• Overview of Python
• Why Choose Python for Programming?
• What is Python?
• Real-World Applications of Python
• Platform Dependent vs Independent Languages
• Key Features of Python
• Limitations of Python
• The Evolution and History of Python

• Installing Python
• Installing and Setting Up PyCharm
• Introduction to IDLE
• How Python Code is Executed
• Writing and Running the “Hello World” Program

• Python Statements, Indentation, and Comments
• How to Print in Python
• Getting User Input in Python
• Understanding Methods, Identifiers, and Variables
• Python Data Types and Variables
• Rules for Declaring Identifiers

• Input and Output Functions in Python
• Reading Data in a Single Line
• Using the print() Function
• Formatted Print Statements
• The Replacement Operator
• Printing with the format() Method

• Different Types of Operators in Python
• Bitwise Operators in Python
• Arithmetic and Assignment Operators
• Number System Conversion in Python
• Identity Operators

• Writing if-else Statements in Python
• Using if-elif Conditions
• Creating Pyramid Patterns with Control Statements
• Transfer Statements: Break and Continue
• Using the Pass Statement
• Introduction to Loops
• Types of Loops: while and for
• Using Nested Loops

• What Are Strings?
• Built-in String Functions
• String Class and Methods
• Common String Operations
• Type Casting in Python

• Understanding Collections in Python
• Working with Lists, Tuples, Sets, and Frozen Sets
• Using Dictionaries in Python
• Bytes and Bytearray Collections

• Understanding Functions in Python
• Types of Functions and Methods
• Creating Functions in Python
• Parameters and Arguments
• Function Arguments and Call by Value
• Types of Function Arguments
• Returning Values from Functions
• Passing Functions as Arguments
• Global Variables and the global Keyword
• Using the eval() Function

• Writing Recursive Functions
• Implementing Factorial with Recursion
• Reversing Numbers with Recursion
• Fibonacci Series with Recursion

• Anonymous Functions with lambda
• Using the filter() Function with lambda
• lambda with the map() Function

• Introduction to Arrays
• Arrays Operations
• Types and Concepts of Arrays
• Using Arrays in Python
• Array Methods in Python
• Creating Arrays with NumPy
• Comparing Arrays in Python
• Understanding Object References
• Difference Between View and Copy
• Exploring NumPy Array Dimensions and Attributes
• Working with Multidimensional Arrays
• Using Matrices in Python with NumPy

• Implementing Binary Search
• Writing a Bubble Sort Algorithm

• Procedural vs Object-Oriented Programming
• Key Concepts of OOP in Python
• Creating Classes and Objects
• Understanding the self Variable
• Using the __init__() Method
• Constructor Overloading in Python
• Exploring Polymorphism and Operator Overloading
• Relational Operator Overloading
• Overloading vs Overriding in Python
• Using Getters and Setters
• Static Variables and Methods
• Working with Inner Classes
• Understanding Is-A vs Has-A Relationships
• Implementing Inheritance and Using the super() Method
• Types of Inheritance in Python
• Role of Constructors in Multiple Inheritance
• Abstract Classes and Methods
• Creating Interfaces in Python

• Basics of Exception Handling
• Common Exception Types
• Using try, except, and finally Blocks
• Exception Handling with Practical Programs
• Using the finally Statement
• Working with Assertions
• Raising Exceptions in Python
• Writing Custom Exceptions

• Introduction to Files in Python
• File Modes (r+, w+, a+)
• Reading, Writing, and Appending Files
• Using with for File Handling
• Handling File Exceptions
• Writing Data to a File with Practical Programs
• Working with readlines() and writelines() Methods
• Counting Lines, Words, and Characters in a File
• Reading Files Character by Character
• Binary File Operations in Python
• Working with the Pickle Module
• Reading and Writing CSV Files
• Using tell() and seek() Methods

• Overview of NumPy
• Steps to Install NumPy in Python
• Installing NumPy in PyCharm IDE

• Different Ways to Create Arrays in NumPy
• Core Concepts of NumPy Arrays
• Creating Arrays in NumPy
• Understanding Attributes of NumPy Arrays
• Practical Exercise on Array Attributes

• Using arange(), linspace(), and logspace() for Array Generation
• Array Initialization with zeros(), ones(), full(), and eye()
• Comparing Arrays in NumPy
• Understanding any(), all(), and where() Methods
• Performing Arithmetic Operations on Arrays
• Using Statistical Functions in NumPy

