AAMS UG 107 BSc Data Science Programme_1 Syllabus Mumbai University by munotes
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AC- 29/6/2021
Item No: 6.41(R)
UNIVERSITY OF MUMBAI
Syllabus
For the
Program: S.Y.B.Sc. Sem -III &IV CBCS
Course: B.Sc. (Data Science)
(Choice Based and Credit System with effect from the
academic year 2021 -22)
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Sr. No. Heading Particulars
1. Title of the Course B.Sc. (Data Science)
2. Eligibility for
Admission F.Y.B.Sc. Data Science / 3 years Diploma
from MSBTE or equivalent
3. Passing Marks 40%
4. Ordinances /
Regulations (if, any) As applicable for all B.Sc. Courses
5. Number of years /
Semesters Three years – Six Semesters
6. Level P.G./ U.G. / Diploma / Certificate
(Strike out which is not applicable)
7. Pattern Yearly / Semester, Choice Based
(Strike out which is not applicable)
8. Status New / Revised
9. To be implemented
from Academic year From the Academic Year 2021 – 2022
Date: 28/06/2021
Dr. Jagdish Bakal Dr. Anuradha Majumdar
BoS Chairperson in Computer Science Dean, Science and Technology
AC - 29/6/2021
Item No:6.41 (R)
UNIVERSITY OF MUMBAI
Syllabus for Approval
Page 5
SEMESTER 3
Course
Code Course Type Course Name Credits Marks
USDS301 DSC Research Methods and Ethics 2 100
USDS3P1 DSC Research Methods and Ethics Practical 2 50
USDS302 DSC Data Structures and Algorithms using
Python 2 100
USDS3P2 DSC Data Structures and Algorithms using
Python Practical 2 50
USDS303 SEC Economics 2 100
USDS3P3 SEC Economics Practical 2 50
USDS304 DSC Data Warehousing and Mining 2 100
USDS3P4 DSC Data Warehousing and Mining Practical 2 50
USDS305 DSC Linear Algebra and Discrete
Mathematics 2 100
USDS3P5 DSC Linear Algebra and Discrete
Mathematics Practical 2 50
Total 20 750
SEMESTER 4
Course
Code Course Type Course Name Credits Marks
USDS401 DSC Testing of Hypothesis 2 100
USDS4P1 DSC Testing of Hypothesis Practical 2 50
USDS402 DSC Big Data 2 100
USDS4P2 DSC Big Data Practical 2 50
USDS403 SEC Fundamentals of Accounting 2 100
USDS4P3 SEC Fundamentals of Accounting Practical 2 50
USDS404 DSC Artificial Intelligence 2 100
USDS4P4 DSC Artificial Intelligence Practical 2 50
USDS405 DSC Numerical Methods 2 100
USDS4P5 DSC Numerical Methods Practical 2 50
Total 20 750
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Table of Contents
Research Methods and Ethics ................................ ................................ ................................ ......... 6
Research Methods and Ethics Practical ................................ ................................ .......................... 9
Data Structures and Algorithms Using Python ................................ ................................ ............. 11
Data Structures and Algorithms Using Python Practical ................................ .............................. 14
Economics ................................ ................................ ................................ ................................ ..... 16
Economics Practical ................................ ................................ ................................ ...................... 20
Data Warehousing and Mining ................................ ................................ ................................ ..... 21
Data Warehousing and Mining Practical ................................ ................................ ...................... 24
Linear Algebra and Discrete Mathematics ................................ ................................ ................... 26
Linear Algebra and Discrete Mathematics Practical ................................ ................................ .... 28
Testing of Hypothesis ................................ ................................ ................................ ................... 31
Testing of Hypothesis Practical ................................ ................................ ................................ .... 34
Big Data ................................ ................................ ................................ ................................ ........ 35
Big Data Practical ................................ ................................ ................................ ......................... 37
Fundamentals of Accounting ................................ ................................ ................................ ........ 38
Fundamentals of Accounting Practical ................................ ................................ ......................... 40
Artificial Intelligence ................................ ................................ ................................ .................... 42
Artificial Intelligence Practical ................................ ................................ ................................ ..... 44
Numerical Methods ................................ ................................ ................................ ....................... 46
Numerical Methods Practical ................................ ................................ ................................ ........ 48
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SEMESTER 3
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Research Methods and Ethics
B. Sc. (Data Science) Semester – III
Course Name: Research Methods and Ethics Course Code: USDS301
Periods per week (1 Period is 50 minutes) 5
Credits 2
Hours Marks
Evaluation System Theory Examination 2½ 75
Internal -- 25
Course Objectives:
1. To impart analytical skill in solving complex problems.
2. To foster the ability to critically think in developing robust, extensible and highly
maintainable solutions to simple and complex problems.
3. To explore the unknown and unlock new possibilities in different dimensions of the
system.
4. To portray accurately the characteristics of a particular individual, situation or a group
under study.
Unit Details Lectures
I Research Methodology -An Introduction: Meaning of Research,
Objectives of Research, Motivation in Research, Types of Research,
Research Approaches, Significance of Research, Research Methods
versus Methodology, Research and Scientific Method, Importance of
Knowing How Research is Done, Research Process, Criteria of Good
Rese arch, Problems Encountered by Researchers in India
Defining the Research Problem: What is a Research Problem?,
Selecting the Problem, Necessity of Defining the Problem, Technique
Involved in Defining a Problem, An Illustration
Research Design: Meaning of Research Design, Need for Research
Design,Features of a Good Design, Important Concepts Relating to
Research Design, Different Research Designs, Basic Principles of
Experimental Designs.
12
II Sampling Design: Census and Sample Survey, Implications of a Sample
Design, Steps in Sampling Design, Criteria of Selecting a Sampling
Procedure, Characteristics of a Good Sample Design, Different Types of
Sample Designs, How to Select a Random Sample?, Random Sample
from an Infinite Universe, Comp lex Random Sampling Designs
12
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Measurement and Scaling Techniques: Measurement in Research,
Measurement Scales, Sources of Error in Measurement, Tests of Sound
Measurement, Technique of Developing Measurement Tools, Scaling,
Meaning of Scalin g, Scale Classification Bases, Important Scaling
Techniques, Scale Construction Techniques
Methods of Data Collection: Collection of Primary Data, Observation
Method, Interview Method, Collection of Data through Questionnaires,
Collection of Data through Schedules, Difference between
Questionnaires and Schedules, Some Other Methods of Data Collection,
Collection of Secondary Data, Selection of Appropriate Method for Data
Collection, Case Study Method,
(i) Guidelines for Constructing Questionnaire/Schedule
(ii) Guid elines for Successful Interviewing
(iii) Difference between Survey and Experiment
III Processing and Analysis of Data: Processing Operations, Some
Problems in Processing, Elements/Types of Analysis, Statistics in
Research, Measures of Central Tendency, Measures of Dispersion,
Measures of Asymmetry (Skewness), Measures of Relationship, Simple
Regression Analysis, Multiple Correlation and Regression, Partial
Correlation, Association in Case of Attributes, Other Measures , Summary
Chart Concerning Analysis of Data
Sampling Fundamentals: Need for Sampling, Some Fundamental
Definitions, Important Sampling Distributions, Central Limit Theorem,
Sampling Theory, Sandler‘s A-test, Concept of Standard Error,
Estimation, Estimating the Population Mean (m), Estimatin g Population
Proportion, Sample Size and its Determination, Determination of Sample
Size through the Approach Based on Precision Rate and Confidence
Level, Determination of Sample Size through the Approach, Based on
Bayesian Statistics
Testing of Hypothese s: What is a Hypothesis?Basic Concepts
Concerning Testing of Hypotheses, Procedure for Hypothesis Testing,
Flow Diagram for Hypothesis Testing, Measuring the Power of a
Hypothesis Test, Tests of Hypotheses, Important Parametric Tests,
Hypothesis Testing of Means, Hypothesis Testing for Differences
between Means, Limitations of the Tests of Hypotheses
12
IV Interpretation of Data and Paper Writing – Layout of a Research
Paper, Journals in Computer Science, Impact factor of Journals, When
and where to publish ?, UGC -CARE, Web of Science, SCOPUS, IEEE,
ACM, Ethical issues related to publishing, Copyright, Data Privacy,
Plagiarism and Self -Plagiarism, Software for detection of Plagiarism.
ShodhShudhhi (PDS), smallseotools.com
Use of Encyclopedias, Research Guides, Handbook etc., Academic
Databases for Computer Science and Information Technology Discipline.
Google Scholar, shodhganga, IEEE Xplore, Resear chGate, IDELS,
DASH
Use of tools / techniques for Research: Chicago, Turabian, MLA and
12
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APA Style, Reference Management Software like EndNote, Zotero or
Mendeley; Software for paper formatting like LaTeX/MS Office/
Scrivener/Open Office/Google Doc/DropBox Paper .
