Department Events
Statistics for Data Science (Theory)
This module introduces students to:
1. The fundamentals of statistical techniques.
2. Understand the role of statistics for analysing and interpreting data meaningfully.
Course | Learning outcome (at course level) | Learning and teaching strategies | Assessment Strategies | |
Course Code | Course Code | |||
24CBDA 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 |
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).
Histogram, Frequency Polygon, Frequency curve and Ogives. Measures of Central Tendency – Definition, different measures of Central Tendency, merits and demerits. Partition Values.
Definition, different measures of Dispersion, merits and demerits. Coefficient of variation. Relative dispersions
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.
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).
SUGGESTED TEXT BOOKS
- Goon, A.M., Gupta, M.K. and Dasgupta, B. (1991): Fundamentals of Statistics, Volume I, The World Press PvtLtd , Calcutta
- Gupta, S.C. and Kapoor, V.K.: (2000) Fundamentals of Mathematical Statistics, S Chand & Company, New Delhi, tenth edition.
- Mood Alexander M., Graybill Frankline and Boes Duane C. (2007): Introduction to Theory of Statistics, McGraw Hill & Company Third Edition
- Gupta, O.P.: Mathematical Statistics, Kedarnath Publication, Meerut
- Yule, G. Udny and Kendall, M.G. (1999): An Introduction to the theory of Statistics,14th Edition.
- Hooda, R.P. (2002): Introduction to Statistics: Macmillan India Ltd. 1st edition.
- Speigel M.R., (1967): Theory and Problem of Statistics, Schaum’s Series.
SUGGESTED REFERENCE BOOKS
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:
- Probability, Academia: https://www.academia.edu/39708554/Probability_and_Statistics_for_Data_Science
- Probability ,slide share: https://www.slideshare.net/ferdinjoe/probability-theory-for-data-scientists-193754474?from_search=0
JOURNALS:
- Journal of Machine Learning Research (JMLR),ACM, https://dl.acm.org/journal/jmlr
