Item No.

1: BOS Meeting 02/03/2020, Annexure_A, Revised structure and syllabus for 1st and 2nd semester of B.Sc. Data Science

Annexure_A

Symbiosis Skill & Open University

Program Structure of B.Sc. Data Science (2019-20)
Se Course Hours Credits
Courses
m code L T P S Total L T P S Total
DS101 Basics of Data Science 1 0 2 1 4 1 0 1 1 3
DS102 Introduction to C Programming 2 0 2 1 5 2 0 1 1 4
DS103A Probability & Statistics 1 1 2 1 5 1 1 1 1 4
Semester-I

Linear Algebra and Basics of

DS104 1 1 2 1 5 1 1 1 1 4
Calculus
Data Analysis using Spread
DS105 2 0 2 1 5 2 0 1 1 4
Sheet(VBA)
DS106 Banking Part-I 1 0 2 1 4 1 0 1 1 3
IDSC101 Communication Skills I 0 0 0 3 3 0 0 0 3 3
31 Total 25
Object Oriented Programming
DS201 2 0 2 1 5 2 0 1 1 4
using C++
DS202A Data Structures and Algorithm 2 0 2 1 5 2 0 1 1 4
DS203 Python Programming –I 2 0 2 1 5 2 0 1 1 4
Semester-II

Database Management System

DS204 2 0 2 1 5 2 0 1 1 4
(DBMS) with SQL
DS205 R Programming 2 0 2 1 5 2 0 1 1 4
Introduction to Business
DS206 1 0 2 1 4 1 0 1 1 3
Domain-II
IDSC102 Communication Skill II 0 0 0 3 3 0 0 0 3 3
1
DS207 INTERNSHIP/Project 0 0 0 16 0 0 0
6 8
32 Total 34
DS301 Python Programming-II 2 0 2 1 5 2 0 1 1 4
Big Data Architecture and
DS302 2 0 2 1 5 2 0 1 1 4
Ecosystem
Semester-III

DS303 Machine Learning-I 1 0 2 1 4 1 0 1 1 3

DS304 Core Java 2 0 2 1 5 2 0 1 1 4
Data Mining and Data
DS305 2 0 2 1 5 2 0 1 1 4
Warehousing
DS306 Advanced Business Domain-I 1 0 2 1 4 1 0 1 1 3
IDSC201 Personal Enhancement Skill I 0 0 0 3 3 0 0 0 3 3
31 Total 25
DS401 Machine Learning-II 1 0 2 1 4 1 0 1 1 3
Semester-IV

Big Data Technologies using

DS402 2 0 2 1 5 2 0 1 1 4
Hadoop
DS403 SPSS Programming 1 0 2 1 4 1 0 1 1 3
DS404A Internet and Web Technology 2 0 2 1 5 2 0 1 1 4

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 Item No.1: BOS Meeting 02/03/2020, Annexure_A, Revised structure and syllabus for 1st and 2nd semester of B.Sc. Data Science

Introduction to Artificial
DS405 1 0 2 1 4 1 0 1 1 3
Intelligence
DS406 Advanced business Domain-II 1 0 2 1 4 1 0 1 1 3
IDSC202 Personal Enhancement Skill II 0 0 0 3 3 0 0 0 3 3
1
DS407 INTERNSHIP/Project 0 0 0 16 0 0 0 0 8
6
29 Total 31
DS501 Apache Spark and Scala 2 0 2 1 5 2 0 1 1 4
DS502 Data Visualization using Tools 2 0 2 1 5 2 0 1 1 4
Semester-V

DS503 Data Security and Compliances 2 0 2 1 5 2 0 1 1 4

Basics of Data Management
DS504 2 0 2 1 5 2 0 1 1 4
using Cloud
DS505 Introduction to Deep Learning 1 0 2 1 4 1 0 1 1 3
IDSC301 Professional Skills 0 0 0 3 3 0 0 0 3 3
27 Total 22
DS601 Environmental Studies (Online) 2 0 0 1 3 2 0 0 1 3
Semester-VI

Full Time Industrial Training/ 4

DS602 0 0 0 48 0 0 0 24 24
Internship/Project/Dissertation 8

27 Total 27
25+34+25+31+22+
B.Sc. (Data Science) TOTAL CREDIT 164
27

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 Item No.1: BOS Meeting 02/03/2020, Annexure_A, Revised structure and syllabus for 1st and 2nd semester of B.Sc. Data Science

Committee for Curriculum Development

Under Graduate Course in Data Science

Course Name: Basics of Data Science

Course Code: DS101
UG/PG : B.Sc. Data Science
Number of Credits: 3

Learning Objective(s)

1. Understand and apply the basic concepts of data science.

2. Understand the various machine learning Algorithms
3. Understand the ethics and practices of documentation.

Pedagogy
1. Class Room Teaching
2. Practical
3. Skills

Pre-Learning
1. No Pre-requisites

Course Outline

Unit Topic No of Hours

1. Introduction of Data 1.1 What is data Science?
Science 1.2 What is data analysis?
1.3 Importance of data science
1.4 Roles and team in data Science
9
1.5 Importance of mathematics, statistics, computer
science and IT in data science.
1.6 Ethics in data science
1.7 What is Big data
2. Data Sources and 2.1 What is data?
Programming 2.2 Types of data- Numeric, Text, Images, structured
data, unstructured data 12
2.3 Methods of collection of data
2.4 Steps in data science

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 Item No.1: BOS Meeting 02/03/2020, Annexure_A, Revised structure and syllabus for 1st and 2nd semester of B.Sc. Data Science

2.5 Existing Data ,APIs ,Scraping , Creating Data ,Data

Exploration
2.6 Skills in Data science- Excel spreadsheets ,R-
programming , Python-programming, SQL, SPSS, STATA,
SAS, Web Formats etc.
3.1 What is report?
3. Report Writing 3.2 Parameters of report
15
3.3 Structure of report
3.4 Do’s and don’ts of report writing
4. Introduction of 4.1 What is machine learning?
Machine Learning 4.2 Why learn? When it is required?
4.3 Types of Machine Learning- Supervised learning,
Unsupervised learning ,Reinforcement learning 15
4.4 Comparison between traditional software algorithm
and machine learning algorithm

5. Communication 5.1. Interpretability

5.2. Actionable insights
5.3. Visualization for presentation 9
5.4. Reproducible research

Suggested Internal Evaluation Methods

Description Allotted marks

Continuous Assessment Theory (Internal) 50
Continuous Assessment Practical (Internal) 40
Term End Examination Theory 50
Term End Practical 40
Skill Assessment 20
TOTAL 200

