Course

Unit 1

Introduction, Causality and Experiments, Data Preprocessing: Data cleaning, Data reduction, Data transformation, Data discretization. Visualization and Graphing: Visualizing Categorical Distributions, Visualizing Numerical Distributions, Overlaid Graphs, plots, and summary statistics of exploratory data analysis. (15 hrs).

Unit 2

Randomness, Probability, Introduction to Statistics, Sampling, Sample Means and Sample Sizes, Descriptive statistics – Central tendency, dispersion, variance, covariance, kurtosis, five-point summary, Distributions, Bayes Theorem, Error Probabilities; Permutation Testing, Statistical Inference. (15 hrs)

Unit 3

Hypothesis Testing, Decisions and Uncertainty, Comparing Samples, A/B Testing, P-Values, Causality, Frequency Analysis, Assessing Models, Estimation, Prediction, Confidence Intervals, Inference for Regression, Classification, Graphical Models, Updating Predictions. (15 hrs)

 Course Outcomes:

CO1: Understand the various data visualization methods.

CO2: Understand the basics of the descriptive statistics.

CO3: Understand and apply the basic concepts of correlations and regressions to the given data.

CO4: Understand and apply the basic concepts of sampling techniques and simple hypothetical testing to the given data.

CO-PO Mapping

Textbook(s)

Adi Adhikari and John DeNero, “Computational and Inferential Thinking: The Foundations of Data Science”, e-book.

Reference(s)

1. Data Mining for Business Analytics: Concepts, Techniques and Applications in R, by Galit Shmueli, Peter C. Bruce, Inbal Yahav, Nitin R. Patel, Kenneth C. Lichtendahl Jr., Wiley India, 2018.
2. Rachel Schutt & Cathy O’Neil, “Doing Data Science” O’ Reilly, First Edition, 2013.