Introduction to Python and Basics

• History
• Features
• Definition & Applications
• Features, Versions & Working
• Anaconda & Different IDEs for Python
• Introduction to IDE's - Jupiter Notebook, Spyder & Google Colab

Data Types and Control Flows

• Literals, reserved words and input functions
• Data Types: int, float, bool, str
• Decision Control Flows: If / Nested If / If-else / If-elif-else
• Control Flow Loops: While loop, For loop, While-else, For-else
• Operators: Arithmetic, Relational or Comparison, Logica

Lists, Tuples, Sets, and Dictionaries

• Operators (Bitwise, Assignment, Ternary)
• List: Traversing Elements, Operations, Methods, Membership Operator, Comprehension, Ways of Accessing values.
• Tuples: Creating Tuples, Ways of Accessing Values, Tuple Vs Immutability, Tuple Comprehension
• Sets: Ways of Accessing Values, Manipulating and Accessing Sets, Set Operations, Creating Sets
• Dictionary: Why Dictionary, creating a Dictionary, Accessing Values, Updating Dictionaries, Functions of Dictionary

File Handling and Strings

• File Handling: Types of Files, Opening and Closing Files, Writing, Appending, and Reading Files
• Strings: String Literals, Single, Double & Triple Quotes, Raw Strings and Operations on strings
• Dictionary: Accessing Values, Updating Dictionaries, Functions of Dictionary

Iterators & Generators

• Iterator vs Iterable, Containers, Generators in Python.

Regular Expressions

• Match function
• Search function
• Matching VS Searching
• Modifiers
• Patterns
• Uses of Regular Expressions - Text Analytics, import re, Character Classes, Backslash, Alteration, Quantifiers

OOPs concept

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

Introduction to NumPy, Pandas & Matplotlib

• Introduction to NumPy, Install NumPy
• Array Creation, Array Reshaping, Indexing, Operations
• Introduction to Pandas, Slicing Data, Slicing Data Frame

Introduction to Data Preprocessing

• Filtering DataFrame, Transforming DataFrame, Advanced Indexing

Exploratory Data Analysis (EDA)

• Data Cleaning Techniques, Handling Missing Data, Handling Categorical Data
• Introduction to EDA, 2D Scatter-plot, 3D Scatter-plot, Pair plots
• Univariate, Bivariate, and Multivariate Analysis, Box-plot
• Variance and Standard Deviation, Median, IQR (Interquartile Range)

Advanced Pandas and Data Visualization

• Advanced Pandas, Advanced Indexing, Data Preparation
• Handling Missing Data, handling Categorical Data, Data Cleaning

Data Visualization

• Introduction to Data Visualization, Plotting with Matplotlib
• Scatter Plots, Line Plots, Bar Plots, Pie Charts, Heatmaps

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