Internal Theory Assessment – 50 Marks

 Unit Tests/ Formative assessment/ case studies/mini project

Internal Practical Assessment – 40 Marks

 Journal Completion – 10 Marks

 Completion of Experiment / Activities – 10 Marks each, Best three
Term end Practical – 40 Marks

 Slip based term end practical examination

Skill Assessment –

 Completion of Skill Journal – 10 Marks (average of all skills)

 Slip based skill assessment – 10 Marks

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 Item No.1: BOS Meeting 02/03/2020, Annexure_A, Revised structure and syllabus for 1st and 2nd semester of B.Sc. Data Science

Committee for Curriculum Development

Under Graduate Course in Data Science

Course Name: Introduction to C Programming

Course Code: DS102
UG/PG : B.Sc. Data Science
Number of Credits: 4

Learning Objective(s)

1. Solve the given problem using the syntactical structures of C language

2. Develop , execute and document computerized solution for various problem solving using the
features of C language
3. Read and write C program that uses pointers , structures and files

Pedagogy
1. Class Room Teaching
2. Practical
3. Skills
Pre-Learning
 Logic and reasoning

Course Outline

Unit Topic No of
Hours
Unit 1:Problem solving: 1.1 Overview, importance of C
Algorithm and Flowchart 1.2 character set.
1.3 C tokens :keywords and identifiers, Constants and
variables
1.4 Data types, Declaration of variables, Defining
10
symbolic constants,
1.5 Operator and expression
1.6 Arithmetic, relational, logical, assignment
operators, increment and decrement, conditional,
bitwise and special operators, expressions.
Unit 2: Data Input-Output 2.1 Basic structure of C program
2.2 Character Input and Output
2.3 String input and output
2.4 Formatted input and output
15
2.5 control structures: Decision making –if, switch
statement Loop, control-while, do while and for
statement, nested loops, break and continue
statement, go to statement.

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 Item No.1: BOS Meeting 02/03/2020, Annexure_A, Revised structure and syllabus for 1st and 2nd semester of B.Sc. Data Science

Unit 3: Array 3.1 Introduction

3.2 Array declaration and initialization
3.3 Type-one dimensional ,two dimensional and multi- 10
dimensional arrays , array of string , string handling
functions
Unit 4: Function and 4.1 Introduction
structure 4.2 standard library function
4.3 User define functions
4.4 Declaration(prototype),
4.5 definition, function call
4.6 parameter passing-by value and by reference. 20
4.7 Introduction , defining structure, Declaration ,
Initialization of structure
4.8 Array of structures, Arrays within structure, nesting
of structure.

Unit 5: Pointers and File 5.1 Introduction

handling 5.2 pointer variables
5.3 Operators, declaration, initialization
5.4 Pointer arithmetic
5.5 Pointer and arrays 20
5.6 Structure and pointers.
5.7 File system basics , file operation, file opening
modes, string I/O in files, record I/O in files , command
line arguments

Suggested Internal Evaluation Methods

Description Allotted marks

Continuous Assessment Theory (Internal) 50
Continuous Assessment Practical (Internal) 40
Term End Examination Theory 50
Term End Practical 40
Skill Assessment 20
Internal Theory Assessment – 50 Marks

 Unit Tests/ Formative assessment/ case studies/mini project

Internal Practical Assessment – 40 Marks

 Journal Completion – 10 Marks

 Completion of Experiment / Activities – 10 Marks each, Best three
Term end Practical – 40 Marks

 Slip based term end practical examination

Skill Assessment –

 Completion of Skill Journal – 10 Marks (average of all skills)

 Slip based skill assessment – 10 Marks

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 Item No.1: BOS Meeting 02/03/2020, Annexure_A, Revised structure and syllabus for 1st and 2nd semester of B.Sc. Data Science

Committee for Curriculum Development

Under Graduate Course in Data Science

Course Name: Probability & Statistics

Course Code: DS103A
UG/PG: B.Sc. Data Science
Number of Credits: 4
Learning Objective(s)
1. Able to compute and interpret measures of centre and spread of data.
2. Able to compute and interpret measures of skewness & kurtosis.
3. Apply key concepts of probability, including discrete and continuous random variables,
probability distributions, conditioning, independence, expectations, and variances.
4. Apply the basic rules and theorems in probability including Bayes’s theorem and the Central
Limit Theorem (CLT).
5. Define and explain the different statistical distributions (e.g., Normal, Binomial, and Poisson)
and the typical phenomena that each distribution often describes.
6. Apply the concepts of hypothesis testing and p-value.
Pedagogy
1. Class Room Teaching
2. Practical
3. Tutorial
4. Skill
Pre-Learning
1. (10 + 2) level Mathematics.
2. Basic Knowledge of computer.

Course Outline

Sr. Module/Units Detailed Topic wise Syllabus No. of Hours

No
1  Data collection and summary. 16
 Stem/leaf plots and histograms.
Descriptive statistics  Measures of central tendency-Mean, median,
mode, Quartiles, deciles, percentiles.

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 Item No.1: BOS Meeting 02/03/2020, Annexure_A, Revised structure and syllabus for 1st and 2nd semester of B.Sc. Data Science

 Measures of Dispersion- Range, Quartile deviation,

variance, Standard deviation, coefficient of
variation.
 Box plot
 Measures of skewness- Positive skew, Negative
skew, symmetric.
 Measures of kurtosis- Leptokurtic, Platykurtic,
Mesokurtic.
2 Probability Theory  Permutations and combinations 15
 Relative frequency. Probability as a limit.
 Events. Union and intersection. Addition rule.
 Exclusive events. Independent events.
Multiplication rule.
 Conditional probability. Total probability theorem,
Bayes' theorem.
3 Discrete probability  Discrete random variables. 12
distributions  Probability mass function & Expectations. Algebra
of expectations. Variance
 Bernoulli distribution-Mean, variance &
applications.
 Binomial distribution- Mean, variance applications.
 Poisson distribution-Mean variance & applications
 Derivation of the Poisson. Approximation to the
binomial.
4 Continuous  Density function as limit of histograms. Properties 12
probability of probability density function (pdf).
distributions:  Cumulative distribution function (cdf).
 Uniform Distribution- Mean and variance.
 Exponential distribution- Mean & variance, Lack of
memory property, applications
 Distributions of functions of random variables;
change of variables formula.
 Normal distribution- Mean, median, mode
variance, properties of normal distribution Use of
tables.
 Central limit theorem. Additivity.
5 Testing of  Population, Sample space, statistic, standard error. 20
Hypothesis  Hypothesis testing: Null hypothesis, alternative
hypothesis, acceptance region, rejection region,
Type-I & Type-II errors. P-value
 Large sample tests for testing single mean,
difference of two means.
 Small sample tests for testing single mean,
difference of two means.
 Paired t-test
 Chi-square test for goodness of fit.

