TYBSc Data _1 Syllabus Mumbai University

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AC – 27/06/2023
Item No. 6.3 (R)


UNIVERSITY OF MUMBAI

Revised Syllabus for
T.Y.B.Sc. (Data Science )
(Sem. V & VI)
(CBCS)

(With effect from the academic year 2023 – 2024 )

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University of Mumbai



Syllabus for Approval
Sr.
No. Heading Particulars
1
O: _____________ Title of Course

T.Y.B.Sc. (Data Science )
2 O: _____________ Eligibility Ordinance no. O.5051
Circular no. UG/284 of 2007 dated 16th
June 2007, Natural Progression for Second
Year B.Sc. Data Science

3
R: ______________ Passing Marks 40%

4 No. of years/Semesters: 3 Years/ 6 Semesters
5 Level: P.G. / U.G./ Diploma / Certificate
( Strike out which is not applicable)
6 Pattern: Yearly / Semester
( Strike out which is not applicable)
7 Status: Revised / New
( Strike out which is not applicable)
8 To be implemented from Academic
Year :
From Academic Year: 2023 -24




Prof. Shivram S. Garje,
Dean ,
Faculty of Science and Technology

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SEMESTER 6
Course
Code Course Type Course Name Credits Marks
USDS601 DSC Machine Learning 2 100
USDS6P1 DSC Machine Learning Practical 2 50
USDS602 DSC Exploratory Data Analysis 2 100
USDS6P2 DSC Exploratory Data Analysis Practical 2 50
USDS603 SEC Internet of Things 2 100
USDS6P3 SEC Internet of Things Practical 2 50
USDS604 DSC Applied Business Analytics 2 100
USDS6P4 DSC Applied Business Analytics Practical 2 50
Elective 2 (Select Any one of the following)
USDS605a DSE Sports Analytics
2 100 USDS605b DSE Healthcare Analytics
USDS605c DSE Data Governance
Compulsory (Project Implementation)
USDS6P5 DSC Project Implementation 2 50
Total 20 750

SEMESTER 5
Course
Code Course Type Course Name Credits Marks
USDS501 DSC Computer Vision 2 100
USDS5P1 DSC Computer Vision Practical 2 50
USDS502 DSC Data Engineering 2 100
USDS5P2 DSC Data Engineering Practical 2 50
USDS503 DSC Robotic Process Automation 2 100
USDS5P3 DSC Robotic Process Automation Practical 2 50
USDS504 SEC Campus to Corporate 2 100
USDS5P4 DSC Project Dissertation 2 50
Elective 1 (Select Any one of the following)
USDS505a DSE Social Media Analytics
2 100 USDS505b DSE Business Forecasting
USDS505 c DSE Marketing and Retail Analytics
Compulsory Practical
USDS5P5 DSC Data Visualisation with Tableau 2 50
Total 20 750

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Contents
USDS501: Computer Vision ................................ ................................ ................................ .... 1
USDS5P1: Computer Vision Practical ................................ ................................ ................... 3
USDS502: Data Engineering ................................ ................................ ................................ ... 4
USDS5P2: Data Engineering Practical ................................ ................................ .................. 6
USDS505c: Robotic Process Automation ................................ ................................ ............... 8
USDS5P3: Robotic Process Automation Practical ................................ .............................. 11
USDS504: Campus to Corporate ................................ ................................ .......................... 14
USDS5P4: Project Dissertation and Implementation – 1 ................................ ................... 16
USDS505a: Social Media Analytics ................................ ................................ ...................... 18
USDS505b: Business Forecasting ................................ ................................ ......................... 20
USDS505c: Marketing and Retail Analytics ................................ ................................ ....... 23
USDS5P5: Data Visualisation with Tableau Practical ................................ ....................... 25
USDS601: Machine Learning ................................ ................................ ............................... 28
USDS6P1: Machine Learning Practical ................................ ................................ ............... 31
USDS602: Exploratory Data Analysis ................................ ................................ ................. 33
USDS5P2: Exploratory Data Analysis Practical ................................ ................................ . 35
USDS603: Internet of Things ................................ ................................ ................................ 37
USDS6P3: Internet of Things Practical ................................ ................................ ............... 40
USDS604: Applied Business Analytics ................................ ................................ ................. 41
USDS6P4: Applied Business Analytics Practical ................................ ................................ 43
USDS605a: Sports Analytics ................................ ................................ ................................ . 46
USDS605b: Healthcare Analytics ................................ ................................ ......................... 48
USDS605c: Data Governance ................................ ................................ ............................... 51
USDS6P5: Project Dissertation and Implementation – 2 ................................ ................... 53
Evaluation Scheme ................................ ................................ ................................ ................. 54

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Semester V

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USDS501: Computer Vision

B. Sc (Data Science) Semester – V
Course Name: Computer Vision Course Code: USDS501
Periods per week (1 Period is 50 minutes) 5
Credit 2
Hours Marks
Evaluation System Theory Examination 2½ 75
Internal -- 25

Course Description:
This course is an introduction to the field of Computer Vision (CV), Computer vision is a field
of artificial intelligence (AI) that enables computers and systems to derive meaningful
information from digital images, videos and other visual inputs — and take actions or make
recommendations based on that information. If AI enables computers to think, computer vision
enables them to see, observe and understand.
Computer vision works much the same as human vision, except humans have a head start.
Human sight has the advantage of lifetimes of context to train how to tell objects apart, how
far away they are, whether they are moving and whether there is something wrong in an image.
Computer vision trains machines to perform these functions, but it has to do it in much less
time with cameras, data and algorithms rather than retinas, optic nerves and a visual cortex.
Because a system trained to inspect products or watch a production asset can analyze thousa nds
of products or processes a minute, noticing imperceptible defects or issues, it can quickly
surpass human capabilities .

Course Objectives:
 To introduce students the fundamentals of image formation.
 To introduce students the fundamentals of Image Proce ssing .
 To introduce students the various features of Image
 To introduce students to the major ideas, methods, and techniques of computer vision
and pattern recognition.
 To give knowledge to students about Applications of Computer Vision.

Unit Details Lectures
I Image Formation and Image Processing : Introduction to Computer
Vision and Basic Concepts of Image Formation, Introduction and
Goals of Computer Vision, Image Formation and Radiometry,
Geometric Transformation, Geometric Camera Models, Image
Reconstruction from a Series of Projections 12
II Image Processing Concepts : Fundamentals of Image Processing,
Image Transforms, Image Filtering, Colour Image Processing,
Mathematical Morphology, Image Segmentation 12
III Image Features : Image Descriptors and Features, Texture
Descriptors, Colour Features, Edge Detection, Object Boundary and 12

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Shape Representations, Interest or Corner Point Detectors, Histogram
of Oriented Gradients, Scale Invariant Feature Transform, Speeded up
Robust F eatures, Saliency
IV Recognition: Fundamental Pattern Recognition Concepts,
Introduction to Pattern Recognition, Linear Regression, Basic
Concepts of Decision Functions, Elementary Statistical Decision
Theory, Gaussian Classier, Parameter Estimation, Clustering for
Knowledge Representatio n, Dimension Reduction, Template
Matching, Artificial Neural Network for Pattern Classification,
Convolutional Neural Networks, Autoencoder 12
V Applications of Computer Vision: Machine Learning Algorithms
and their Applications in Medical, Image Segmenta tion, Motion
Estimation and Object Tracking, Face and Facial Expression
Recognition, Gesture Recognition, Image Fusion, Programming
Examples 12


Books and References:
Sr. No. Title Author/s Publisher Edition Year
1. Computer Vision and
Image Processing Manas Kamal
Bhuyan CRC Press 1st 2020
2. Computer Vision: A
Modern Approach David A. Forsyth,
Jean Ponce Pearson 2nd 2012
3. Machine Vision R. Jain, R. Kasturi,
and B. G. Schunk McGraw -
Hill 1st 1995
4. Image Processing,
Analysis, and Machine
Vision Milan Sonka,Vaclav
Hlavac, Roger Boyle Thomson
Learning 3rd 2007
5. Computer and Robot
Vision Robert Haralick and
Linda Shapiro Addison -
Wesley 1st 1993

Course Outcome s:
Upon the successful completion of this course, students will be able to:

 Understand the fundamentals of image formation.
 Use and Demonstrate operations of Image Processing.
 Relate and Explain various features of Image .
 Understand, Identify and Examine various image patterns.
 Design and develop practical and innovative image processing and com puter vision
applications or systems.

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USDS5 P1: Computer Vision Practical
B. Sc (Data Science) Semester – V
Course Name: Computer Vision Practical Course Code: USDS5P1
Periods per week (1 Period of 50 minutes) 3
Credits 2
Hours Marks
Evaluation System Practical Examination 2½ 50
Internal -- --

Course Objectives:
 To understand and use the Image features.
 To learn Histogram operations .
 To learn Image Filtering.
 To learn and understand Image colours .
 To understand image filtering techniques.

Sr. List of Practical
1 Basic operation on Image
A Program to change the Brightness of Image.
B To Flip the image around the vertical and horizontal line.
C Display the color components of the image.
D Display of gray scale images.
E To find the negative of an image.

2 Using histogram for image quality analysis
A Calculate the Histogram of a given image.
B Histogram Equalization.

3 Program for Image Filtering
A Low pass filter => 1)Average filter2)Weighted Average filter3)Median filter
High pass filters using=>1) Sobel operator2) Laplacian operator
B Design non -linear filtering.

4 Edge detection with gradient and convolution of an Image

5 Finding Threshold of Images
A Program to find threshold of grayscale image.
B Program to find threshold of RGB image.

6 Program to estimate and subtract the background of an image.

7 Program to convert color image to gray and hsv.

8
A Determination of edge detection using operators.
B 2-D DFT and DCT.
C Filtering in Frequency domain.

9
A Display of colour images.
B Conversion between colour spaces.

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A DWT of images
B Segmentation using watershed transform

Course Outcome s:
Upon the successful completion of this course, students will be able to:

 Identify various Image features.
 Experiment with Histogram operations.
 Apply Image Filtering.
 Understand and make use of Image colours.
 Apply and make use of image filtering techniques.
USDS502: Data Engineering

B. Sc. (Data Science) Semester – V
Course Name: Data Engineering Course Code: USDS502
Periods per week (1 Period is 50 minutes) 5
Credits 2
Hours Marks
Evaluation System Theory Examination 2½ 75
Internal -- 25
Course Description:
Data engineering refers to the building of systems to enable the collection and usage of data.
This data is usually used to enable subsequent analysis and data science; which often involves
machine learning. Making the data usable usually involves substantial compute and storage, as
well as data processing and clea ning.

Course Objectives:
● To know the Data Engineering basics and Lifecycle.
● To understand the Data Architecture Design with various options available.
● To learn the Data generation and Storage.
● To understand Ingestion process and know about Queries, Modeli ng, and
Transformation.
● To learn Data Analytics, Machine Learning and to know the importance of Security and
Privacy.

