Key Program Highlights

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Batch Starts:

Batch - 26th April 2019

Offered at:

Amity Campus


6 Months


8-10 Hours per week



Post Gradute certificate in Data Science awarded by Amity University


Rs. 200000

Options- One Time Payment, Interest Free EMI Plans


    Offered at Amity Campus


  • 24/7 access to study material & video lectures
  • Offline sessions with corporate leaders at Amity University Campus
  • Comprehensive focus on tools and tactics
  • Dedicated Academic Counsellor
  • Real-world Projects & Case Studies
  • Get Alumni Status from Amity University

About Program

Amity's PG Program in Data Science is a rigorous program designed by top academicians & experts to help working professionals develop frameworks and acquire skills to take up roles in data science.

  • Data Science Landscape in India:
    • Hottest career option for future. Harvard Business Review, termed it the sexiest job of the 21st century. Wired had famously quipped: “Tell your kids to be data scientists, not doctors.”
    • Humongous amount of data being generated by industry. There is a dearth of qualified professionals to make sense of this data.
    • A Data Scientist can work in a industry vertical he wants to and is not restricted to any particular sector. You could end up working in business, energy, supply chain,government, or healthcare. Pick what interests you.
    • Data is reshaping the landscape for many job roles, without knowledge of data it would be tough to sustain jobs in the future.

Program Structure & Format

Candidates can complete the 6-months course of study through:
  • High-quality Recorded video lectures with illustrative presentations
  • Offline sessions with corporate leaders at Amity University Campus
  • e-learning activities with Case studies discussions & Assignments
  • Instructor-led hands-on sessions to learn popular tools & platforms
Our Campus for Contact Classes
  • Amity Bangalore, Kormangala

  • Amity Mumbai, Malad

  • Amity Noida, Sector 125

Contact Sessions at our Campus


Our team of world-class faculties have years of experience as professional trainers and have worked closely with analytics adopters across various sectors. Their industry exposure will help you to understand the everyday nitty-gritty to be a successful Data Scientist.

Ashish Gilotra

  • 20+ Years of Experience in Machine Learning
  • ME - BITS Pilani - Software Engineering
  • Ex, Head ML & AI (HT Media), VP - AuthBridge

Dr. Suresh Varadarajan

  • 26+ Years of Experience
  • PhD, Engineering - Marquette University
  • BE, IISc Bangalore | MTech, IIT Kanpur

Dr. Himanshu Choudhary

  • Data Scientist, Soft Maverick Technologies Inc.
  • Ex-Associate Professor, The Northcap University
  • Ex-Academic Associate, IIM Ahmedabad


  • Alumni - IIM Bangalore
  • Data Analyst - Dell EMC
  • Ex Engineering - Infosys

Dr. Karthic Narayanan

  • MSc & PhD - Nanyang Technical University (NTU)
  • Experience in Modelling using Stats, Algo Modelling
  • Has worked with leading Hedge Funds for Advisory in Futures, Commodities & Options

Dr. Sakshi Babbar

  • PhD - Data Mining - University of Sydney, Australia
  • Worked on Predicting Water Quality Index of Yamuna River
  • 14 Years experience in Education

Dr Anish Roychowdhury

  • PhD - IISc Bangalore
  • MS - Louisiana State University
  • Data Science Manager at one of the MNCs

Dr Mohit Kumar Goel

  • PhD - Ecole polytechnique fédérale de Lausanne
  • - Indian institute of Technology, Kharagpur