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 Item No.1: BOS Meeting 02/03/2020, Annexure_A, Revised structure and syllabus for 1st and 2nd semester of B.Sc. Data Science

 Chi-square test for independence of two events.

Suggested Internal Evaluation Methods:

Description Allotted marks

Internal Theory 50
Internal Practical 40
Term end Theory 50
Term end Practical 40
Skill Assessment 20
TOTAL 200

Internal Theory Assessment – 50 Marks

 Unit Tests/ Formative assessment/ case studies/mini project

Internal Practical Assessment – 40 Marks

 Journal Completion – 10 Marks

 Completion of Experiment / Activities – 10 Marks each, Best three
Term end Practical – 40 Marks

 Slip based term end practical examination

Skill Assessment –

 Completion of Skill Journal – 10 Marks (average of all skills)

Slip based skill assessment – 10 Marks

Page 9 of 39
 Item No.1: BOS Meeting 02/03/2020, Annexure_A, Revised structure and syllabus for 1st and 2nd semester of B.Sc. Data Science

Committee for Curriculum Development

Under Graduate Course in Data Science

Course Name: Linear Algebra and Basics of Calculus

Course Code: DS104

UG/PG : B.Sc. Data Science

Number of Credits: 4

Learning Objective(s)
Student will be able to Understand

1. Use matrix algebra and the related matrices to linear transformations

2. Compute and use determinants
3. Compute and use eigenvectors and eigenvalues, Determine and use orthogonality
4. Demonstrate algebraic facility with algebraic topics including linear, quadratic, exponential,
logarithmic, and trigonometric functions.
5. Solve system of equations by matrix method and iterative methods.
6. Define, differentiate, and integrate functions represented using power series expansions,
including Taylor series, and solve related problems.
7. Find partial derivatives.
8. Compute limits, derivatives, and definite & indefinite integrals for several variables.
9. Use technological tools such as computer algebra systems or graphing calculators for
visualization and calculation of linear algebra concepts.

Pedagogy
1. Class Room Teaching
2. Practical
3. Tutorial
4. Skill

Pre-Learning
1) This course is aimed at graduate students. Students are expected to –
2) Knowledge of HSC (10+2) level Mathematics

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 Item No.1: BOS Meeting 02/03/2020, Annexure_A, Revised structure and syllabus for 1st and 2nd semester of B.Sc. Data Science

3) Knowledge of computer
Course Outline
Sr. Module/Units Detailed Topic wise Syllabus No. of Hours
No
1 Unit.1. 1.1 Matrix & type of matrices 10
Matrices and 1.2 Basics operations of matrices,
their 1.3 Properties of matrices
Applications 1.4 Inverse of matrix,
1.5 Elementary row or column operations
1.6 Row echelon form & row reduced echelon form
1.7 Rank & trace of Matrix,
1.8 Normal and PAQ form of matrix
1.9 Applications of Matrix
2 Unit.2 2.1 System of linear Equations 15
System of 2.2 Homogeneous and non-homogeneous system
Linear 2.3 Solving linear system of equations by inversion
Equations method,
2.4 Gauss Elimination method
2.5 Matrix factorization
2.6 LU decomposition
2.7 Cholesky method
2.8 Jacobi's method
2.9 Gauss-Seidel method
2.10 successive over-relaxation methods (SOR)
3 Unit.3. 3.1 Eigenvalues & eigenvectors 15
Eigen values 3.2 Properties of eigenvalues
and Eigen 3.3 Diagonalization
Vector 3.4 Canonical forms, other reductions to triangular and
diagonal forms
3.5 Projection matrices
3.6 Singular value decomposition
3.7 Principal Component Analysis (PCA. Methods based on
reduction to Hessenberg or tridiagonal forms (Arnoldi,
Gram-Schmidt)
3.8 Gram Schmidt process of orthogonalization
3.9 Power iteration
3.10 inverse iteration
4 Unit.4. 4.1 Sets relations and functions of various kinds 15
Functions, 4.2 Graphs & properties of Logarithm, exponential,
Variables, polynomial functions,
Equations and 4.3 Basic geometry and theorems
Graphs 4.4 trigonometric functions , identities
4.5 Real numbers, basic properties
4.6 Graphing and plotting

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 Item No.1: BOS Meeting 02/03/2020, Annexure_A, Revised structure and syllabus for 1st and 2nd semester of B.Sc. Data Science

4.7 Cartesian and polar coordinates & Euclidian Distance

5 Unit.5. 5.1 Functions of many variables 20

Calculus of 5.2 Limit & continuity for several variables
single variable 5.3 Differentiability of several variables
and 5.4 Partial Differentiation
multivariable 5.5 Maxima & Minima
5.6 Infinite series, Taylors series
5.7 Multiple Integration
5.8 Application of derivatives & integration for several
variables

Suggested Internal Evaluation Methods:

Description Allotted marks

Internal Theory 50
Internal Practical 40
Term end Theory 50
Term end Practical 40
Skill Assessment 20
TOTAL 200

Internal Theory Assessment – 50 Marks

 Unit Tests/ Formative assessment/ case studies/mini project

Internal Practical Assessment – 40 Marks

 Journal Completion – 10 Marks

 Completion of Experiment / Activities – 10 Marks each, Best three
Term end Practical – 40 Marks

 Slip based term end practical examination

Skill Assessment –

 Completion of Skill Journal – 10 Marks (average of all skills)

 Slip based skill assessment – 10 Marks

Page 12 of 39
 Item No.1: BOS Meeting 02/03/2020, Annexure_A, Revised structure and syllabus for 1st and 2nd semester of B.Sc. Data Science

Committee for Curriculum Development

Under Graduate Course in Data Science

Course Name: Data Analysis Using Spread Sheet (VBA)

Course Code: DS105

UG/PG : B.Sc. Data Science

Number of Credits: 4

Learning Objective(s)
Student will be able to Understand

1. Enter and edit data in worksheet

2. Build worksheet
3. Preview and print worksheets and workbooks
4. Create, sort, and filter lists/data
5. Summarize data with data analysis, PivotTables, and Pivot Charts
6. If, nested if, VLOOKUP, HLOOKUP
7. Import and export data from other applications.

