• 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

• Why SQL?
• Importance of SQL
• Introduction to SQL
• What are the Purpose of SQL
• How it is Used

• Introduction to Database Management System (DBMS)
• Introduction to Relational Database Management System (RDBMS)
• DBMS vs RDBMS

• Difference between Database and Database Server
• Single Database
• Database Server?
• What is Client Server Architecture
• Installation of MySQL

• Introduction to Commands in SQL
• Types of Commands in SQL
• Data Types in MySQL
• String Data Type in MySQL
• Numeric Data Type in MySQL
• DDL Command in SQL

• DML Command Statements in SQL – INSERT, SELECT, DELETE, UPDATE Commands
• Install SQLyog
• Use MySQL Workbench
• SQL Relational Operators
• Between and In Command in SQL
• LIKE Operator in SQL

• SQL Aggregate Functions
• Use of aggregate functions in SQL
• SQL aggregate function Advantages
• SQL Nested Queries
• Use of nested query

• What is ORDER BY Clause in SQL
• Use of ORDER BY Clause
• What are the Advantages of ORDER BY Clause
• Use GROUP BY and HAVING Clause in SQL
• Aliases in SQL
• Use of Aliases in SQL

• Constraints in SQL
• Use of Constraints in MySQL
• SQL NULL Values
• IS NULL and IS NOT NULL Values in SQL

• What is Primary Key and Foreign Key Relationship in SQL
• SQL CHECK Constraint
• SQL DEFAULT Constraint
• SQL NULL Function
• SQL Auto Increment

• Joins in SQL
• Different Types of SQL Joins
• inner join, left join, right join, outer join, full join in SQL
• Why we use Joins in SQL?
• How to join tables in SQL?
• How to Fetch records from multiple tables

• What is Statistics
• Importance in Data Science
• Understanding Data
• Types of Statistics: Descriptive vs. Inferential
• Types of Data: Qualitative vs. Quantitative
• Populations vs. Samples

• Central Tendency: Mean, Median, Mode
• Dispersion
• Range, Variance, Standard Deviation
• Coefficient of Variation

• Skewness and Kurtosis
• Understanding Normal Distribution
• Visualization Techniques
• Histograms, Box Plots, Bar Charts, and Pie Charts
• Scatter Plots and Correlation

• Basics of Probability
• Axioms of Probability
• Conditional Probability
• Bayes’ Theorem
• Applications of Bayes’ Theorem

• Random Variables
• Discrete Variables
• Probability Distributions: PMF, PDF and CDF
• Binomial, Poisson, Exponential, Uniform, Log-Normal

• Sampling and Sampling Distributions
• Hypothesis Testing
• Defining Null and Alternative Hypotheses
• Type I and Type II Errors
• Using p-Value and Significance Levels
• Applying Z-test, T-test, Chi-Square Test, and ANOVA
• Confidence Intervals
• Margin of Error

• 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

• Introduction to Business Intelligence
• Business Intelligence Stages
• BI Use Cases
• Overview of Power BI
• Power BI Benefits
• Power BI Real-world Applications

• How to Install and Set up Power BI
• Download Power BI Desktop
• Installation Guide Steps
• Initial Setup and Configuration of Power BI
• Architecture and Components of Power BI

• Power BI Desktop, Service, Mobile
• How These Components Work Together
• Introduction to Power BI Desktop
• Power BI Desktop Installation
• Power BI Desktop Interface
• Key Features and Tools of Power BI
• Different Panes and Menus

• Get Data from Different Data Sources
• How to Identify and Connect to a Data Source
• Types of Data Sources in Power BI
• How to Get Data from Flat Files and Relational Data Sources
• Importing Data from Excel, CSV, and Other Flat Files
• Connecting to Databases

• Introduction to Power Query
• Power Query Editor Interface
• Features and Functionalities of Power Query
• Power Query Ribbon and Tabs Overview
• Organizing and Structuring Data Tables
• Creating Relationships Between Tables
• Analyzing Data Quality and Handling Data Issues
• Changing Column Data Types for Accuracy
• Managing Data Source Settings