Course Curriculum 6 months | 5 Modules

  • Module I – Introduction to Data Science
    • What is Data Science and why is it so important?
    • Overview of Data Science and Analytics
    • Mathematics for Data Science
      1. Probability and Inferential Statistics
      2. Linear Algebra
      3. Calculus
    • Introduction to Python and R
      1. Basic Python Programming constructs
      2. Functions and OOP in Python, NumPy basics
      3. Learn Pandas basic concepts, work with Series
      4. Work with Pandas DataFrame (Advanced)
      5. Fundamentals of R
      6. Univariate statistics in R
      7. Data preparation using R
    • Data Visualization techniques
      1. Data Visualization in R
      2. Data Visualization in Python using Matplot
      3. Data Visualization in Tableau
  • Module II – Machine Learning (Supervised Learning - I)
    • Generalised Linear Models
      1. Linear Regression
      2. Ridge and Lasso Regression
      3. Logistic Regression
    • Classification
      1. Random Forest
      2. Decision Trees
      3. Support Vector Machines
      4. KNN
      5. Naïve Bayes
      6. Usage
    • Boosting Algorithms using Python
      1. Concept of weak learners
      2. Introduction to boosting algorithms
      3. Adaptive Boosting
      4. Extreme Gradient Boosting (XGBoost)
    • Case Study
  • Module III – Machine Learning (UnSupervised Learning - II)
    • Clustering
      1. K-Means
      2. K Nearest Neighbours
      3. Association Rule Learning
    • Dimensionality Reduction
      1. Need for dimensionality reduction
      2. Principal Component Analysis (PCA)
      3. Singular Value Decomposition(SVD)
      4. T-distributed Stochastic Neighbor Embedding (t-SNE)
    • Reinforcement Learning
      1. Markov Decision
      2. Monte Carlo Prediction
  • Module IV – Deep Learning
    • Time Series Forecasting
      1. Logistic
      2. Time Series (ARIMA)
      3. Assignment & Case Study (Time Series)
    • Neural Networks
      1. Convolutional Neural Networks (CNN)
      2. Recurrent Neural Networks (RNN)
      3. Long Short-Term memory (LSTM)
      4. Gated recurrent units (GRUs)
    • Case Study
  • Module V – Big Data Analytics
    • Introduction to Big data and Hadoop
    • HDFS and YARN
    • MapReduce and Sqoop
    • Basics of Hive and Impala
    • Working with Hive and Impala
    • Types of data formats
    • Advanced Hive Concept and Data File
    • Apache Flume and HBase
    • Pig
    • Basics of Apache Spark
    • RDDs in Spark
    • Implementation of Spark Applications
    • Spark Parallel Processing
    • Spark RDD Optimizatoin Techniques
    • Spark Algorithm
    • Spark SQL
    • Case Study

Case Studies

Case 1

Improving Customer Engagement at VMWare through Analytics

Case 2

Linear Regression a high level overview

Case 3

Larsen and Toubro: Spare Parts Forecasting

Case 4

Big Data Dreams: A Framework for Corporate Strategy

Program Fees

  • One Time Payment

    Rs. 200000

    • Faster payment with Credit Card, Debit Card, Net Banking
  • Cardless EMI Payment

    • Pay Hassle free Zero percent EMI in 12 months.
    • No Credit Card required
  • Not interested in Contact Sessions ?

    • Can't attend Contact Sessions Explore our PG Diploma Program Click here

Learning Outcomes

By the end of the Post Graduate Program, you will be able to
  • Learn to make sense of organizational data, develop processes for managing data and use data to inform key business decisions
  • Learn how to combine data visualization and predictive models to increase the accuracy of your predictions
  • Learn to formulate a business question as a scientific hypothesis that can be tested using statistical methods
  • Create and validate regression models that can be used to determine the effect of attributes on a decision and predict likely outcomes
  • Gain the expertise to use a wide range of modern statistical tools and software packages like R, Python, Spark, Excel, MySQL,Hadoop
  • Learn to do visual analytics with free access to paid software packages like Tableau


Earn a Certificate of Post Graduate Program in Data Science from Amity University

Add the certificate to your CV and improve your job/business prospects

Evaluation: Assignments & Case Studies

  • Online Quiz
    • Be responsible for your own learning with optional online quizzes at the end of each module.
    • Test your subject understanding and determine your readiness for program completion.
  • Assignments
    • Apply concepts learned in each module with in-depth assignments designed to make you reflect, adapt and apply knowledge to real life scenarios.
  • Case Studies
    • Get hands-on experience with real-life case studies
    • Build a portfolio of demonstrable work by referring to these cases published at Harvard Business Publishing’s case portal
Request a Call Back from our Admission Counsellors to know more