UNIT I - INTRODUCTION

Data Science: Benefits and uses – facets of data  Data Science Process: Overview – Defining research goals – Retrieving data – Data preparation  Exploratory Data analysis – build the model – presenting findings and building applications - Data Mining  Data Warehousing – Basic Statistical descriptions of Data.

UNIT II - DESCRIBING DATA 

Types of Data - Types of Variables   Describing Data with Tables and Graphs –Describing Data with Averages - Describing Variability Normal Distributions and Standard (z) Scores.

UNIT III - DESCRIBING RELATIONSHIPS

Correlation –Scatter plots –correlation coefficient for quantitative data –computational formula for correlation coefficient – Regression –regression line –least squares regression line – Standard error of estimate – interpretation of r2 –multiple regression equations –regression towards the mean.

UNIT IV - PYTHON LIBRARIES FOR DATA WRANGLING

Basics of Numpy arrays –aggregations –computations on arrays –comparisons, masks, boolean logic – fancy indexing – structured arrays – Data manipulation with Pandas – data indexing and selection – operating on data – missing data – Hierarchical indexing – combining datasets – aggregation and grouping – pivot tables.

UNIT V - DATA VISUALIZATION

Importing Matplotlib – Line plots – Scatter plots – visualizing errors – density and contour plots – Histograms – legends – colors – subplots – text and annotation – customization – three dimensional plotting - Geographic Data with Basemap Visualization with Seaborn.

1. David Cielen, Arno D. B. Meysman, and Mohamed Ali, “Introducing Data Science”, Manning Publications, 2016. (Unit I).
2. 2. Robert S. Witte and John S. Witte, “Statistics”, Eleventh Edition, Wiley Publications, 2017. (Units II and III).
3. Jake VanderPlas, “Python Data Science Handbook”, O’Reilly, 2016. (Units IV and V).

4. Allen B. Downey, “Think Stats: Exploratory Data Analysis in Python”, Green Tea Press,2014.