• Fact and Dimension Tables
• Relationships and Cardinality (One-to-Many, Many-to-Many)
• Star Schema in Data Modeling
• Benefits of Using a Star Schema

• Introduction to Data Modeling and DAX
• Creating Relationships and Managing Cross-filter Directions
• DAX Syntax and Functions Overview
• Context in DAX
• Creating Calculated Tables and Columns
• Practical Examples of Calculated Tables
• Measures and Calculations Using DAX
• Date and Time Functions in DAX
• Best Practices for Using DAX in Your Data Model

• Creating Reports from Raw Data
• Adding and Customizing Visualization Items
• Choosing Appropriate Visualization Types
• Formatting and Configuring Power BI Charts
• Using and Importing Custom Visuals
• Applying and Customizing Themes
• Configuring Conditional Formatting
• Applying Sorting, Slicing, and Filtering
• Creating Data Hierarchies for Navigation
• Drilldown/Drillup with Interactive Visuals
• Using AI Visuals (Q & A, Key Influencers, Decomposition Tree, Smart Narratives)
• Publishing Reports to Power BI Service

• Creating and Configuring Workspaces
• Assigning Workspace Roles and Permissions
• Configuring and Updating Workspace Apps

• Building Dashboards in Power BI
• Managing and Organizing Dashboard Tiles
• Pinning Live Report Pages to Dashboards for Real-time Updates

• What is DSA using Python?
• Why Python is a great choice for DSA
• Why DSA with Python
• Types of Data Structures

• Introduction to Stack in DSA Python
• Stack using List
• Stack using Collection Module in DSA Python
• Stack using Queue Module in DSA Python
• Convert Infix to Postfix Expression using Stack
• Convert Infix to Prefix in DSA

• What is a Queue in Data Structures?
• Queue using List in DSA Python
• Queue using Collection Module in DSA Python
• Linear Queue using Collection Module
• Circular Queue in DSA Python
• Insert, Display and Delete in Circular Queue
• D Queue in Data Structures
• Deque using Collection Module
• Priority Queue in DSA Python
• Priority Queue using List
• Priority Queue using Module

• What is Linked List in DSA Python?
• Create, Display, Insert and Delete in Linked List in DSA Python
• Searching in Linked List
• Sorting in Linked List
• Stack Linked List in DSA Python
• Queue Linked List in DSA Python

• What is a Circular Linked List?
• Create, Display, Insert and Delete in DSA Python

• What is Doubly Linked List in DSA Python?
• Create, Display and Reverse Display in Doubly Linked List in DSA Python
• Insertion and Display in Doubly Linked List in DSA Python

• What is Circular Doubly Linked List in DSA Python
• Creation and Display in Circular Doubly Linked List in DSA Python
• Insertion and Reverse Display in Circular Doubly Linked List in DSA Python
• Deletion in Circular Doubly Linked List in DSA Python

• What is Recursion in DSA Python?
• Types of Recursion
• Tail Recursion in DSA Python

• Linear Search in DSA Python
• Binary Search in DSA Python

• Linear Sort in DSA Python
• Bubble Sort in DSA Python
• Selection Sort
• Insertion Sort
• Radix Sort
• Shell Sort
• Quick Sort
• Two way Merge Sort
• Merge Sort

• What is Tree in Data Structures using Python?
• Binary Tree and Binary Search Tree in DSA Python
• Tree Traversals in DSA Python
• Tree Traversal Methods – Inorder, Preorder, Postorder
• Creation of Tree from Inorder to Preorder in DSA Python
• Creation of Binary Search Tree
• Inorder, Preorder and Postorder in BST in DSA Python
• Searching and Sorting in BST in DSA Python
• Delete Node From Tree in DSA Python

• Strictly Binary Tree
• AVL Tree
• Red Black Tree
• Threaded Binary Tree in DSA
• B Tree in DSA Python
• B+ Tree in DSA Python
• Heap Tree in DSA Python