V Ethics in business research: What Are Research Ethics? Ethical
Treatment of Participants, Ethics and the Sponsor, Researchers and Team
Members, Professional Standards, Resources for Ethical Awareness
Think like a Researcher: The Language of Research, Concepts,
Constructs, Definitions, Variables, Propositions and Hypotheses, Theory,
Models, Research and the Scientific Method, Sound Reasoning for
Useful Answers
E-Research : Introduction, The Internet as o bject of analysis, Using
websites to collect data from individuals. Virtual ethnography,
Qualitative research using online focus groups, Qualitative research using
online personal interviews, Online social surveys, Ethical considerations
in e-research, The state of e-research
12
Books and References:
Sr. No. Title Author/s Publisher Edition Year
1. Research Methodology –
Methods and techniques C. R. Kothari New Age
International
(P) Ltd., --- ---
Publishers
2. Business Research Methods Donald R.
Cooper Pamela
Schindler McGraw -
Hill/Irwin 12th Ed
3. Business Research Methods Allan Bryman
Emma Bell OXFORD
University
Press --- ---
4. RESEARCH
METHODOLOGY - a step
by step guide for beginners Ranjit Kumar SAGE
Publication
Ltd --- ---
5. Research Methods for
Business Students Mark Saunders
Philip Lewis
Adrian Thornhill Pearson
Education
Limited --- ---
Course Outcome:
CO 1: Learner understands the reasons for doing research, the applications of research,
characteristics and requirements of the research process, types of research and Research
paradigms.
CO 2: Learner is applying major approaches to information gathering, the relationship between
attitudinal and measurement scales Methods for exploring attitudes in research.
CO 3: Learner is able to analyze data in qualitative and quantitative studies, application of IT in
data analysis.
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CO 4: Learner is able to write a research report and use Information Technology in Research
CO 5: Learneris practicing ethical codes and practices of c onduct research.
Research Methods and Ethics Practical
B. Sc. (Data Science) Semester – III
Course Name: Research Methods and Ethics
Practical Course Code: USDS3P1
Periods per week (1 Period is 50 minutes) 3
Credits 2
Hours Marks
Evaluation System Practical Examination 2½ 50
Internal --
List of Practical:
1. Introduction to LaTex
a. Report Writing: report style having chapter, section and subsection, article style
having section, subsection and subsubsection , Automatic generation of table of
contents, toc file to store the information that goes into the table of contents,
Automatic numbering of section numbers
b. Equations and Numbering Equations: Creating an equation, writing multiple
equations, Aligning multiple equations, creating matrices in Latex, label
command, Cross referencing with ref command
c. Tables and Figures: Tables and Figures Creating tables and figures in LaTeX
d. Bibliography: Bibliography Creating Bibliography in LaTeX
2. Introduction to EndNote, Zotero or Mendeley
a. Integration with Word and adding citation and creating bibliographies
b. Creating your own library
c. Creating references from website
d. Creating references manually
3. Visit the college library or nearby research center or from internet collect 5 tittles
of research papers/thesis and classify them according to types of research,
Discuss how the problems are delineated, how they are relevant to scientific
method etc.
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4. Identify 2 researchable problems relevant to your context and knowledge
disciplines and justify the significance of their study.
5. Preparation of a review article
6. Identification of variables of a research study and their classification in terms of
functions and level of measurement
7. Preparation of a sampling design given the objectives and research
questions/hypotheses of a research study
8. Preparation of questionnaire for micro -level educational survey.
9. Prepare 1 proposal on an identified research problem
10. Checking and removing plagiarism using Plagiarism Detection Software
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Data Structures and Algorithms Using Python
B. Sc. (Data Science) Semester – III
Course Name: Data Structures and Algorithms Using
Python Course Code: USDS302
Periods per week (1 Period is 50 minutes) 5
Credits 2
Hours Marks
Evaluation System Theory Examination 2½ 75
Internal -- 25
Course Objectives:
1. To learn the essential Python data structures.
2. To learn the significant Python implementation of popular data structures
3. To learn about various data structure algorithms and design paradigms
4. To acquire knowledge of how to create complex data structures.
5. To acquire basic understanding of complex data structures such as trees an d graphs and
their applications
Unit Details Lectures
I Python Objects & Object -Oriented Programming: Goals,
Principles, and Patterns, Overview of data types and objects, Classes
and object programming, Class Definitions, Inheritance, Data
encapsulation and properties, Namespaces and Object -Orientation,
Shallow and Deep Copying
Python Data Types and Strutures: Modules for data structures and
algorithms - Collections, Deques, ChainMap objects Counter, Counter
objects, Ordered dictionaries defaultdict, Learning about named tuples
Arrays
Principles of Algorithm Design: An introduction to algorithms,
Algorithm design paradigms Recursion and backtracking,
Backtracking, Divide and conquer - long multiplication The recursive
approach Runtime analysis Asymptot ic analysis Big O notation,
Composing complexity classes Omega notation, Theta notation,
Amortized analysis
12
II Lists and Pointer Structures :Arrays -Pointer structures 12
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Singly linked lists -Singly linked list class, The append operation, A
faster append operation, Getting the size of the list, Improving list
traversal, Deleting nodes, List search, Clearing a list
Doubly linked lists -A doubly linked list node Doubly linked list class
Append operation The delete operation List sea rch
Circular lists -Appending elements, Deleting an element in a circular
list, Iterating through a circular list
Stacks : Stack implementation, Push operation, Pop operation, Peek
operation, Bracket -matching application,
III Queues :List-based queues, Stack -based queues Node -based queues,
Application of queues Media player queues
Trees : Terminology, Tree nodes, Tree traversal ,Depth -first traversal -
In-order traversal and infix notation, Pre -order traversal and prefix
notation, Post -order traver sal and postfix notation, Breadth -first
traversal, Binary trees -Binary search trees,Binary search tree
implementation, Binary search tree operations, Finding the minimum
and maximum nodes Inserting nodes Deleting nodes, Searching the tree,
Benefits of a bi nary search tree, Balancing trees, Expression
trees,Parsing a reverse Polish expression, Heaps , Ternary search tree
12
IV Hashing and Symbol Tables: Hashing - Perfect hashing functions Hash
tables -Storing elements in a hash table, Retrieving elements from the
hash table, Testing the hash table, Using [] with the hash table, Non -
string keys, Growing a hash table, Open addressing, Chaining, Symbol
tables
Graphs and Other Algorithms: Graphs -Directed and undirected graphs,
Weighted graphs, Graph representa tions,Adjacency lists, Adjacency
matrices, Graph traversals - Breadth -first traversal, Depth -first search,
Other useful graph methods, Priority queues and heaps - Insert operation,
Pop Operation, Selection Algorithm
12
V Sorting: Sorting algorithms - Bubble sort algorithms, Insertion sort
algorithms, Selection sort algorithms, Quick sort algorithms
Selection Algorithms: Selection by sorting, Randomized selection -
Quick Select, Deterministic selection -Pivot selection Median of medians
Partitioning step
Pattern Matching Algorithms: The brute -force algorithm, The Rabin -
Karp algorithm
12
Books and References:
Sr. No. Title Author/s Publisher Edition Year
1. Hands -On Data Structures
andAlgorithms with Python Basant Agarwal,
Benjamin Baka Packt
Publishing 2nd 2018
2. Data Structure and
algorithm Using Python Rance D. Necaise Wiley India
Edition 2016
3. Data Structure and
Algorithm in Python Michael T.
Goodrich,
RobertomTamassia Wiley India
Edition 2016
Page 15
, M. H. Goldwasser
4. Data Structure and
Algorithmic Thinking with
Python NarasimhaKaruma
nchi Careermonk
Publications 2015
5. Fundamentals of Python:
Data Structures Kenneth Lambert Delmar
Cengage
Learning 2018
Course Outcomes:
CO 1 : Learner is capable of choosing appropriate data structure in Python for specified problems
and algorithms.
CO 2 : Learner is able to implement Linked list and Stack data structure in various domains.
CO 3 : Learner is able to implement Tree and Queue data structures and use their operatio n.
CO 4 : Learner has ability to apply of Hashing techniques, Symbol Table and Graph Algorithms
appropriately.
CO 5 : Learner has skills to handle sorting, searching and pattern matching on various data
structures.