• Reference vs View vs Copy in Arrays
• Combining Arrays with Concatenation
• Merging Two Arrays in NumPy
• Merging Arrays with concatenate(), stack(), vstack(), hstack(), and Depth Methods
• Techniques to Split Arrays
• Splitting Arrays with split(), array_split(), vsplit(), and hsplit()

• Overview of Pandas for Data Analysis
• Installing Pandas on Windows
• Setting Up Pandas in PyCharm
• How to Download a Dataset for Analysis

• Understanding Series in Pandas
• Key Properties of Pandas Series
• Performing Mathematical Operations on Series

• Introduction to Pandas DataFrames
• Creating DataFrames from Excel, CSV, and Clipboard
• Multiple Ways to Create a DataFrame in Pandas
• Exporting DataFrames to CSV and Excel
• Understanding DataFrame Attributes
• Slicing DataFrames for Analysis
• Sorting DataFrames in Pandas
• Removing Duplicate Entries from DataFrames
• Handling Missing Data with fillna() and dropna()
• Using loc() and iloc() for Data Selection
• Filtering Data in Pandas DataFrames

• Introduction to Advanced Data Analysis Techniques
• Merging DataFrames with Join
• Performing Joins Without a Common Column in Pandas
• Concatenating DataFrames for Complex Analysis
• Using the where() Function in Pandas
• Grouping Data with Pandas groupby()
• Performing Aggregations in Pandas

• SQL-like Operations in Pandas
• Writing SQL-Equivalent Queries Using Pandas
• SQL Queries Translated for Pandas DataFrames
• Using isin() and not isin() for DataFrame Filtering
• Finding the Largest Values with nlargest()
• Inserting, Deleting, and Updating Data in Pandas DataFrames

• Overview of Matplotlib for Data Visualization
• Installing Matplotlib in PyCharm
• Setting Up Matplotlib in Python
• Steps to Design a Basic Chart

• Using Markers in Matplotlib
• Different Types of Markers in Charts
• Drawing Lines in Matplotlib
• Customizing Line Properties (Style, Width, and Color)
• Modifying Lines in Charts
• Changing Title, x-axis, and y-axis Colors and Fonts
• Customizing Fonts for Titles and Axes in Charts
• Adding a Legend in Matplotlib
• Adding Grid Lines to Charts
• Applying Grids to Graphs in Matplotlib

• Using Subplots in Matplotlib
• How to Plot Subplots in Python
• Using xticks(), yticks(), xlabel(), ylabel(), xlim(), and ylim()
• Creating a Scatter Plot in Matplotlib
• Using Cmap and ColorBar in Scatter Plots
• Creating Vertical and Horizontal Bar Graphs
• Plotting Multiple Bars in a Single Graph
• Creating a Pie Chart in Matplotlib
• Creating Histogram Graphs

• The Role of Data Visualization in Data Science
• Why Use Seaborn for Visualization?
• Comparison of Seaborn and Matplotlib
• Installing Seaborn and Setting Up the Environment
• Seaborn’s Key Features and Benefits

• Creating Line Plots and Bar Plots
• Scatter Plots for Bivariate Data
• Histograms and KDE Plots for Distribution Analysis
• Using distplot() and displot() for Data Distributions
• Creating Heatmaps for Correlation Analysis

• Introduction to Data Science
• The Evolution of Data Science
• Big Data vs. Data Science
• Key Terminologies in Data Science
• Data Science vs. AI and Machine Learning
• Data Science vs. Analytics

• A Real-World Use Case
• Preparing Data for Analysis
• Building a Machine Learning Model
• Making Predictions with the Model
• Delivering Business Value with Data Science
• The Data Science Project Workflow
• Stages of a Data Science Project
• Roles: Data Engineer, Data Scientist, ML Engineer

• Types of Analytics
• Descriptive Analytics
• Diagnostic Analytics
• Predictive Analytics
• Prescriptive Analytics

• Data Science in Finance and Banking
• Data Science in Retail
• Data Science in Healthcare
• Data Science in Logistics and Supply Chain
• Data Science in the Technology Industry
• Data Science in Manufacturing
• Data Science in Agriculture

• What is Machine Learning? How is it Different from AI?
• The Machine Learning Workflow
• Popular Machine Learning Algorithms
• Reinforcement Learning

• What is Classification in Machine Learning?
• Logistic Regression
• k-Nearest Neighbors (k-NN)
• Decision Trees and Random Forests
• Support Vector Machines (SVM)
• Naive Bayes Classifier

