## AAMS UG 107 BSc Data Science Programme_1 Syllabus Mumbai University by munotes

## Page 2

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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

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, 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

## Page 20

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.

## Page 47

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