Statistics for Data Science

Paper Code: 
25CBDA113
Credits: 
03
Periods/week: 
03
Max. Marks: 
100.00
Objective: 

This module introduces students to:

1. The  fundamentals of statistical techniques.

2. Understand the  role of statistics for analysing and  interpreting data meaningfully.

 

Course Outcomes: 

Course

Learning outcome

(at course level)

Learning and teaching strategies

Assessment

Strategies

Course

Code

Course

Code

 

 

 

 

 

 

 

 

 

 

 

 

 

 

25CBDA

113

 

 

 

 

 

 

 

 

 

 

 

 

 

Statistics for Data Science (Theory)

CO13.  Define   and   use the  basic  terminology of statistics.

CO14.  Classify the  data and      prepare     various diagrams and  graph. CO15.Demonstrate  the use   of   descriptive   data analysis    in    real    world problems.

CO16.       Apply        the concept   of    elementary correlation                 and regression theory on real world  applications. CO17.       Identify       the problem       and        apply appropriate      laws       of probability     and     Bayes theorem. CO18.Contribute effectively in

course-specific interaction

Approach in teaching:Interactive Lectures, Discussion, Reading assignments,Demonstration.

 

Learning activities for the students: Self learning assignments,

Effective  questions, Seminar presentation

Class test, Semester end examinations, Quiz,

Practical Assignments, Presentation

 

9.00
Unit I: 
Qualitative and Quantitative classification

Discrete   and    continuous   classification,  Geographical   and    Chronological classification. Construction  of   frequency  tables,  frequency  distribution  for   continuous  and   discrete data, cumulative frequency distributions (inclusive and  exclusive methods).

9.00
Unit II: 
Graphical presentation of data

Histogram,   Frequency   Polygon,  Frequency   curve    and    Ogives.    Measures   of Central Tendency    –    Definition,   different    measures    of    Central     Tendency,   merits   and demerits. Partition Values.

 

9.00
Unit III: 
Measure of Dispersion

Definition,  different   measures   of   Dispersion,   merits   and    demerits.    Coefficient   of variation. Relative  dispersions

 

9.00
Unit IV: 
Correlation and Regression

Correlation,   Scatter    Diagram,   Karl    Pearson’s    Coefficient   of    Correlation   and     its properties.    Spearman’s      Rank       Correlation      Coefficient.      Regression-Fitting       of Regression   Lines, Regression Coefficients with properties.

 

9.00
Unit V: 
Random experiment and probability

Random    Experiment,    Trial,     Events     and     their     types.    Classical,     Statistical   and Axiomatic  definition of  probability and  its  properties (simple). Addition  and  Multiplication theorems of  Probability  and   their   application,   Conditional  Probability  and   Independent events. Bayes’ theorem and  its application (simple  questions).

 

ESSENTIAL READINGS: 

1.   Goon,  A.M., Gupta, M.K. and  Dasgupta, B. (1991): Fundamentals of Statistics, Volume

I, The World Press  PvtLtd , Calcutta

2.   Gupta, S.C. and  Kapoor,  V.K.: (2000) Fundamentals of Mathematical Statistics, S Chand

& Company, New Delhi, tenth edition.

3.   Mood Alexander M., Graybill Frankline  and  Boes Duane C. (2007): Introduction to

Theory  of Statistics, McGraw Hill & Company Third Edition

4.   Gupta, O.P.: Mathematical Statistics, Kedarnath Publication, Meerut

5.   Yule, G. Udny and  Kendall,  M.G. (1999): An Introduction to the  theory of Statistics,14th

Edition.

6.   Hooda, R.P. (2002): Introduction to Statistics: Macmillan  India  Ltd. 1st  edition.

7.   Speigel  M.R., (1967): Theory  and  Problem of Statistics, Schaum’s Series.

 

REFERENCES: 

SUGGESTED READINGS:

1.   Meyer,  P.L. (1970) : Introductory Probability and  Statistical Application, Addision Wesley.

2.   Rohatgi, V.K. and  Saleh, A.K. Md. Ehsanes (2009): An Introduction to Probability Theory  and  Statistics, Second Edition,  John  Wiley and  Sons.

3.   Bhat,  B.R (1981): Modern  Probability Theory, New Age Publishers, Third edition.

4.   Kingman, J.F. & Taylor,  S.J.  (1996): Introduction to Measure and  Probability, Cambridge Univ. Press.

e-RESOURCES:

1.   Probability, Academia:

https://www.academia.edu/39708554/Probability_and_Statistics_for_Data_Science

2.   Probability ,slide  share:  https://www.slideshare.net/ferdinjoe/probability-theory-for- data-scientists-193754474?from_search=0

JOURNALS:

1.   Journal of Machine  Learning Research (JMLR),ACM, https://dl.acm.org/journal/jmlr

 

Academic Year: 
2025-26
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