There are 11 modules available for this course, an extensive introduction to data science and analytics using python

Introduction to data science with oython

1. What is Python?
   • Why Python?
   • Python Philosophy And The Zen Of Python
   • A Brief History Of Python
   • Features of Python
   • Installation on Windows Gnu/Linux and Unix Systems
2. Programming Basics
   • Hello World Program
   • Comments
   • Literal Constants
   • Strings
   • The Format Method
3. Basics Continued
   • Raw String
   • Escape Sequences
   • Variables
   • Data Types
   • Object
   • How To Write Python Programs
   • Indentation
4. Operators and Expressions
   • Operators
   • Evaluation Order
   • Changing The Order Of Evaluation
   • Associativity
   • Expressions
5. Control Flow
   • The if Statement
   • The while Statement
   • The for Loop
   • The break Statement
   • The continue Statement
6. Control Flow
   • The if Statement
   • The while Statement
   • The for Loop
   • The break Statement
   • The continue Statement
7. Functions
   • Function Parameters
   • Local Variables
   • Using The Global Statement
   • Default Argument Values
   • Keyword Arguments
   • VarArgs Parameters
   • Keyword Only Parameters
   • The return Statement
   • DocStrings
8. Linear Algebra, Statistics
   • Array ,Metrix, Metrix operation Eigen Value
   • Eigen Vector,orthogonality Mean
   • Median
   • Mode
   • Variance and Standard Deviation
   • Probability
   • Random Variables Probability Density
   • Functions Normal Distribution
   • Gaussian Distribution
   • Conditional Probability Bayes' Theorem
   • Bernoulli Distribution Binomial distribution
   • Vector
   • Dotproduct
   • Co-variance
9. Data and Data Processing
   • Unstructured vs. Structured data Qualitative and Quantitative
   • Data: Discrete data and Continuous data
   • Data and Information
   • Nominal and Ordinal Data
   • Data Preprocessing Data Cleaning Data Integration
   • Data Transformation Data Reduction Normalization Standardization
   • Handling Missing Data
10. Python/Anaconda
    • Introduction to python and anaconda
    • Conditional Statements
    • Looping, Control Statements
    • Lists, Tuple ,Dictionaries
    • String Manipulation
    • Functions Installing Packages
11. Introduction of Various Tool
    • Introduction of Anaconda
    • Working on spyder
    • Jupyter notebook
12. Working on Various Python Library
    • Installing library and packages for machine learning and data science
    • Matplotlib,Seaborn
    • Scipy and Numpy
    • Pandas
    • IPython toolkit
    • scikit-learn
13. Data Analysis Using Pandas
    • Introduction to Pandas
    • Data Type of Pandas
    • Creating DataFrame using Pandas
    • Importing and Exporting Database
    • Working with Complex Data
    • Data Mining using Pandas