PGD in Data Science & AI

Program Details

Data Science is currently one of the hottest and trending career option for the decade. The demand for data scientists is growing into becoming huge where the number as quoted by industry is much higher than the available candidates. Hence, it can be concluded that, choosing Data Science as a career option has a lot of scope and will remain so in the near future and there is possible scope of gap in demand and supply for job roles pertaining to data scientists, which includes professionals for information management, software development, data analytics, artificial intelligence, data discovery and its various other related fields. As per the industrial demand, Python, R, Machine Learning etc. have now become the most sought after skills.

PG Diploma program in Data Science and AI for executives aim towards making working professionals/ technology graduates, belonging to various diverse sectors, equipped skill sets pertaining to data science field. The program includes in-depth concepts of Machine Learning, Neural Networks, and Business Intelligence along with introductory concepts of AI.

Course Content

  1. Introduction to Data Science and Applications

    This course gives an overview of the basic concepts of data, big data, data processing, Data manipulations and tools that data analysts work with. The course cover introduction of various tools that are used by data scientists to find the insignificant insights or patterns in the data.

  2. Basic Statistics

    The course covers fundamentals of Basic statistics which helps the learner to understand the how to measure the data in a scientific manner and how to compute and apply measures of central tendency, measures of dispersion, measures of skewness & kurtosis tools while analysing the data. This course also covers a brief introduction of probability and probability distributions which is useful while using data science tools.

  3. Advanced Statistics

    This course covers how to measure the relationship between the variables using correlation and regression techniques. It also covers how to use different sampling methods and test the claim and using hypothesis testing. The course covers fundamentals of statistical inference which helps the learner to understand the process of drawing conclusions about populations or scientific truths from data using different modes of performing inference.

  4. R Programming

    This course provides an in-depth understanding of R, R-studio, and R packages. Learner will learn how to program in R with the various types of functions, data structure, and perform data visualizations, data manipulation, pre-processing and summary statistics using R for any specific need.

  5. Python Programming

    This course helps the learner to understand the essential concepts of Python programming like data types, basic operators, and functions. Leaners will perform high-level computing using NumPy, SciPy packages along with the Pandas package used for data analysis and manipulation. Learner will gain expertise in machine learning using the Scikit package, matplotlib library for data visualization and web scrapping using BeautifulSoup.

  6. Machine Learning with R and Python

    The course covers Data Processing, Exploratory data analysis, Supervised Learning (Regression, classification), Unsupervised learning (Clustering, Dimensionality Reduction).

  7. Business Intelligence Tools

    This course helps to learner to visualize the data and present the information in the forms of reports and dashboards by performing business analysis using tableau or Power BI.

  8. Natural Language Processing

    Students can learn, how to use NLTK module in Python for developing various skill set pertaining to Natural Language Processing, how to build a Spam Detector, how to build a Twitter, Sentiment Analyser, Latent Semantic Analysis.

  9. Neural Networks

    This course covers Artificial Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks, Boltzmann Machines, Tensor flow with Python/R.

  10. Capstone Project

    Project in AI or automation in one of our focus sectors including Retail, Banking, Manufacturing, Education, SCM and others.


  • Experienced Trainers having worked in multinational enterprises and technology projects to provide industrial exposure.
  • 70% learning through hands-on, practical, case studies and projects.
  • Content curated with the help of sector skill councils and industry needs.
  • Flexible batch timings to suit working professionals.
  • Comprehensive 12 month program giving opportunity to ride the data science career.
  • Optional Module on Basics of Math for Machine Learning – before start of the program.
  • We provide 2 months Industry Internships

Program Structure


Introduction to Data Science and Applications

Machine Learning with R and Python

Basic Statistics

Business Intelligence Tools

Advanced Statistics

Natural Language Processing

R Programming

Neural Networks

Python Programming

Capstone Project

Project/Case Study

Program Structure:-Download pdf file

Fees Structure

Fees Structure For Indian Students

Program FeesAmount in INRPayment Terms
Academic Fees (Note 1)INR 1,85,000/- per year
Caution Money (Refundable)INR 10,000/- (Refundable)Refundable University Caution Money to be paid at the time of admission

Note 1

Academic Fees includes Tuition fees, Regular exam fees, Library books & e-journals, Laboratory fees , Practical and Skills Journal. These fees are refundable according to UGC policy. Backlog Exam Fee shall be charged separately. Medical Fees of Rs. 5500/- for basic medical facilities on campus are Non-Refundable.

