
PG Diploma
Post Graduate in data science course provides training to develop Statistical, Computational and Programming Skills. The increasing importance of data analysis in several fields including banking, finance, entertainment, pharmaceutical, environment, economics, engineering and many more motivated us to curate a course for full time working professionals and new graduates to upskill and become eligible for lucrative job prospects.
The program aims at teaching students:
- Theoretical and practical aspects of statistical concepts like inference, probability, Bayes Theorem, modeling
- Programming skills like Python and R programming
- Data visualization tools for real-world scenarios
- Database management to clean, transform and query data.
- Implementing machine learning algorithms to design solutions for data-oriented problems
Key highlights

Gateway to foreign universities
Complete 16 years of education as required by most of the international institutions while getting an excellent foundation in the data science field.

University of Mumbai
Globally recognised degree from the prestigious 160 years old University of Mumbai. Campus placed at renowned Patkar-Varde college having ‘A+ Grade’ by NAAC.

Industry approved curriculum
The program has been designed and taught by eminent industry experts to bring education in sync with workplace realities ensuring holistic learning.

Capstone Projects & Case Studies
Capstone projects drawn from real-world problems allow students to create a product that can be used to practice their skills and showcase to potential employers.
Programming softwares / tools covered
Training Methodology
“Knowledge has to be improved, challenged, and increased constantly, or it vanishes.”
Classroom Learning
Theory
In data science, mathematical skills are as important as programming skills. Subjects like Statistics, Linear algebra and Big data helps to build a profound fundamental to build reliable and efficient models
Practical
You need good programming and analytical skills to become a good data scientist which comes by practicing several tools required to solve big data problems, automate processes and write efficient algorithms.

Application Based Learning

Case studies
Companies are using analytics to improve their process and the scale of the data they use to do this has increased tremendously over the last few years making it extremely important to learn through real-world case studies.
Industry connects
Professionals from the industry share their experiences though such sessions and talk about how data science continues to gain widespread acclaim & demand across almost every major sector.
Eligibility Criteria
Course Syllabus
This semester introduces students to statistical analysis and several programming tools, data visualisation to transform and clean the data and database management to create, retrieve, update and manage data.
Paper title
- Database Management Systems
- Big Data architecture and ecosystem
- Statistical Methods
- Data Visualization – Using Power BI
- R Programming
- Python Programming
Semester I | |||
---|---|---|---|
Course Code | Course Type | Course Title | Credits |
PUSDSBA 101 | Core Subject | Database Management Systems | 4 |
PUSDSBA 102 | Core Subject | Big Data architecture and ecosystem | 4 |
PUSDSBA 103 | core Subject | Statistical Methods | 4 |
PUSDSBA 104 | Core Subject-Practical | Data Visualization – Using Power BI | 4 |
PUSDSBA 405 | Core Subject | R Programming | 4 |
PUSDSBA 406 | Core Subject | Python Programming | 4 |
Total Credits | 24 |
This semester focuses on Time series forecasting and Machine Learning that automates analytical model building. Students learn to work on big data and advanced SQL to systematically extract information and draw insights.
Paper title
- Machine Learning and Deep Learning
- Time Series Analysis and Forecasting
- NLP and Recommendation Engine
- Distributed Processing using HADOOP
- Electives
- Social Media and Marketing Analytics
- Financial Analytics
Semester II | |||
---|---|---|---|
Course Code | Course Type | Course Title | Credits |
PUSDSBA 201 | Core Subject | Machine Learning | 4 |
PUSDSBA 202 | Core Subject | Time Series Analysis | 4 |
PUSDSBA 203 | core Subject | Advance SQL | 4 |
PUSDSBA 204 | Core Subject | NLP | 4 |
PUSDSBA 205 | Core Subject | Project Work | 4 |
PUSDSBA 206 | Core Subject | Elective | 4 |
Total Credits | 24 |