Unit Details Lectures
I Foundation and Building Blocks : Data Engineering Described, What
Is Data Engineering? Data Engineering Skills and Activities, Data
Engineers Inside an Organization,
The Data Engineering Lifecycle : What Is the Data Engineering
Lifecycle? Major Undercurrents Across the Data Engineering Lifecycle 12
II Designing Good Data Architecture : What Is Data Architecture?
Principles of Good Data Architecture, Major Architecture Concepts,
Examples and Types of Data Architecture, Who’s Involved with 12

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Designing a Data Architecture?
Choosing Technologies Across the Data Engineering Lifecycle: Team
Size and Capabilities, Speed to Market, Interoperability, Cost
Optimization and Business Value, Today Versus the Future: Immutable
Versus Transitory Technologies, Location, Build Versus Buy, Monolith
Versus Modular, Serverless Versus Servers, Optimization, Performance,
and the Benchmark Wars, Undercurrents and Their Impacts on Choosing
Technologies
III Data Generation in Source Systems : Sources of Data: How Is Data
Created? Source Systems: Main Ideas, Source System Practical Details,
Whom You’ll Work With, Undercurrents and Their Impact on Source
Systems
Storage : Raw Ingredients of Data Storage, Data Storage Systems, Data
Engineering Storage Abstractions, Big Ideas and Trends in Sto rage,
Whom You’ll Work With, Undercurrents 12
IV Ingestion : What Is Data Ingestion? Key Engineering Considerations for
the Ingestion Phase, Batch Ingestion Considerations, Message and
Stream Ingestion Considerations, Ways to Ingest Data, Whom You’ll
Work With, Undercurrents
Queries, Modelling , and Transformation : Queries, Data Modelling ,
Transformations, Whom You’ll Work With, Undercurrents 12
V Serving Data for Analytics, Machine Learning, and Reverse ETL :
General Considerations for Serving Data, Analyti cs, Machine Learning,
What a Data Engineer Should Know About ML, Ways to Serve Data for
Analytics and ML, Reverse ETL, Whom You’ll Work With,
Undercurrents
Security and Privacy : People, Processes, Technology
The Future of Data Engineering : The Decline of C omplexity and the
Rise of Easy -to-Use Data Tools, The Cloud -Scale Data OS and Improved
Interoperability, “Enterprisey” Data Engineering, Titles and
Responsibilities Will Morph, Moving Beyond the Modern Data Stack,
Toward the Live Data Stack
Serialization a nd Compression Technical Details : Serialization
Formats, Columnar Serialization, Hybrid Serialization, Database
Storage Engines, Compression: gzip, bzip2, Snappy, Etc,
Cloud Networking : Cloud Network Topology, Data Egress Charges,
Availability Zones, Reg ions, GCP -Specific Networking and
Multiregional Redundancy, Direct Network Connections to the Clouds,
CDNs, The Future of Data Egress Fees 12

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Books and References:
Sr. Title Author/s Publisher Edition Year
1. Fundamentals of Data
Engineering Joe Reis and Matt
Housley O'Reilly
Media 1st 2022
2. Learning Spark: Lightning -Fast
Data Analytics Jules S. Damji,
Brooke Wenig O'Reilly
Media 2nd 2020
3. Kafka: The Definitive Guide:
Real-Time Data and Stream
Processing at Scale Neha Narkhede,
Gwen Shapira &
Todd Palino O'Reilly
Media 1st 2017
4. Data Pipelines Pocket
Reference James Densmore O'Reilly
Media 1st 2021
5. Data Engineering with Python Paul Crickard Packt
Publishing 1st 2020

Course Outcomes:
After completion of the course, a student should be able to:
● To remember and explain the Data Engineering basics and Lifecycle.
● To apply the Data Architecture Design with various options available.
● To create the Data from source and make use of Storage.
● To understand Ingestion process and know about Queries, Modeling, and
Transformation.
● To Illustrate Data Analytics, Machine Learning and to Explain the importance of
Security and Privacy.





USDS5P2 : Data Engineering Practical

B. Sc (Data Science) Semester – V
Course Name: Data Engineering Practical Course Code: USDS5P2
Periods per week (1 Period of 50 minutes) 3
Credits 2
Hours Marks
Evaluation System Practical Examination 2½ 50
Internal -- --

Course Objectives:
● To know the working of Resilient Distributed Dataset .
● To understand the structure and working of Dataframe .
● To know use of Array and Map Operations .
● To understand Spark SQL Joins, Schema, StructType & Functions .
● To learn Spark Data Source API, Streaming .

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List of Practical
Sr Practical Details
1 Practicals on RDD (Resilient Distributed Dataset) with Scala Operations and
transformations.
a) Use of next mentioned operations and functions: Parallelize, Read text file, Read
CSV, Create RDD, Actions, Pair Functions, Repartition and Coalesce, Shuffle
Partitions, Broadcast Variables, Accumulator Variables and Convert RDD to
DataFrame
b) Use of next mentioned operations : Read multiple text fil es into RDD, Read CSV file
into RDD, Create an empty RDD, RDD Pair Functions and Generate DataFrame from
RDD

2 Practical on the DataFrame operations
a) Demonstrate the use of next mentioned operations: Create an empty DataFrame,
Create an empty DataSe t, use of Rename nested column, Adding or Updating a column
on DataFrame, Drop a column on DataFrame, Adding literal constant to DataFrame,
Changing column data type, Pivot and Unpivot a DataFrame, Create a DataFrame
using StructType & StructField schema
b) Use of next mentioned operations: Selecting the first row of each group, Sort
DataFrame, Union DataFrame, Drop Rows with null values from DataFrame, Split
single to multiple columns, Concatenate multiple columns, Replace null values in
DataFrame , Remove duplicate rows on DataFrame, Remove distinct on multiple
selected columns, Spark UDF
3 Practical on the Spark Array and Map operations
a) Use of next mentioned operations: Create an Array (ArrayType) column on
DataFrame, Create a Map (MapType) c olumn on DataFrame, Convert an Array to
columns, Create an Array of struct column, Explode an Array and map columns,
Explode an Array of structs, Explode an Array of map columns to rows
b) Use of next mentioned operations: Create a DataFrame with nested A rray, Explode
nested Arrays to rows, Flatten nested Array to single Array, Convert array of String to
a String column

4 Spark Aggregate : Group rows in DataFrame, Get Count distinct on DataFrame, Add
row number to DataFrame, Select the first row of each group
5 Spark SQL Joins, Spark SQL Schema, StructType & SQL Functions
a) Use of next mentioned operations: Use of Spark SQL Join, Join multiple
DataFrames, Inner join two tables/DataFrame, Self join, Join tables on multiple
columns, Convert case cla ss to a schema, Create array of struct column, Flatten nested
column
b) Use of next mentioned functions : Date and Time Functions, String Functions, Array
Functions, Map Functions, Aggregate Functions, Window Functions, Sort Functions,
JSON Functions
6 Spark SQL : Demonstrate the use of next functions : createDataFrame(), where() &
filter(), withColumn(), withColumnRenamed(), drop(), distinct(), groupBy(), join(),
map() vs mapPartitions(), foreach() vs foreachPartition(), pivot(), union(), collect(),
cache() & persist(), udf()
7 Spark Data Source API
a) Use of next operations : Process JSON from a Text file, Read & Write CSV file, Read
and Write JSON file, Read & Write Parquet file, Read & Write XML file, Read &
Write Avro files

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b) Use of next operations : Read & Write HBase using “hbase -spark” Connector, Read
& Write from HBase using Hortonworks, Read & Write ORC file, Read Binary File
c) Use of File conversions operations from each type to other : CSV , Parquet, JSON,
Avro, Text fil e
8 Practical of Spark Streaming
a) Use of next operations : OutputModes Append vs Complete vs Update, Read JSON
Files From Directory with Scala Example, Read data From TCP Socket with Scala
Example, Consuming & Producing Kafka messages in JSON format
b) Use of next operations : Consuming & Producing Kafka messages in Avro format,
from_avro and to_avro functions, Avro data from Kafka topic using from_avro() and
to_avro(), Batch Processing using Kafka Data Source
9 Spark MLlib : Demonstrate use of Estima tor, Transformer, and Param
10 Spark HDFS : Demonstrate the use of next operations : Processing files from
Hadoop HDFS (TEXT, CSV, Parquet, Avro, JSON), Processing TEXT files from
Amazon S3 bucket, Processing JSON files from Amazon S3 bucket, Processing CS V
files from Amazon S3 bucket, Processing Parquet files from Amazon S3 bucket,
Processing Avro files from Amazon S3 bucket

Course Outcome:
Upon the successful completion of this course, students will be able to:
 To apply and build the working of Resilient Distributed Dataset .
 To analyse and evaluate working of Dataframe.
 To perform and use Array and Map Operations.
 To apply and experiment with Spark SQL Joins, Schema, StructType & Functions .
 To effective use Spark Data Source API, Streaming .



USDS505c: Robotic Process Automation
B. Sc (Data Science) Semester – V
Course Name: Robotic Process Automation Course Code: USDS505c
Periods per week (1 Period is 50 minutes) 5
Credit 2
Hours Marks
Evaluation System Theory Examination 2½ 75
Internal -- 25

Course Description:
Robotic process automation (RPA) is a form of business process automation technology based
on metaphorical software robots (bots) or on artificial intelligence (AI)/digital workers. It is
sometimes referred to as software robotics (not to be confused with robot software).

In traditional workflow automation tools, a software developer produces a list of actions to
automate a task and interface to the back -end system using internal application programming
interfaces (APIs) or dedicated scripting language. In contrast, RPA systems develop the action

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list by watching the user perform that task in the application's graphical user interface (GUI),
and then perform the automation by repeating those tasks directly in the G UI. This can lower
the barrier to the use of automation in products that might not otherwise feature APIs for this
purpose.

Course Objectives:

 To make the students aware about the automation today in the industry.
 To make the students aware about the tools used for automation.
 To help the students automate a complete process

Unit Details Lectures
I Robotic Process Automation: Scope and techniques of automation, About
UiPath
Record and Play: UiPath stack, Downloading and installing UiPath Studio,
Learning UiPath Studio, Task recorder, Step -by-step examples using the
recorder. 12
II Sequence, Flowchart, and Control Flow: Sequencing the workflow,
Activities, Control flow, various types of loops, and decision making, Step -
by-step example using Sequence and Flowchart, Step -by-step example using
Sequence and Control flow
Data Manipulation : Variables and scope, Collections, Ar guments –
Purpose and use, Data table usage with examples, Clipboard management,
File operation with step -by-step example, CSV/Excel to data table and vice
versa (with a step -by-step example) 12
III Taking Control of the Controls : Finding and attaching windows, Finding
the control, Techniques for waiting for a control, Act on controls – mouse
and keyboard activities, Working with UiExplorer, Handling events, Revisit
recorder, Screen Scraping, When to use OCR, Types of OCR available , How
to use OCR, Avoiding typical failure points
Tame that Application with Plugins and Extensions : Terminal plugin,
SAP automation, Java plugin, Citrix automation, Mail plugin, PDF plugin,
Web integration, Excel and Word plugins, Credential management,
Extensions – Java, Chrome, Firefox, and Silverlight 12
IV Handling User Events and Assistant Bots : What are assistant bots?,
Monitoring system event triggers, Hotkey trigger, Mouse trigger, System
trigger ,Monitoring image and element triggers, An example of monitoring
email, Example of monitoring a copying event and blocking it, Launching
an assistant bot on a keyboard event
Exception Handling, Debugging, and Logging : Exception handling,
Common exceptions and ways to handle them, Logging and taking
screenshots, Debugging techniques, Collecting crash dumps, Error reporting 12

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V Managing and Maintaining the Code : Project organiz ation, Nesting
workflows, Reusability of workflows, Commenting techniques, State
Machine, When to use Flowcharts, State Machines, or Sequences,
Using config files and examples of a config file, Integrating a TFS
server
Deploying and Maintaining the Bot : Pu blishing using publish
utility, Overview of Orchestration Server, Using Orchestration Server
to control bots, Using Orchestration Server to deploy bots, License
management, Publishing and managing updates 12


Books and References:
Sr. No. Title Author/s Publisher Edition Year
1. Learning Robotic Process
Automation Alok Mani
Tripathi Packt 1st 2018
2. Robotic Process Automation
Tools, Process Automation
and their benefits:
Understanding RPA and
Intelligent Automation Srikanth
Merianda Createspace
Independent
Publishing 1st 2018
3. The Simple Implementation
Guide to Robotic Process
Automation (Rpa): How to
Best Implement Rpa in an
Organization Kelly
Wibbenmeyer iUniverse 1st 2018


Course Outcome s:
Upon the successful completion of this course, students will be able to:

 Understand and implement the mechanism of business process and can provide the
solution in an optimize way.
 Apply the features use for interacting with database plugins.
 Apply and Use the plug -ins and other controls used for pr ocess automation.
 Implement and handle the different events, debugging and managing the errors.
 Test and deploy the automated process.

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USDS5P3: Robotic Process Automation Practical

B. Sc (Data Science) Semester – V
Course Name: Robotic Process Automation Practical Course Code: USDS5P3
Periods per week (1 Period of 50 minutes) 3
Credits 2
Hours Marks
Evaluation System Practical Examination 2½ 50
Internal -- --

Course Objectives:
1. UiPath Fundamentals: The course aims to provide a solid foundation in UiPath RPA
software. Students will learn about the UiPath platform, its components, and key features.
They will gain a thorough understanding of UiPath Studio, Orchestrator, and other UiPath
tools necessary for building and managing software robots.

2. Workflow Design and Development: The primary objective is to equip students with the
skills needed to design and develop automation workflows using UiPath Studio. Students
will learn how to use the drag -and-drop interface, activities, variables, a nd conditions to
create efficient and robust automation solutions.

3. Data Manipulation and Integration: The course focuses on teaching students how to work
with data in UiPath. They will learn how to extract data from various sources, manipulate
and transfo rm it using UiPath activities, and integrate it with other systems or applications.