Pedagogy
1. Class Room Teaching
2. Practical
3. Tutorial
4. Skill

Pre-Learning
1) This course is aimed at graduate students. Students are expected to –
2) Knowledge of HSC (10+2) level Mathematics
3) Knowledge of computers

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 Item No.1: BOS Meeting 02/03/2020, Annexure_A, Revised structure and syllabus for 1st and 2nd semester of B.Sc. Data Science

Course Outline
Sr. No Module/Units Detailed Topic wise Syllabus (In bullet points) Total
Hours
1 Introduction to  Introduction to spreadsheets,
Spreadsheets  Basic operations in Excel.
 Data types,
 Scales of measurement-Nominal, Ordinal, Interval,
Ratio,
10
 Data interpretation.
 Advantage & Disadvantage of Excel,
 Tabular presentation of data tables, Objective of
tabulation,
 Main parts of data table, Limitations of data table.
2 Spreadsheet Functions  Introduction to spreadsheet function.
to Organize Data Mathematical functions:- EXP, FACT, INT, ABS,
MOD,SQRT, POWER, ROUND, SUMPRODUCT;
 Statistical Functions: COUNT, AVERAGE, MODE,
MAX, MIN, MEDIAN,RANGE, CORREL, VAR.P, 20
VAR.S,STDEVA
 Financial functions: FV, PMT, PV, NPV, PPMT, NPER
Also loops like IF, nested IF, VLOOKUP and
HLOOKUP functions in Excel.
3 Introduction to  Introduction to the Data filtering data sorting
Filtering, Pivot Tables, capabilities of Excel,
and Charts  Features of pivotal tables,
15
 creation of pivotal tables,
 pivotal table fields,
 Construction of Pivot chart
4 Advanced Graphing  Difference between graphs & charts,
and Charting  Constructing various Line, Bar and Pie charts.
 Using the Pivot chart features of Excel.
Understanding and constructing 15
 Histograms and Scatterplots, Advantage &
disadvantage of graph & chart, Limitations of
graphs & charts
5 Introduction of VBA  What is VBA ,
 Inserting a bunch of text,
 Automating repetitive operations, Creating a
custom command,
 Creating a custom button, 15
 Developing new worksheet functions
 Creating custom add-ins for Excel,
 Creating complete, macro-driven applications,
 Advantages and Disadvantages of VBA

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 Item No.1: BOS Meeting 02/03/2020, Annexure_A, Revised structure and syllabus for 1st and 2nd semester of B.Sc. Data Science

Suggested Internal Evaluation Methods:

Description Allotted marks

Internal Theory 50
Internal Practical 40
Term end Theory 50
Term end Practical 40
Skill Assessment 20
TOTAL 200

Internal Theory Assessment – 50 Marks

 Unit Tests/ Formative assessment/ case studies/mini project

Internal Practical Assessment – 40 Marks

 Journal Completion – 10 Marks

 Completion of Experiment / Activities – 10 Marks each, Best three
Term end Practical – 40 Marks

 Slip based term end practical examination

Skill Assessment –

 Completion of Skill Journal – 10 Marks (average of all skills)

 Slip based skill assessment – 10 Marks

Page 15 of 39
 Item No.1: BOS Meeting 02/03/2020, Annexure_A, Revised structure and syllabus for 1st and 2nd semester of B.Sc. Data Science

Committee for Curriculum Development

Under Graduate Course in Data Science

Course Name: Banking Part-I

Course Code: DS106

UG/PG : B.Sc. Data Science

Number of Credits: 3

Learning Objective(s)
Student will be able to Understand

1. Demonstrate basic understanding of processes and working of retail banking operations.

2. Illustrate and enlist various liability and para banking products.
3. Interpret the importance of KYC.
4. Understand the process of bank account opening and various processes involved such as cash,
clearing and transfer transactions.

Pedagogy
1) Class Room Teaching
2) Practical
3) Skill

Pre-Learning
Basic knowledge of arithmetic, counting

Course Outline
Sr. Module/Units Detailed Topic wise Syllabus Total Hours
No
1 Unit.1. 1.1 Characteristics 10
Introduction to Retail 1.2 Advantages and constraints of retail banking
Branch Banking 1.3 Role within the bank operations,
1.4 Applicability of retailing banking
concepts
1.5 Distinction between Retail and
Corporate/wholesale Banking

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 Item No.1: BOS Meeting 02/03/2020, Annexure_A, Revised structure and syllabus for 1st and 2nd semester of B.Sc. Data Science

2 Unit.2 2.1 Introduction to deposits 10

Introduction to  Demand Deposits: Current account, current
Liability(Deposit) / deposits premium scheme, Savings account,
Assets products ‘No-frills saving bank account’, Premium of
saving bank plus account
 Term Deposits: Recurring Deposits, Recurring
deposit premium account, Fixed deposits,
Special term deposits, Hybrid deposits/Multi
option deposit scheme (MODS), Tailor-made
deposit schemes, Deposit schemes for NRIs,
2.2 NROs, NREs, FCNR scheme
2.3 Deposit Insurance,
2.4 DICGC
2.5 Home Loans, Home Improvement Loans, Home
Décor Loans, Auto/Vehicle Loans, Personal Loans,
Educational Loans (Eligibility, Purpose, Amounts,
Margin, Security, Disbursement, Moratorium,
Prepayment issues, Repayments / Collection). Loan
against property
3 Unit.3 3.1 Various channels of services: ATM, Credit Cards: 10
Overview of Para its operation
banking products and 3.2 advantages as to customer, Merchant
services Establishment and bank
3.3 Disadvantages of credit cards, Debit Card, Gift
Card, Smart Card, Mobile banking services,
Telebanking, Internet Banking, Electronic fund
Transfer: NEFT, RTGS, IMPS, UPI
3.4 Agency Services, Locker services and its
procedure; Cash Management Services.
3.5 Third Party Services: MFs, Insurance, Trading
account, Selling of gold coins and advisory
services/underwriting
3.6 Demat account: its benefits and documentation
etc.
4 Unit.4. 4.1 Savings accounts – Various variants of SB accounts 10
Account opening and its account opening process.
process- Retail 4.2 Current accounts – Various variants of CA and its
Liability products account opening process.
4.3 Recurring accounts – Various variants of RD
accounts and its account opening process.
4.4 Fixed Deposit accounts – Various variants of FD
accounts and its account opening process

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 Item No.1: BOS Meeting 02/03/2020, Annexure_A, Revised structure and syllabus for 1st and 2nd semester of B.Sc. Data Science

5 Unit.5. 5.1 Clearing, Transfer and Cash Transactions in Bank 10

Banking operations- Branch. Cash Retention Limit in Branch, Cash
cash, clearing and Management Process in banks.
transfers. 5.2 Types of clearing.
Collection of instruments;
Inward clearing, outward clearing
5.3 CTS and its benefits.
Process of clearing and CTS.

