Live projects + Internship + 100% Job Support
Mode : Online + Offline
Duration: 12 Months
Data Science course
Data Science course
Month-wise Curriculum
Month 1: Foundations of Programming & Tools
- Introduction to Data Science & Career Paths
- Python Basics (syntax, data types, loops, functions)
- Jupyter Notebook & Google Colab
- Git & GitHub for version control
- Mini Project: Python automation script
Month 2: Data Handling & Analysis
- Numpy (arrays, vectorized operations)
- Pandas (dataframes, data cleaning, transformation)
- Working with CSV, Excel, JSON
- Exploratory Data Analysis (EDA) basics
- Mini Project: Sales dataset analysis
Month 3: Statistics & Probability for Data Science
- Descriptive Statistics (mean, median, variance, std)
- Probability & Distributions
- Hypothesis Testing, P-value, Z/T-tests
- Correlation & Covariance
- Case Study: Customer churn statistical analysis
Month 4: Data Visualization
- Matplotlib & Seaborn (bar, line, scatter, heatmaps)
- Advanced Visualizations (pair plots, box plots, violin plots)
- Tableau / Power BI Basics (dashboards & storytelling)
- Project: Create an interactive dashboard for sales/finance data
Month 5: SQL & Databases
- SQL Basics (Select, Where, Group By, Joins)
- Advanced SQL (Window functions, Subqueries, CTEs)
- Connecting Python with SQL
- Case Study: E-commerce user & order analysis using SQL
Month 6: Machine Learning – Foundations
- Introduction to Machine Learning
- Supervised Learning (Linear Regression, Logistic Regression)
- Model Evaluation (Accuracy, Precision, Recall, F1, ROC-AUC)
- Scikit-learn Basics
- Project: Predict house prices
Month 7: Machine Learning – Advanced
- Decision Trees, Random Forests, Gradient Boosting (XGBoost/LightGBM)
- Feature Engineering & Feature Selection
- Hyperparameter Tuning (GridSearch, RandomSearch)
- Project: Loan default prediction
Month 8: Unsupervised Learning & Natural Language Processing (NLP)
- Clustering (K-Means, Hierarchical, DBSCAN)
- Dimensionality Reduction (PCA, t-SNE)
- Introduction to NLP
- Text Cleaning, Bag of Words, TF-IDF
- Project: Sentiment analysis on social media data
Month 9: Deep Learning Basics
- Neural Networks (Perceptron, Activation Functions)
- TensorFlow / PyTorch Introduction
- Image Data (CNN basics)
- Project: Handwritten digit classification (MNIST)
Month 10: Advanced Deep Learning & AI Applications
- Convolutional Neural Networks (CNN) for Computer Vision
- Recurrent Neural Networks (RNN, LSTM) for Time Series & NLP
- Transfer Learning (ResNet, VGG)
- Case Study: Fake news classification OR Image recognition
Month 11: Big Data, Cloud & Deployment
- Introduction to Big Data & Hadoop Spark basics
- Cloud Platforms (AWS / GCP / Azure for ML models)
- Model Deployment (Flask, FastAPI, Streamlit)
- MLOps Basics (CI/CD for ML)
- Project: Deploy ML model as a web app
Month 12: Capstone Project + Placement Training
- Work on 2 Capstone Projects (end-to-end, real datasets)
- Resume & LinkedIn Building for Data Science
- Mock Interviews & Aptitude Tests
- Job Applications Support (Internship + 100% Placement Assistance)
Program Benefits
Key Features
- Work on 5+ Live Projects.
- Metro City + Remote Internship Opportunities.
- Government-Recognized Certificate.
- Dedicated Job & Interview Assistance.
- Build ATS-Friendly Resume & Online Portfolio.