4. Exception Handling and Error Management: Error handling is a critical aspect of RPA
development. Students will learn techniques for identifying and handling exceptions tha t
may occur during automation execution. They will also understand how to implement error
logging and notifications to ensure smooth and reliable automation processes.

5. Orchestrator Configuration and Management: UiPath Orchestrator is a central component
for managing and monitoring software robots. The course aims to teach students how to
configure and manage Orchestrator, including creating and scheduling jobs, managing
assets and queues, and monitoring automation performance.

6. Advanced UiPath Features: The course cover s advanced features and capabilities of
UiPath. It include s topics such as screen scraping, OCR (Optical Character Recognition),
Citrix automation, web automation, and working with APIs. Students will gain exposure to
a wide range of UiPath functionalities to handle complex automation scenarios.

Practical 1: RPA Basic s: Sequences and Flowcharts
a. Create a simple sequence -based project.
b. Create a flowchart -based project.
c. Automate UiPath Number Calculation (Subtraction, Multiplication, D ivision of
numbers).
d. Create an automation UiPath project using different types of variables (number,
datetime, Boolean, generic, array, data table)

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Practical 2: Decision making and looping
a. Consider an array of names. We have to find out how many of them start with the
letter "a". Create an automation where the number of names starting with "a" is.
counted and the result is displayed.
b. Demonstrate switch statement with an example.
c. Create an automation To Print numbers from 1 to 10 with break after the writeline
activity inside for each activity
d. Create an automation using Do..While Activity to print numbers from 5 to 1
e. Create an automation using Delay Activity between two writeline activities to
separate their execution by 5 seconds
f. Create an automation to demonstrate use of decision statements (if)

Practical 3: Types of Recording
a. Basic Recording using Toolbar
b. Basic Recording using Notepad
c. Desktop Recording using Tool bar
d. Desktop Recording by creating a workflow
e. Web Recording e.g. Find the rating of the movie from imdb web site
f. Web Recording manually

Practical 4: Excel Automation
a. Automate the process to extract data from an excel file into a data table and vice versa
b. Create an automation To Write data to specific cell of an excel sheet.
c. Create an automation To Read data to specific cell of an excel sheet.
d. Create an automation To append data to specific cell of an excel sheet.
e. Create an automation To sort a table of an excel sheet.
f. Create an automation To filter a table of an excel sheet

Practical 5: Different controls in UiPath
a. Implement the attach window activity.
b. Automate using Anchor Base.
c. Automate using Element Exists.
d. Automate using Find Children control.
e. Use Get Ancestor control
f. Use Find Relative control

Practical 6: Keyboard and Mouse Events
a. Demonstrate the following activities in UiPath:
i. Mouse (click, double click and hover)
ii. Type into
iii. Type Secure text
b. Demonstrate the following events in UiPath:
i. Element triggering event
ii. Image triggering event
iii. System Triggering Event
c. Automate the process of launching an assistant bot on a keyboard event.

Practical 7: Screen Scraping and Web Scraping methods
a. Automate the following screen scraping methods using UiPath: i) Full Text

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b. ii) Native
c. iii) OCR
d. Demonstrate Data Scraping and display values in Message box.
e. Demonstrate Screen Scraping for a pdf, web page and image file.

Practical 8: PDF Automation and Exception Handling
a. Read PDF With OCR
b. Merge PDF’s into one
c. Get PDF Total Page count Using Regex
d. Demonstrate Exception Handling using UiPath

Practical 9: Email Automation
a. Configure Email using UiPath
b. Read Emails
c. Send Email with Attachment
d. Save Email Attachments
e. Reply to Email

Practical 10: Orchestrator management and mini project
a. Deploy bots to Orchestrator
b. Run jobs from Orchestrator
c. Queue Introduction:
i. Add items to Queue.
ii. Get Queue item from Orchestrator
d. Build UiPath Chatbot using Google dialogflow

Course Outcomes:

Upon the successful completion of this course, students will be able to:

1. Recall and describe the fundamental concepts of RPA and UiPath. Memorize key features and
functionalities of UiPath Studio and Orchestrator.
2. Explain the principles and mechanisms of RPA and how UiPath enables process automation.
3. Interpret the components and tools within the UiPath ecosystem and their respective roles.
4. Summarize the data manipul ation and integration capabilities of UiPath.
5. Utilize UiPath Studio to create automation workflows and sequences for simple business processes.
6. Apply data extraction and manipulation techniques using UiPath activities.
7. Implement exception handling mechanis ms to manage errors during automation execution.
8. Analyze and evaluate existing business processes to identify potential automation opportunities.
Assess the suitability of different UiPath features and functionalities for specific automation
scenarios.
9. Evaluate the efficiency and effectiveness of automation workflows and propose improvements.
10. Evaluate the performance and reliability of automated processes developed using UiPath.
11. Critically assess the suitability of UiPath Orchestrator for managing and monit oring automation.
12. Analyze the impact of RPA implementation on business processes and make recommendations for
optimization.
13. Design and develop complex automation workflows using UiPath Studio, incorporating advanced
functionalities such as screen scraping, OCR, and API integration.
14. Create comprehensive automation solutions that integrate multiple systems and applications using
UiPath.

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USDS504: Campus to Corporate

B. Sc (Data Science) Semester – V
Course Name: Cloud Computing Course Code: USDS504
Periods per week (1 Period is 50 minutes) 5
Credit 2
Hours Marks
Evaluation System Theory Examination 2½ 75
Internal -- 25

Course Description:
The "Campus to Corporate" course is designed to equip students with the necessary
communication and interpersonal skills required to transition successfully from a campus
environment to a professional corporate setting. The course covers various aspects of
communication, including active listening, effective speaking, interviews, business
communication, negative news and crisis communication, intercultural and international
business communication, group communication, teamwork, leadership, data interpretation, and
logical reasoning. Students will develop a comprehensive understanding of these topics
throu gh theoretical discussions, practical exercises, and real -world case studies.

Course Objectives:
1. Develop active listening skills and understand the importance of empathy in
communication.
2. Enhance speaking skills for confident and clear communication.
3. Gain knowledge about different types of interviews and improve interview performance.
4. Understand intrapersonal and interpersonal communication dynamics and conflict
management in the workplace.
5. Learn effective strategies for delivering negative news and crisis communication.

Unit Details Lectures
I Active Listening: Meaning and Art of Listening, Importance of
Listening and Empathy in Communication, Reasons for Poor
Listening, Poor Listening Habits, Traits of a Good Listener,
Listening Modes and Types, Barriers to Effective Listening,
Listening for General Content and Specific Information
Effective Speaking: Basic Sounds of English, Word Stress,
Sentence Stress, Intonation, Achieving Confidence, Clarity, and
Fluency, Vocal Cues
Interviews: Objectives of Interviews, Types of Interviews, Job
Interviews, Résumés, Media Interviews, Press Conferences 12
II Intrapersonal and Interpersonal Business Communication:
Intrapersonal Communication, Self -Concept and Dimensions of Self,
Interpersonal Needs, Social Penet ration Theory, Rituals of
Conversation and Interviews, Conflict in the Work Environment
Negative News and Crisis Communication: Delivering a Negative
News Message, Eliciting Negative News, Crisis Communication Plan,
Press Conferences 12

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III Intercultural and International Business Communication:
Intercultural Communication: How to Understand Intercultural
Communication, Common Cultural Characteristics, Divergent
Cultural Characteristics,
International Communication: The Global Marketplace, St yles of
Management, The International Assignment 12
IV Group Communication: Use of Body Language in Group,
Discussions, Group Discussions, Organizational GD, GD as Part of
Selection Process, Meetings, Conferences
Group Communication, Teamwork, and Leadership: Group Life
Cycles and Member Roles, Group Problem Solving, Business and
Professional Meetings, Teamwork and Leadership 12
V Data Interpretation: Tabulation, Bar Graphs, Pie Chart, Line
Graphs
Logical Reasoning: Argument forms, structure of categorical
propositions, Mood and Figure, Formal and Informal fallacies, Uses
of language, Connotations and denotations of terms, Classical square
of opposition, deductive and inductive reasoning, Analogies, Venn
diagram 12


Books and References:
Sr. No. Title Author/s Publisher Edition Year
1. Business
Communication for
Success
University of
Minnesota University
of
Minnesota 2015
2. Technical
Communication:
Principles and Practice
Meenakshi Raman Oxford
University
Press 3rd
Edition 2015

Course Outcome:
Upon the successful completion of this course, students will be able to:

1. Apply active listening techniques and overcome barriers to become a better listener.
2. Demonstrate improved speaking skills with clarity, confidence, and fluency.
3. Utilize interview techniques to enhance job interview performance and create impactful
résumés.
4. Apply interpersonal communication skills to build effective relationships and manage
conflicts in professional settings.
5. Effec tively deliver negative news messages, develop crisis communication plans, and
handle press conferences in challenging situations.

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USDS5P4: Project Dissertation and Implementation – 1
B. Sc (Data Science) Semester – V
Course Name: Project D issertation and Implementati on
– 1 Course Code: USDS5P4
Periods per week (1 Period of 50 minutes) 3
Credits 2
Hours Marks
Evaluation System Practical Examination 2½ 50
Internal -- --

Course Description:

The Project Dissertation course in B.Sc Data Science allows students to independently
undertake a research project in the field of data science. Through this course, students will
apply their knowledge and skills to identify, plan, execute, and document a research study.
They will address a rea l-world data science problem, demonstrating critical thinking, research
competence, and effective communication skills. The course culminates in the submission of a
comprehensive project dissertation, showcasing students' ability to conduct meaningful
research in the field of data science.

Course Objectives:
1. To provide students with an opportunity to apply the knowledge and skills acquired
throughout the B.Sc Data Science program in a real -world project.
2. To enable students to independently plan, design, an d execute a research project in the
field of data science.
3. To develop students' critical thinking and problem -solving abilities through the
identification and analysis of complex data science problems.
4. To enhance students' research and project management skills, including data collection,
data analysis, and result interpretation.
5. To foster effective communication and presentation skills by requiring students to
document and present their research findings in a comprehensive project dissertation.

The proje ct report should contain the following:
Table of Contents
 What is Data Science Project Report?
 6 Fundamental Steps to Create a Data Science Report
o Define the Data Science Project Topic and Problem Statement
o Explain How You Intend to Address the Problem
o Describe the Dataset and its Attributes
o Outline the Design of Your Data Science Project
o Conduct an In -depth Project Analysis
o Wrap Up Your Project
 How to Write a Detailed Project Report on Data Science?
o Defining the Data Science Problem Statement
o Discuss Yo ur Approach to Address the Problem Statement
o Explain the Dataset and its Attributes

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o Define the Project Structure/Layout
o Analyze the Steps in Detail
o Summarize the Project Outcomes and Add References
 10 Main Components of a Data Science Project Report
o Project Title/Topic
o Table of Contents
o Abstract or Project Summary
o Introduction
o Dataset Description
o Methods and Algorithms
o Project Analysis
o Final Results
o Conclusion and Future Scope
o References
o Best Practices for A Data Science Pro ject Report
o Define the Objective of The Project Report
o Focus More on the Outcomes, Not the Report
o Develop a Thorough Project Strategy in Advance
o Document Everything - Data, Design, Algorithms
Course Outcome:
Upon the successful completion of this course, students will be able to:

1. Students will demonstrate the ability to identify and formulate a research problem in
the field of data science and define clear objectives and research questions.
2. Students will independently plan and execute a research project, including selecting
appropriate research methodologies and data collection techniques.
3. Students will analyze and interpret data using advanced data analysis techniques,
demonstrating proficiency in statistical analysis, machine learning, or other relevant
methodologies.
4. Students will present thei r research findings in a well -structured, comprehensive project
dissertation, adhering to academic writing standards and providing appropriate
references.


5. Students will effectively communicate their research findings through oral
presentations, demonstra ting clear and concise delivery of information and responding
to questions and feedback from peers and evaluators.

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USDS505a: Social Media Analytics
B. Sc (Data Science) Semester – V
Course Name: Social Media Analytics Course Code: USDS505a
Periods per week (1 Period is 50 minutes) 5
Credit 2
Hours Marks
Evaluation System Theory Examination 2½ 75
Internal -- 25

Course Description:

This course on Social Media Analytics provides an in -depth exploration of key concepts, tools,
and techniques for leveraging social media data. Students will learn about social media
intelligence, monitoring metrics, customer profiling, social network analysis, text analytics,
and recommender systems. The course emphasizes hands -on learning to develop p ractical
skills in extracting insights from social media data and making data -driven decisions. By the
end of the course, students will be equipped to navigate the complex landscape of social media
analytics and apply these insights in real -world scenarios .

