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PGD in Data Science & AI

Program Details


According to International Data Corporation (IDC), a technology research company, global data analytics and big data market is likely to reach USD 57 billion by 2020 with a CAGR of 23%. This also means that there is going to be a bigger gap in demand and supply of data scientists, including professionals for information management, software development, data analytics, artificial intelligence and data discovery. Hence, Python, R, SAS, Machine Learning etc. have become the most sought after skills. PG Diploma in Data Science and AI with R and Python is aimed at equipping IT professionals, working professionals or technology graduates with the know-how of the data science field. The program will introduce machine learning as well Neural Networks, Artificial Intelligence and Business Intelligence. This program will also help the IT enterprises from the perspective of implementing machine learning and developing the resources to handle more intellectual work.

Highlights


  • FIRST classroom program in Data Science and Artificial Intelligence
  • Experienced Trainers having worked in multinational enterprises and technology projects
  • 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

Content


  1. Introduction to Data Science and Applications

    This course gives an overview of the basic concepts of data and tools that data analysts work with. The course cover various methods of obtaining data with various formats, also cover the basics of cleaning and visualising data.

  2. Statistical Inference

    This will cover 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.

  3. R Programming

    Module 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 using the various graphics available in R. The course also covers the GitHub and working on the same.

  4. 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 BeautifulSoup for web scraping.

  5. Machine Learning with R and Python

    Module covers Data Processing, EDA Regression (Linear Regression, Support Vector Machine, Decision Tree, Random Forest Walk ) Classification (Logistic Regression, K-NN, SVM, Naïve Bayes, Decision Tree, Random Forest) Clustering (K- Means , Hierarchical) Association Rule Mining Eclat Dimensionality Reduction Model Selection & BoostingInstall R, RStudio

  6. Business Intelligence Tools

    Introduction to Advanced BI tools like Power BI or Qlik is covered

  7. Natural Language Processing

    Learn - How to build a Spam Detector, How to build a Twitter Sentiment Analyzer, NLTK, Latent Semantic Analysis – in this module.

  8. Neural Networks

    Topics covered include Artificial Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks, Boltzmann Machines, Tensorflow with Python

  9. Capstone Project

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

Duration, Timing & Venue


Duration: 12 months including Capstone Project

Week-end Batch: Saturday and Sunday, 5 hours each day, 10 am to 3.30 pm / 1 pm to 6.30 pm

Week-day Batch: Tuesday, Thursday, 3 pm to 6 pm & Friday 3 pm to 7 pm


Symbiosis Skills & Open University (SSOU),
Kiwale, Village – Kiwale,
Adjoining Pune Mumbai Expressway,
Pune - 412101 , Maharashtra, India.

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:-http://www.ssou.ac.in/payment-of-fees/

Cancellation & Refund Policy:- http://www.ssou.ac.in/refund-and-cancellation-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

OR

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

Important Dates


  • Admissions Open– 10 February 2019
  • Selection of Batch Announced – 15 June 2019
  • Last Date of Application – 14 June 2019
  • Optional ‘Basics of Math for Machine Learning’ begins – 20 June 2019
  • Regular Classes Begin – 1 July 2019

For any other information, you may call us at 020-67187777, 67187768 or admissions@ssou.ac.in

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 BankChainAsset.com 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 AIonthefly.io 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