• Course Duration: 1 Year (Flexible Batches Available)
• Eligibility: Graduates, Working Professionals, Non-IT Career Changers, and Tech Students.
• Mode: Online (Live Interactive Sessions) / Classroom Training
• Prerequisites: Basics of Computer Fundamentals, We start from absolute scratch!

Course Modules (Syllabus)

Module 1: Python Basics & Core Programming (The Fundamentals)

• Introduction to Python, Anaconda, and Jupyter Notebook Setup
• Variables, Data Types (Strings, Integers, Floats, Booleans)
• Python Data Structures: Lists, Tuples, Dictionaries, and Sets
• Conditional Statements (if-else) and Loops (for, while)
• Writing Custom Functions and Handling Errors (Try-Except)

Module 2: Data Manipulation with NumPy & Pandas (The Core of Analytics)

• NumPy: Working with Arrays, Mathematical Operations, and Indexing
• Pandas DataFrames: Importing Data (CSV, Excel, SQL)
• Data Cleaning: Handling missing values, filtering data, and removing duplicates
• Data Transformation: Grouping, Merging, Joining, and Concatenating datasets
• Time-Series Data Handling (Analyzing trends over time)

Module 3: Data Visualization (Telling Stories with Data)

• Introduction to Exploratory Data Analysis (EDA)
• Creating Charts with Matplotlib: Line plots, Bar charts, and Histograms
• Advanced Visualizations with Seaborn: Heatmaps, Scatter plots, Box plots, and Violin plots
• Designing interactive dashboards using Plotly and Streamlit

Module 4: SQL & Database Integration with Python

• Connecting Python to Relational Databases (MySQL / PostgreSQL / SQLite)
• Writing SQL Queries inside Jupyter Notebook
• Exporting analyzed data from Python back into databases or Excel reports

Module 5: Introduction to Statistics & Predictive Analytics

• Applied Statistics: Mean, Median, Mode, Variance, and Standard Deviation
• Correlation vs. Causation analysis
• Introduction to Machine Learning: Linear Regression for forecasting business trends

Module 6: Live Capstone Projects

• E-Commerce Sales Analysis: Cleaning and analyzing retail data to find top-selling products.
• Financial Trend Forecasting: Analyzing stock market or housing price trends using data.
• Deploying your analytics project portfolio on GitHub.

Key Tools & Libraries You Will Learn

• Development Environment: Jupyter Notebook, VS Code, Google Colab
• Data Core Libraries: Pandas, NumPy
• Visualization Libraries: Matplotlib, Seaborn, Plotly
• Project Deployment: GitHub, Streamlit

Why Choose Our Python Course?

• Hands-on Practice: 15+ mini-assignments, 5 case studies, and 2 major live projects.
• Career Support: Resume optimization, mock interviews, and job placement assistance.

Career Opportunities After This Course

• Python Data Analyst
• Business Analyst
• Data Operations Analyst
• Junior Data Scientist

Start Your Data Analytics Journey Today!