Course Objectives:

 Understand the fundamentals of social media analytics and its relevance in today's digital
landscape.
 Learn different types of social media analytics and their applications, including customer
profiling, location analytics, action an alytics, mobile/app analytics, and Google Analytics.
 Gain knowledge of social network analysis, including network structure, egocentric
networks, network metrics, and clustering techniques.
 Explore text analytics techniques used in social media, including data types, deployment
models, and text mining algorithms.
 Develop practical skills in building recommender systems in social media using techniques
such as association rule mining, collaborative filtering, and similarity measures

Unit Details Lectures
I Introduction to Social Media
Social Media Data, Social Media Intelligence & Listening, Social
Media Monitoring Metrics, Types of Social Media tools, Theories
in Media Research, Long Tail, electronic word -of-mouth (eWOM),
Power Law& Populari ty. 12
II Types of Social Media Analytics,
Knowing your customers –Seven layer Approach, Location
Analytics, Action Analytics, Mobile/App Analytics, Google
Analytics . 12
III Social Network Analysis
Introduction to Networks, Common network terms, Network
structure, Types of Networks, Egocentric Networks, Network
analysis metrics, Strong and Wea k Ties, Clustering and Grouping . 12

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IV Text Analytics in Social Media
Text Analytics data types, Deployment models, Purpose of text
analytics, Text analytics value creation cycle, Text Mining
algorithms . 12
V Recommender Systems in Social Media
Overview – Association rule mining – Collaborative filtering – User -
based similarity – Item-based similarity Hands -on: Recommender
System. 12


Books and References:
Sr. No. Title Author/s Publisher Edition Year
1. “Networks, Crowds,
and Markets:
Reasoning about a
Highly Connected
World” David Easley and
Jon Kleinberg Cambridge
University
Press 3rd
Edition 3rd
Edition 2017
2. “Analysing Social
Media Networks with
Node XL” “Derek Hansen Ben
Shneiderman Marc
Smith
ItaiHimelboim,
Morgan Kaufmann 2nd
Edition 2019
3. "Social Media Mining:
An Introduction" Huan Liu,
Mohammad Ali
Abbasi , and Reza
Zafarani Cambridge
University
Press. 1st
Edition 2014

Course Outcome:
Upon the successful completion of this course, students will be able to:

 Demonstrate a comprehensive understanding of social media analytics concepts, theories,
and tools.
 Apply various social media analytics techniques to extract insights and make informed
decisions.
 Perform social network analysis to uncover patterns, relationships, and influential nodes
within social networks.
 Utilize text analytics methods to extr act meaningful information from social media text
data.
 Design and implement recommender systems for social media platforms, considering user
preferences and item similarities to enhance user experiences.

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USDS505b: Business Forecasting
B. Sc (Data Science) Semester – V
Course Name: Business Forecasting Course Code: USDS505b
Periods per week (1 Period is 50 minutes) 5
Credit 2
Hours Marks
Evaluation System Theory Examination 2½ 75
Internal -- 25

Course Description:
Business forecasting refers to the tools and techniques used to predict developments in
business, such as sales, expenditures, and profits. The purpose of business forecasting is to
develop better strategies based on these informed predictions. Past data is collected and
analy sed via quantitative or qualitative models so that patterns can be identified and can direct
demand planning, financial operations, future production, and marketing operations.
The use of forecasts in business management is indispensable for nearly every d ecision in every
industry. The use of business forecasting provides information that helps business managers
identify and understand weaknesses in their planning, adapt to changing circumstances, and
achieve effective control of business operations.

Cour se Objectives:

 Students will have a command of business theory and practice in the field of business
forecasting.
 To learn different forecasting models/techniques both quantitative and qualitative.
 Students will use reasoned and ethical judgment when anal yzing problems and making
decisions.
 Students will be able to understand complex business situations and provide solutions to
improve current business practices.
 Students will be effective communicators.

Unit Details Lectures
I Fundamental Considerations in Business Forecasting : Getting Real
about Uncertainty, What Demand Planners Can Learn from the Stock
Market, Toward a More Precise Definition of Forecastability,
Forecastablity: A New Method for Benchmarking and Driving
Improv ement, Forecast Errors and Their Avoidability, The Perils of
Benchmarking, Can We Obtain Valid Benchmarks from Published
Surveys of Forecast Accuracy? Defining “Demand” for Demand
Forecasting, Using Forecasting to Steer the Business: Six Principles, The
Beauty of Forecasting
Methods of Statistical Forecasting : Confessions of a Pragmatic
Forecaster , New Evidence on the Value of Combining Forecasts , How
to Forecast Data Containing Outliers , Selecting Your Statistical
Forecasting Level , W hen Is a Flat -line Fo recast Appropriate?
Forecasting by Time Compression , Data Mining for Forecasting: An
Introduction , Process and Methods for Data Mining for Forecasting ,
Worst -Case Scenarios in Forecasting: How Bad Can Things Get? Good 12

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Patterns, Bad Patterns
II Forecasting Performance Evaluation and Reporting: Dos and Don’ts
of Forecast Accuracy Measurement: A Tutorial, How to Track Forecast
Accuracy to Guide Forecast Process Improvement, A “Softer” Approach
to the Measurement of Forecast Accuracy, Measuring Fore cast
Accuracy, Should We Define Forecast Error as e = F – A or e = A – F?
Percentage Error: What Denominator? Percentage Errors Can Ruin Your
Day, Another Look at Forecast -Accuracy Metrics for Intermittent
Demand, Advantages of the MAD/Mean Ratio over the MAPE, Use
Scaled Errors Instead of Percentage Errors in Forecast Evaluations, An
Expanded Prediction -Realization Diagram for Assessing Forecast
Errors, Forecast Error Measures: Critical Review and Practical
Recommendations, Measuring the Quality of Intermi ttent Demand
Forecasts: It’s Worse than We’ve Thought! Managing Forecasts by
Exception, Using Process Behavior Charts to Improve Forecasting and
Decision Making, Can Your Forecast Beat the Naïve Forecast?
Process and Politics of Business Forecasting : FVA: A Reality Check
on Forecasting Practices , Where Should the Forecasting Function
Reside? Setting Forecasting Performance Objectives , Using Relative
Error Metrics to Improve Forecast Quality in the Supply Chain , Why
Should I Trust Your Forecasts? , High on Complexity, Low on Evidence:
Are Advanced Forecasting Methods Always as Good as They Seem? ,
Should the Forecasting Process Eliminate Face -to-Face Meetings? The
Impact of Sales Forecast Game Playing on Supply Chains. Role of the
Sales Force in Forecasting , Good and Bad Judgment in Forecasting:
Lessons from Four Companies , Worst Practices in New Product
Forecasting , Sales and Operations Planning in the Retail Industry , Sales
and Operations Planning: Where Is It Going 12
III Artificial Intelligence and Machine Learning in Forecasting : Deep
Learning For Forecasting, Deep Learning For Forecasting: Current
Trends And Challenges, Neural Network –Based Forecasting Strategies,
Will Deep And Machine Learning Solve Our Forecasting Pro blems?
Forecasting The Impact Of Artificial Intelligence: The Emerging And
Long -Term Future, Forecasting The Impact Of Artificial Intelligence:
Another Voice, Smarter Supply Chains Through Ai, Continual Learning:
The Next Generation Of Artificial Intellige nce, Assisted Demand
Planning Using Machine Learning, Maximizing Forecast Value Add
Through Machine Learning And Behavioral Economics, The M4
Forecasting Competition – Takeaways For The Practitioner
Big Data in Forecasting : Is Big Data The Silver Bullet For Supply -
Chain Forecasting? How Big Data Could Challenge Planning Processes
Across The Supply Chain 12
IV Forecasting Methods : Modeling, Selection, and Monitoring : Know
Your Time Series, A Classification Of Business Forecasting Problems,
Judgmental Model Selection, A Judgment On Judgment, Could These
Recent Findings Improve Your Judgmental Forecasts? A Primer On
Probabilistic Demand Planning, Benefits And Challenge s Of Corporate
Prediction Markets, Get Your Cov On, Standard Deviation Is Not The
Way To Measure Volatility, Monitoring Forecast Models Using Control
Charts, Forecasting The Future Of Retail Forecasting 12

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Forecasting Performance : Using Error Analysis To Improve Forecast
Performance, Guidelines For Selecting A Forecast Metric, The Quest For
A Better Forecast Error Metric: Measuring More Than The Average
Error, Beware Of Standard Prediction Intervals From Causal Models
V Forecasting Process : Communicati on, Accountability, and S&OP : Not
Storytellers But Reporters, Why Is It So Hard To Hold Anyone
Accountable For The Sales Forecast? Communicating The Forecast:
Providing Decision Makers With Insights, An S& Op Communication
Plan: The Final Step In Support Of Company Strategy, Communicating
Forecasts To The C -Suite: A Six -Step Survival Guide, How To Identify
And Communicate Downturns In Your Business, Common S& Op
Change Management Pitfalls To Avoid, Five Steps To Lean Demand
Planning,The Move To Defensive Business Forecasting
Case Studies : Business Demand Forecast Case Study, Sales Forecasting
Case study, Demand Forecasting for a Call Centre , Market Trend for
Product Forecasting 12


Books and References:
Sr. No. Title Author/s Publisher Edition Year
1. Business Forecasting Michael Gilliland,
Len Tashman Wiley 1st 2016
2. Business Forecasting Michael Gilliland,
Len Tashman, et.al. Wiley 1st 2021
3. Demand -Driven
Forecasting: A
Structured Approach to
Forecasting Charles W. Chase Jr. Wiley 2nd 2013
4. Sales and Market
Forecasting for
Entrepreneurs Tim Berry Business
Expert Press 1st 2010

Course Outcome:
Upon the successful completion of this course, students will be able to:

 Explain various notions/concepts/principles in time series analysis and forecasting.
 Choose and use the standard techniques of time series analysis to analyse real data, and
build appropriate forecasting models.
 Review and interpret models and forecasting results critically.
 Construct written work, which is logically and profe ssionally presented.
 Master a modern statistical computer language and Master problem solving as a team.

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USDS505 c: Marketing and Retail Analytics
B. Sc (Data Science) Semester – V
Course Name: Marketing and Retail Analytics Course Code: USDS505 c
Periods per week (1 Period is 50 minutes) 5
Credit 2
Hours Marks
Evaluation System Theory Examination 2½ 75
Internal -- 25

Course Description:
Marketing analytics enables marketers to measure, manage and analyse customer preferences
and trends, as well as evaluate marketing performance to maximize its effectiveness. Students
will develop an understanding how to use marketing analytics to predict outcomes.

Course Objectives:

 Students will have a command of business theory and practice in the field of business
forecasting.
 To learn different forecasting models/techniques both quantitative and qualitative.
 Students will use reasoned and ethical judgment when analyzing problems and making
decisions.
 Students will be able to understand complex business sit uations and provide solutions to
improve current business practices.
 Students will be effective communicators.

Unit Details Lectures
I Summarize Marketing Data, Pricing, Forecasting 12
II Customer Analysis, Customer Value, Market Segmentation 12
III Forecasting New Product Sales, Retailing, Advertising , Marketing
Research Tools, Internet and Social Marketing 12
IV Retailing Analytics: An Introduction, Retail and Data Analytics,
Importance of Geography and Demographics 12
V In-Store Marketing and Presentation, Store Operations and Retail Data,
Loyalty Marketing 12


Books and References:
Sr. No. Title Author/s Publisher Edition Year
1. Marketing Analytics Wayne L. Winston John Wiley
& Sons -- --
2. Retailing Analytics Emmett Cox John Wiley
& Sons -- --

Course Outcome:

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On successful completion of this course, students will be able to:
1. Demonstrate the use of analytical tools in marketing.
2. Choose appropriate data sources and analytical tools to assess marketing performance.
3. Apply analytics tools to a variety of data collected by marketers.
4. Translate the results of quantitative analyses into managerial insights for marketing
decision -making.
5. Explain and illustrate how marketing analytics are used in an integrated manner to solve
strateg ic marketing problems.

