Data Science & AI Growth Track Overview
This Data Science & AI Growth Track assumes foundational data-analytics skills (basic statistics, Excel/SQL and introductory Python) as prerequisites. The program is focused on machine-learning engineering and applied AI — teaching you to frame predictive problems, design experiments, engineer features for models, and build reproducible end-to-end pipelines. Classes are small and practical — available in Nashik or live online — with emphasis on building a portfolio of production-ready projects and receiving mentor feedback.
The curriculum emphasizes industry workflows and tooling: Python (pandas, NumPy), advanced feature engineering, scikit-learn, XGBoost and ensemble methods, model selection and robust evaluation, plus time-series forecasting and scalable pipeline development. You will learn best practices for reproducibility, versioning, basic MLOps, and containerized model deployment so models can move reliably from prototype to production.
The AI module covers deep-learning concepts and popular frameworks (TensorFlow / PyTorch), including CNNs, RNNs/Transformers and practical NLP and computer-vision applications using transfer learning. Responsible AI topics, model interpretability, and deployment considerations are integrated throughout. On completion you will have capstone projects, mentor reviews, and career support to pursue roles such as Machine Learning Engineer, Data Scientist or MLOps/AI Platform Engineer.
Data Science & AI Growth Track Highlights
- Data Engineering & Wrangling : Collect, clean, integrate and prepare large datasets for analysis using Python and SQL.
- Exploratory Data Analysis & Visualization : Communicate insights with Matplotlib, Seaborn, Plotly and dashboarding tools.
- Advanced Python & Libraries : Master pandas, NumPy, scikit-learn, XGBoost and efficient data workflows.
- Statistical Modeling & Inference : Apply hypothesis testing, regression, and experimental design for sound decisions.
- Machine Learning & Model Deployment : Build, evaluate and deploy supervised and unsupervised models to production; includes MLOps basics.
- Deep Learning & Neural Networks : Hands-on with CNNs, RNNs and Transformers using TensorFlow or PyTorch.
- NLP & Computer Vision : Practical projects in text processing, sentiment analysis, and image recognition.
- Time Series & Forecasting : Techniques for trend, seasonality and demand forecasting with real datasets.
- Capstone Projects & Career Support : Real client projects, portfolio development, mentoring, and interview preparation.
Data Science & AI Growth Track Curriculum
This is the recommended order, but some courses may be taken in a different order. See the FAQ for more details.
Benefits Provided
Learn the concepts and skills covered in this program or your tuition is on us. See See details and terms & conditions.
Hands-on training
Work on projects proven to boost retention
Learn from experts
Experienced educators who are driven to help you succeed
Retake & Class Recordings
Refresh the materials for free within one year
1-on-1 Mentoring
Every student has a different spark which is enchanced by mentors
Data Science & Machine Learning Projects Example
Learn practical skills by working on real projects with instructor guidance and lectures to strengthen and improve your skill.