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Data Structures and Algorithms Using Python
Practical
B. Sc. (Data Science) Semester – III
Course Name:Data Structure and Algorithm Using
Python Practical Course Code: USDS3P2
Periods per week (1 Period is 50 minutes) 5
Credits 2
Hours Marks
Evaluation System Theory Examination 2½ 75
Internal -- 25
List of Practical:
1 General Python Programs
A Write Python Program to demonstrate the use of various Python Data Types and
Structures
B Write Python Program to demonstrate OOP Concepts including Class, Objects,
Inheritance and encapsulation.
C Write Python Program to implement array and operations of arrays.
2 List and Pointer Structure
A Write Python Program to create singly linked list and various operations on it..
B Write Python Program to create doubly linked list and various operations on it.
c Write Python Program to create circular linked list and various operations on it.
3 Stacks and Queues
a. Write Python Program to implement stack and demonstrate push, pop and peek
operations.
b. Write Python Program to implement stack for Bracket -matching application
c. Write Python Program to implement list based queues and demonstrate various
operations on it.
d. Write Python Program to implement stack based queues and demonstrate various
operations on it.
e. Write Python Program to implement Node based queues and demonstrate various
operations on it.
f. Write Python Program to implement queue data structure for simulating media
player playlist queues.
4 Trees
a. Write Python Program to implement tree data structure and demonstrate depth -
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first traversal
b. Write Python Program to implement tree data structure and demonstrate breadth -
first traversal
c. Write Python Program to implement binary search tree to find the minimum
node.
d. Write Python Program to implement binary search tree to find the minimum
node.
e. Write a Python implementation to demonstrate the insert and delete method to
add/delete the nodes in the BST.
f. Python implementation to search the node in the BST
g. Write a python program build up a tree for an expression written in postfix
notation and evaluate it.
5 Hashing and Symbol Tables
a. Write a Python Program to demonstration of computing Hash for given strings.
b. Write a Python program to implement hash table for storing and searching values
from it.
c. Write aa Python Program to create Symbol Table
6 Graphs
a. Write a Python program to store and display Graph data structure using
adjacency matrix.
b. Write a Python Program to implement Graph traversal (BFS/DFS) based on
above practical.
c. Write a Python program to implement priority queue and heap operations
7 Searching
a. Write a Python Program for implementation in Python for the linear search on an
unordered list of items
b. Write a Python Program for implementation in Python for the linear search on an
ordered list of items
c. Write a Python Program for implementation of the binary search algorithm on an
ordered list of items
d. Write a Python Program for implementation of implementation of the interpolation
search algorithm
8 Sorting
a. Write a Python Program for implementing Insertion Sort.
b. Write a Python Program for implementing Bubble Sort.
c. Write a Python Program for implementing Quick Sort.
d. Write a Python Program for implementing Selection Sort.
9 Selection Algorithms
a. Write a Python Program to implement Randomized Selection
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b. Write a Python Program to implement Deterministic Selection
10 Application
a. Write a Python Program to create an application for storing Polynomial
b. Write a Python Program to create an application for adding two Polynomials
Economics
B.Sc. ( Data Science) Semester – III
Course Name: Economics Course Code:USDS303
Periods per week (1 Period is 50 minutes Lectures 5
Credits 2
Hours Marks
Evaluation System Theory
Examination 2½ 75
Theory
Internal -- 25
Course Objectives:
1. To understand Fundamental economic ideas and the operation of the economy on a
national scale.
2. Basic Understanding of production, distribution and consumption of goods and services,
the exchange process, the role of government, the national income and its distribution,
GDP, consumption function, savings function, investment spending and the multiplier
principle
3. Acquire ba sic knowledge of the influence of government spending on income and output.
4. Develop ability to analyze monetary policy, including the banking system and the Federal
Reserve System.
Unit Details Lectures
I First Principles: What is the difference between macro and micro
economics? The central choices of economic decision making: what,
how and for whom to produce? The participants in the market
economy Key concepts used in economic analysis: Scarcity, choice,
opportunity cost Marginal analysis and choice Ce teris Paribus or
‗everything else held constant.‘ Positive and normative economics and
using theories and models to measure economic events Criteria for
evaluation of economic policy and policy proposals Economic
systems – the market economy, mixed economies & command
economies Review of expressing relationships between economic
variables using graphs
Economic Models: Trade -offs and Trade: Defining the resources 12
Page 19
used in the production of goods and services The production
possibility frontier applied to the concept of opportunity cost/tradeoffs
and to marginal costs and benefits; increasing marginal opportunity
costs. Productive efficiency; inefficient choices and unat tainable
choices, Using the frontier to illustrate economic growth, attainment
of new resources, technological change, and more efficient
production. Comparative advantage and the gains from trade The
circular flow of income, product and services in the ec onomy
II Supply and Demand: Product and Resource Markets – Role of
households (consumers) and firms What is a market? Consumer
demand and the ―Law of Demand‖ Law of Demand: the inverse
relationship between price and quantity demanded Change in quantity
demand ed vs. shift in d emand: the concept of ―ceteris paribus‖ Causes
of a shift in demand: changes in income, expectations, number of
consumers, tastes and preferences; Normal and inferior goods Law of
Supply: The positive relationship between price and quantity supplied.
Change in quantity supplied vs. a shift in supply C auses of a shift in
Supply: changes in cost of resources, prices of related goods,
technology, expectations of producers, number of producers
Applications (examples) of Demand and Supply graphs; Market
demand, market supply and market equilibrium Governmen t price
controls: price ceilings, price floors
Macroeconomics: Theory and Policy: The Business Cycle in Market
Economies; short -term vs. long -term growth trend Expansion, peak,
decline, trough Emergence of modern -day macroeconomic policy to
moderate effects of recessions: Keynesian policy/government
spending and taxation to stimulate aggregate demand Components of
aggregate demand and aggregate supply Shifts in the AD and AS
curves: What do they show? The roots of macroeconomics: John
Maynard Keynes a nd the Great Depression Classical vs. Keynesian
economics; the short -run vs. long run model of macroeconomic
equilibrium The Keynesian short -run model and the classical
economists‘ long -run model Keynes‘ challenge to Say‘s Law: the
Demand Driven Economy Wa ge and Price inflexibility; The role of
Government Concerns of Inflation (boom times) and deflation (severe
economic downturns) The impact of recession on trade imbalances 12
III Unemployment and Inflation: How is the labor force defined? Who
is in the la bor force? Measuring employment and unemployment.
Who is not counted in the Government‘s official count of the
unemployed? Types of unemployment; cyclical unemployment and
the business cycle. The difference between the ‗household survey‘
(the civilian labo r force) and the ‗establishment survey‘ (number of
payroll jobs added by employers). The labor force participation rate
Unemployment and the changes in the global economy
Gross Domestic Product: Measuring the economy‘s output of goods
and services; Government Sector: federal state and local government 12
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in the economy The financial sector; the international sector The three
markets: goods and service, labor market, money market Nominal and
real GDP; The difference between GNP and GDP Expen diture
Measure of GDP: consumption by households, businesses,
government and the rest of the world (Net exports) Income Measure
of GDP: Income from labor, rent, interest, proprietors‘ income, profit
Value added approach vs. measure of final goods and servi ces
produced What GDP Does Not Include; alternative measures of GDP
IV Measuring inflation the consumer price index: What does it say
about the state of the economy? Real vs. nominal income and earnings
Real and Nominal rates of interest Costs and causes of inflation
Fiscal policy: Defining fiscal policy: taxation and spending to achieve
macroeconomic goals The role of government in the U.S. economy
Fiscal policy and the Recession of 2007 – 2009 The Employment Act
of 1946 A history of U.S. fiscal policy s ince the early 20th century
The multiplier effect Government spending and taxation Automatic
stabilizers: the income tax, unemployment insurance Discretionary tax
and spending policy Progressive, proportional and regressive taxes
and their impacts Fiscal P olicy Lags The circular flow diagram with
government spending and taxation Budget deficits and surpluses;
Government debt and deficits: Are they the same thing? 12
V Money, Banking and the Federal Reserve System: What is money?
Commodity and fiat monies; the barter system Money as a medium of
exchange; Money supply defined: M1 and M2 Gold and the money
supply: Why was the gold standard adopted (1873) and why was it
later eliminated (1971)? Monetary role of banks; Establishment of
bank reserves; The T -accou nt (assets and liabilities) Bank regulation:
the FDIC deposit insurance; capital requirements; the discount
window at the Fed.
