Course

Unit 1

Introduction, Toolboxes: Python, fundamental libraries for data Scientists. Integrated development environment (IDE). Data operations: Reading, selecting, filtering, manipulating, sorting, grouping, rearranging, ranking, and plotting

Unit 2

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, Randomness, Probability, Introduction to Statistics, Sampling, Sample Means and Sample Sizes.

Unit 3

Data Acquisition, Data Entry, Signal Reception, Data Extraction. Data Warehousing, Data Cleansing, Data Staging, Data Processing, Data Architecture. Data Mining, Clustering/Classification, Data Modeling, Data Summarization. Exploratory/Confirmatory, Predictive Analysis.

Unit 4

Descriptive statistics Central tendency, dispersion, variance, covariance, kurtosis, five point summary, Distributions, Bayes Theorem, Error Probabilities; Permutation Testing, Statistical Inference; Hypothesis Testing, Assessing Models, Decisions and Uncertainty, Comparing Samples, A/B Testing, P-Values, Causality.

Unit 5

Estimation, Prediction, Confidence Intervals, Inference for Regression, Classification , Graphical Models, Updating Predictions.

Text Books / References

Text Books

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

References:

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.

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