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USDS5P 5: Data Visualisation with Tableau Practical

B. Sc (Data Science) Semester – V
Course Name: Data Visualisation with Tableau
Practical Course Code: USDS5P5
Periods per week (1 Period of 50 minutes) 3
Credits 2
Hours Marks
Evaluation System Practical Examination 2½ 50
Internal -- --

Course Description:
This course introduces students to Tableau, a powerful data visualization and analytics tool.
From installing Tableau Desktop to creating interactive dashboards and sharing insights,
students will learn data preparation, visualization techniques, advanced analytics, and how to
leverage Tableau Online, Tableau Server, and Tableau Public. Additionally, they will gain
proficiency in data cleaning, formatting, and combining using Tableau Prep. By the end of the
course, students will have the skills to analyze d ata effectively and create impactful
visualizations with Tableau.


Course Objectives:
a. To familiarize students with the Tableau software and its key features.
b. To develop students' understanding of data preparation techniques and best practices in
Tableau.
c. To enable students to create a variety of visualizations using different chart types and
design principles in Tableau.
d. To introduce students to advanced analytics capabilities in Tableau for trend analysis,
forecasting, and cluster analysis.
e. To equip stude nts with the skills to share and present insights effectively using Tableau
Online, Tableau Server, and Tableau Public.

Sr. List of Practical
1. Introduction to Tableau - Install, prepare data, navigate workspace, create visualizations,
save/share workbooks.
2. Adding Data Sources - Set up connectors, select tables, perform joins/unions, edit metadata, add
hierarchies/calculated fields, optimize performance.
3. Creating Data Visualizations - Explore chart types, design bar/line/highlight/heatmap/bulle t
charts, understand visualization anatomy.
4. Aggregate Functions and Calculated Fields - Use aggregates/calculated fields, handle text/date
fields, apply logical functions/parameters, search text fields.
5. Table Calculations and Level of Detail Calculatio ns - Perform different calculations, apply
quick/customized table calculations, implement level of detail expressions.
6. Maps in Tableau - Create symbol/filled/density maps, add layers/pie charts, use viz in tooltip,
explore alternative map services, analyze spatial data.
7. Advanced Analytics in Tableau - Identify trends/forecasts/clusters, utilize analytics pane,
incorporate lines/forecasts, perform cluster analysis.
8. Interactive Dashboards - Considerations for dashboard creation, create/place charts , add
titles/navigation/buttons/actions, follow best practices.

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9. Sharing Insights with Tableau - Utilize Tableau Online/Server, publish to Tableau Public, embed
visualizations in websites.
10. Data Preparation with Tableau Prep - Connect to data, perform wi ldcard unions,
inspect/clean/format data, remove unneeded fields, combine data with unions/joins, run/save
flows.

Course Outcome:
Upon the successful completion of this course, students will be able to:
1. Students will be able to install Tableau Desktop and navigate the Tableau workspace
proficiently.
2. Students will demonstrate competence in data preparation, including data connection, joining,
data types, and calculated fields in Tableau.
3. Students will design and create visually compelling and informative visualizations using
various chart types and visualization techniques in Tableau.
4. Students will utilize advanced analytics features in Tableau for trend analysis, forecasting, and
cluster analysis.
5. Students will effectively share their visualizations and insights using Tableau Online, Tableau
Server, and Tableau Public, and demonstrate an understanding of best practices in data sharing
and presentation.

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Semester VI

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USDS601: Machine Learning

B. Sc (Data Science) Semester – VI
Course Name: Machin e Learning Course Code: USDS601
Periods per week (1 Period is 50 minutes) 5
Credit 2
Hours Marks
Evaluation System Theory Examination 2½ 75
Internal -- 25

Course Description:
This course introduces the field of Machine Learning (ML) and its various applications.
Students will gain a comprehensive understanding of the underlying concepts, algorithms, and
techniques used in ML. The course covers supervised and unsupervised learning methods,
classifica tion algorithms, regression models, performance evaluation metrics, clustering
techniques, dimensionality reduction, and association rule mining. Students will also explore
the advantages, disadvantages, and challenges associated with ML

Course Objectives :

 To introduce students to the fundamental concepts and principles of Machine Learning.
 To familiarize students with various types of machine learning algorithms and their
applications.
 To provide hands -on experience in implementing and evaluating machine learning
models.
 To develop critical thinking and problem -solving skills in the context of machine
learning.
 To enable students to apply machine learning techniques to real -world problems and
datasets.
 To understand the limitations and challenges of machi ne learning and develop strategies
to address them.

Unit Details Lectures
I Introduction to Machine Learning: Machine Learning(ML), Need
for Machine Learning, ML from Knowledge -driven to Data Driven,
Applications of Machine Learning, Problems suitable for Machine
Learning, Advantages, Disadvantages and Challenges of Machine
Learning, Challenges of ML. General architecture of ML systems ,
Underlying Concepts in Machine Learning : Inductive Learning,
Generlization, Bias and Variance, Overfitting and Underfitting,
Parametic and Non Parametric algorithms
Types of Machine Learning: Supervised and Unsupervised
Learning, Workflow, Semisupervise d Learning, Reinforced Learning, 12
II Introduction to Classification Algorithms: Concept of
Classification, Binary classification, Multi -Class Classification, Multi
Label Classification 12

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K-Nearest Neighbor Method: need and Working of KNN,
Computing Distance, Pros and Cons of KNN,
Decsion Tree based Algorithm: Terminologies and assumptions,
Working of Decsion Trees, ID3 Alsogithm, Attribute selection
Methods(Entropy, Gini Impurity, Information Gain)
III Support Vector Machines: Workin of SVM, SVM Concepts -
Support Vectors, Hard Margin, soft Margin, Kernels, Advantages and
Disadvantages of SVM
Probablistic Learning: Introduction to Bayes Learning,
Interpretation of Bayes Rule, Benefits and shortfalls of Bayesian
Learning, Naïve Bayes Classifier, Chracteristics of Naïve Bayes
Regression Methods: Linear Regression Models, Logistic
Regression 12
IV Performance Evaluation: Classification Metrics -Accuracy,
Sensitivity, Precision, F1 Score, ROC/AUC Curve, Cross Validation
Unsupervised Learning: Concept of unsupervised Learning,
Importance and Challenges of unsupervised Learning, Clustering and
its applications
Hierarchical Clusturing: Introduction, Types of Hierarchical
Clustering, Issues with Hierarchical Cluste ring, 12
V Partition algorithm: K-means Clustering, steps of K -means
Clustering, Issues, Stength and Weakness of K -means clustering.
Curse of Dimensionality.
Dimensionality Reduction: Crieteria for Reduction, Feature
Reduction and Selection, Principal Co mponent Analysis(PCA)
Association rule Mining: Basic Concepts, Market Basket Analysis,
and Apriori algorithm. 12


Books and References:
Sr. No. Title Author/s Publisher Edition Year
1. Machine Learning:
Concepts, Techniques
and Applications T V Geetha S,
Sendhilkumar CRC Press,
Taylor and
Francis 1st
Edition 2023
2. Machine Learning for
Decision Sciences with
Case Studies in Python S. Sumathi, Suresh V.
Rajappa CRC Press,
Taylor and
Francis 1st
Edition 2022
3. Introduction to Machine
Learning with Python
Andreas C.
Müller, Sarah Guido O'Reilly
Media, Inc. 1st
Edition 2016
4. Machine Learning for
Beginners Harsh Bhasin BPB 1st
Edition 2020
5. Machine Learning S Sridhar Oxford
University
Press 1st
Edition 2021
6. Machine Learning
Ruchi Doshi, Kamal
Kant Hiran BPB 1St
Edition 2021

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Course Outcome s:
Upon the successful completion of this course, students will be able to:

 Understand the foundational concepts and principles of Machine Learning
 Apply supervised and unsupervised learning techniques, including classification algorithms
and clustering algorithms
 Evaluate the performance of Machine Learning models using classification metrics,
ROC/AUC curve analysis, and cross -validation techniques.
 Implement regression models (such as linear regression and logistic regression) and
understand their applications in predictive analysis.
 Utilize dimensionality reduction techniques (Like PCA) for feature reduction and selection,
and apply association rul e mining algorithms (such as the Apriori algorithm) for discovering
meaningful patterns in datasets.

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USDS6P1: Machine Learning Practical

B. Sc (Data Science) Semester – VI
Course Name: Machin e Learning Practical Course Code: USDS6P1
Periods per week (1 Period of 50 minutes) 3
Credits 2
Hours Marks
Evaluation System Practical Examination 2½ 50
Internal -- --

Course Description:

This course provides hands -on experience in implementing various machine learning
algorithms and techniques. Students will gain proficiency in applying classification algorithms
like K -Nearest Neighbor (KNN), decision trees, Support Vector Machines (SVM), and Naïve
Bayes, as well as regression models such as linear regression and logistic regression. They will
also learn about evaluating model performance, utilizing clustering techniques like hierarchical
clustering and K -means clustering, and applying dime nsionality reduction methods,
particularly Principal Component Analysis (PCA). Through practical exercises using real -
world datasets, students will develop the necessary skills to implement and assess machine
learning models effectively.

Course Objectives :

 Gain practical experience in implementing machine learning algorithms.
 Apply machine learning techniques to real -world datasets and problem scenarios.
 Develop skills in model evaluation and performance assessment.
List of Suggested Practical
Sr Practical Details
1 Implementing a K -Nearest Neighbor (KNN) algorithm (e.g. to classify handwritten digits).
2 Building a decision tree model using the ID3 algorithm (e.g. to predict whether a customer will
churn or not).
3 Developing a Support Vector Machine (SVM) model (e.g. to classify email messages as spam or
not spam).
4 Building a Naïve Bayes classifier (e.g. to classify movie reviews as positive or negative
sentiment).
5 Implementing linear regression (e.g. to predict housing prices based on f eatures such as size and
location).
6 Using logistic regression (e.g. to predict whether a credit card transaction is fraudulent or not).
7 Evaluating a classification model using metrics such as accuracy, precision, recall, and F1 score.
8 Applying hierarchical clustering (e.g. to group customer segments based on their purchasing
behavior).
9 Implementing the K -means clustering algorithm (e.g. to identify distinct clusters in a customer
demographic dataset).
10 Utilizing Principal Componen t Analysis (PCA) for dimensionality reduction to improve the
efficiency and interpretability of a model.

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Course Outcome s:
Upon the successful completion of this course, students will be able to:

 Practical implement machine learning algorithms such as KNN, decision trees, SVM,
Naïve Bayes, linear regression, and logistic regression.
 Apply these algorithms to real -world datasets and problem scenarios.
 Be Proficien t in evaluating and assessing the performance of machine learning models
using appropriate m etrics.
 Appl y clustering techniques, including hierarchical clustering and K -means
clustering, for grouping and segmentation tasks.
 Understanding and practical application of dimensionality reduction techniques,
particularly PCA, for feature extraction and interpretation.

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USDS602: Exploratory Data Analysis

B. Sc (Data Science) Semester – VI
Course Name: Exploratory Data Analysis Course Code: USDS602
Periods per week (1 Period is 50 minutes) 5
Credit 2
Hours Marks
Evaluation System Theory Examination 2½ 75
Internal -- 25

Course Description:
Exploratory data analysis (EDA) is used by data scientists to analyse and investigate data sets
and summarize their main characteristics, often employing data visualization methods. It helps
determine how best to manipulate data sources to get the answers you need, making it easier
for data scientists to discover patterns, spot anomalies, test a hypothesis, or check assumptions.
EDA is primarily used to see what data can reveal beyond the formal modelling or hypothesis
testing task and provides a provides a better understanding of data set variables and the
relationships bet ween them. It can also help determine if the statistical techniques you are
considering for data analysis are appropriate.