• Address Calculation of Array in DSA Python
• Address Calculation of 3D Array in DSA Python
• Address Calculation of Lower & Upper Bound of Array in DSA
• Tower of Hanoi in DSA Python

• What is a Graph in DSA?
• Graph Representation
• Graph Algorithms
• Kruskal’s Algorithm
• Prim’s Algorithm for Minimum Spanning Tree in DSA
• Dijkstra’s Algorithm
• Dijkstra Algorithm for Directed Graph
• Floyd Warshall Algorithm
• Breadth First Search Algorithm
• Depth First Search Algorithm
• What is Hashing

Analyze and classify movie reviews as positive or negative using natural language processing techniques. This project explores text preprocessing and sentiment classification.

Build and train a convolutional neural network to classify images into 10 different categories. This computer vision project introduces deep learning frameworks and image preprocessing.

Detect fraudulent credit card transactions using anomaly detection algorithms. This project focuses on handling imbalanced datasets and implementing effective classification models.

Develop a recommendation engine to suggest movies to users based on their viewing history. This collaborative filtering project delves into matrix factorization and similarity measures.

Classify news articles into predefined categories using NLP techniques. This text classification project covers tokenization, vectorization, and model training for multi-class classification.

Predict weekly sales for Walmart stores across different departments using historical sales data. This project involves time series forecasting and multivariate regression.

Build a model to predict customer churn based on usage patterns and demographic information. This project focuses on handling imbalanced datasets and implementing classification algorithms.

Predict future energy consumption levels using historical usage data and external factors. Focuses on time series forecasting and model deployment.

Develop a deep learning model to detect emotions from facial images. Focuses on convolutional neural networks (CNNs) and image preprocessing.

Develop a system that predicts stock price movements using live market data and technical indicators. Implement streaming data processing to update predictions continuously.

Predict future sales for a retail store using historical sales data and promotional information. This project focuses on time series analysis and regression techniques to help optimize inventory and marketing strategies.

Automatically categorize e-commerce products into predefined categories using product descriptions and metadata. This project leverages text classification and machine learning algorithms.

Build a computer vision model to recognize and classify traffic signs from images. This project involves deep learning with convolutional neural networks (CNNs) and image preprocessing techniques.

Predict which employees are likely to leave the company based on their demographic and job-related features. This classification project focuses on handling imbalanced datasets and feature selection.

Build a model to classify songs into their respective genres using audio features. This project involves audio data processing, feature extraction using libraries like Librosa, and applying machine learning algorithms for multi-class classification.

Develop a classification model to identify spam emails based on their content and metadata. This project emphasizes text preprocessing, feature extraction, and implementing machine learning algorithms for binary classification.

Analyze and classify blockchain transactions to identify patterns, anomalies, and potential fraudulent activities. This project delves into network analysis, feature engineering from transaction data, and applying machine learning algorithms for anomaly detection.

Develop a classification model to diagnose diseases from patient medical records. This project emphasizes data preprocessing, handling missing values, and building accurate diagnostic models.

Build a real-time language translation model using neural machine translation techniques. This natural language processing project involves sequence-to-sequence models and attention mechanisms to facilitate multilingual communication.

Build a classification model to predict loan approval outcomes based on applicant demographics, financial history, and loan details. This project emphasizes handling imbalanced datasets and feature selection to improve prediction accuracy.

Create a model to recognize emotions from speech recordings using audio signal processing and machine learning algorithms. This project explores feature extraction from audio data and classification techniques to interpret human emotions.

Develop a natural language processing (NLP) model to classify news articles as real or fake based on their content and metadata. This project emphasizes text preprocessing, feature extraction, and implementing classification algorithms to combat misinformation.

Build a natural language processing (NLP) model to detect emotions in text data, such as tweets or customer reviews. This project involves text preprocessing, sentiment analysis, and classification algorithms to interpret human emotions accurately.

Create a machine learning model to verify the authenticity of handwritten signatures. This computer vision project involves image preprocessing, feature extraction, and implementing classification algorithms to detect forgeries.