Monetary Policy: The structure of the Federal Reserve System How
the Fed regulates the money supply: reserve requirements, the
discount rate, open market operations; the goals of monetary policy
The federal funds rate; fed funds market Banking legislation and
deregulation since the 1980 ‘s Growth of the ―Shadow Banking
System‖ and the financial crisis of 2007 -2009 The role of credit, debit
cards and electronic money in the money supply Role of financial
intermediaries – modern depository institutions Savings and Loan
crisis of the late 1980‘s The financial crisis of 2008 and the Federal
Reserve‘s policy response How the banking system creates and
expands money in circulation The difference between treasury bonds
and bonds issued by the Fed Fed Policies during the 2007 – 2009
Recession 12
,
Books and References:
Sr. No. Title Author/s Publisher Edition Year
Page 21
01 Macroeconomics Krugman and
Wells, Eds., Worth
Publishers 3 rd 2012
02 Macroeconomics Leeds, Michael
A., von Allmen,
Peter and
Schiming,
Richard C Pearson
Education 1st 2006
03 Lectures in Quantitative
Economics
with Python Thomas J.
Sargent and John
Stachurski ---- ---- 2019
Course Outcome
CO1: Learner understands the basic economic decisions that underline the economic process:
What and how to produce goods and services and how they are distributed.
CO2: Learner is able to apply of the concepts of scarcity, choice and opportunity cost to analyze
the workings of a market economy.
CO3: Learneris able to demonstrate a firm knowledge of the interrelationships among consumers,
government, business and the rest of the w orld in the U.S. macroeconomy.
CO4: Learner is able to identify the process of how the nation‘s output of goods and services is
measured through the national income and product accounts; clearly comprehend the income and
expenditure approaches to measuring national output and national income.
CO5: Learner is capable to clearly illustrate the specific roles and functions of monetary and
fiscal policy in the economy and explain how these are applied to the process of shaping
economic policy and stabilizing the economy, specifically regarding controlling inflation,
promoting full employmentand facilitating economic growth.
Page 22
Economics Practical
B. Sc. (Data Science) Semester – III
Course Name: Economics Practical Course Code: USDS3P3
Periods per week (1 Period is 50 minutes) 3
Credits 2
Hours Marks
Evaluation System Practical Examination 2½ 50
Internal --
List of Practical:
1 Application to Asset Pricing using Geometric series for elementary economics in
R/python/scilab/matlab .
2 Cass -Koopmans Optimal GrowthModel using R/python/scilab/matlab.
3 a. Job Search andSeparation using R /python/scilab/matlab .
b. Modeling Career Choice using R/python/scilab/matlab.
4 Consumption and Tax Smoothing with Complete and Incomplete Markets using
R/python/scilab/matlab.
5 A Lake Model of Employment and UnemploymentusingR/python/scilab/matlab.
6 Cattle Cycles model usingR/python/scilab/matlab.
7 Von Neumann Growth Model using R/python/scilab/matlab.
8 The Lucas Asset Pricing Model using R/python/scilab/matlab.
9 Implement the optimal government plan model using R /python/scilab/matlab .
10 Credible Government Policies in Chang Model R/python/scilab/matlab.
Page 23
Data Warehousing and Mining
B. Sc. (Data Science) Semester – III
Course Name: Data Warehousing and Mining Course Code: USDS304
Periods per week (1 Period is 50 minutes) 5
Credits 2
Hours Marks
Evaluation System Theory Examination 2½ 75
Internal -- 25
Course Objectives:
1. To understand business intelligence for an enterprise and review data warehouse with
architectural types and architectural building blocks
2. To discuss and understand changing dimensions and learn about aggregate tables and
determine their usage.
3. To learn basics of data mining, understand the need and the process of data mining in
contrast with machine learning.
4. To study the use of classification and clustering techniques for Data Mining.
5. To appreciate the use of various data mining algorithms and learn abo ut their specific
applications.
Unit Details Lectures
I THE COMPELLING NEED FOR DATA WAREHOUSING: Escalating
Need for Strategic Information, Failures of Past Decision -Support
Systems, Operational Versus Decision -Support Systems, Data
Warehousing —The Only Viable Solution, Data Warehouse Defined, The
Data Warehousing Movement, Evolution of Business Intelligence
DATA WAREHOUSE: The Building Blocks: Defining Features, Data
Warehouses and Data Marts, Architectural Types, Overview of The
Components, Metadata in The Data Warehouse
TRENDS IN DATA WAREHOUSING: Continued Growth in Data
Warehousing, Significant Trends, Emergence of Standards, Web -Enabled
Data Warehouse
ARCHITECTURAL COMPONENTS: Understanding Data Warehouse
Architecture, Distinguishing Characteristics, Architectural Framework,
Technical Architecture, Architectural Types
THE SIGNIFICANT ROLE OF METADATA: Why Metadata Is
Important, Metadata Types By Functional Areas, Business Metadata,
12
Page 24
Books and References: Technical Metadata, How To Provide Metadata
II PRINCIPLES OF DIMENSIONAL MODELING: From Requirements to
Data Design, The Star Schema, Star Schema Keys, Advantages of The
Star Schema, Star Schema: Examples
DIMENSIONAL MODELING: ADVANCED TOPICS: Updates to The
Dimension Tables, Miscellaneous Dimensions, The Snowflake Schema,
Aggregate Fact Tables, Families of Stars
DATA EXTRACTION, TRANSFORMATION, AND LOADING: ETL
Overview, ETL Requirements and Steps, Data Extraction, Data
Transformation, Data Loading, ETL Summary, Other Integration
Approaches
12
III INTRODUCTION TO DATA MINING: Introduction to Data Mining,
Need of Data Mining, What Can Data Mining Do and Not Do? Data
Mining Applications, Data Mining Process, Data Mining Techniques,
Difference between Data Mining and Machine Learning
BEGINNING WITH WEKA AND IRIS DATASET IN R: About Weka,
Installing Weka, Understanding Fisher‘s Iris Flower Dataset, Preparing
the Dataset, Understanding A
RFF, Working with a Dataset in Weka, Working with the Iris dataset in R
Data Preprocessing: Need for Data Preprocessing, Data Preprocessing
Methods
CLASSIFICATION: Introduction to Classification, Types of
Classification, Input and Output Attributes, Guidelines for Size and
Quality of the Training Dataset, Introduction to the Decision Tree
Classifier, Naive Bayes Method, Understanding Metrics to Assess the
Quality of Classifiers
12
IV CLUSTER ANALYSIS: Introduction to Cluster Analysis, Applications
of Cluster Analysis, Desired Features of Clustering, Distance Metrics,
Major Clustering Methods/Algorithms, Partitioning Clustering,
HIERARCHICAL CLUSTERING ALGORITHMS:
Web Mining and Search Engines: Introduction, Web Content Mining,
Web Usage Mining, Web Structure Mining, Hyperlink Induced Topic
Search algorithm, In troduction to Modern Search Engines, Working of a
Search Engine, PageRank Algorithm, Precision and Recall
12
V INTRODUCTION TO ASSOCIATION RULE MINING: Defining
Association Rule Mining, Representations of Items for Association
Mining, The Metrics to Evaluate the Strength of Association Rules, The
Naive Algorithm for Finding Association Rules, Approaches for
Transaction Database Storage
THE APRIORI ALGORITHM, Closed and Maximal Itemsets, The
Apriori –TID Algorithm for Generating Association Mining Rules, Direct
Hashing and Pruning (DHP), Dynamic Itemset Counting (DIC), Mining
Frequent Patterns without Candidate Generation (FP Growth)
12
Page 25
Sr. No. Title Author/s Publisher Edition Year
1. DATA WAREHOUSING
FUNDAMENTALS FOR
IT PROFESSIONALS PAULRAJ
PONNIAH Wiley Second 2010
2. Data Mining and Data
Warehousing : Principles
and Practical Techniques Parteek Bhatia Cambridge
University
Press First 2019
3. The Data Warehouse
Toolkit Ralph Kimball
Margy Ross Wiley Third 2013
4. Encyclopedia of Data
Warehousing and Mining John Wang Information
Science
Reference Second 2008
5. Data Mining and Data
Warehousing S.K. Mourya
Shalu Gupta Alpha Science
International
Ltd First 2013
Course Outcomes:
CO1: Learner is able to demonstrate knowledge of business intelligence, data warehouse with
clear understanding of architectural types and will be able to establish the relationship between
architectural building blocks.
CO2: Learner is able to elaborate changing dimensions with respect to current trends & using
aggregate tables.
CO3 : Learner is able to handle the processes of data preprocessing, data transformation and data
reduction.
CO4: Learner has knowledge of using variou s Data Mining techniques for classification and
clustering.
CO5: Learneris able to align the Data Mining techniques for analyzing the datasets using tools
like Weka, R or Python
Page 26
Data Warehousing and Mining Practical
B. Sc. (Data Science) Semester – III
Course Name: Data Warehousing and Mining Practical Course Code: USDS3P4
Periods per week (1 Period is 50 minutes) 3
Credits 2
Hours Marks
Evaluation System Practical Examination 2½ 50
Internal --
List of Practical:
1. Data warehouse design
a. Design dimension tables.
b. Design fact tables.
c. Create an indexed view and rebuild columnstore indexes.
