Overview :

  •                What is Data Science? – Introduction
  •               Roles and Responsibilities of a Data Scientist
  •               Life cycle of Data Science project
  •               Tools and Technologies used

Course Content:

 

Module 1: Python Programming

  • Introduction to Python with Anaconda Distribution
  • Introduction to Jupiter Notebook
  • Crash Course on Python Programming
  • Types of Operators
  • Python Data Types
  • List
  • Tuple
  • Dictionary
  • Sets
  • Data Types Operations & Methods
  • Flow Controls
  • If…..Else Statements
  • If ….Elif ….Else Statements
  • For Loops
  • While Loops
  • Functions
  • List Compressors
  • Lambda, Map and Filter

Module 2: Introduction to Essential Python Libraries for Data Science

  • Numpy, Pandas, Matplotlib, Seaborn and Scikit Learn Libraries

Module 3: Numpy

  • Introduction to Numpy Library
  • Numpy Arrays
  • Numpy Indexing and Selection
  • Numpy Operations

Module 4: Pandas

  • Introduction to Pandas Library
  • Pandas Series and Data Frames
  • Pandas Indexing and Selection
  • Pandas Operations

Module 5: Data Mugging / Wrangling with Pandas

  • Handling Missing Data
  • Group by Method
  • Merging, Joining and Concatenating Data Frames.
  • Pivot Table
  • Reshaping the Data Frame
  • Cross Tab / Contingency Table

Module 6: Data Visualization

  • Various types of Plots and their Applications
  • Introduction to Matplotlib Library
  • Creation of plots
  • Plot Styles
  • Introduction to Seaborn Library
  • Distribution Plots
  • Categorical Plots
  • Matrix Plots
  • Regression Plots
  • Pandas Built-in Visualizations