Data wrangling is the process of transforming and mapping data from one raw data form into another format with the intent of making it more appropriate and valuable for a variety of downstream purposes such as analytics. Here I have combined multiple Data Wrangling steps intop groups on which success of any Data Analytics / Science project depends. Topics to be covered: • Data Cleaning: Missing Value Treatment, Outlier Treatment, Data Validation • Data Manipulation: Subsetting, Indexing, Groupby, Aggregation, Pivot tables, Data Merge, Reshaping, Creating new variables, Sorting. Relevant Libraries: • Python — Pandas, Numpy, Scipy, Matplotlib, Seaborn, folium, bukeh • R — dplyr, sqldf, data.table, stringr, tm, ggplot2, ggviz, RWorldMap