Note 2

  • Hostel & Mess Fees include Hostel Stay and Mess/Dining charges.
  • Laundry facility is available to student on payment basis.

Note 3

  • Academic fees shall be increased up to 15% each year as per the University policies during the period of the program at the discretion of the Management.
  • Revised fees will be applicable to new and existing students.

Other Notes

  • In case a crash course is conducted, a separate fee between Rs.5,000 to Rs.10,000 will be charged. The same shall be notified separately.
  • Nominal fees of Rs.500/- is charged for every non-credit mandatory IDSC course per student.
  • Education loan facility available from nationalised banks.
  • Pay per use bus service for city trip for hostel students @ Rs. 100/- per trip (to & fro).

Discretionary Quota 5% Seats are reserved as Discretionary Quota Seats. Only students with high academic record are considered for Discretionary Quota Seats. The fees for Discretionary Quota Seats will be double the Academic fees for Indian Students to be paid to the University.

  • Start of any academic program is subject to minimum enrolment. In case a program does not commence, student will be refunded entire fees as well as the application form charges.

Payment of Fees:-

Cancellation & Refund Policy:-

Eligibility & Fees

Graduate or post graduate with good numerical and analytical skills, minimum 50% at graduate exam, orientation to mathematical subjects during graduate or post graduate studies, work experience preferred


IT professional with 2 years of experience in software development or databases – and having successfully completed courses in Linear Algebra and Statistics

Applicants will be interviewed and selected

Fees: Rs. 1,85,000


Program Fees Amount in INR Payment Terms

Academic Fees (Note 1)

INR 222,000/- per year

Installment 1 (Payable on Admission)
INR 121,000/-(Academic Fees (New Admission))
INR 111,000/-(Academic Fees (Existing))
Installment 2 (Payable by 30/10/2020) INR 111,000/-(Academic Fees (New Admission))
INR 111,000/-(Academic Fees (Existing))

Medical Fees (Non-Refundable)

INR 7000/-

Caution Money (Refundable)

INR 10,000/- (Refundable)

Refundable University Caution Money to be paid at the time of admission

Hostel & Mess Fees (Note 2)

- 2 Seater Non AC Room

INR 1,70,000/-

Hostel + Mess Fees to be paid at the time of admission

Hostel Caution Money (Refundable)

INR 10,000/- (Refundable)

Refundable Hostel Caution Money to be paid at the time of admission

1. Academic Fees includes: Tution fees, Regular exam fees, Practial Journal Fees, Library books & e-journals, Laboratory fees.
2. Hostel Charges includes Hostel Stay, Mess/Dining Charges.
3. Academic fee shall be increase up to 15% each year as per the University policies during the period of the program at the discreation of the Management.
4. Caution Money (Refundable) for all full time programs Rs. 10,000/-

Payment of Fees:-

Cancellation & Refund Policy:-

Important Dates

  • Admissions Open – 2 Dec 2019
  • Last Date of Application – 31 July 2020
  • Regular Classes Begin – August 2020

For any other information, you may call us at 020-27187766, 27187768 or

Next Steps

  • If you need more information on the content, we would like to suggest to you to attend one of our weekly counselling seminars. For any other information, you may call us at 020-27187768/66,8007001777,8806062789 or

  • *Dates are subject to change

Trainers & Mentors

Sudin Baraokar

Sudin is an Emerging Tech Innovator and Collaborator having served as Head of Innovation in SBI, Barclays, IBM & GE. Currently, he is a Global IT & Innovation Advisor to Global 1,000 Enterprises and Fintechs. He is also a Founder of a disruptive Fintech that is building the next generation Asset Exchange Platform, Asset Payment Network and Asset Management using Blockchain and AI and a DeepTech Startup and that automates the API, Model and Code Build using OTA and AI.

Prabhakar Singh

At present, Prabhakar is working as Senior Consultant Strategy Operations with Palladium and PCMC City Transformation Office. Earlier, he has had experience working on cutting edge technologies at KPMG, Vinfotech as Product Manager, Principal Consultant and at Adwortech as Founder. His domain expertise include Healthcare, Banking, Smart Cities, Construction etc.

Dr. Samee Sayyad

Dr. Samee is a researcher in Big Data, having completed his PhD in 2018. He has been teaching at SSOU as well as has experience of teaching subjects including Data Mining, Algorithms, Programming and Big Data. He works as Assistant Professor with SSOU