Data Science with Python Training

Data Science with Python Syllabus

Introduction

Execution steps

  • Why do we need Python?
  • Program structure
  • Interactive Shell
  • Executable or script files
  • User Interface or IDE

Memory management and Garbage collections

Data Types and Operations

  • Object creation and deletion
  • Object properties
  • Numbers
  • Strings
  • List
  • Tuple
  • Dictionary
  • Other Core Types

Statements and Syntax

  • Assignments, Expressions and prints
  • If tests and Syntax Rules
  • While and For Loops
  • Iterations and Comprehensions

File Operations

  • Opening a file
  • Using Files
  • Find and replace

Functions

  • Function definition and call
  • Function Scope
  • Arguments
  • Function Objects
  • Anonymous Functions

Modules and Packages

  • Module Creations and Usage
  • Module Search Path
  • Module Vs. Script
  • Package Creation and Importing

Classes

  • Classes and instances
  • Classes method calls
  • Inheritance and Compositions
  • Static and Class Methods
  • Bound and Unbound Methods
  • Operator Overloading
  • Polymorphism

Exception Handling

  • Default Exception Handler
  • Catching Exceptions
  • Raise an exception
  • User defined exception

Advanced Concepts

  • Defining Panda
  • Pandas – Creating and Manipulating Data
  • How to Create Data Frames?
  • Importance of Grouping and Sorting
  • Plotting Data

Data Science

  • Standard deviation
  • Correlation and covariance
  • Outliers
  • Hypothesis Testing
  • Chi square
  • Anova
  • T test
  • Linear regression
  • Multiple Regression
  • Logisitc Regression
  • Variable selection on model
  • Clustering
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