6 Unit.6. 6.1 Role and Objectives of BO/Insurance 5

Customer service and Ombudsman/SCORES/ BCSBI
customer data
management
7 Unit.7. 7.1 PMLA Act 2002 and its provisions. 5
Introduction to Stages involved in money laundering.
KYC/AML and risk 7.2 Role of KYC in prevention of money laundering.
management

Suggested Internal Evaluation Methods:

Description Allotted marks

Internal Theory 50
Internal Practical 40
Term end Theory 50
Term end Practical 40
Skill Assessment 20
TOTAL 200

Internal Theory Assessment – 50 Marks

 Unit Tests/ Formative assessment/ case studies/mini project

Internal Practical Assessment – 40 Marks

 Journal Completion – 10 Marks

 Completion of Experiment / Activities – 10 Marks each, Best three
Term end Practical – 40 Marks

 Slip based term end practical examination

Skill Assessment –

 Completion of Skill Journal – 10 Marks (average of all skills)

 Slip based skill assessment – 10 Marks

Page 18 of 39
 Item No.1: BOS Meeting 02/03/2020, Annexure_A, Revised structure and syllabus for 1st and 2nd semester of B.Sc. Data Science

Committee for Curriculum Development

Under Graduate Course in Data Science

Course Name: Communication Skills I

Course Code: IDSC101
UG/PG : B.Sc. Data Science
Number of Credits: 3

Learning Objective(s)
Student will be able to Understand

1. To develop four aspects of English language, Listening, reading, writing, speaking

2. To be familiar with communication skills including non-verbal communication
3. To understand structure of language
4. To overcome grammatical mistakes
5. To boost the self confidence
6. To help student for holistic development

Pedagogy
1. Skill
Pre-Learning
 12th pass ( Passed), First year admitted

 Basic knowledge of English Language

Course Outline

Sr. No Unit Detailed Topic wise Syllabus Total Hours

1 Orientation & Pre  Recap – What have you already learned in 7
assessment IDSC?
 Why this session:
 Pre assessment of learning
 One to one interaction
 Interview
 Icebreaking
2 Introduction to effective Inputs will be given through activities 6
communication Skills-I  Group brain storming
 Quiz
 Vocabulary games

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 Item No.1: BOS Meeting 02/03/2020, Annexure_A, Revised structure and syllabus for 1st and 2nd semester of B.Sc. Data Science

 Role play
 Grammar quiz and games
3 Listening Skills Inputs will be given through activities 7
 Listening games
 Audio Visual shows and presentations
 Role plays
 Mock sessions of reading
4 Reading Skills Inputs will be given through activities 6
 Reading exercises
 News reading
 Article reading
 Reading comprehension
 Reading with correct pronunciations
5 Spoken communication  Pronunciational games 6
skills  Small & big group conversations on different
Phonetics-correct themes and scenario
pronunciation Skill  Correct Spoken utterances and common
errors
 Use the domain of Phonetics in acquiring
knowledge of sound system in English
language.
6 Nonverbal  Using dynamics of body language 8
Communications  Class addressing
Presentation skills  Group and individual presentation on given
topic
 Peers assessment
 Observation.
7 Professional Skills as per 5
sector specific need  Selecting minimum two skill enhancement
(NSQF) proposed by NSQF
 Preparing students to develop requires
trades as per specific need of skills { it will
vary course to course}

Evaluation System

Description Allotted marks

Continuous Assessment Theory (Internal) 0
Internal 0
External 0
Term End Practical 0
Skill Assessment 20
TOTAL 20

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 Item No.1: BOS Meeting 02/03/2020, Annexure_A, Revised structure and syllabus for 1st and 2nd semester of B.Sc. Data Science

Committee for Curriculum Development

Under Graduate Course in Data Science

Format to submit Syllabus

Course Name: Object oriented Programming Using C ++

Course Code: DS201

UG/PG : B.Sc. Data Science

Number of Credits: 4

Learning Objective(s)
1. Students will understand the Programming concepts of C++
2. Students will understand the object oriented features.
3. Students will understand and apply the C++ to solve various Data Science problems.

Pedagogy
1. Class Room Teaching
2. Practical
3. Skills

Pre-Learning
Basic understanding of any scripting Language.

Course Outline

Unit Topic No of Hours

01 Introduction to C++: 8

1.1. Basic Concepts of object oriented Programming,

1.2. Basic Syntax and Structure
1.3. Data types and Modifiers
1.4. variables in C++
1.5. Operators in C++
1.6. type conversion
1.7. size-of and typedef in C++

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02 Decision Making & Control statements 12

2.1. Branching : If, if-else, Switch, go to
2.2. Loops: for, while, do while
2.3. break Continue and goto
03 Object and Classes 15
3.1. Structures and classes,
3.2. Defining class and member functions,
3.3. Accessing member of class,
3.4. Defining objects,
3.5. Array of class objects,
3.6. Pointers and classes,
3.7. Unions and classes,
3.8. Nested classes.
3.9. Constructors,
3.10. Copy constructors,
3.11. Destructors,
3.12. Inline member functions,
3.13. Friend functions,
3.14. this pointer
04 Inheritance 10
4.1. Single Inheritance,
4.2. Base Classes,
4.3. Derivations of classes,
4.4. multiple inheritance,
4.5. member access control,
4.6. Container classes,
4.7. Function overloading,
4.7. operator overloading,

05 Polymorphism 20
5.1. Polymorphism
5.2. Early & late binding
5.3. virtual functions
5.4. pure virtual functions
5.5. abstract base class
5.6. constructor under inheritance
5.7. destructor under inheritance
5.8. virtual destructor
5.9. Virtual base classes.

6. Template and Exceptional Handling 10

6.1. Function template
6.2. class template
6.3. exception handling

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Suggested Internal Evaluation Methods

Description Allotted marks

Internal Theory 50
Internal Practical 40
Term end Theory 50
Term end Practical 40
Skill Assessment 20
TOTAL 200

Internal Theory Assessment – 50 Marks

 Unit Tests/ Formative assessment/ case studies/mini project

Internal Practical Assessment – 40 Marks

 Journal Completion – 10 Marks

 Completion of Experiment / Activities – 10 Marks each, Best three
Term end Practical – 40 Marks

 Slip based term end practical examination

Skill Assessment –

 Completion of Skill Journal – 10 Marks (average of all skills)

 Slip based skill assessment – 10 Marks

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 Item No.1: BOS Meeting 02/03/2020, Annexure_A, Revised structure and syllabus for 1st and 2nd semester of B.Sc. Data Science

Committee for Curriculum Development

Under Graduate Course in Data Science

Course Name: Data Structure and Algorithm

Course Code: DS202A
UG/PG : B.Sc. Data Science
Number of Credits: 4

Learning Objective(s)
1. Understand and apply the concepts of Data structure in real life.
2. Understand the basics of Data structure and algorithm.
3. Understand the sorting techniques.
4. Understand and apply the concepts of Data structure in real life scenarios.

