Course

Unit 1

Data Science – Introduction, Overview of Python, Expressions, Data types, Arrays. (Chapters 1 to 6)

Unit 2

Data Visualization—Bar charts, Histograms, Line Charts, Scatterplots, Overlaid plots. (Chapter 7)

Unit 3

Functions and Tables, Joins, Iteration, Simulation, Sampling. (Chapters 8 to 10)

Unit 4

Empirical Distributions, Assessing Models, Decisions and Uncertainty, A/B Testing, Causality, Bootstrap, Designing Experiments. (Chapters 11 to 14)

Unit 5

Linear Regression, Least Squares, Classification, Case Studies. (Chapters 15 to 18)

Course outcomes

CO1: Able to design visualizations that represent the relationships contained in complex data sets and adapt them to highlight the ideas you want to communicate
CO2: Able to support the visualizations with written and verbal explanations on their interpretation. +
CO3: Able to use leading open source software packages to create and publish visualizations
CO4: Ability to understand Regression and Classification problems.
CO5: Enable clear interpretations of big, complex and real world data

1. Ani Adhikari, John DeNero and David Wagner, Computational and Inferential Thinking: The Foundations of Data Science, 2nd Edition.
2. Web Resource: http://data8.org/fa21/