Course Objectives:
 To understand importance of data and its types in Exploratory Data Analysis.
 To understand difference between E DA and summary statistics in context of
interpretation.
 To understand the importance of data pre -processing for Exploratory Data Analysis.
 To understand the significance of missing value imputations in better EDA
interpretations.
 To understand the importance measure of central tendency in describing the quick view
of data set.
 To understand the importance of measure of dispersion and its interpretation in spread
ness of data.
Unit Details Lectures
I INTRODUCTION TO DATA AND ITS TYPES : Definition and
importance of data, classification of data : based on observation –
Cross Sectional, times series and panel data, based on measurement –
ratio, interval, ordinal and nominal, based on availability – primary,
secondary, tertiary, base d on structural form – structured, semi
structured and unstructured, based on inherent nature – quantitative
and qualitative, concepts on sample data and population, small sample
and large sample, statistic and parameter, types of statistics and its
applic ation in different business scenarios, frequency distribution of
data. 12
II INTRODUCTION TO EXPLORATORY DATA ANALYSIS
(EDA) : Definition of EDA, difference between EDA with classical
and Bayesian Analysis, comparison of EDA with Classical data
summary me asures, goals of EDA, Underlying assumptions in EDA,
importance of EDA in data exploration techniques, introduction to 12

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different techniques to test the assumptions involved in EDA, role of
graphics in data exploration, introduction to unidimensional,
bidim ensional and multidimensional graphical representation of data.
III DATA PREPARATION : Introduction to data exploration process
for data preparation, data discovery, issues related with data access,
characterization of data, consistency and pollution of data, duplicate
or redundant variables, outliers and leverage data, noisy data, missing
values, imputation of missing and empty places, with different
techniques, missing pattern and its importance, handling non
numerical data in missing places. 12
IV UNIVARIATE DATA ANALYSIS : Description and summary of
data set, measure of central tendency – mean: Arithmetic, geometric
and harmonic mean – Raw and grouped data, confidence limit of
mean, median, mode, quartile and percentile, interpretation of quartile
and percentile values, measure of dispersion, concepts on error, range,
variance, standard deviation, confidence limit of variance and standard
deviation, coefficient of variation, mean absolute deviation, mean
deviation, quartile deviation, interquartile r ange, concepts on
symmetry of data, skewness and kurtosis, robustness of parameters,
measures of concentration. 12
V BIVARIATE DATA ANALYSIS : Introduction to bivariate
distributions, association between two nominal variables, contingency
tables, Chi -Squar e calculations, Phi Coefficient, scatter plot and its
causal interpretations, correlation coefficient, regression coefficient,
relationship between two ordinal variables – Spearman Rank
correlation, Kendall’s Tau Coefficients, measuring association
between mixed combination of numerical, ordinal and nominal
variables. 12

Books and References:
Sr. No. Title Author/s Publisher Edition Year
1. Exploratory Data
Analysis John W Tukey Addison
Wesley 1st 1977
2. Exploratory Data
Analysis in Business
and Economics Thomas Cleff
Springer
1st 2014
3. Graphical Exploratory
Data Analysis S.H.C. du Toit
A.G.W. Steyn R.H.
Stumpf Springer 1st 1986
4. Hand book of Data
Visualization Chun -houh Chen,
Wolfgang Härdle Springer 1st 2008

Course Outcome s:
Upon the successful completion of this course, students will be able to:
 Understand importance of data and its types in Exploratory Data Analysis.
 Classify EDA and summary statistics in context of interpretation.
 Understand the significance of missing va lue imputations in better EDA interpretations.
 Analyse the measure of central tendency in describing the quick view of data set.
 Categorize measure of dispersion and its interpretation in spread ness of data.

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USDS5P2: Exploratory Data Analysis Practical

B. Sc (Data Science) Semester – VI
Course Name: Exploratory Data Analysis Practical Course Code: USDS6P2
Periods per week (1 Period of 50 minutes) 3
Credits 2
Hours Marks
Evaluation System Practical Examination 2½ 50
Internal -- --

Course Objectives:
1. Understand the data and its types for the appropriate exploratory data analysis.
2. Understand the importance of Exploratory Data Analysis over summary statistics.
3. Understand the importance Univariate statistics in EDA
4. Plot Univariate statistic al graphs for the better representation and interpretation.
5. Plot bivariate statistical graphs for the better representation and interpretation.

List of Suggested Practical
Sr. Practical Details
1 Managing Data Frames with the dplyr package

2 Use dplyr Grammar for inbuilt data set car.

3 Use group by(), %>%,mutate(), rename(),arrange(), filter(), select()

4 Use the data set air quality from inbuilt data sets library.
a) Use summary statistics and find the important key values from the output
b) Use boxplot and find the interquartile range. Interpret the boxplot and inner and outer
fencing of outliers
c) Check the missing value in the data set and fine the suitable solution for the missing
values.
d) Using histogram, find the distribution of data and gi ve proper comment over the dataset.

5 Use bar plot and identify the difference between bar plot and histograms. Conclude the
appropriate use of bar plot and histogram.

6 Explore the two dimensional data

7 Scatter plot between two variables

8 Five number summary in exploratory data analysis

9 Multiple histogram and multiple boxplots , Multiple scatter plots and colouring the graph

10 Lattice system in R environment and Graphical window in R and its uses

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Course Outcome s:
Upon the successful completion of this course, students will be able to:

 Experiment with exploratory data analysis; use its features in the field of data science.
 Make use of data and its types for the appropriate exploratory data analysis.
 Understand th e importance of Exploratory Data Analysis over summary statistics.
 Interpret and make use of Univariate statistics in EDA
 Build Univariate statistical graphs for the better representation and interpretation.
 Build bivariate statistical graphs for the better representation and interpretation.

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USDS603: Internet of Things

B. Sc (Data Science) Semester – VI
Course Name: Internet of Things (IOT) Course Code: USDS603
Periods per week (1 Period is 50 minutes) 5
Credit 2
Hours Marks
Evaluation System Theory Examination 2½ 75
Internal -- 25

Course Description:
The internet of things lies at the intersection of the physical and digital world. Across industries,
it encompasses an extensive network of web -connected devices that use embedded systems
like sensors and processors to collect, analyze, and act on data fr om various environments.
Through their connection to the internet, each device can communicate over a network without
needing human intervention, which can come in handy for many different tasks.
This course is designed to give an overview of the Internet of Things graduate certificate. It
closely maps to subject focus areas, and is intended to assist the student in understanding the
focus areas. The faculty in this short course also teach graduate courses within the IoT graduate
certificate.

Course Objec tives:
 To learn the main elements of Internet of Things (IoT) systems and how to design and
build them.
 To learn embedded programming and IoT hardware components such as
microprocessors, microsensors and energy harvesters
 To understand how data moves bet ween devices, apps and the cloud
 To able to design & develop application based IOT Devices
 To learn the infrastructure for supporting IoT deployments .

Unit Details Lectures
I The Internet of Things: An Overview : The Flavour of the Internet of Things,
The “Internet” of “Things”, The Technology of the Internet of Things,
Enchanted Objects, Who is Making the Internet of Things?
Design Principles for Connected Devices: Calm and Ambient Technology,
Magic as Metaphor, Privacy, Keeping Secrets, Whose Data Is It Anyway?
Web Thinking for Connected Devices, Small Pieces, Loosely Joined, First -
Class Citizens on The Internet, Graceful Degradation, Affordances.
Internet Principles: Internet Communications: An Overview, IP, TCP, The
IP Protocol Suite (TCP/IP), UDP, I P Addresses, DNS, Static IP Address
Assignment, Dynamic IP Address Assignment, IPv6, MAC Addresses, TCP
and UDP Ports, An Example: HTTP Ports, Other Common Ports, Application
Layer Protocols, HTTP,
HTTPS : Encrypted HTTP, Other Application Layer Protocols. 12
II Thinking About Prototyping: Sketching, Familiarity, Costs versus
Ease of Prototyping, Prototypes and Production, Changing Embedded
Platform, Physical Prototypes and Mass Personalisation, climbing into
the Cloud, Open Source versus Closed Source, Why Closed? Why 12

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Open? Mixing Open and Closed Source, Closed Source for Mass
Market Projects, Tapping into the Community.

Prototyping Embedded Devices: Electronics, Sensors, Actuators,
Scaling Up the Electronics, Embedded Computing Basics,
Microcontrollers, System -on-Chips, Choosing Your Platf orm, Arduino,
developing on the Arduino, Some Notes on the Hardware, Openness,
Raspberry Pi, Cases and Extension Boards, Developing on the
Raspberry Pi, Some Notes on the Hardware, Openness.
III Prototyping the Physical Design: Preparation, Sketch, Iterate, and
Explore, Nondigital Methods, Laser Cutting, Choosing a Laser Cutter,
Software, Hinges and Joints, 3D Printing, Types of 3D Printing,
Software, CNC Milling, Repurposing/Recycling.

Prototyping Online Components: Getting Started with an API,
Mashing Up APIs, Scraping, Legalities, writing a New API,
Clockodillo, Security, implementing the API, Using Curl to Test, Going
Further, Real -Time Reactions, Polling, Comet, Other Protocols, MQ
Telemetry Transport, Extensible Messaging and Presence Protocol,
Constrained Application Protocol. 12
IV Techniques for Writing Embedded Code: Memory Management,
Types of Memory, Making the Most of Your RAM, Performance and
Battery Life, Libraries,Debugging.

Business Models: A Short History of Business Models, Space and
Time, From Craft to Mass Production, The Long Tail of the Internet,
Learning from History, The Business Model Canvas, Who Is the
Business Model For? Models, Make Thing, Sell Thing, Subscriptions,
Customisation, be a Key Resource, Provide Infrastructure : Sensor
Networks, take a Percentage, Funding an Internet of Things Startup,
Hobby Projects and Open Source, Venture Capital, Government
Funding, Crowdfunding, Lean Startups. 12
V Moving to Manufacture: What Are You Producing? Designing Kits,
Designing Pr inted circuit boards, Software Choices, The Design
Process, Manufacturing Printed Circuit Boards, Etching Boards,
Milling Boards. Assembly, Testing, Mass -Producing the Case and
Other Fixtures, Certification, Costs, Scaling Up Software, Deployment,
Correctn ess and Maintainability, Security, Performance, User
Community.

Ethics: Characterizing the Internet of Things, Privacy, Control,
Disrupting Control,Crowdsourcing, Environment, Physical Thing,
Electronics, Internet Service, Solutions, The Internet of Things as Part
of the Solution, Cautious Optimism, The Open Internet of Things
Definition. 12

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Books and References:
Sr. No. Title Author/s Publisher Edition Year
1. Designing the Internet
of Things Adrian McEwen,
Hakim Cassimally WILEY 1st 2014
2. Internet of Things –
Architecture and
Design Raj Kamal McGraw
Hill 1st 2017
3. Getting Started with the
Internet of Things Cuno Pfister O‟Reilly 6th 2018
4. Getting Started with
Raspberry Pi Matt Richardson and
Shawn Wallace SPD 3rd 2016

Course Outcome:
Upon the successful completion of this course, students will be able to:

 Describe what IoT is and how it works today and Recognise the factors that contributed
to the emergence of IoT
 Design and program IoT devices and Use real IoT protocols for communic ation
 Secure the elements of an IoT device
 Design an IoT device to work with a Cloud Computing infrastructure.
 Transfer IoT data to the cloud and in between cloud providers and Define the
infrastructure for supporting IoT deployments

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USDS 6P3: Internet of Things Practical

B. Sc (Data Science) Semester – VI
Course Name: Internet of Things Practical Course Code: USDS6P3
Periods per week (1 Period of 50 minutes) 3
Credits 2
Hours Marks
Evaluation System Practical Examination 2½ 50
Internal -- --

Course Objectives:
 To Learn the concept of Internet of Things
 To understand interfacing of various sensors with Arduino/Raspberry Pi.
 To learn to transmit data wirelessly between different devices.
 To understand how to upload/download sensor data on cloud and server.
 To learn various SQL queries from MySQL database.

Sr Practical Details
0 Starting Raspbian OS, Familiarising with Raspberry Pi Components and interface,
Connecting to ethernet, Monitor, USB.

1 Displaying different LED patterns with Raspberry Pi.

2 Displaying Time over 4 -Digit 7 -Segment Display using Raspberry Pi

3 Raspberry Pi Based Oscilloscope

4 Controlling Raspberry Pi with telegram bot .

5 Fingerprint Sensor interfacing with Raspberry Pi

6 Raspberry Pi GPS Module Interfacing

7 IoT based Web Controlled Home Automation using Raspberry Pi

8 Visitor Monitoring with Raspberry Pi and Pi Camera

9 Interfacing Raspberry Pi with RFID.

10 Installing Windows 10 IoT Core on Raspberry Pi

Course Outcome s:
Upon the successful completion of this course, students will be able to:
 Understand the concept of Internet of Things
 Implement interfacing of various sensors with Arduino/Raspberry Pi.
 Demonstrate the ability to transmit data wirelessly between different devices.
 Demonstrate an ability to upload/download sensor data on cloud and server.
 Examine various SQL queries from MySQL database.

