STATISTICS FOR DATA SCIENCE

Paper Code: 
CBDA 113
Credits: 
3
Periods/week: 
3
Max. Marks: 
100.00
Objective: 

This module introduces students to:

1. The fundamentals of statistical techniques.

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

Course Outcomes (COs).

Course Outcome (at course level)

Learning and teaching strategies

Assessment Strategies

On completion of this course, the students will:

CO11. Define and use the basic terminology of statistics.

CO12.  Classify the data and prepare various diagrams and graph.

 CO13. Demonstrate the use of descriptive data analysis in real world problems.

CO14. Apply the concept of elementary probability theory on real world applications.

CO15. Identify the problem and apply appropriate laws of probability and Bayes theorem.

 

Approach in teaching:

Interactive Lectures, Group Discussion, Tutorials, Case Study, Demonstration

 

Learning activities for the students:

Self-learning assignments, Exercises related with Machine Learning algorithm, presentations

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, 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, 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:

JOURNALS:

 

Academic Year: 
2022-23
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