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