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USDS 604: Applied Business Analytics

B. Sc (Data Science) Semester – VI
Course Name: Applied Business analytics Course Code: USDS604
Periods per week (1 Period is 50 minutes) 5
Credit 2
Hours Marks
Evaluation System Theory Examination 2½ 75
Internal -- 25

Course Description:

The applied business analytics course is designed for students keen to advance their knowledge
on data analytics. The dynamic curriculum in applied business analytics helps the participant
to understand and apply analytics in the business context. Topics c overed are fundamentals of
probabilities, linear regression, and other business data analysis using Microsoft excel.
Business cases from marketing analytics, HR analytics, financial analytics and operation
analytics are covered as part of this course.

Course Objectives:
 To learn b asics of statistical concepts like probability distribution, hypothesis testing
etc.
 To learn about Business Intelligence Tools for Data Analysis.
 To learn about the business analytics methods for discovering the knowledge
 To unde rstand Regression Analysis with Time Series Analysis and forecasting
 To learn to understand various modelling techniques for Optimization and simulation.


Unit Details Lectures
I Introduction to business analytics, describing the distribution of a
variable, finding relationships among variables 12
II Business Intelligence Tools for Data Analysis, Probability and
Probability Distributions, Decision making under uncertainty. 12
III Sampling and Sampling Distributions, Confidence Interval
Estimation, Hypothesis Testing 12
IV Regression Analysis: Estimating Relationships, Regression Analysis:
Statistical Inference, Time Series Analysis and forecasting 12
V Optimization modelling, simulation modelling, analysis of variance
and experimental design 12

Books and References:
Sr. No. Title Author/s Publisher Edition Year
1. Business Analytics
Data Analysis and
decision making S.Christian Albright
and Wayne L.
Winston Cengage 7th 2020
2. The Applied Business
Analytics Casebook: Matthew Drake O’Reilly 3rd 2013

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Applications in Supply
Chain Management,
Operations
Management, and
Operations Research
3. Applied Business
Analytics: Integrating
Business Process, Big
Data, and Advanced
Analytics Nathaniel Lin FT Press
Analytics 1st 2015

Course Outcome s:
Upon the successful completion of this course, students will be able to:

 Understand basics of statistical concepts like probability distribution, hypothesis testing
etc.
 Experiment with Business Intelligence Tools for Data Analysis.
 Make use of the business analytics methods for discovering the knowledge
 Apply Regression Analysis with Time Series Analysis and forecasting
 Apply and Construct various modelling techniques for Optimization and simulation.

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USDS 6P4: Applied Business Analytics Practical

B. Sc (Data Science) Semester – VI
Course Name: Applied Business Analytics Practical Course Code: USDS6P4
Periods per week (1 Period of 50 minutes) 3
Credits 2
Hours Marks
Evaluation System Practical Examination 2½ 50
Internal -- --

Course Description:
This course " provides students with practical skills in using analytics techniques to solve real -
world business problems. The course covers various topics, including data analysis,
probability, hypothesis testing, regression analysis, time series analysis , optimization
modeling, and experimental design. Students will learn how to collect, analyze, and interpret
data to make informed business decisions. They will also gain proficiency in using business
intelligence tools and programming languages such as Py thon for data analysis and
visualization.
Course Objectives:
a. Develop a solid understanding of the fundamental concepts and techniques in business
analytics.
b. Acquire practical skills in data collection, data exploration, and descriptive statistics.
c. Apply st atistical methods to analyze relationships between variables and make
informed business decisions.
d. Utilize business intelligence tools and programming languages for data analysis and
visualization.
e. Gain hands -on experience in applying various analytics tec hniques such as regression
analysis, time series analysis, optimization modeling, and experimental design to solve
business problems.


Sr Practical Details
1. Introduction to Business Analytics:
a. Collect data from a real -life business scenario and perform exploratory data
analysis (EDA) to gain insights into the dataset.
b. Analyze customer data to identify trends and patterns that can be used for
business decision -making.
2. Describing the Distribution of a Variable:
a. Obtain a dataset and calculate descriptive statistics (mean, median, mode,
variance, etc.) for a specific variable of interest.
b. Create visualizations (histograms, box plots) to depict the distribution of a
variable and analyze its characteristics.
3. Finding Relationships Among Variables:
a. Use a dataset with multiple variables and perform correlation analysis to
determine the strength and direction of relationships between pairs of variables.
b. Apply regression analysis to identify the relationship between an independent
variable (e.g., advertising expenditure) and a dependent variable (e.g., sales
revenue).

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4. Business Intelligence Tools for Data Analysis:
a. Utilize a business intelligence tool (e.g., Tableau, Power BI) to extract insights
from a dataset and create interactive visualizations for effect ive data analysis.
5. Probability and Probability Distributions:
a. Simulate a probability experiment (e.g., rolling dice) using programming and
calculate the probabilities of different outcomes.
b. Generate random numbers from various probability distributions (normal,
uniform, exponential) and analyze their properties.
6. Decision Making under Uncertainty:
a. Develop a decision tree model to make business decisions considering
uncertainties and associat ed probabilities at each decision point.
b. Apply the concept of expected value to evaluate different decision alternatives
and select the optimal one.
7. Sampling and Sampling Distributions:
a. Conduct a survey and collect data from a sample population, ensuring proper
sampling techniques are employed.
b. Use the Central Limit Theorem to analyze the sampling distribution of a sample
mean and estimate population parameters.
8. Confidence Interval Estimation:
a. Calculate confidence intervals for population means or proportions using
sample data and interpret the results in a business context.
b. Apply bootstrapping techniques to estimate confidence intervals for non -
parametric statistics.
9. Hypothesis Testing:
a. Formulate null and alternative hypotheses related to a busin ess problem,
conduct a hypothesis test using appropriate statistical tests, and interpret the
results.
b. Perform A/B testing on a website or marketing campaign to evaluate the
effectiveness of different strategies and make data -driven decisions.
10. Regression Analysis and Time Series Analysis:
a. Develop a regression model to predict future sales based on historical data,
assess model performance, and interpret the significance of predictor variables.
b. Apply time series analysis techniques (e.g., ARIMA, exponentia l smoothing)
to forecast future demand for a product or service, and evaluate the accuracy
of the forecasts.
11. Optimization Modeling and Simulation Modeling:
a. Formulate an optimization model (e.g., linear programming, integer
programming) to solve a real -world business problem and analyze the optimal
solution.
b. Use simulation modeling to evaluate different business scenarios, such as
capacity planning, inventory management, or pricing strategies, and assess
their impact on performance metrics.
12. Analysis of V ariance and Experimental Design:
a. Design and conduct an experiment to study the effects of different factors on a
specific response variable, analyze the results using analysis of variance
(ANOVA), and draw conclusions.
b. Implement a factorial experiment and analyze the main effects and interaction
effects of factors using statistical techniques.

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Course Outcome s:
Upon the successful completion of this course, students will be able to:

1. Apply data analysis techniques effectively.
2. Analyze relationships among variables for informed decision -making.
3. Utilize business intelligence tools for data analysis and visualization.
4. Make optimal decisions under uncertainty.
5. Conduct statistical inference for making meaningful conclusions.
6. Apply advanced analytics techniques to solve complex business problems.

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USDS605a: Sports Analytics

B. Sc. (Data Science) Semester – VI
Course Name: Descriptive Statistics Course Code: USDS605a
Periods per week (1 Period is 50 minutes) 5
Credits 2
Hours Marks
Evaluation System Theory Examination 2½ 75
Internal -- 25

Course Objectives:
● To understand the Cricket analytics and cricketr package.
● To understand the use of cricketr package for analysing performances of cricketers.
● To understand the use of cricketr package template.
● To understand the use of Cricpy package for analysing performances of cricketers.
● To understand Cricket analysis with Machine Learning using Octave.

Unit Details Lectures
I Introduction, Cricket analytics with cricketr , Introducing cricketr! : An
R package to analyze performances of cricketers, Taking cricketr for a
spin – Part 1, cricketr digs the Ashes! 12
II cricketr plays the ODIs! cricketr adapts to the Twenty20 International!
Sixer – R package cricketr’s new Shin y avatar, Re -introducing cricketr:
An R package to analyze performances of cricketers 12
III cricketr sizes up legendary All -rounders of yesteryear, cricketr flexes
new muscles: The final analysis, The Clash of the Titans in Test and ODI
cricket, Analyzing performances of cricketers using cricketr template 12
IV Cricket analytics with cricpy, Introducing cricpy:A python package to
analyze performances of cricketers, Cricpy takes a swing at the ODIs
Analysis of Top 4 batsman 12
V Cricpy takes guard for the Twenty20s, Analyzing batsmen and bowlers
with cricpy template, Average runs against different opposing teams,
Other cricket posts in R, Analyzing cricket’s batting legends – Through
the mirage with R, Mirror, mirror … the best batsman of them all?
Cricket analysis with Machine Learning using Octave, Informed choices
through Machine Learning – Analyzing Kohli, Tendulkar and Dravid,
Informed choices through Machine Learning -2 Putting together Kumble,
Kapil, Chandra 12


Books and References:
Sr.
No. Title Author/s Publisher Edition Year
1. 1. Cricket analytics with
cricketr and cricpy :
Analytics harmony with R
and Python Tinniam V
Ganesh Paperback 4th 2023

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2. 2. Cricket 2.0: Inside the T20
Revolution Tim Wigmore
& Freddie
Wilde Treeshade 1st 2022
3. 3. The Three Ws of West Indian
Cricket: A Comparative
Batting Analysis Keith A. P.
Sandiford &
Arjun Tan Paperback 1st s 2002

Course Outcomes:
After completion of the course, a student should be able to:
● To remember and understand the Cricket analytics and its procedures.
● To apply cricketr package for analysing performances of cricketers.
● To understand the use of cricketr package template.
● To analysing performances of cricketers using of Cricpy package.
● To apply and e valuate Cricket analysis with Machine Learning using Octave .

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USDS605b: Healthcare Analytics

B. Sc (Data Science) Semester – VI
Course Name: Healthcare Analytics Course Code: USDS605b
Periods per week (1 Period is 50 minutes) 5
Credit 2
Hours Marks
Evaluation System Theory Examination 2½ 75
Internal -- 25

Course Description:
Health care analytics is a subset of data analytics that uses both historic and current data to
produce actionable insights, improve decision making, and optimize outcomes within the
health care industry. Health care analytics is not only used to benefit health care organizations
but also to improve the patient experience and health outcomes.
The health care industry is awash with valuable data in the form of detailed records. Industry
regulations stipulate that health care providers must retain many of these records for a set period
of time.
This means that health care has become a site of interest for those working with “big data,” or
large pools of unstructured data. As a still -developing field, big data analytics in health care
offers the potential to reduce operation costs, improve efficiency, and treat patients.

Course Objectives:
 To learn basics about Healthcare Analytics.
 To understand the attributes of Electronic Medical Record to learn about Computing
Foundation.
 To understand Measuring Techniques of Healthcare Quality .
 To learn about Making Predictive Models in Healthcare .
 To know about Various Healthcare Predictive Mode ls and learn about Healthcare and
Emerging Technologies.

