Course Duration
01
4 Months
Structured learning path
Data Science + Generative AI
Python • Statistics • SQL • Machine Learning • Deep Learning • GenAI • MLOps
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Curriculum
02
18 Modules
Step-by-step learning
Hands-On
03
Real-Time Projects
Build practical applications
Career Support
04
Interview Prep
Placement-focused guidance
Complete Curriculum
Data Science Learning Roadmap
18 practical modules planned across a 16-week learning path. Click any module to view the topics covered.
Modules
Month 1
Month 2
Month 3
Month 4
Learning Plan
W1
W2
W3
W4
W5
W6
W7
W8
W9
W10
W11
W12
W13
W14
W15
W16
Python Foundations for Data Science
- Python syntax, variables, data types and operators
- Conditional statements, loops and functions
- Lists, tuples, dictionaries and sets
- Object-oriented programming fundamentals
- Working with files, exceptions and reusable modules
NumPy for Numerical Computing
- NumPy arrays, dimensions and data types
- Array indexing, slicing and reshaping
- Vectorized operations and broadcasting
- Mathematical, statistical and linear algebra operations
- Performance-oriented numerical computing
Pandas for Data Analysis
- Series and DataFrame fundamentals
- Importing CSV, Excel, JSON and database data
- Filtering, sorting, grouping and aggregation
- Merging, joining and concatenating datasets
- Time-series and categorical data handling
Data Cleaning & Preprocessing
- Handling missing values and duplicate records
- Outlier detection and treatment
- Data type conversion and normalization
- Encoding categorical variables
- Building reusable preprocessing pipelines
Statistics & Probability
- Descriptive statistics and data distributions
- Probability concepts and Bayes theorem
- Sampling methods and confidence intervals
- Correlation, covariance and statistical relationships
- Probability distributions for data science
Hypothesis Testing & Statistical Inference
- Null and alternative hypotheses
- Z-test, t-test and chi-square test
- ANOVA and comparison of groups
- P-values, significance and confidence levels
- A/B testing for business decisions
Exploratory Data Analysis & Visualization
- EDA workflow and analytical thinking
- Matplotlib and Seaborn visualizations
- Univariate, bivariate and multivariate analysis
- Business storytelling with charts
- Creating insight-driven analytical reports
SQL for Data Science
- SQL fundamentals and relational databases
- SELECT, WHERE, GROUP BY and HAVING
- Joins, subqueries and CTEs
- Window functions and analytical queries
- Connecting Python with SQL databases
Machine Learning Fundamentals
- Supervised vs unsupervised learning
- Training, validation and test datasets
- Bias, variance, overfitting and underfitting
- Feature engineering and feature selection
- Scikit-learn workflow and ML pipelines
Regression Models
- Simple and multiple linear regression
- Polynomial regression
- Regularization: Ridge and Lasso
- Regression metrics: MAE, MSE, RMSE and R²
- Real-time regression use cases and projects
Classification Models
- Logistic regression
- K-Nearest Neighbors
- Decision trees and random forests
- Support Vector Machines
- Classification metrics, ROC-AUC and confusion matrix
Unsupervised Learning
- K-Means clustering
- Hierarchical clustering
- DBSCAN
- Principal Component Analysis
- Customer segmentation and pattern discovery projects
Ensemble Learning & Model Optimization
- Bagging and boosting concepts
- Random Forest, XGBoost and Gradient Boosting
- Hyperparameter tuning with GridSearchCV and RandomizedSearchCV
- Cross-validation strategies
- Model comparison and production selection
Deep Learning Foundations
- Neural networks, neurons and activation functions
- Forward propagation and backpropagation
- TensorFlow / Keras fundamentals
- Building and training dense neural networks
- Regularization, dropout and model optimization
NLP, Computer Vision & Modern AI
- Text preprocessing and vectorization
- Sentiment analysis and text classification
- CNN fundamentals for image data
- Transfer learning concepts
- Introduction to transformers and modern AI models
Generative AI for Data Scientists
- LLMs and prompt engineering fundamentals
- Using ChatGPT, Claude and Gemini for analytics workflows
- Embeddings and vector databases
- RAG-based analytical applications
- LangChain and LlamaIndex fundamentals
MLOps, APIs & Cloud Deployment
- Saving and serving trained ML models
- Building prediction APIs with Flask / FastAPI
- Git and GitHub workflow for data science
- Docker fundamentals for ML applications
- AWS deployment, monitoring and model lifecycle concepts
Capstone Projects & Placement Preparation
- End-to-end real-world Data Science capstone project
- Business problem definition and dataset preparation
- Model building, evaluation and deployment
- GitHub portfolio, resume and LinkedIn project presentation
- Mock interviews, case studies and placement preparation
Select a module to view the detailed topics.
