Department Events
DATA ANALYSIS
The course will enable the students to
- Appreciate the role of statistics in computers.
- Familiar with applying various tests on datasets for data analysis.
Course Outcomes (COs):
Course Outcome (at course level) | Learning and teaching strategies | Assessment Strategies |
|---|---|---|
The students will: CO77. Organize, present and interpret statistical data, both numerically and graphically, CO78. Use various methods to compute the probabilities of events. CO79. Perform a regression analysis, and compute and interpret the coefficient of correlation. CO80. Analyze and interpret statistical data using appropriate probability distributions, e.g. binomial and normal. CO81. Construct and interpret confidence intervals to estimate means, standard deviations CO82. Perform parameter and non- parameter testing techniques on different applications based problems. | Interactive Lectures, Discussion, Tutorials, reading assignments, Demonstrations. Self-learning assignments, Effective questions, Seminar presentation, giving tasks, Performing practical | Class test, Semester end examinations, Quiz, Solving problems in tutorials, Assignments, Presentation, Individual and group projects |
Frequency distributions, Graphical representation of data (Bar Chart, Histograms, Pie Chart, Box-Plots). Measures of Central Tendency (mean, median, mode), Measures of Dispersion (Range, QD, MD, SD), five number summary.
Concept of bivariate and multivariate data. Correlation definition and assumptions. Properties of correlation coefficient. Karl Pearson’s coefficient of correlation and Spearman Rank Correlation. Linear Regression - Definition, Fitting of two lines of regression, Regression coefficients with simple properties.
Classical Theory of Probability, Law of total and compound probability, Conditional probability, Baye’s theorem (simple question based on the theorem). Concept of random variable and types of random variables. Probability distribution function and some important probability distributions (Binomial, Poisson and Normal).
Basics of statistical inference, point estimation, interval estimation and hypothesis testing. Concept of Sampling Distribution and Standard Error. Introduction to standard sampling distributions (chi-square, t and F). Large sample tests for variables.
Applications of standard sampling distributions which includes application of t-test for testing the significance of single mean & difference in two means (independent and paired-t), Chi-square test for testing normal population variance, test for goodness of fit, independence of attributes using 2x2 and RXC contingency tables, application of F test for testing of equality of two variances.
1. S.C. Gupta and V.K. Kapoor, “Fundamentals of Mathematical Statistics”, Eleventh edition, S. Chand & Company, 2002.
2. Ross Sheldon M., “Introduction to the Theory of Probability”, Elsevier Publication.
Suggested READINGS:
1. D. Ball and G. D. Buckwell, “Statistics A Level”, Second edition,Macmillan Press Ltd, 1991.https://link.springer.com/book/10.1007/978-3-319-46162-5
2. A.M.Goon, M.K.Gupta and B.Das Gupta, “Fundamental of Statistics” Vol I, Calcutta University Press.
3. B.L. Agarwal, “Basic Statistics”, New Age Publications.
4. S.P. Gupta, “Statistical Methods”, Sultan Chand Publishers
5. D. C. Sancheti, V. K. Kapoor, “Statistical Methods”, Sultan Chand and Sons.
6. D.N. Elhance& others “Fundamentals of Statistics”.
7. Glyn Davis and BrankoPecar, “Business Statistics using Excel”, Second Edition, Oxford University Press, 2013.
E-RESOURCES:
1. https://link.springer.com/book/10.1007/978-3-319-46162-5
2. www.nptel.ac.in
3. www.jntuk coeerd.in
4. https://epgp.inflibnet.ac.in/
5. https://www.academia.edu/
6. https://www.slideshare.net/
JOURNALS:
1. Journal of Information Display, https://www.tandfonline.com/journals/tjid20
2. EPJ Data Science, https://epjdatascience.springeropen.com/