Pedagogy
1. Class Room Teaching
2. Practical
3. Skills

Pre-Learning
1. Knowledge of C language.

Course Outline
Sr. No Unit Topic No of Hours
1. Unit.1 1.1. Concept of data structure 10
Introduction to data 1.2. Data type, Data object
structures 1.3. ADT
1.4. Need of Data Structure
1.5. Types of Data Structure
1.6. Algorithm and flowchart
1.7. Complexity: Space & Time
1.8. Asymptotic Notations
2. Unit.2 2.1. Arrays 10
Linear data structures 2.2. Dynamically Allocated Arrays,
One and two Dimensional
2.3. Structures and Unions
2.4. Searching Algorithms
2.4.1. Linear Search
2.4.2. Binary Search
2.5. Sorting algorithms
2.5.1. bubble sort

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2.5.2. Insertion sort

2.5.3. Merge sort
2.5.4. Quick Sort
2.5.5. Bucket Sort
2.5.6. Radix Sort
3. Unit.3 3.1. Introduction to List , Implementation 15
Linked List of List
3.2. static & dynamic representation,
3.3. Types of Linked List
3.3.1 Singular Link list: Creation,
Operation
3.3.2 Double Linked List: Creation,
Operation
3.3.3. Circular Linked List Creation
and Operation
3.3.4. Header Linked List
3.4. Applications of Linked List –
polynomial manipulation,
3.5. Generalized linked list – concept &
representation
4. Unit.4 4.2. Introduction to Stacks 15
Stacks & Queues 4.3. Representation-static & dynamic
4.4. Operations , Application - infix to
postfix & prefix, postfix evaluation,
4.5. Recursion using implicit stack ,
Concept of Multiple stacks
4.6. Introduction to Queues
4.7. Representation -static & dynamic
Operations
4.8. Circuler queue
4.9. DeQue
4.10. priority queues
4.11. Concept of Multiple Queues
5. Unit.5 5.1. Introduction to Trees and its 15
Trees terminologies, types of trees, memory
representation.
5.2. Tree Traversal algorithm in Binary Trees
5.3. Insertion in Binary Search Tree
5.4. Deletion in Binary Search Tree
5.5. AVL Trees: types of Rotations
5.5.1 Insertion in AVL Trees
5.5.2 Deletion in AVL Trees
6. Unit.6 Graphs 10
Graphs 6.1. Introduction to Graphs
6.2. Breadth First Search
6.3. Depth First Search

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 Item No.1: BOS Meeting 02/03/2020, Annexure_A, Revised structure and syllabus for 1st and 2nd semester of B.Sc. Data Science

Evaluation System:

Description Allotted marks

Internal Theory 50
Internal Practical 40
Term end Theory 50
Term end Practical 40
Skill Assessment 20
TOTAL 200

Internal Theory Assessment – 50 Marks

 Unit Tests/ Formative assessment/ case studies/mini project

Internal Practical Assessment – 40 Marks

 Journal Completion – 10 Marks

 Completion of Experiment / Activities – 10 Marks each, Best three
Term end Practical – 40 Marks

 Slip based term end practical examination

Skill Assessment –

 Completion of Skill Journal – 10 Marks (average of all skills)

Slip based skill assessment – 10 Marks

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 Item No.1: BOS Meeting 02/03/2020, Annexure_A, Revised structure and syllabus for 1st and 2nd semester of B.Sc. Data Science

Committee for Curriculum Development B.Sc. in Data Science

Course Name: Python Programming-I

Course Code: DS203
UG/PG: B.Sc. Data Science
Number of Credits: 4
Learning Objective(s)
1. Student will able to understand why Python is a useful scripting language for developers.
2. Students will able to learn how to use lists, tuples, and dictionaries in Python programs.
3. Student will able to learn how to use indexing and slicing to access data in Python programs.
4. Student will able to learn how to identify Python object types.
5. Student will able to learn how to write loops and decision statements in Python.
6. Student will able to learn how to write functions and pass arguments in Python.
Pedagogy
1. Class Room Teaching
2. Practical
3. Skill
Pre-Learning:
Experience with a high level language (C/C++) is suggested. Prior knowledge of Object-Oriented
concepts is helpful but not mandatory.

Course Outline

Sr. Module/Units Detailed Topic wise Syllabus

Hours
No
1 1.1 Introduction of python ,
1.2 History & characteristics ,
Introduction of 1.3 Variables,
python programming 1.4 Identifier Naming 10
1.5 Declaring Variable and Assigning Values
1.6 Multiple Assignment, Tokens, Keywords Literals
1.7 Python Data types and operators
2 Decision Making and 2.1 If Statement
Loops 2.2 If - else Statement, 10
2.3 Nested if Statement

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2.4 Loops- for loop, while loop, do….while loop, break,

continue & pass statement
2.5 Advantage of loop
3 Python Strings and 3.1 Strings indexing and splitting,
List 3.2 Reassigning strings, String Operators
3.3 Built-in String functions.
3.4 Python List: List indexing and splitting,
15
3.5 Updating List values,
3.6 Python List Operations
3.7 Python List Built-in functions,
3.8 List built-in methods, nesting of list
4 tuple and sets 4.1 Tuple indexing and splitting,
4.2 Basic Tuple operations,
4.3 Python Tuple inbuilt functions,
10
4.4 List VS Tuple.
4.5 Sets: Creating a set, Set operations, union of two sets,
Set comparisons, Built-in set methods.
5 Dictionary and 5.1 Creating the dictionary,
Function 5.2 accessing the dictionary values,
5.3 Updating dictionary values,
5.4 Deleting elements using Del keyword,
5.5 Iterating Dictionary, 15
5.6 Built-in Dictionary functions, Built-in Dictionary
methods
5.7 Function: Function Calling, Call by reference, types of
argument, Lambda function
6 File Handling 6.1. How Python Handle Files?
6.2. Types Of File in Python
6.3. Python File Handling Operations
6.3.1. Open
6.3.2. Read
6.3.3. Write
6.3.4. Close
6.3.5. Rename
15
6.3.6. Delete
6.4. Reading and writing text files
6.5. Writing Text Files
6.6. Appending to Files and Challenge
6.7. Writing Binary Files Manually
6.8. Encoding in Files
6.9. Python File Methods
6.10. Using write along with with() function

Evaluation System:

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Description Allotted marks

Internal Theory 50
Internal Practical 40
Term end Theory 50
Term end Practical 40
Skill Assessment 20
TOTAL 200