Hands-on Learning
Capstone Projects
Practice real application flows while building projects you can discuss during interviews.
We Learn – Groceries Application
We Learn – Food Application
We Learn – Banking Application
We Learn – Delivery Application
We Learn – Airlines Application
We Learn – LMS Application
Career Preparation
Build Job-Ready Profiles
Present your skills professionally across the platforms recruiters commonly review.
GitHub
Showcase code, projects, repositories and consistent development work.
Build a stronger professional identity and improve recruiter visibility.
Portfolio
Present your projects, technical skills and practical experience clearly.
AI Skills
Learn the AI Tools Used by Modern Developers
Use AI to code faster, solve problems, build projects, prepare for interviews and stay ready for modern software jobs.
✦ChatGPT
AIClaude
GeminiGitHub Copilot
Cursor AI
PerplexityAIOpenAI API
LangChain
🤗Hugging Face
Replit AIWindsurf AI
AI Agents
AI for Coding
Write, debug and understand code faster with modern AI coding assistants.
AI for IT Jobs
Prepare resumes, interviews, projects and workplace tasks using AI.
AI for Career Growth
Stay updated with tools used by developers, startups and product teams.
Student Outcomes
Our Alumni
Surya
Infosys
Farhath
L&T
Manasa
Capgemini
Sana Lekana
Tech Mahindra
Ganesh
Genpact
Gnaneshwar
Amazon
Ravi
Hexaware
Harinath
AdobeSurya
InfosysFarhath
L&TManasa
CapgeminiSana Lekana
Tech MahindraGanesh
GenpactGnaneshwar
AmazonRavi
HexawareHarinath
AdobeRavi
HexawareHarinath
AdobeSurya
InfosysFarhath
L&TManasa
CapgeminiSana Lekana
Tech MahindraGanesh
GenpactGnaneshwar
AmazonRavi
HexawareHarinath
AdobeSurya
InfosysFarhath
L&TManasa
CapgeminiSana Lekana
Tech MahindraGanesh
GenpactGnaneshwar
Amazon
Common Questions
Frequently Asked Questions
Everything you need to know about our Data Science Training Program.
You will learn Python, NumPy, Pandas, data cleaning, statistics, probability, SQL, exploratory data analysis, machine learning, deep learning, NLP, computer vision, Generative AI, MLOps, APIs and cloud deployment.
No. The curriculum begins with Python foundations and progresses step by step through numerical computing, data analysis, statistics, SQL and machine learning.
Yes. The course includes descriptive statistics, probability, hypothesis testing, ANOVA, A/B testing, SQL fundamentals, joins, subqueries, CTEs, window functions and connecting Python with SQL databases.
Yes. It covers regression, classification, clustering, PCA, ensemble learning, random forests, XGBoost, gradient boosting, hyperparameter tuning and cross-validation.
Yes. The curriculum includes neural networks, TensorFlow/Keras, NLP, computer vision, CNN concepts, transfer learning, transformers and modern AI models.
Yes. You will learn LLM and prompt-engineering fundamentals, ChatGPT, Claude and Gemini for analytics workflows, embeddings, vector databases, RAG-based applications, LangChain and LlamaIndex fundamentals.
Yes. The curriculum includes model serving with Flask/FastAPI, Git and GitHub workflow, Docker fundamentals, AWS deployment, monitoring and model lifecycle concepts.
The final module includes an end-to-end real-world Data Science capstone, business problem definition, model building, evaluation and deployment, GitHub portfolio work, resume and LinkedIn presentation, mock interviews and case studies.
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