Post Graduate Program in Data Science with Specialization
- 3 Assured Interviews
- Eligibility: Graduate
- Duration: 8 Months
- 5-month Core Foundation of Data Science
- 3-month Specialization : AI/ML or Data Engineering
- 180+ live hours in total of Expert-led sessions
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Artificial Intelligence & Machine Learning
Deep Learning Foundations & Architectures Transformer- & BERT-based model architectures
Computer Vision & NLP Mastery Train & evaluate CV and NLP models
Generative AI & MLOps Capstone Design generative AI solutions, integrate MLOps, apply insights to strategic decisions
Data Engineering
Build & Manage Data Platforms DuckDB, Airbyte & more
Scale Batch & Real-Time Workflows Spark & Delta Lake pipelines, Kafka & Flink analytics
Ensure Quality, Governance & Cost Efficiency dbt, Great Expectations, Monte Carlo
Curriculum Overview: Data & Analytics Mastery
Module 1: SQL & Database Mastery
Query relational data: filtering, joins, aggregations, CTEs, window functions
Design schemas for analytics and reporting
Apply SQL for segmentation and performance tracking
Module 2: Python for Data Workflows
Write clean, efficient Python code
Manipulate lists, dictionaries, and sets
Automate CSV I/O and streamline with lambda functions
Module 3: Statistics for Decision Making
Summarize datasets: mean, dispersion, skewness
Apply probability distributions in real-world contexts
Conduct hypothesis testing: p-values, t-tests, ANOVA
Module 4: Exploratory Data Analysis (EDA)
Explore data: univariate, bivariate, multivariate
Handle missing values, outliers, and transformations
Create pivot tables, group summaries, and correlation visuals
Module 5: Machine Learning with Python
Build models: regression, classification, clustering
Execute feature engineering and model tuning
Evaluate with metrics and validation techniques
Amazon uses massive review data to understand customer behavior—who bought what, how they rated it, and what kind of buyer they are. It categorizes purchases by customer types: sellers, “bought it”, “not worth it”, and matrix factorization. The system picks the best 5 products per user and presents them in a mini web app for personalized suggestions.
Netflix predicts whether viewers will finish a series or drop off. It uses viewer ratings, genre data, and release timing to build predictive models. These models help Netflix decide which shows to promote and when to release them. Includes a dashboard where staff can tweak show details and receive weekly updates.
Airbnb helps hosts determine appropriate pricing. It uses city data, listing details, and seasonal trends (e.g., “summer weekend”) to suggest pricing. Employs three pricing models for accuracy. Hosts receive a simple pricing suggestion with a safe range.
Starbucks stores forecast the right number of drinks using daily sales, offers, and weather data. Patterns like “rainy Mondays boost latte sales” are used. Facebook’s Prophet model predicts next-month demand. The data is wrapped into a one-page dashboard where baristas enter their store ID and get drink prep plans for the week.
Tesla uses data analytics to catch battery failures early to avoid costly replacements. The data includes charge cycles, voltage curves, and signs like “capacity drop.” Tesla trains a rule-based model and a neural net to analyze how early and accurately these risks are flagged. Highlights key voltage curve triggers in a visual chart, with a short brief estimating yearly warranty claims saved.
Complete the program and earn 3 industry-recognized certificates from Hero Vired, HarvardX, and FutureSkills Prime. Perfect for boosting your resume, LinkedIn profile, or job applications.
These certificates validate your expertise in data science, data engineering, and analytics—helping you stand out in roles across tech, finance, and business operations.
1. I’m new to data science and AI. Can I keep up? Yes! The program is beginner-friendly. It starts with foundational topics like Python and SQL before progressing to AI and machine learning concepts.
2. Will the curriculum be overwhelming? The curriculum is structured to build your skills gradually. You’ll move from basics to advanced topics in a paced, digestible format.
3. Is there hands-on experience or just theory? You’ll work on real-world applications and projects, not just theory. The program emphasizes practical learning.
4. What coding languages and tools will I learn? You’ll learn Python, SQL, and tools relevant to AI/ML or Data Engineering, depending on your chosen specialization.
5. Do I need prior coding experience? No prior experience is required. The course starts from scratch and builds up your coding proficiency.
6. Will I learn to clean and work with messy data? Yes. Data cleaning and preprocessing are core parts of the curriculum, especially in Exploratory Data Analysis (EDA).
7. What is EDA and why is it important? EDA helps you understand data patterns, spot anomalies, and prepare data for modeling. It’s a vital step in any data project.
8. What is machine learning, and is it necessary? Machine learning enables systems to learn from data and make predictions. It’s central to modern AI applications and covered extensively.
9. How much math do I need to know? Basic understanding of statistics and linear algebra is helpful, but the course teaches the necessary math as you go.
10. How does this program prepare me for real-world AI/ML roles? You’ll build and deploy models, work with image/text data, and learn to monitor and validate AI solutions—skills directly applicable to industry roles.
11. What makes this AI/ML program stand out? It offers a blend of foundational learning, specialization options, hands-on projects, and career support including 3 assured interviews.
| Category | AI & ML Specialization | Data Engineering Specialization |
| Program Name | Data Science with Specialization in AI & ML | Data Science with Specialization in Data Engineering |
| Duration | Foundations + 5 Months Specialization = 9 Months | Foundations + 5 Months Specialization = 9 Months |
| Benefits |
- Any bachelor’s degree - No coding experience needed - STEM background preferred |
- Any bachelor’s degree - No coding experience needed - STEM background preferred |
| Eligibility | Beginner & Intermediate | Beginner & Intermediate |
| Projects | 12+ | 12+ |
| Level | Beginner & Intermediate | Beginner & Intermediate |
| Career Support | Yes | Yes |
| Learning Outcomes |
- Smart predictions with AI/ML - ML algorithm mastery - Supervised/unsupervised models - Deep learning - NLP & computer vision - Recommender systems |
- Handle large datasets - Build data pipelines - Big data tools - Cloud platforms - Data workflows - Security & governance strategies |
Data Science with Specialization in Artificial Intelligence
In partnership with edX and Harvard
EMI starts at ₹6,416/month
Total price: ₹1,99,000 + GST
Data Science with Specialization in Data Engineering
In partnership with edX and Harvard
EMI starts at ₹6,416/month
Total price: ₹1,99,000 + GST
A Business Analytics and Data Science & AI course is a highly valuable course in India. While a business analyst course focuses on the statistical study and practice of business data, a data science course is used to gain valuable B2B insights. A data scientist course, on the other hand, is used for analyzing trends, technologies, and strategies in the business world. This course is designed to help you understand the key concepts and strategies of data science applications.