2. Data Warehouse with Azure
a. Create an Azure SQL Data Warehouse Project.
b. Develop tables in Azure SQL Data Warehouse.
c. Migrate Data Warehouse to Azure.
d. Pause and remove Azure data warehouse.
3. Data Warehouse implementation and use
a. Cleanse data with SQL Server Data Quality Services.
b. Create custom knowledge base.
c. Install Master Data Services and IIS.
d. Configure MDS and deploy sample MDS model.
e. Install MDS excel add -in and Update master data in excel.
f. Consume the data from the warehouse.
4. Working with Data and Data Preprocessing
a. Demonstrate the use of ARFF files taking input and display the output of the files.
b. Create your own excel file. Convert the excel file to .csv format and prepare it as ARFF
files.
c. Preprocess and classify Customer datase t.http: //archive.ics.uci.edu/ml/
d. Perform Preprocessing, Classification techniques onAgriculture dataset.
(http:// archive.ics.uci.edu/ml/)
Page 27
e. Preprocess and classify Weather datase t.http: //archive.ics.uci.edu/ml/
f. Perform data Cleansing of customer dataset. http://archive.ics.uci.edu/ml/
www.kdnuggets.com/datasets/
5. Performing classification on data sets
a. Building a Decision Tree Classifier in Weka
b. Applying Naïve Bayes on Dataset for classification
c. Creating the Testing Dataset
d. Decision Tree Operation with R
e. Naïve Bayes Operation using R
f. Classify the dataset using decision tree. www.kdnuggets.com/datasets/
6. Simple Clustering
a. Perform Clustering technique on Customer dataset. http://archive.ics.uci.edu/ml/
b. Perform Clustering technique on Agriculture dataset. http://archive.ics.uci.edu/ml/
c. Perform Clustering technique on Weather dataset. http://archive.ics.uci.edu/ml/
7. Implementing Clustering with Weka and R
h. Clustering Fisher‘s Iris Dataset with the Simple k -Means Algorithm
i. Handling Missing Values
j. Results Analysis after Applying Clustering
k. Classification of Unlabeled Data
l. Clustering in R using Simple k -Means
8. Implementing Apriori Algorithm with Weka and R
a. Applying Predictive Apriori in Weka
b. Applying the Apriori Algorithm in Weka on a Real World Dataset
c. Applying the Apriori Algorithm in Weka on a Real World Larger Dataset
d. Applying the Apriori Algorithm on a Numeric Dataset
9. Implementing Association Mining with R
a. Applying Association Mining in R
b. Application of Association Mining on Numeric Data in R
c. Perform Association technique on Agriculture dataset.
http://archive.ics.uci.edu/ml/,www.kdnuggets.com/datasets/
d. Perform Association technique on Agriculture dataset.
http://archive.ics.uci.edu/ml/ www.kdnuggets.com/datasets/
e. Perform Association technique on Weather dataset.
10. Web Mining
a. Implement Hyperlink Induced Topic Search (HITS) algorithm
b. Implement PageRank Algorithm
Page 28
Linear Algebra and Discrete Mathematics
B. Sc. (Data Science) Semester – III
Course Name: Linear Algebra and Discrete
Mathematics Course Code: USDS305
Periods per week (1 Period is 50 minutes) 5
Credits 2
Hours Marks
Evaluation System Theory Examination 2½ 75
Internal -- 25
Course Objectives
1. To analyze the solution set of a system of linear equations.
2. To interpret existence and uniqueness of solutions geometrically.
3. To formulate, solve, apply, and interpret properties of linear systems.
Unit Details Lectures
I Matrices and Gaussian Elimination: Introduction, The Geometry of
Linear Equations, An Example of Gaussian Elimination, Matrix Notation
and Matrix Multiplication, Triangular Factors and Row Exchanges,
Inverses and Transposes, Special Matrices and Applications
Vector Spaces: Vector Spaces and Subspaces, Solving Ax= 0 and Ax= b,
Linear Independence, Basis, and Dimension, The Four Fu ndamental
Subspaces, Graphs and Networks, Linear Transformations
12
II Orthogonality: Orthogonal Vectors and Subspaces, Cosines and
Projections onto Lines, Projections and Least Squares, Orthogonal Bases
and Gram -Schmidt, The Fast Fourier Transform
Determinants: Introduction, Properties of the Determinant, Formulas for
the Determinant, Applications of Determinants
12
III Eigenvalues and Eigenvectors: Introduction, Diagonalization of a
Matrix, Difference Equations and Powers Ak, Differential Equations and
eAt, Complex Matrices, Similarity Transformations
12
IV Positive Definite Matrices: Minima, Maxima, and Saddle Points, Tests
for Positive Definiteness, Singular Value Decomposition, Minimum
Principles, The Finite Element Method
12
Page 29
Computations with Matrices: Introduction, Matrix Norm and Condition
Number, Computation of Eigenvalues, Iterative Methods for Ax= b
V Linear Programming and Game Theory: Linear Inequalities, The
Simplex Method, The Dual Problem, Network Models, Game Theory 12
Books and References:
Sr. No. Title Author/s Publisher Edition Year
1 Linear Algebra and Its
Applications Gilbert Strang Cengage
Publication Fourth
Edition ----
2. Advanced Linear Algebra David Surowski
3. Linear Algebra, Theory and
Applications Kenneth Kuttlet
Course Outcome:
CO 1: Learner is able to perform common matrix operations such as addition, scalar
multiplication, multiplication, and transposition.
CO 2: Learner is able to describe how the determinant of a product of matrices relates to the
determinant of the individual matrices.
CO 3: Learner expresses clear understanding of the concept of a ‗solution to a game’ and also
the limitations on the applicability of the theory .
Page 30
Linear Algebra and Discrete Mathematics Practical
B. Sc. (Data Science) Semester – III
Course Name:Linear Algebra and Discrete
Mathematics Practical Course Code: USDS3P5
Periods per week (1 Period is 50 minutes) 3
Credits 2
Hours Marks
Evaluation System Practical Examination 2½ 50
Internal --
List of Practical:
1 Matrices and Gaussian Elimination
a Multiplication and transpose of matrix using R/python/scilab/matlab.
b Inverses of matrix inR/python/scilab/matlab without using any inbuilt package.
c Inverses of matrix inR/python/scilab/matlab using any inbuilt package like
numpy.
d Linear equation with n unknowns using Gauss Elimination Method using
R/python/scilab/matlab.
2 Vector
a Addition, subtraction, multiplication anddivision of vector using
R/python/scilab/matlab.
b dot product & cross product of vector using R/python/ scilab/matlab
c Visualising vector Linear Transformations using R/python/scilab/matlab
3
a Computes the orthonormal vectors using the GS algorithmusing
R/python/scilab/matlab.
b Projections and Least Squaresusing R/python/scilab/matlab.
c Fast Fourier Transform using R/python/scilab/matlab.
4
a Finding determinant of matrix inR/python/scilab/matlab without using any inbuilt
package.
Page 31
b Finding determinant of matrix inR/python/scilab/matlab using any inbuilt
package.
5
a Compute the eigenvalues and right eigenvectors of a given square array using
R/python/scilab/matlab.
b Program to test diagonalizable matrix using R/python/scilab/matlab.
6
a Tests for Positive Definitenessusing R/python/scilab/matlab.
b Singular Value Decompositionusing R/python/scilab/matlab.
c The Finite Element Method using R/python/scilab/matlab. (Only
Demonstration)
7 Simplex Method using R/python/scilab/matlab. (Only Demonstration)
8 The Dual Problem using R/python/scilab/ matlab. (Only Demonstration)
9 Implementing Network Models using R/python/scilab/matlab. (Only
Demonstration)
10 Implementing Game Theory using R/python/scilab/matlab. (Only
Demonstration)
Page 32
SEMESTER -4
Page 33
Testingof Hypothesis
B. Sc. (Data Science) Semester – IV
Course Name: Testing of Hypothesis Course Code: USDS401
Periods per week (1 Period is 50 minutes) 5
Credits 2
Hours Marks
Evaluation System Theory Examination 2½ 75
Internal -- 25
Course Objectives:
1. To impart statistical significance in solving complex problems.
2. To critically test in developing robust, extensible and highly maintainable solutions to
simple and complex problems.
3. To implement various statistical functions using suitable prog ramming languages and
packages.
4. To scientifically test the unknown and unlock possibilities in different dimensions of the
system.
