Python
- Introduction to Python, Operators in Python, Conditional Statement, Loops in Python
- Installing Python and Setting Up the Environment
- Writing Your First Python Program
- Python Syntax, Variables & Data Types
- Conditional Statements (if, else, elif)
- Loops in Python (for, while)
- Functions and Scope
- Working with Lists, Tuples, Sets & Dictionaries
- File Handling in Python
- Error Handling and Exceptions
- Error Handling and Exceptions
- Basics of Object-Oriented Programming (OOP)
- Mini Projects and Practice Exercises
SQL
- Understanding RDBMS (Relational Database Management Systems)
- Installing and Setting Up SQL Environment
- Creating and Managing Databases
- Creating Tables and Understanding Data Types
- Inserting, Updating, and Deleting Data (CRUD Operations)
- Using SELECT Queries to Retrieve Data
- Filtering Data with WHERE Clause
- Using Aggregate Functions (COUNT, SUM, AVG, MIN, MAX)
- GROUP BY and HAVING Clauses
- Working with JOINs (INNER, LEFT, RIGHT, FULL)
- Using Filters, Slicers, and Drill-Downs
Tableau
- Installing Tableau Public / Tableau Desktop
- Understanding Tableau Interface and Workspace
- Connecting to Various Data Sources (Excel, CSV, SQL, etc.)
- Working with Dimensions and Measures
- Creating Basic Charts (Bar, Line, Pie, etc.)
- Filters, Sorting, and Grouping Data
- Using Calculated Fields and Table Calculations
- Creating Maps and Geographic Visualizations
- Applying Parameters and Actions
- Building Interactive Dashboards
- Formatting and Customizing Visuals
- Publishing and Sharing Dashboards
- Real-World Dashboard Case Study / Mini Project
Power Bi
- Installing Power BI Desktop
- Power BI Interface Overview
- Connecting to Data Sources (Excel, SQL, Web, etc.)
- Data Cleaning & Transformation Using Power Query
- Understanding Data Models and Relationships
- Creating Visuals: Bar, Line, Pie Charts & More
- Sharing and Collaborating on Reports
- Real-Time Dashboard Project / Case Study
Excel
- Introduction to Excel Tables and Named Ranges
- Protecting Sheets and Using Passwords
- Basic Data Analysis Project Using Excel
- Understanding Workbooks, Worksheets & Cells
- Basic Formulas and Functions (SUM, AVERAGE, COUNT, etc.)
- Using Logical Functions (IF, AND, OR, NOT)
- Working with Text Functions (LEFT, RIGHT, CONCATENATE, etc.)
- Sorting, Filtering, and Conditional Formatting
- Data Validation and Drop-Down Lists
- Lookup Functions (VLOOKUP, HLOOKUP, XLOOKUP)
- Pivot Tables and Pivot Charts
- Creating Charts and Graphs (Bar, Line, Pie, etc.)
- Working with Date and Time Functions
- Introduction to Excel Tables and Named Ranges
- Protecting Sheets and Using Passwords
- Basic Data Analysis Project Using Excel
Machine Learning
- ML Fundamentals, EDA, Linear Regression, Logistic Regressio,n Decision Tree, Random Forests, Ensemble Techniques
- Difference Between AI, ML, and Deep Learning
- Types of Machine Learning: Supervised, Unsupervised, Reinforcement
- Understanding Data: Features, Labels, and Preprocessing
- Splitting Data: Train-Test Split & Cross Validation
- Supervised Learning Algorithms (Linear Regression, Decision Trees, etc.)
- Unsupervised Learning Algorithms (K-Means, PCA, etc.)
- Evaluating Model Performance (Accuracy, Precision, Recall, F1-Score)
- Overfitting vs. Underfitting & Regularization Techniques
- Introduction to Scikit-Learn and ML Libraries in Python
- Model Deployment Basics
- Real-World Use Cases and Applications of ML
- Mini Project: Predictive Model Using Python
Deep Learning
- Introduction to AI, Introduction to DL, Artificial Neural Network Deep Dive,Computer Vision,Computer Vision with OpenCV, Convolution Neural Network
- Overview of Artificial Neural Networks (ANNs)
- Understanding Neurons, Layers, Weights & Activation Functions
- Types of Neural Networks (ANN, CNN, RNN, etc.)
- Forward Propagation and Backpropagation Concepts
- Using Keras and TensorFlow for Deep Learning
- Building a Basic Neural Network in Python
- Understanding Recurrent Neural Networks (RNNs) and LSTMs for Sequence Data
- Evaluating Deep Learning Models (Loss, Accuracy, Confusion Matrix)
- GPU vs CPU Training, Batch Size, Epochs & Optimization
- Real-World Applications (Image Recognition, NLP, Recommendation Systems)
- Deep Learning Mini Project (e.g., Digit Classifier or Sentiment Analysis)
