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INTRODUCTION TO DATA SCIENCE

Basic terminologies of data science, Types of data, Five steps of data science, Arrays and vectorized computation using NumPy - The NumPy ndarray: A multidimensional array object, Universal functions: Fast element-wise Array functions, Array-oriented Programming with arrays, File input and output with arrays, Linear algebra, Pseudo random number generation.

DATA EXPLORATION WITH PANDAS

Process of exploring data, Pandas data structures – Series, Data frame, Index objects; Essential functionality, Summarizing and computing descriptive statistics – Correlation and covariance, Unique values, Value counts and membership; Data loading, Storage, and file formats - Reading and writing data in text format , Binary data formats

DATA CLEANING, PREPARATION AND DATA WRANGLING

Handling missing data, Data transformation, String manipulation - String object methods, Regular expressions, Vectorized string functions in Pandas; Data wrangling: join, Combine Page 27 of 35 and reshape - Hierarchical indexing, Combining and merging datasets, Reshaping and pivoting.

DATA VISUALIZATION WITH MATPLOTLIB

Plotting and visualization- A brief matplotlib API primer, Plotting with Pandas and Seaborn, Other python visualization tools; Data aggregation and Group operations Group By mechanics, Data aggregation, Apply: General split-apply-combine, Pivot tables and Cross-tabulation.

TIME SERIES ANALYSIS

Date and time data types and tools, Time series basics, Date ranges, Frequencies, and shifting Resampling and frequency Conversion – Downsampling, upsampling and Resampling with periods.