5. To write the reports of analytical results generated by the system.
Unit Details Lectures
I Introduction to Hypothesis Testing: Hypothesis Tests, Stating a
Hypothesis, Types of Errors and Level of Significance, Statistical Tests
and P -Values, Making a Decision and Interpreting the Decision,
Strategies for Hypothesis Testing, Characteristics of a good hypothesis,
Steps for hypothesis testing
Hypothesis Testing for the Mean (σ Known): Using P -Values to Make
Decisions, Using P -Values for a z -Test, Rejection Regions and Critical
Values, Using Rejection Regions for a z -Test, Critical Values in a t -
Distribution, The t -Test for a Mean µ, Using P -Values with t -Tests, Sums
and case studies
Packages used for Hypothesis testing: Introduction to statistical
functions in R / Python / Excel, Packages used for finding P -value to
make decision and hypothesis t esting.
12
II Goodness of fit tests: Anderson -Darling, Chi -square test, Kolmogorov -
Smirnov, Ryan -Joiner,Shapiro -Wilk ,Jarque -Bera, Lilliefors
Variance tests: Chi-square test of a single variance, F -tests of two
variances,Tests of homogeneity
Wilcoxon rank -sum/Mann -Whitney U test,Sign test
Contingency tables: Chi-square contingency table test, G contingency
table test, Fisher's exact test, Measures of association, McNemar's test
Packages used for Hypothesis testing: Packages used for finding
goodness of fit test, variance test, Wilcoxon rank -sum / Mann -Whitney U
test and sign test, Using Contingency table in R / Python / Excel.
12
Page 34
III Analysis of variance and covariance: ANOVA , Single factor or one -
way ANOVA, Two factor or two-way and higher -way ANOVA ,
MANOVA, ANCOVA
Non-Parametric ANOVA: Kruskal -Wallis ANOVA, Friedman ANOVA
test, Mood's Median
Packages used for Hypothesis testing: Packages used for finding
Anova, Manova, Ancova and Non -Parametric Anova in R / Python /
Exce l.
12
IV Regression and smoothing: Least squares, Ridge regression, Simple and
multiple linear regression, Polynomial regression, Generalized Linear
Models (GLIM), Logistic regression for proportion data, Poisson
regression for count data, Non -linear regression, Smoothing and
Generalized Additiv e Models (GAM), Geographically weighted
regression (GWR), Spatial series and spatial autoregression - SAR
models, CAR models, Spatial filtering models
Time series analysis and temporal autoregression: Moving averages,
Trend Analysis, ARMA and ARIMA (Box -Jenkins) models, Spectral
analysis
12
V Communicating and Documenting the Results of Analyses:
Introduction, The Difficulty of Good Communication, Communication
Hurdles: Graphical Distortions, Communication Hurdles: Biased Samples
& Sample Size, Preparing Data for Statistical Analysis, Guidelines for a
Statistical Analysis and Report, Documentation and Storage of Results
,Supplementary Exercise
Data Storytelling: What is a Data Story?, The Art and Science of
Storytelling, Planning the Data Story, Elements of the Data Story, Parts
of the Data Story, Framing and Formatting of the Data Story, False
Narratives and Data Storytelling
Infographics: What is an Infographic?, Why are Infographics Useful?
Types of Infographics, Infographic Design Elements, St eps in Designing
an Infographic, Best Practices in Designing an Infographic
12
Books and References:
Sr. No. Title Author/s Publisher Edition Year
1 Hypothesis Testing --- Pearson
Higher
Education --- ---
2 Statistical Analysis
Handbook Dr. Michael J de
Smith The
Winchelsea
Press,
Drumlin
Security
Ltd, 2018
Ed 2018
Page 35
Edinburgh
3 An Introduction to
Statistical Methods
and Data Analysis R. Lyman Ott&
Michael
Longnecker Thomson
Learning --- ---
Course Outcome:
CO 1: Learner is developing null and alternative hypotheses to test for a given situation.
CO 2: Learner is able to differentiateone - and two -tailed hypothesis tests.
CO 3: Learner is able to do samplinga normal distribution and random sampling.
CO 4: Learner is using stati stical models and their associations in performing hypothesis
testing.
CO 5: Lerner is writing the reports and interpreting the data using the various
programming languages and packages.
Page 36
Testing of Hypothesis Practical
B. Sc. (Data Science) Semest er – IV
Course Name: Testing of Hypothesis Practical Course Code: USDS4P1
Periods per week (1 Period is 50 minutes) 3
Credits 2
Hours Marks
Evaluation System Practical Examination 2½ 50
Internal --
List of Practical:
Practical can be performed using R / Python / scilab / matlab / SPSS / MS Excel
1 Hypothesis Testing for the Mean
a Perform testing of hypothesis using one sample t -test.
b Perform testing of hypothesis using two sample t -test.
c Perform testing of hypothesis using paired t -test.
d Perform testing of hypothesis using Z -test.
2 Goodness -of-fit test
a Perform goodness -of-fit test using chi -squared test.
b Perform goodness -of-fit test using KS -test.
c Perform testing of hypothesis using chi -squared Test of Independence
3 Variance Testing
a. Using Chi -square test of a single variance
b. Using F -tests of two variances
c. Testing of homogeneity
4 Analysis of variance and covariance
a. Perform testing of hypothesis using one -way ANOVA.
b. Perform testing of hypothesis using two -way ANOVA.
c. Perform testing of hypothesis using Multivariate ANOVA (MANOVA)
d. Perform testing of hypothesis using one -way ANOVA.
5 Regression
a. Perform simple linear regression
b. Perform multiple linear regression
c. Perform polynomial regression
6 Perform spatial series and spatial auto -regression
7 Perform time series analysis using Moving averages
8 Perform time series analysis using Trend Analysis
9 Perform Spectral analysis
10 Creating ―Infographics‖ using secondary data available on inte rnet. (Use Canva /
Adobe Spark / Prezi / Vennage
Page 37
Big Data
B. Sc. (Data Science) Semester – IV
Course Name: Big Data Course Code: USDS402
Periods per week (1 Period is 50 minutes) 5
Credits 2
Hours Marks
Evaluation System Theory Examination 2½ 75
Internal -- 25
Course Objectives
1. To develop core abilities to make data -driven decisions through big data.
2. To provide an overview of an exciting growing field of big data analytics.
3. To introduce the tools required to manage and analyze big data like Hadoop, NoSql
MapReduce.
4. To teach the fundamental techniques and principles in achieving big data analytics with
scalability and streaming capability.
Unit Details Lectures
I Introduction to Big Data Analytics:
Defining Big Data analytics: Discovering value from large data
sets, Exploiting data to optimize decision -making
Planning your analytics life cycle project: Outlining steps in the life
cycle,Contrasting traditional analytics with Big Data analytics
Representing Big Data with R and Rattle:
Preparing the data: Loading data for knowledge discovery,Spotting
outliers in the data,Transforming and summarizing data
Visualizing data characteristics: Revealing changes over
time,Displaying proportions within your data,Leveraging charts to
display relationships,Displaying relationships across categories 12
II Modeling and Predictive Data Analysis: Categorizing analytic
approaches: Predictive vs. descriptive analytics, Supervised vs.
unsupervised learning
Applying appropriate mining techniques: Discovering unknown
groups through clustering, Detecting relationships with association
rules, Uncovering decision tree classifications, Identifying patterns
with time series analysis 12
III Leveraging Analytics with RHadoop
Expanding the analytic capabilities of your organization
Exploring the MapReduce and Hadooparchitecture,Creating and
executing HadoopMapReducejobs,Integrating R and Hadoop
with RHadoop,Examining the components of RHadoop,Creating
modules for RHadoopjobs,ExecutingRHadoopjobs,Monitoring
job execution flow 12
Page 38
Building a Recommendation Framework: Streamlining business decisions
Considering motivations for a recommender engine,Leveraging
recommendations based on collaborative filtering, Exploring the
architecture of the recommendation framework, Building
programming components, Executing the recommendation
model, Performing tradeoff analysis
IV Mining Unstructured Data
Investigating business value within unstructured data
Making a business case for unstructured data mining, Extending
business intelligence with mining tools
Implementing text mining and social network analysis
Analyzing the structure of text mining, Evaluating mining
approaches, Building a text mining framework, Inspecting social
network interactions 12
V Planning and Implementing a Complete Data Analytics Solution
Transforming business objectives to analytic projects
Arguing your business case for analytics, Mapping analytics
models to business objectives, Identifying performance metrics
targets
Implementing the analytics life cycle
Finding core data sets, Preparing the data for analysis, Modeling
the data, Executing the model, Communicating results
Ensuring a Successful Data Analytics Solution
Identifying barriers to Big Data analytics, Managing and
mitigating risks, Employing an implementation checklist 12
Reference Books:
1. VigneshPrajapati, “Big Data Analytics with R and Hadoop”, Packt
Publishing House.