Internal Theory Assessment – 50 Marks

 Unit Tests/ Formative assessment/ case studies/mini project

Internal Practical Assessment – 40 Marks

 Journal Completion – 10 Marks

 Completion of Experiment / Activities – 10 Marks each, Best three
Term end Practical – 40 Marks

 Slip based term end practical examination

Skill Assessment –

 Completion of Skill Journal – 10 Marks (average of all skills)

 Slip based skill assessment – 10 Marks

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 Item No.1: BOS Meeting 02/03/2020, Annexure_A, Revised structure and syllabus for 1st and 2nd semester of B.Sc. Data Science

Committee for Curriculum Development

Under Graduate Course in Data Science

Course Name: Data Base Management System (DBMS) with SQL

Course Code: DS204
UG/PG : B.Sc. Data Science
Number of Credits: 4

Learning Objective(s)
1. Students will understand the Concepts of data base Management System
2. Students will understand concept of handling relational data base
3. Students will understand and apply the SQL to solve various Data Base Operations

Pedagogy
1. Class Room Teaching
2. Practical
3. Skills

Pre-Learning
Basic understanding of any scripting Language.

Course Outline

Unit Topic No of
Hours
Unit1. 1.1 Introduction to Database Management system 10
Introduction: 1.2 Characteristics of the Database Approach
1.3 Advantage of using a Database Approach
1.4 Database System concepts and Architecture
1.5 Data Models
1.6 Schemes and Instances
1.7 DBMS Architecture and Data Independence
Unit 2: 2.1 Database Modelling using the ER Model 15
ER Models, 2.2 Using High-Level conceptual Data Models for Database
Relational Models design
2.3 An example Database Application
2.4 Entity types, Entity Sets, Attributes and keys
2.5 Relationships, Relationship types, roles and Structural
Constraints, Week Entity types
2.6 Refining the ER Design for the Company Database
2.7 ER Diagrams, naming conventions and design Issues, the
Relational Data Model.
Unit 3: 3.1 Functional Dependencies and Normalization for Relational 15
Database Designing Database

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 Item No.1: BOS Meeting 02/03/2020, Annexure_A, Revised structure and syllabus for 1st and 2nd semester of B.Sc. Data Science

3.2 Informal Design Guidelines for Schemes

3.3 Functional Dependencies
3.4 Normal Forms based on Primary keys
3.5 General Definitions of Second and Third Normal forms
3.6 Relational Database Design Algorithms and Further
Dependencies
3.7 Algorithms for Relational Database Schema Design
Unit 4: 4.1 Data definition 15
SQL-The Relational 4.2 Constraints and Schema changes in SQL 2
Database Standard: 4.3 Basic Quires in SQL, More Complex SQL Quires
4.4 Insert, Delete and Update Statements in SQL
4.5 Views (Virtual Tables) in SQL
4.6 Integrity constrains
4.7 Triggers
4.8 Functional dependencies.
Unit 5: 5.1 Transaction Processing Concepts 20
Transaction 5.2 Introduction to Transaction Processing, Transaction and
Processing: System Concept
5.3 Desirable properties of Transactions
5.4 Scheduling and Recoverability
5.5 Serializability of Scheduling
5.6 Transaction Support in SQL, Concurrency control
techniques

Evaluation System:

Description Allotted marks

Internal Theory 50
Internal Practical 40
Term end Theory 50
Term end Practical 40
Skill Assessment 20
TOTAL 200
Internal Theory Assessment – 50 Marks

 Unit Tests/ Formative assessment/ case studies/mini project

Internal Practical Assessment – 40 Marks

 Journal Completion – 10 Marks

 Completion of Experiment / Activities – 10 Marks each, Best three
Term end Theory – 50 Marks

 Formative assessment
Term end Practical – 40 Marks

 Slip based term end practical examination

Skill Assessment – 20 Marks

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 Item No.1: BOS Meeting 02/03/2020, Annexure_A, Revised structure and syllabus for 1st and 2nd semester of B.Sc. Data Science

Committee for Curriculum Development

Under Graduate Course in Data Science

Course Name: R Programming

Course Code: DS205
UG/PG: B.Sc. Data Science
Number of Credits: 4
Learning Objective(s)
1. Student will able to understand basic concepts such as data type and index and use them in
their work.
2. Students will able to use the basic functions.
3. Student will able to conceptualize and create loops to solve different types of problems
4. Student will able to import, review, manipulate and summarize data-sets in R.
5. Student will able to create and edit visualizations with R.
6. Student will able to perform appropriate statistical tools & tests using R
Pedagogy
1. Class Room Teaching
2. Practical
3. Skill
Pre-Learning
Students are expected to –
1) Have no previous programming experience (but GREAT if you do!)
2) Have some basic statistical & mathematical knowledge and a desire to learn more

Course Outline

Sr. Module/Units Detailed Topic wise Syllabus Total

No Hours

1 Introduction to R 1.1 Introduction of R. 10

Programming. 1.2 Installation of R.
1.3 Data types
1.4 If, If….else, switch statement.
1.5 Loops: repeat loop, while loop, for loop
1.6 Break statement & next statement.

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1.7 Built-in Functions

1.8 User defined Functions
2 Strings and Vectors 2.1 Rules Applied in String Construction 20
2.2 String Manipulation.
2.3 Vector
2.4 List
2.5 Matrix
2.6 Arrays
2.7 Factors
2.8 Data frames
2.9 R packages
3 R Data interfaces: 3.1 Input CSV file 10
3.2 Reading a CSV File
3.3 Analyzing the CSV File.
3.4 Input as xlsx File,
3.5 Reading the Excel File.
3.6 Working on binary file & XML files.
4 R charts and Graphs: 4.1 Construction of Pie Chart using R 15
4.2 C0nstruction of Bar chart using R
4.3 Construction of Box-plot using R
4.4 Construction of Histogram using R
4.5 Construction of Line chart using R
4.6 Construction of scatter plot using R
4.7 Construction of Matrix plot using R
5 R Statistics: 5.1 Measures of central tendency (Mean, mode, median, 20
percentile etc. & dispersion (variance, standard deviation
etc.) using R.
5.2 Linear regression using R
5.3 Multiple linear regression using R
5.4 Logistic regression using R
5.5 Computation of probabilities using R
5.6 Testing of hypothesis using R.

