Installing Python on your computer; Jupiter notebooks. Using pip or similar command for installing various packages such as Pandas Matplotlib or Numpy. Reading data from csv files into Panda data frame. Using Matplotlib to plot one or more series of data. Descriptive statistics. Linear regression and Logistic regression.

Probability, Games of Chance, Conditional, Marginal and Joint Probability, Bayes’ Law, Sampling methods and empirical rules, sample mean and central limit theorem, standard normal distribution and probability table. Student’s t-distribution.

Framing mutually exclusive hypothesis, setting a significance level for type I error and type II error. Testing for means - single sample, testing for proportions - single sample. Testing for difference of means - two large samples, testing for difference of proportions - two large samples, small samples and t- test statistic. hypothesis tests for paired samples, Chi-square test for independence of population attributes. Chi-square test for goodness of fit of distribution, correlation coefficient for categorical data,

Scatterplots and regression line, ordinary least squares regression, estimating the coefficients, testing for their significance, coefficient of determination, F statistic and fitness of overall model. Applications of regression, multiple regression, estimating betas for multiple regression. Odds, log of odds. Sigmoid function, Classification with logistic regression; Issues using regression for binary classification

Installing R and/or RStudio on your computer. Installing packages. Reading csv files into R. Calculating summary measures. Counts and pivot tables in R. Sampling from various distributions. Hypothesis tests. Linear regression and logistic regression

Experimental design, completely randomised design, randomised block design, factorial experiment, one way ANOVA for more than two populations, Pearson’s correlation coefficient, Spearman’s rank correlation.

Market Basket Analysis and Associative Rule Mining, Text analysis, Decision Trees, Data Visualisation, Cluster Analysis.

Qualitative methods and quantitative methods. Method of Delphi, Jury of Executive Opinion etc. Calculating a moving average. Exponential smoothing. Exponential smoothing with trend and seasonality components.

Time Series Analysis, seasonality, stationarity, decomposition a series into trend, seasonality, cycles and random variation. Box-Jenkins methodology. Dealing with non- stationarity, Tests for stationarity.