2. Bart Baesens, Analytics in a Big Data World: The Essential Guide to Data
Science and its Applications (Wiley and SAS Business Series), Wiley
3. ArvindSathi, Big Data Analytics Disruptive Technologies For Changing The
Game, MC Press LLC
4. Viktor Mayer Schonberger, Kenneth Cukier, Big Data,
5. Adam Jorgensen, James Rowland -Jones, John Welch and Dan Clark,
“Microsoft Big Data Solutions”, Wiley
6. http://www.bigdatauniversity.com
Course Outcome:
CO 1: Learner understands the key issues in big data management and its associated applications
in intelligent business and scientific computing.
CO 2: Lerner is acquiring fundamental techniques and algorithms like Hadoop, Map Reduce and
NO SQL in big data analy tics.
CO 3: Learner is able to interpret business models and scientific computing paradigms, and
apply software tools for big data analytics.
CO 4: Learner understands adequate perspectives of big data analytics in various applications
like recommender sys tems, social media applications etc.
Page 39
Big Data Practical
B. Sc. (Data Science) Semester – IV
Course Name: Big Data Practical Course Code: USDS4P2
Periods per week (1 Period is 50 minutes) 3
Credits 2
Hours Marks
Evaluation System Practical Examination 2½ 50
Internal --
List of Practical:
1 a Install, configure and run Hadoop and HDFS
b Implement word count/ frequency program using MapReduce
2 Implement an Mapreduce program that process a weather dataset
3 Exploring Hadoop Distributed File System (HDFS)
4 Implement an application that store big data in Hbase/ Mongodb/ Pig using
Hadoop/R
5 Implement a program in Pig
6 Configure the Hive and implement the application in Hive
7 Illustrate the working of Jaql
8 a Implement Decision tree classification technique
b Implement SVM Classification technique
9 a Regression Model: Import a data from web storage.
Name the dataset and do Logistic Regression to find out relation between
variables that are affecting the admission of a student in an institute based on his
or her GRE score, GPA obtained and rank of the student. Also check the model is
fit or not require (foreign), require (Mass)
b MULTIPLE REGRESSION MODEL: Apply multiple regressions, if data have a
continuous independent variable. Apply on above dataset.
10 a CLASSIFICATION MODEL:
a. Install relevant package for classification.
b. Choose classifier for classification problem.
c. Evaluate the performance of classifier.
b CLUSTERING MODEL
a. Clustering algorithms for unsupervised classification. b. Plot the cluster data
using R visualizations.
Page 40
Fundamentals of Accounting
B. Sc. (Data Science) Semester – IV
Course Name: Fundamentals of Accounting Course Code: USDS403
Periods per week (1 Period is 50 minutes) 5
Credits 2
Hours Marks
Evaluation System Theory Examination 2½ 75
Internal -- 25
Course Objectives
1: To be able to track a company‘s finances in their numerous forms, from credits, debits, and
profitability to payroll and tax filing.
2: To analyzing the organization‘s financial health and apply data science principles/practices
on that information to plot current and future strategies for growth.
Unit Details Lectures
I Accounting in Action: Identify the activities and users associated with
accounting, Explain the building blocks of accounting: ethics, principles,
and assumptions, State the accounting equation, and define its
components, Analyze the effects of business transactions on the
accounting equation, Describe the fou r financial statements and how they
are prepared
The Recording Process: Describe how accounts, debits, and credits are
used to record business transactions, Indicate how a journal is used in the
recording process, Explain how a ledger and posting help in t he recording
process, Prepare a trial balance
12
II Adjusting the Accounts: Explain the accrual basis of accounting and the
reasons for adjusting entries, Prepare adjusting entries for deferrals,
Prepare adjusting entries for accruals, Describe the nature and purpose of
an adjusted trial balance
Completing the Accounting Cycle: Prepare a worksheet, Prepare
closing entries and a post -closing trial balance, Explain the steps in the
accounting cycle and how to prepare correcting entries, Identify the
sections of a classified balance sheet
12
III Accounting for Merchandising Operations: Describe merchandising
operations and inventory systems, Record purchases under a perpetual
inventory system, Record sales under a perpetual inventory system,
Apply the steps in the accounting cycle to a merchandising company,
Compare a multiple -step with a single -step income statement
Inventories: Discuss how to classify and determine inventory, Apply
inventory cost flow methods and discuss their financial effects, Indicate
12
Page 41
the effects of inventory errors on the financial statements, Explain the
statement presentation and analysis of inventory
Accounting Information Systems: Explain the basic concepts of an
accounting information system, Describe the nature and purpose of a
subsidiary ledger, Record transactions in special journals
IV Fraud, Internal Control, and Cash: Discuss fraud and the principles of
internal control, Apply internal control principles to cash, Identify the
control features of a bank account, Explain the reporting of cash.
Accounting for Receivables: Explain how companies recognize
accounts receivable, Describe how companies value accounts receivable
and record their disposition, Explain how companies recognize notes
receivable, Describe how companies value notes receivable, record their
disposition, a nd present and analyze receivables
Plant Assets, Natural Resources, and Intangible Assets: Explain the
accounting for plant asset expenditures, Apply depreciation methods to
plant assets, Explain how to account for the disposal of plant assets,
Describe ho w to account for natural resources and intangible assets,
Discuss how plant assets, natural resources, and intangible assets are
reported and analyzed
12
V Current Liabilities and Payroll Accounting: Explain how to account
for current liabilities, Discuss how current liabilities are reported and
analyzed, Explain how to account for payroll
Accounting for Partnerships: Discuss and account for the formation of
a partnership, Explain how to account for net income or net loss of a
partnership, Explain how to account for the liquidation of a partnership
12
Books and References:
Sr. No. Title Author/s Publisher Edition Year
1 ACCOUNTING
PRINCIPLES Jerry J. Weygandt
Paul D. Kimmel
Donald E. Kieso WILEY 12th
2 MS-EXCEL FOR
CHARTERED
ACCOUNTANTS CA SUNIL B
GABHAWALLA WIRC -
ICAI ---- ----
Course Outcome:
CO 1: Lerner understands the laws governing the business, typical business administration
schemes, and the ethics of accountancy, statistics, and accounting theory.
CO 2: Learner understands the record keeping of financial transactions and further
implementations in relevant area.
Page 42
Fundamentals of Accounting Practical
B. Sc. (Data Science) Semester – IV
Course Name: Fundamentals of Accounting Practical Course Code: USDS4P3
Periods per week (1 Period is 50 minutes) 3
Credits 2
Hours Marks
Evaluation System Practical Examination 2½ 50
Internal --
List of Practical:
Practical can be performed using MS Excel / Google Sheet / WPS Office
1 Introduction to Spreadsheet
a Data entry using spreadsheet :
Text, Number, Formula, Function, Auto fill, Auto Correct and Data Validation
b Using Total and Subtotal:
+, sum() , Quick sum, subtotal(), sumif(), conditional sums, sorting of data
2 Advance functions
a Lookup(), HLOOKUP(), VLOOKUP(), date functions, numeric functions, string
functions, Index(), Match()
b Financial Functions
3 Using paste special
a To demonstrate different types of paste options available in paste special.
4 Analysing Data
a Data tables
b Scenarios
c Goal Seek
5 Pivot Table
a Creating a Pivot Table
b Layout of the PivotTable
6 Auditing Tools
a Auditing Toolbar
b Documenting a Sheet
c Migrating Data from Other Software
d Common Audit Techniques
7 Application of spreadsheet
a Creating Balance Sheet and Balance Sheet Summary
Page 43
b Creating Income Statement and Income Statement Summary
c Creating Cash Flow Statement and Cash Flow Statement Summary
8 Creating a General Ledger
9 Create a Loan Amortization table using the PMT function
10 Automate common tasks using Macros
a Using Global Macros
( download and use Currency conversion, Number to Word conversion etc)
b Creating own Macros (Income tax & GST Macros)
Page 44
Artificial Intelligence
B. Sc. (Data Science) Semester – IV
Course Name: Artificial Intelligence Course Code: USDS404
Periods per week (1 Period is 50 minutes) 5
Credits 2
Hours Marks
Evaluation System Theory Examination 2½ 75
Internal -- 25
Course Objectives:
1. To introduce and appreciate use of AI and the theory underlying for solving problems.
2. To Learn about Rational Intelligent Agent and Agent types to solve problems
3. To learn about representing difficult real life problems as state space representation and
solving them using AI techniques.
4. To understand the basic issues of knowledge representation and develop skills for
reasoning and handling uncertainty
5. To introduce advanced topics of AI for solving complex problems.
Unit Details Lectures
I Intelligent Systems and Intelligent Agents:
Introduction to AI, AI Problems and AI techniques, Solving problems
by searching, Problem Formulation. State Space Representation
Structure of Intelligent agents, Types of Agents, Agent Environments
PEAS representation for an Agent.