Unit Details Lectures
I Introduction to Healthcare Analytics : What is healthcare analytics?
Healthcare analytics uses advanced computing technology, Healthcare
analytics acts on the healthcare industry, Healthcare analytics improves
medical care, Foundations of healthcare analytics, Healthcare, Mathematics,
Computer science, History of healthcare analytics, Examples of healthcare
analytics, Using visualizations to elucidate patient care, Predicting
future diagnostic and treatment events, Measuring provider quality and
performance, Patient -facing treatments for diseas e
Healthcare Foundations: Healthcare delivery, Healthcare industry basics,
Healthcare financing, Healthcare policy
Electronic Medical Record -1: The history and physical, Metadata and chief
complaint, History of the present illness, Past medical history, Medications,
Family history, Social history, Allergies, Review of systems , Physical
examination , Additional objective data (lab tests, imaging, and other
diagnostic tests) , Assessment and plan 12
II Electronic Medical Record -2 : The progress (SOAP) clinic al note,
Standardized clinical codesets, International Classification of Disease (ICD), 12

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Current Procedural Terminology (CPT), Logical Observation Identifiers
Names and Codes (LOINC), National Drug Code (NDC), Systematized
Nomenclature of Medicine Clinical Terms (SNOMED -CT), Breaking down
healthcare analytics, Population, Medical task, Data format, Disease
Machine Learning Foundations : Model frameworks for medical decision
making, Tree -like reasoning, Probabilistic reasoning and Bayes theorem,
Criterion tables and the weighted sum approach, Pattern association and
neural networks, Machine learning pipeline, Loading the data, Cleaning an d
preprocessing the data, Exploring and visualizing the data, Selecting
features, Training the model parameters, Evaluating model performance
Computing Foundations : Introduction to databases, Data engineering with
SQL, Case details : Predicting mortality for a cardiology practice, Starting
an SQLite session, Data engineerin g, one table at a time with SQL
III Measuring Healthcare Quality : Introduction to healthcare
measures, US Medicare value -based programs, The Hospital Value -Based
Purchasing (HVBP) program, Domains and measures, The clinical care
domain, The patient - and caregiver -centered experience of care domain,
Safety domain, Efficiency and cost reduction domain, The Hospital
Readmission Reduction (HRR) program, The Hospital -Acquired Conditions
(HAC) program, The healthcare -acquired infections domain, The
patient safety domain, The End -Stage Renal Disease (ESRD) quality
incentive program, The Skilled Nursing Facility Value -Based Program
(SNFVBP), The Home Health Value -Based Program (HHVBP), The Merit -
Based Incentive Payment System (MIPS), Quality, Advancing care
information, Improvement activities, Cost, Other value -based programs, The
Healthcare Effectiveness Data and Information Set (HEDIS), State
measures, Comparing dialysis facilities using P ython, Downloading the data,
Importing the data into your Jupyter Notebook session, Exploring the data
rows and columns,Exploring the data geographically, Displaying dialysis
centers based on total performance, Alternative analyses of dialysis centers,
Com paring hospitals, Downloading the data, Importing the data into your
Jupyter Notebook session, Exploring the tables, Merging the HVBP tables 12
IV Making Predictive Models in Healthcare : Introduction to predictive
analytics in healthcare, Our modeling task predicting discharge statuses for
ED patients, Obtaining the dataset, The NHAMCS dataset at a glance,
Downloading the NHAMCS data, Downloading the ED2013 file,
Downloading the list of survey items, Downloading the documentation file,
Starting a Jupy ter session, Importing the dataset, Loading the metadata,
Loading the ED dataset, Making the response variable, Splitting the data into
train and test sets, Preprocessing the predictor variables, Visit information,
Month, Day of the week, Arrival time, Wai t time, Other visit information,
Demographic variables, Age, Sex, Ethnicity and race, Other demographic
information, Triage variables, Financial variables, Vital signs, Temperature,
Pulse, Respiratory rate, Blood pressure, Oxygen saturation, Pain level,
Reason-for-visit codes, Injury codes, Diagnostic codes, Medical history,
Tests, Procedures, Medication codes, Provider information, Disposition
information, Imputed columns, Identifying variables, Electronic medical
record status columns, Detailed medication information, Miscellaneous
information, Final preprocessing steps, One -hot encoding, Numeric
conversion, NumPy array conversion, Building the models, Logistic
regression, Random forest, Neural network, Using the models to make
predictions, Improving our m odels 12
V Healthcare Predictive Models: Predictive healthcare analytics,
Overall cardiovascular risk, The Framingham Risk Score, 12

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Cardiovascular risk and machine learning, Congestive heart failure,
Diagnosing CHF, CHF detection with machine learning, Other
applications of machine learning in CHF, Cancer, What is cancer?
ML applications for cancer, Important features of cancer, Routine
clinical data, Cancer -specific clinical data, Imaging data, Genomic
data, Proteomic data, breast cancer prediction, T raditional screening
of breast cancer, Breast cancer screening and machine learning,
Readmission prediction, LACE and HOSPITAL scores, Readmission
modelling, Other conditions and events
Healthcare and Emerging Technologies : Healthcare analytics and
the internet, Healthcare and the Internet of Things, Healthcare
analytics and social media, Healthcare and deep learning, What is deep
learning, briefly? Deep learning in healthcare, Obstacles , ethical
issues, and limitations, Obstacles, Ethical issues, Limitatio ns


Books and References:
Sr. No. Title Author/s Publisher Edition Year
1. Healthcare Analytics
Made Simple Vikas Kumar Packt
Publishing 1st 2018
2. Healthcare Analytics:
Foundations and Frontiers Ross M. Mullner
Edward M. Rafalski T&F /
Routledge 1st 2020
2. Hands -On Healthcare
Data Andrew Nguyen Shroff /
O'Reilly 1st 2022
4. AI-First Healthcare: AI
Applications Kerrie L. Holley Shroff /
O'Reilly 1st 2021
5. Healthcare Data Analytics Chandan K. Reddy,
Charu C. Aggarwal Chapman
and
Hall/CRC 1st 2020

Course Outcome:
Upon the successful completion of this course, students will be able to:

 Remember and relate Healthcare Analytic basics .
 Understand and Experiment with the attributes of Electronic Medical Record to learn
about Computing Foundation.
 Apply and Evaluate Measuring Techniques of Healthcare Quality.
 Design and Build Predictive Models in Healthcare.
 Discuss and Modify Various Healthcare Predictive Models and l earn about Healthcare
and Emerging Technologies.

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USDS605c: Data Governance

B. Sc (Data Science) Semester – VI
Course Name: Data Governance Course Code: USDS605c
Periods per week (1 Period is 50 minutes) 5
Credit 2
Hours Marks
Evaluation System Theory Examination 2½ 75
Internal -- 25

Course Description:

This course provides a comprehensive overview of Data Governance, focusing on its
importance, principles, processes, and tools. Students will learn about the fundamental
concepts and practices of Data Governance, including its role in driving business value and the
impact of data on organizational outcomes. The course covers various aspects of Data
Governance, such as the development and implementation of Data Governance programs,
considerations for Data Governance in the public cloud, and the management of data
throughout its life cycle. Additionally, the course explores topics related to data quality, data
protection, monitoring, and the establishment of a culture of data privacy and security.

Course Objectives:
 Understand the concept of Data Governance and its significance in organizations.
 Identify the roles and responsibilities of individuals involved in Data Governance
initiatives.
 Explore the tools and processes used in Data Governance, including the enterprise
dictionary and policy management.
 Learn how to apply Data Governance practices throughout the data life cycle, from data
transformation to monitoring and change management.
 Develop the skills to design and implement eff ective Data Governance programs that
align with organizational priorities and goals.

Unit Details Lectures
I Introduction to Data Governance: Understanding Data Governance, Why
Data Governance is important? Examples of Data Governance in Action,
Business Value of Data Governance , Developing a Data Governance,
Preparing for Data Governance, Data Governance in Public Cloud
Driving Values through Data: Understanding Impact of Bigdata,
Identifying the Roles of data, improving outcomes with data, 12
II Ingredients of of Data Governance Tools: The Enterprise Dictionary, The
People: Roles, Responsibilities, and Hats, The Process - Diverse Companies,
Diverse Needs and Approaches to Data, People and Process Together -
Considerations, Issues, and Some Successful Strategies
Data Governance over a Data Life Cycle: What Is a Data Life Cycle?
Phases of a Data Life Cycle, Data Life Cycle Management, Applying
Governance over the Data Life Cycle, Operationalizing Data Governance 12

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Governance of Data: Data Transformance, Lineage, Policy Management,
Simulation, Monitoring, Change Management
III Developing Data Governance: Organisations priorities and outcomes,
Identifying data governance roles and responsibilities, Data Governance
leadership groups, Designing and Implementing Data Governance Program
Improving Data Quality: Data Quality, Its Importance, Data Quality as
part of Data Governance Program, 12
IV Data Protection: Planning Protection, Data Protection in the Cloud,
Physical Security, Data Exfiltration, Identity and Access Management,
Keeping Data Protection Agile, Data Protection Best Practices
Monitoring : What is Monitoring? Why and who should perform
monitoring? Monitoring system, Monitoring Criteria and Process 12
V Culture of Data Privacy and Security: Data Culture, Benefits of Data
Governance to Business, Intension, Training, and Communications,
Maintaining Agility, Interplay with Legal and Security, Incident Handling,
Transparency, Responding to data governance challenges and risks 12


Books and References:
Sr. No. Title Author/s Publisher Edition Year
1. Data Governance The Definitive
Guide Evren Eryurek, Uri
Gilad, Valliappa
Lakshmanan O’Reilly 1st
Edition 2021
2. Data Governance for Dummies Jonathan Reichenta Wiley 1st
Edition 2023
3. Practitioner’s Guide to
Operationalizing Data
Governance Mary Anne Hopper Wiley 1st
Edition 2023
4. Data Governance for Managers
Lars Michael Bollweg Springer 1st
Edition 2022
5. Data Governance and Data
Management: Contextualizing
Data Governance Drivers,
Technologies, and Tools Rupa Mahanti Springer 1st
Edition 2021

Course Outcome s:
Upon the successful completion of this course, students will be able to:

 Demonstrate a comprehensive understanding of Data Governance, including its
importance and benefits for organizations.
 Analyze real -world examples of Data Governance in action and evaluate their impact
on business value.
 Apply Data Governance principles an d tools, such as the enterprise dictionary and
policy management, to support effective data management practices.
 Implement Data Governance practices throughout the data life cycle and effectively
operationalize Data Governance within an organization.
 Deve lop strategies for improving data quality, ensuring data protection, establishing
effective monitoring systems, and fostering a culture of data privacy and security.

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USDS6P5: Project Dissertation and Implementation – 2

B. Sc (Data Science) Semester – VI
Course Name: Project Dissertation and
Implementation – 2 Course Code: USDS6P5
Periods per week (1 Period of 50 minutes) 3
Credits 2
Hours Marks
Evaluation System Practical Examination 2½ 50
Internal -- --

Course Description:
The Project Implementation course in B.Sc Data Science builds upon the knowledge and skills
acquired in previous semesters, focusing on the practical implementation of data science
projects. Students will work on real -world projects, applying data science methodologies and
techniques to solve complex problems. They will gain hands -on experience in data collection,
preprocessing, feature engineering, and model development. Through project management and
teamwork, students will learn to effectively plan, exec ute, and monitor projects. The course
emphasizes critical thinking and problem -solving, enabling students to analyze project
outcomes and propose recommendations for improvement.

Course Objectives:

1. To enable students to apply the knowledge and skills acquired during the B.Sc Data
Science program in the implementation of a data science project.
2. To provide students with hands -on experience in implementing data science
methodologies and techniques to so lve real -world problems.
3. To develop students' project management skills by planning, executing, and
monitoring a data science project.
4. To enhance students' collaboration and teamwork abilities by working in groups to
implement a data science project.
5. To fo ster students' critical thinking and problem -solving skills by addressing
challenges and making informed decisions during the project implementation phase.
The documentation and the details are same as mentioned in semester 5.
Course Outcome:
Upon the successful completion of this course, students will be able to:

1. Students will successfully implement a data science project by applying appropriate
methodologies, algorithms, and tools.
2. Students will demonstrate proficiency in data collection, preprocessi ng, feature
engineering, and model development during the project implementation phase.
3. Students will effectively manage project timelines, resources, and deliverables,
demonstrating project management skills.
4. Students will collaborate and work effectively in teams, demonstrating teamwork,
communication, and coordination skills during project implementation.
5. Students will critically analy se project outcomes, evaluate the effectiveness of their
implemented solution, and propose recommendations for future enh ancements.

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Evaluation Scheme

1. Internal Evaluation (25 Marks).
i. Test: 1 Class test of 20 marks. (Can be taken online)
Q Attempt any four of the following: 20
a.
b.
c.
d.
e.
f.

ii. 5 marks: Active participation in the class, overall conduct, attendance.

2. External Examination: (75 marks)
All questions are compulsory
Q1 (Based on Unit 1) Attempt any three of the following: 15
a.
b.
c.
d.
e.
f.

Q2 (Based on Unit 2) Attempt any three of the following: 15
Q3 (Based on Unit 3) Attempt any three of the following: 15
Q4 (Based on Unit 4) Attempt any three of the following: 15
Q5 (Based on Unit 5) Attempt any three of the following: 15

3. Practical Exam: 50 marks

A Certified copy journal is essential to appear for the practical examination.

1. Practical Question 1 20
2. Practical Question 2 20
3. Journal 5
4. Viva Voce 5

OR

1. Practical Question 40
2. Journal 5
3. Viva Voce 5

Prof. Shivram S. Garje,
Dean ,
Faculty of Science and Technology

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