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Python for Data Science

Python is a general purpose programming language which is rich in library and moreover it is open source. The popularity of python is increasing day by day because of its community and library. Python can be used for various purpose like web development, machine learning, artificial intelligence along with data science. Students who have interest in data analysis and data science can learn python along with its popular libraries like Numpy, Pandas and Matplotlib.

The course is designed in such a way that even a beginner can start learning python for Data Science. However, one should first learn Python fundamental programming concept. At first we teach fundamental programming concept in Python for about a month and then we start Python for data science.

Who can join Python for Data Science?

Anybody who has an interest to learn data analysis in python can join this training. It is not compulsory to have a programming background.


Module 1: Generators and Other Iterables

  • Iterables
  • Saving memory with generators
  • Generator expressions
  • Generator functions
  • Generator classes
  • Stacking generators

Module 2: Data Structures

  • How to store data
  • The basics: list and tuples
  • Named access with dictionaries
  • Named tuples: best of both worlds
  • Using Classes as data structures

Module 3: Serializing Data

  • Pickle
  • JSON
  • CSV
  • XML

Module 3: Consuming data from the web

  • Web data sources
  • Data via URL
  • RESTful data
  • Screen-scraping

Module 5: Excel Spreadsheet

  • The xlrd, xlwr, and xluti modules
  • Reading an existing spreadsheet
  • Creating a spreadsheet from scratch
  • Modifying an existing spreadsheet

Module 6: Analyzing datasets

  • Sorting data filtering values
  • Basic statistics
  • Leveraging NumPy
  • Using Pandas

Module 7: Numpy

  • NumPy Basic
  • Creating arrays
  • Indexing and slicing
  • Large number of sets
  • Transforming data
  • Advance tricks

Module 8: Pandas

  • Pandas overviews
  • Data frames
  • Reading and Writing data
  • Data alignment and reshaping
  • Fancy indexing and slicing
  • Merging and joining data sets

Module 9: Matplotlib

  • Creating a basic plot
  • Commonly use plots
  • Ad hoc data visualization
  • Advance uses
  • Exporting images

Module 10: The Python Image Library (PIL)

  • PIL overview
  • Create image library
  • Image processing
  • Displaying images

Module 11: Sympy

  • Basic arithmetic
  • Simplification and expansion
  • Functions
  • Polynomials
  • Solving equations
  • Geometry