Master the Future: Data Science & AI Program
From Curious Learner to Production-Ready Data Scientist.
This 24-week journey is designed to mirror how real Data Science teams work—so that when you graduate, you don't just know the tools, you know how to think like a Data Scientist.
Duration
24 weeks
Format
Live + Project Work
Level
Intermediate–Advanced
Outcome
A portfolio of real, end-to-end Data Science & AI projects.
Phase-wise structure that matches how teams hire and grow DS talent.
Constant feedback on code, thinking, and communication.
Career support: positioning your experience, portfolio, and interviews.
Four Phases, One Clear Trajectory.
Each module ends with a project that you can proudly showcase in your portfolio. You always know why you're learning a concept and where it shows up in Data Science.
1Module 1
THE FOUNDATION
Building the programming and mathematical base required for Data Science.
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Module 1
THE FOUNDATION
Building the programming and mathematical base required for Data Science.
Key Themes
- •Understanding job roles, the data ecosystem, and use cases.
- •CS-101, Linux basics, and Cloud Computing fundamentals.
- •Variables, data types, operators, loops, functions, exception handling, file handling, regular expressions, OOP, Git, and Github.
- •Linear Algebra: Vectors, matrices, tensors, and factorization.
- •Statistics: Descriptive & Inferential statistics, Hypothesis testing.
- •Probability: Conditional probability, Bayesian theory, and distributions.
2Module 2
THE ANALYST
Learning to clean, manage, and visualize data to find insights.
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Module 2
THE ANALYST
Learning to clean, manage, and visualize data to find insights.
Key Themes
- •Introduction to NumPy, SciPy, Pandas, Matplotlib, and Seaborn.
- •Data cleaning, dealing with veracity, filtering, and working with formats like CSV, JSON, and XML.
- •From Basics to Advanced — creating/modifying tables, loading data, and storing query results.
- •Handling missing values, feature engineering, mean removal, and variance scaling.
- •Implementation on real data and reporting with EDA techniques.
- •Visual analytics, creating dashboards/stories, and insights delivery.
3Module 3
THE SCIENTIST
Building predictive models using standard algorithms.
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Module 3
THE SCIENTIST
Building predictive models using standard algorithms.
Key Themes
- •The complete modelling process, common terms, and assumptions.
- •Simple/Multiple Linear Regression, Polynomial Regression, and Lasso/Ridge methods.
- •Logistic Regression, SVM (Support Vector Machines), K-Nearest Neighbor, Decision Trees, Random Forest, and Naive Bayes.
- •k-Means, DBSCAN, and Hierarchical clustering.
- •Principal Component Analysis (PCA) and Linear Discriminant Analysis.
4Module 4
THE INNOVATOR
Handling massive datasets and building advanced AI systems.
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Module 4
THE INNOVATOR
Handling massive datasets and building advanced AI systems.
Key Themes
- •Introduction to PySpark (Essentials & Analytics), Apache Hive, and SQL on Big Data.
- •Working with AWS Sagemaker, Databricks, and Azure ML.
- •Demystifying AI, ANN, RNN & CNN.
By the end of this course, you will:
- Design, train, and evaluate end-to-end ML pipelines.
- Comfortably move between notebooks, scripts, and production-minded codebases.
- Speak the language of both business stakeholders and engineering teams.
- Build a portfolio that demonstrates real, relevant impact, not toy datasets.
Who Is This For?
- •Aspiring Data Scientists and AI Engineers.
- •Software Engineers transitioning to AI and ML roles.
- •Professionals looking to upskill in Data Science.
- •Graduates seeking industry relevant skills.
You'll work with the tools that modern Data Science & AI teams rely on. All projects use realistic datasets.
Ready to step into serious Data Science?
If you're done collecting certificates and finally want depth plus employability, this is your program.