• Overview of Python
• Installation of Python and IDEs (e.g., Anaconda, PyCharm , Visual studio code)
• Running Python programs
• Basic Syntax and Data Types
• Variable Types ,Keywords
• Input and output Functions

• Arithmetic Operators
• Comparison Operators
• Logical Operators
• Assignment Operators
• Membership Operators
• Identity Operators

• Simple if
• If else , If else if ,Nesed if

Python – Loops
• While loop
• For loop
Python - Transfer Statement
• Break , Continue ,Transfer

• Lists: Creation, indexing, slicing, methods
• Tuples:Creation, indexing, immutability
• Dictionaries: Creation, accessing, modifying, methods
• Sets: Creation, methods, set operations

• Math Functions
• String Functions
• Python Default Arguments
• Keyword Arguments
• Arbitrary Arguments
• Lambda Functions

• NumPy: Fundamental package for numerical computing with Python.
• Pandas: Offers data structures and functions needed to manipulate structured data seamlessly.
• Matplotlib: Comprehensive library for creating static, animated, and interactive visualizations in Python.
• Seaborn: Statistical data visualization library based on Matplotlib, provides a high-level interface for drawing attractive and informative graphics.
• Scikit-learn: Machine learning library that provides simple and efficient tools for data mining and data analysis. • SciPy: Used for scientific and technical computing.

• Introduction to NumPy
• What is NumPy?
• Importance of NumPy in data science
• Installation and setup
• Basic structure and terminology
• Creating Arrays
• Creating arrays from lists

• Basic Operations
• Arithmetic operations
• Broadcasting
• Universal functions (ufuncs)
• Indexing and Slicing

• One-dimensional array indexing and slicing
• Multidimensional array indexing and slicing
• Boolean indexing
• Fancy indexing
• Manipulating Arrays

Reshaping arrays • Concatenation and splitting
• Adding and removing elements
• Transposing and swapping axes
Advanced Array Manipulations • Linear Algebra with NumPy
• Dot product
• Matrix multiplication
• Determinant and inverse
• Eigenvalues and eigenvectors
• Array Mathematics

Basic statistical operations: • mean, median, standard deviation
• Random sampling: np.random module
• Cumulative sums and products
• Advanced Functions
Applying functions • Vectorizing functions
• Aggregation functions: sum, min, max, etc.
• Exploratory data analysis (EDA) using NumPy

Pandas Module • Introduction to Pandas
• What is Pandas?
• Importance of Pandas in data science
• Installation and setup
Basic structure and terminology: Series and DataFrame • Creating Data Structures
• Creating Series
• Creating DataFrames from various sources (lists, dictionaries, CSV/Excel files)
• Exploring DataFrame attributes: shape, size, dtype, index, columns

Data Operations • Indexing and Selecting Data
• Indexing and selecting with loc and iloc
• Boolean indexing
• Setting and resetting the index
Data Manipulation • Adding and dropping columns
• Renaming columns and indexes
• Handling missing data: detecting, removing, and filling missing values

Data Alignment and Arithmetic • Alignment of data and arithmetic operations
• Handling different data alignments
• Operations with scalars and functions
Data Wrangling • Combining and Merging DataFrames
• Concatenation using concat()
• Merging and joining DataFrames with merge() and join()
• Understanding the different types of joins
Group By Operations • Splitting data into groups
• Applying functions to groups
• Aggregating data with groupby
• Reshaping and Pivoting

Reshaping with melt() and pivot() • Creating pivot tables
• Using stack() and unstack() for hierarchical indexing
Data Analysis and Visualization • Descriptive Statistics
• Summary statistics
• Correlation and covariance
• Value counts and unique values
• Time Series Analysis
Working with datetime objects • Date range generation
• Resampling, shifting, and lagging data

Data Visualization with Pandas • Plotting basics with Pandas
• Customizing plots
• Integrating with Matplotlib and Seaborn
Advanced Data Operations • Advanced Indexing and Slicing
• MultiIndex (Hierarchical Indexing)
• Cross-section and advanced selection
String Manipulation • String operations in Series
• Handling and manipulating text data

Applying Functions • Applying functions element-wise using apply(), map(), and applymap()
• Using agg() for aggregation
Input and Output Operations • Reading and Writing Data
• Reading data from CSV, Excel, SQL databases, and other formats
• Writing data to various formats
• Handling large datasets with chunking
• Data Cleaning and Preprocessing
Capstone Project Comprehensive project integrating

• Applications of Machine learning
• Machine learning Life cycle
• Gathering Data:
• Data preparation
• Data Wrangling
• Data Analysis
• Train Model
• Test Model
• Deployment
Machine Learning Model • Supervised Learning
• Unsupervised Learning
• Reinforcement Learning

• Linear Regression
• Logistic Regression.
• Decision Tree
• SVM (Support Vector Machine) Algorithm
• Naive Bayes Algorithm
• KNN (K- Nearest Neighbors) Algorithm

Un Supervised Learning - Scikit Learn. • K-Means Clustering
• Hierarchical Clustering
• Association Rule Learning
• Apriori Algorithm
Reinforcement learning (RL) • Key Concepts
• Agent:
• Environment:
• State:
• Q-Learning Algorithm
Visualization Tools • Tableau: Leading data visualization tool for creating detailed and interactive visualizations.
• Power BI: Microsoft's powerful analytics tool that provides interactive visualizations and business intelligence capabilities.