Department Events
Data Analysis
Course Objectives:
The course will enable the students to
1 Appreciate the role of statistics in computers.
2 Familiar with applying various tests on datasets for data analysis.
Course | Learning Outcome (at course level) | Learning and teaching strategies | Assessment Strategies | |
Course Code | Course Title | |||
25GBCA202A | Data Analysis (Theory) | CO91. Interpret statistical data, both numerically and graphically. CO92. Compute and interpret the coefficient of correlation and regression. CO93. Apply various methods to compute the probabilities of events. CO94. Analyze and interpret statistical data using appropriate sampling distributions. CO95. Perform parameter and non- parametric testing techniques on different application based problems. CO96. Contribute effectively in course-specific interaction. | Approach in teaching: Interactive Lectures, Discussion, reading assignments, Demonstrations.
Learning activities for the students: Self-learning assignments, Seminar presentation, giving tasks, Performing practical | Class test, Semester end examinations, Quiz, Assignments, Presentation, Individual and group projects |
Frequency distributions: Graphical representation of data (Bar Chart, Histograms, Pie Chart, BoxPlots). Measures of Central Tendency (mean, median, mode), Measures of Dispersion (Range, QD, MD, SD), five number summary.
Correlation and Regression: 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.
Probability theory: 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).
Statistical Inference: 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.
Sampling Distributions: 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.
ESSENTIAL READINGS:
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
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/
