Data Analytics with Python/Core Python

1. Module : Python Essentials Introduction

  • What is Python...?
  • A Brief history of Python
  • Why Should I learn Python...?
  • Installing Python
  • How to execute Python program
  • Write your first program
  • Variables
  • Numbers
  • Operator
  • if...statement
  • if...else statement
  • Elif...statement
  • The while...Loop
  • The for....Loop
  • Continue, break, pass statement

2. Strings

  • Introduction to Python ‘string’ data type
  • Properties of a string
  • String built-in functions
  • Programming withstrings
  • String formatting

3. Lists and Tuples

  • Introduction to Python ‘list’ data type
  • Properties of a list
  • List built-in functions
  • Programming with lists
  • List comprehension
  • Introduction to Python ‘tuple’ data type
  • Tuples as Read only lists

4. Dictionary and Sets

  • Introduction to Python ‘dictionary’ data type
  • Creating a dictionary
  • Dictionary built-in functions
  • Introduction to Python ‘set’ data type
  • Set and set properties
  • Set built-in functions

5. User defined functions

  • Introduction to functions
  • Function definition and return
  • Function call and reuse
  • Function parameters
  • scope of variable(call by value, call by reference)

6. Modules and Packages

  • Importing module (from, import statement)
  • Anonymous functions(Lambda)
    1. Filter
    2. Map
    3. Reduce
  • Math module
  • Datetime

7. Working with files

  • File objects and Modes of file operations
  • Reading, writing and use of ‘with’ keyword
  • Read(), Readline(), Readlines(), Write(), Writeline()
  • Pickle module

8. Exception Handling in Python

  • Understanding exceptions
  • Try, Except, else and finally
  • Raising exceptions with: raise, assert

9. OOPs Concept

  • Class and object
  • Attributes
  • Inheritance
  • Overloading
  • Overriding
  • Data hiding

10. Regular expressions

  • Match function
  • Search function
  • Matching VS Searching
  • Modifiers
  • Patterns

11. Database(MySQL)

  • Introduction
  • Connections
  • Executing queries
  • Transactions
  • Handling error

12. Mathematical Computing with Python (NumPy)

  • NumPy Overview
  • Properties, Purpose, and Types of ndarray
  • Class and Attributes of ndarray Object
  • Basic Operations: Concept and Examples
  • Initializing arrays: random, ones, zeros
  • Accessing Array Elements: Indexing, Slicing, Iteration, Indexing with Boolean Arrays
  • Shape Manipulation
  • Linear Algebra

13. Data Manipulation with Python (Pandas)

  • Introduction to Pandas
  • Data Structures
  • Series
  • Data Frame
  • Missing Values
  • Data Operations
  • Data Standardization
  • Pandas File Read (CSV,TSV,EXCEL,ACCESS) and Write Support
  • Data Acquisition (Import & Export)

14. Data analysis –Visualization using Matplotlib & Seaborn

  • Introduction exploratory data analysis
  • Descriptive statistics, Frequency Tables and summarization
  • Univariate Analysis (Distribution of data & Graphical Analysis)
  • Creating Graphs-Bar/pie/line chart/histogram/ boxplot/ scatter/columns

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