12
II Searching Techniques:
Uninformed Search: DFS, BFS, Uniform cost search, Depth Limited
Search, Iterative Deepening.
Informed Search: Heuristic functions, Hill Climbing, Simulated
Annealing, Best First Search, A*
Constraint Satisfaction Programming: Crypto Arithmetic, Map Coloring,
N-Queens.
Adversarial Search: Game Playing, Min -Max Search, Alpha Beta Pruning
12
III Knowledge and Reasoning:
Knowledge Based Agent, Overview of Propositional Logic, First Order
Predicate Logic, Inference in First Order Predicate Logic: Forward and
Backward Chaining, Resolution.
12
IV Uncertainity and Reasoning: Uncertainly, Representing Knowledge in
an Uncertain Domain, Bayesian Network, Conditional Probability, Joint
Probability, Bayes‘ theorem, Belief Networks, Simple Inference in
BeliefNetworks. Sequential decsion problems.
12
V Machine Learning: Forms of Learning, Supervised Learning, Learning
Decision Trees, Evaluating and Choosing the Best Hypothesis, Theory of
Learning, Regression and Classification with Linear Models, Artificial
Neural Networks, Nonparametric Models, Support Vector Machines,
Ensemble Learning, Statistical Learning, Introduction to deep learning
concepts
12
Page 45
Books and References:
Sr. No. Title Author/s Publisher Edition Year
1 Artificial Intelligence: A
Modern Approach Stuart J. Russell
and Peter Norvig Pearson Fourth
Edition 2020
2 Artificial Intelligence:
Foundations of
Computational Agents David L
Poole,Alan K.
Mackworth Cambridge
University
Press Second
Edition 2017
3 Artificial Intelligence Kevin Knight
and Elaine Rich McGraw
Hill 3rd
Edition 2017
4 The Elements of Statistical
Learning Trevor Hastie,
Robert
Tibshirani and
Jerome
Friedman
Springer 2013
5 A First Course in
Artificial Intelligence Deepak
Khemani TMH 1st
Edition 2017
Course Outcome:
CO 1 : Leaner understands building blocks of AI.
CO 2: Learner is analyzing problem and solving it by implementing suitable techniques.
CO 3: Learner is applying logic based techniques to solve examples .
CO 4: Learner is able to implement Bayesian approaches.
CO 5 : Learner is using machine learning concepts for solving problems
Page 46
Artificial Intelligence Practical
B. Sc. (Data Science) Semester – IV
Course Name:Artificial Intelligence Practical Course Code: USDS4P4
Periods per week (1 Period is 50 minutes) 3
Credits 2
Hours Marks
Evaluation System Practical Examination 2½ 50
Internal --
List of Practical:
1. Generate the state -space possiblities for the folling problems
a. Water Jug Problem
b. Number Puzzel
2. Write the program to compute the following Uninformed SearchSearch
Algorithms for suitable problem
a. Depth First Search
b. Breadth First Search
3. Write the program to compute the following Informed SearchSearch
Algorithms for suitable problem
a. Hill Climbing
b. Simulated Annealing
c. A* Algorithm
4. Write the program to compute the following Algorithms for suitable
problem
a. Simulate solution for 4 -Queen / N -Queen problem
b. Constraint satisfaction problem: Map Coloring
5. Write the program to compute the following Search Algorithms for suitable
problem
a. simulation of tic – tac – toe game using Min -Max Search
b. Alpha Beta Pruning
c. Water Jug Problem
6. Write the program to compute the following Algorithms for suitable
problem
a. Missionaries and Cannibals
b. Simple Inferencing
7. Write the program to implement decsion tree for suitable problem.
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a. Two Class decsion
b. Multi class decsion
8. Write the program to compute the following Algorithms for suitable
problem
a. Linear Regression
b. Classification Problems
9. Write the program to solve the following problems using suitable AI
method.
a. Traveling salesman problem
b. Number Puzzel Problem
10. Write the program to demonstrate the following
a. Simple neural network
b. Support Vector Machine
Page 48
Numerical Methods
B. Sc. (Data Science) Semester – IV
Course Name: Numerical Methods Course Code: USDS405
Periods per week (1 Period is 50 minutes) 5
Credits 2
Hours Marks
Evaluation System Theory Examination 2½ 75
Internal -- 25
Course Objectives
1. To be able to precisely solve problems using mathematical modeling.
2. To be able to find solution for a solvable to unsolvable problems.
3. To find an answer or solution close to answer, without even knowing what the answer is.
Unit Details Lectures
I Background and Introduction: Differential Equations, Matrix Analysis,
Matrix Eigenvalue Problem, Errors and Approximations, Iterative
Methods
Numerical Solution of Equations of a Single Variable: Numerical
Solution of Equations, Bisection Method, RegulaFalsi Method, Fixed -
Point Method, Newton‘s Method, Secant Method, Equations with Several
Roots
12
II Numerical Solution of Systems of Equations: Linear Systems of
Equations, Numerical Solution of Linear Systems, Gauss Elimination
Method, LU Factorization Methods, Iterative Solution of Linear Systems,
Ill-Conditioning and Error Analysis, Systems of Nonlinear Equations
12
III Curve Fitting and Interpolation: Least -Squares Regression, Linear
Regression, Linearization of Nonlinear Data, Polynomial Regression,
Polynomial Interpolation, Spline Interpolation, Fourier Approximation
and Interpolation
Numerical Differentiation and Integration: Numerical Differentiation,
Finite -Difference Formulas for Numerical Differentiation, Numerical
Integration: Newton –Cotes Formulas, Numerical Integration of
Analytical Functions:
Romberg Integration, Gaussian Quadrature, Improper Integrals
12
IV Numerical Solution of Initial -Value Problems: Introduction, One -Step
Methods, Euler‘s Method, Runge –Kutta Methods, Multistep Methods,
Systems of Ordinary Differential Equations, Stability, Stiff Differential
Equations
12
Page 49
V Numerical Solution of Boundary -Value Problems: Second -Order
BVP, Boundary Conditions, Higher -Order BVP, Shooting Method,
Finite -Difference Method
Matrix Eigenvalue Problem: Matrix Eigenvalue Problem, Power
Method: Estimation of the Dominant Eigenvalue, Inverse Power Method:
Estimation of the Smallest Eigenvalue, Shifted Inverse Power Method:
Estimation of theEigenvalue Nearest a Specified Value, Shifted Power
Method, Deflation Meth ods, Householder Tridiagonalization and QR
Factorization Methods, A Note on the Terminating Condition Used in
HouseholderQR, Transformation to Hessenberg Form (Nonsymmetric
Matrices)
12
Books and References:
Sr. No. Title Author/s Publisher Edition Year
1. 1 Numerical Methods for
Engineers and
Scientists
Using MATLAB Ramin S.
Esfandiari CRC Press Second
Edition 2017
Course Outcome:
CO 1: Learner implementingNumerical Methodsto solve the problems.
CO 2: Learner is computing the numerical results using raw data.
CO 3: Learner will learn numerical different and integration .
CO 4: Learner will learn Numerical Solution of Initial -Value
CO 5: Learner will learn Matrix Eigenvalue
Page 50
Numerical MethodsPractical
B. Sc. (Data Science) Semester – IV
Course Name: Numerical MethodsPractical Course Code: USDS4P5
Periods per week (1 Period is 50 minutes) 3
Credits 2
Hours Marks
Evaluation System Practical Examination 2½ 50
Internal --
List of Practical:
1 Write a program using R/Python/Scilab/Matlab
a Bisection Method
b Regula Falsi Method
c Newton‘s Method
2 Write a program using R/Python/Scilab/Matlab
a Gauss Elimination Method
b LU Factorization Methods
3 Write a program using R/Python/ Scilab/Matlab
a Numerical Differentiation
b Newton –Cotes Formulas
4 Write a program using R/Python/Scilab/Matlab
a Romberg Integration
b Gaussian Quadrature
5 Write a program using R/Python/Scilab/Matlab
a Euler‘s Method
b Runge –Kutta Methods
6 Write a program using R/Python/Scilab/Matlab
a Second -Order BVP
b Higher -Order BVP
7 Write a program using R/Python/Scilab/Matlab for Finite difference method
8 Write a program using R/Python/Scilab/Matlabfor Estimation of the
Dominant Eigenvalue
9 Write a program using R/Python/Scilab/Matlab for Estimation of the
Smallest Eigenvalue
10 Write a program using R/Python/Scilab/Matlab for Estimation of the
Eigenvalue Nearest a Specified Value