• What is Regression in Machine Learning?
• Linear Regression
• Ridge and Lasso Regression
• Decision Trees and Random Forest Regression
• Support Vector Regression (SVR)

• Introduction to Clustering Algorithms
• What is Clustering?
• Differences Between Supervised and Unsupervised Learning
• k-Means Clustering
• Hierarchical Clustering

• Feature Engineering
• Support Vector Machine (SVM)
• Introduction to Decision Trees
• How Decision Trees Work

• Introduction to Naïve Bayes
• How Naïve Bayes Uses Probability
• Text Classification with Naïve Bayes
• Building Naïve Bayes Models in Python
• Introduction to Gradient Boosting and XGBoost

• What is Scikit-Learn and Why is it Important?
• Features of Scikit-Learn for Machine Learning
• Installing Scikit-Learn and Setting Up the Environment
• Understanding the Machine Learning Workflow with Scikit-Learn

• What is Natural Language Processing?
• Applications of NLP
• Text Preprocessing
• Text Classification
• Sentiment Analysis

• Introduction to Artificial Intelligence
• Introduction to Gen AI
• Introduction to Generative Adversarial Networks

Module 1: Introduction to Prompt Engineering

• What is Prompt Engineering?
• Importance in working with AI models like GPT and DALL·E.
• Applications across industries (e.g., content creation, customer support, design).

Module 2: Fundamentals of Prompt Design

• Types of prompts:
  • Instruction-based.
  • Conversational.
  • Contextual.
• Components of effective prompts:
  • Clarity, specificity, and relevance.

Module 3: Challenges in Prompt Engineering

• Understanding limitations of AI models.
• Ethical considerations in designing and using pro

Module 1: Introduction to Generative AI

• What is Generative AI?
• Differences between traditional AI and Generative AI.
• Real-world applications:
  • Chatbots and conversational AI.
  • Image generation (e.g., DALL·E).
  • Video synthesis and editing.

Module 2: Fundamentals of Generative AI

• Understanding Generative Models:
  • How they create new data.
• Types of Generative AI:
  • GANs (Generative Adversarial Networks).
  • VAEs (Variational Autoencoders).
  • Transformers (foundation of GPT, DALL·E, and other tools).

Module 3: Popular Tools and Frameworks

• Overview of deep learning frameworks
• APIs and tools
• Working with pre-trained models to simplify tasks

• What is Deep Learning?
  • Definition and its importance in AI.
  • Comparison with machine learning.
• Applications of Deep Learning
  • Computer vision: Image recognition, object detection.
  • Natural language processing: Speech recognition, chatbots.
  • Applications in healthcare, finance, and autonomous vehicles.
• Artificial Neural Networks (ANNs)
  • Basics of neurons, layers, and their roles.
  • Activation functions: Sigmoid, ReLU, Tanh.
  • Forward propagation and backpropagation.
• Deep Learning Frameworks
  • Introduction to TensorFlow, Keras, and PyTorch.
• Types of Neural Networks
  • Fully connected networks.
  • Convolutional Neural Networks (CNNs) for images.
  • Recurrent Neural Networks (RNNs) for sequential data.
• Practical Implementation

• What are Large Language Models?
  • Definition and key features.
  • Examples: ChatGPT, Bard, Claude, GPT-4.
• Importance of LLMs in AI and Data Science
  • Role in modern AI applications.
  • Real-world use cases in various industries.
• Fundamentals of LLMs
  • How Do LLMs Work?
  • Overview of transformer architecture.
  • Basics of the attention mechanism.
• Tokenization process.
  • Training Large Language Models
  • Data preprocessing for LLMs.
  • Overview of training datasets (Common Crawl, Wikipedia).
• Applications of LLMs
• Natural Language Processing Tasks
• Emerging Use Cases
  • Healthcare, education, and finance.
  • Research and scientific discoveries.

• Introduction to Data Engineering
• What is Big Data?
• Difference between Big Data and Data Science
• Characteristics of Big Data
• Five Vs
• Importance of Big Data in Today’s World
• Evolution of Big Data and Its Role in Business
• Use Cases of Big Data Across Industries

• What is Hadoop?
• Installation of Hadoop
• Architecture of Hadoop
• Hadoop Ecosystem: Hive, Pig, HBase, and Sqoop
• Working with HDFS and MapReduce
  • Setting Up and Navigating HDFS
  • Basic HDFS Commands for File Management
  • How MapReduce Works

• Introduction to Spark
• RDDs
• Data Processing with PySpark
• Spark SQL