Evaluation System:

Description Allotted marks

Internal Theory 50
Internal Practical 40
Term end Theory 50
Term end Practical 40
Skill Assessment 20
TOTAL 200

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 Item No.1: BOS Meeting 02/03/2020, Annexure_A, Revised structure and syllabus for 1st and 2nd semester of B.Sc. Data Science

Internal Theory Assessment – 50 Marks

 Unit Tests/ Formative assessment/ case studies/mini project

Internal Practical Assessment – 40 Marks

 Journal Completion – 10 Marks

 Completion of Experiment / Activities – 10 Marks each, Best three
Term end Practical – 40 Marks

 Slip based term end practical examination

Skill Assessment –

 Completion of Skill Journal – 10 Marks (average of all skills)

 Slip based skill assessment – 10 Marks

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 Item No.1: BOS Meeting 02/03/2020, Annexure_A, Revised structure and syllabus for 1st and 2nd semester of B.Sc. Data Science

Committee for Curriculum Development

Under Graduate Course in Data Science

Course Name: Introduction to Business Domain-II

Course Code: DS206
UG/PG: B.Sc. Data Science
Number of Credits: 3

Learning Objective(s)
1) Develop skills to understand in detail, the processes in retail business.
2) Develop detailed understanding of investments in retail business.
3) Prepare students to query movements of each item through its existence in a business.
4) To enable students to get real time data and generate useful reports.
5) Prepare students to use all the resources in most efficient way.

Pedagogy
1. Class Room Teaching
2. Assignments
3. Skill

Pre- requisite
Basic understanding of retail management & processes

Course Outline

Sr. No Unit Detailed Topic wise Syllabus Total Hours

1. Introduction to ERP  ERP overview 4
 Basic features of ERP systems
 Understand retail at micro level
 Understand needs of stake holders
 Defining the structure of business
 Defining structure of Operations

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 Item No.1: BOS Meeting 02/03/2020, Annexure_A, Revised structure and syllabus for 1st and 2nd semester of B.Sc. Data Science

2. Overview of Retail  Understand locations of business 4

business  Create basic requirements of ERP
 Create basic modules list for each stake holder
 State requirement of data
 Data management systems
3. Management of  Classification of products 4
Products within  Variations in product affecting POS
retail business  Product hierarchy in system
 Creation of SKU’s & items on system
 Define paths of merchandise in the system
4. Merchandise  Concept of MMS 8
Management  Create Merchandising Plans
System  Process of Merchandise Purchase
 Process of handling Merchandise
 Merchandise Budget & monetary control
5. Merchandise  Generating Open to Buy plans by category & 8
Planning & sub categories
Assortment  Plan quantities by item level
 Generate assortment plan
 Vendor-wise allocation of planned buys
 Generate quantity, cost and Sales value reports
6. Sales & Margins  Sales analysis at retail values 8
Management  Location wise analysis
 Sales & Gross profit
 Treatment of top sellers & worst sellers
 Planning mark-downs & mark-ups.
 Build strategic & tactical discount events
 Enhancing the profitability by controlling
merchandise
7. Inventory  Concept – what is IM 8
Management.  Warehouse & stock locations
 How to launch new season
 Automated replenishment process
 Stock transfer process
 Stock counts and shrinkages
8 Customer  Concept of CRM 8
Relationship  Collecting & collating customer data
Management  Understanding modules of CRM
 Integrating and analyzing customers
 Design customer loyalty programs
9 Miscellaneous  Data requirement of Operations team 8
modules  Vendor relationship module
 Audit interface
 Data storage & management options
 Others

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 Item No.1: BOS Meeting 02/03/2020, Annexure_A, Revised structure and syllabus for 1st and 2nd semester of B.Sc. Data Science

Evaluation System:

Description Allotted marks

Internal Theory 50
Internal Practical 40
Term end Theory 50
Term end Practical 40
Skill Assessment 20
TOTAL 200

Internal Theory Assessment – 50 Marks

 Unit Tests/ Formative assessment/ case studies/mini project

Internal Practical Assessment – 40 Marks

 Journal Completion – 10 Marks

 Completion of Experiment / Activities – 10 Marks each, Best three
Term end Practical – 40 Marks

 Slip based term end practical examination

Skill Assessment –

 Completion of Skill Journal – 10 Marks (average of all skills)

 Slip based skill assessment – 10 Marks

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 Item No.1: BOS Meeting 02/03/2020, Annexure_A, Revised structure and syllabus for 1st and 2nd semester of B.Sc. Data Science

Committee for Curriculum Development

Under Graduate Course in Data Science

Course Name: Communication Skills II

Course Code: IDSC102
UG/PG: B.Sc. Data Science
Number of Credits: 3

Learning Objective(s)
The Students will be able to:

 Speak grammatically correct language and enhance their vocabulary.

 Write and comprehend essays and effective business letters.
 Learn and implement reading skills.
 Comprehend and explain any given text
 Think creatively and critically as and when needed.
 Learn about image building and use their image as their armour.

Pedagogy
Skill
Pre- requisite
IDSC101
Course Outline

Sr. No Module/Units Detailed Topic wise Syllabus (In bullet points) Total
Hours
1 Pre- assessment and Importance of having good vocabulary 7
Vocabulary Tips and techniques for learning new vocabulary.
Development: Homophones, Homonyms, One word Substitution,
Words often misspelt/confused, jargons and slangs, root
idioms/phrases and metaphors/similes root words,
suffixes and prefixes, Using dictionaries and thesaurus.
2 Reading skills: Reading strategies and techniques for developing speed. 6
SQ3R technique.
Note taking and Note making
Seven step ladder to writing effective paraphrase and
summary.

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3 Study Skills Brain storming 6

Individual & group counselling
Writing assignments
Presentations
4 Effective Formal Writing Business letter writing – formats and styles. 7
– Business letters and Different kinds of business letters - Equiry, Order, Sales,
emails: Complaint, adjustment, etc.
Writing official emails – style, language, copy criteria etc.
5 Thinking Skills: Thinking Skills – Introduction to Creative thinking and 6
Critical thinking.
 Creative thinking - Thinking out of the Box
 Critical thinking - Need for critical thinking in
profession – Do’s and Dont’s.
 From ridiculous to possible.
 Working model for critical thinking.
 Case Study on Thinking Skills.

6 Impression creation: Factors contributing to first impression – 6
 Mental images and other personality traits.
 Impact of Cross cultural differences in first
impression.
 Killer instincts of First impression.
 Presentation on “Initial 30 seconds and 30 years
of your Career”.
7 Professional Skills as per  Selecting minimum two skill enhancement 7
sector specific need proposed by NSQF.
(NSQF)  Preparing students to develop requires trades as
per specific need of skills { it will vary course to
course

Evaluation System

Description Allotted marks

Internal Theory -
Internal Practical -
Term end Theory -
Term end Practical -
Skill Assessment 20
TOTAL 20
 Presentation in viva / experiment – 10 Marks
Skill Assessment –

 Completion of Skill Journal – 5 Marks

 Completion of Activities / Projects during Skill Sessions – 5 Marks
 Viva voce – 10 Marks

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