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MAIN TOOLS & LANGUAGES THAT YOU WILL LEARN TO BECOME A TOP DATA SCIENTIST

Key Program Highlights

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Batch Starts:

Batch - January 2019

Duration:

12 Months

Preparatory Session:

15 days to prepare yourself prior to start of program

Effort:

7-10 Hours per week

(recommended)

Certification:

Post Graduate Diploma in Data Science (PGD-DS) awarded by Amity University

Fees:

Rs. 145000/- (All-Inclusive)

Options- One Time Payment, Cardless EMI Payment

PROGRAM FORMAT:

Online + Optional Campus Sessions

UNIQUE FEATURES:

  • One-on-One Mentoring
  • Industry-driven comprehensive curriculum
  • 24/7 access to study material & video lectures
  • Live interactions with Data Scientists and Corporate leaders
  • Real-world Projects & Case Studies
  • Includes offline face to face meetups with peers and top experts
  • Get Alumni Status from Amity University

About Program

A rigorous program designed by top academicians & experts to help working professionals develop frameworks and acquire skills to take up roles in data science.

    • Asia’s No. 1 online education platform.
    • Ruling in the area of online education for more than two decades.
    • Only internationally accredited online university with over 200,000 international students across 135 countries.
    • Most preferred online degree by employers and recruiters across the globe.
  • Why Study Data Science?
    • 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.
  • Why Amity’s PG Diploma in Data Science?
    • You gain a well-rounded foundation and deeper understanding to create solutions using ML & AI.
    • You master essential concepts and exceptional skills to train intelligent machines and AI systems effectively.
    • You attain practical mastery over a wide range of ML applications in healthcare, retail, financial services and manufacturing industries.

Program Structure & Format

Candidates can complete the 12-months course of study through:
  • Recorded video lectures and other study material from faculty with real-world expertise
  • Optional contact sessions at Amity Campuses with industry mentors and experts to solve queries and doubts
  • Online coursework that includes case studies, mini-projects and assignments
  • Hands-on exercises in Python, R and Other Data tools
  • Work on real life data sets.
  • Capstone Data Science Project to work on practical business cases.
Our Campuses for Contact Classes

  • Amity Bangalore, Kormangala

  • Amity Chennai, Gopalapuram

  • Amity Hyderabad, Punjagutta

  • Amity Mumbai, Malad & Kurla

  • Amity Pune, Senapati Bapat Road

  • Amity Noida, Sector 125

  • Amity Dubai, UAE

  • Amity Singapore, 3 Kay Siang Road

If the contact classes are not available in your city, don’t worry. You will be provided access to the recorded lectures of contact sessions of another nearby city.

Eligibility & Selection

  • For whom
    • Working professionals in IT / Analytics / Statistics / Big Data / Machine Learning
    • Fresh graduates from Engineering / Mathematics / IT backgrounds
    • Professionals looking to develop skills to do statistical analysis to support decision making
    • Final year students completing their graduation on or before December 2019
  • Eligibility
    • BE / B.Tech / BCA / MCA / B.Sc. (Maths) / M.Sc (Maths) with a minimum of 50% aggregate marks is compulsory
    • Candidates with Mathematics, Statistics background will be given preference.
    • Minimum of two years full-time work experience after graduation or post-graduation is required.
    • For meritorious fresher students / Young Professionals (CGPA > 7/10), the work experience will be relaxed on the basis of application.

Faculty

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

NAVEEN BHANSALI

  • 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

Course Curriculum 12 months | 8 Modules

  • Module I – Introduction to Data Science
    • What is Data Science and why is it so important?
    • Overview of Data Science and Analytics
    • Statistics - Foundation Primer
      1. Plots: Scatter Plots, Histogram, Bar Chart, Box Plot etc.
      2. Numerical Measures : Mean,Median,quartile,percentile etc.
      3. Basic Probability, Conditional Probability,Bayes’ Theorem
      4. Random Variables, PDFs, CDFs, Binomial & Normal
      5. Distributions, Sampling Distributions & the Central Limit Theorem
  • Module II – Introduction to Python and R
    • Basic Python Programming constructs
    • Functions and OOP in Python, NumPy basics
    • Learn Pandas basic concepts, work with Series
    • Work with Pandas DataFrame (Advanced)
    • Fundamentals of R
    • Univariate statistics in R
    • Data preparation using R
  • Module III – Data Visualization techniques
    • Data Visualization in Python using Matplot
    • Data visualization in R
    • Data Visualizatoin in Tableau
    • Case Study
  • Module IV – Machine Learning Basics
    • Converting business problems to data problems
    • Understanding supervised and unsupervised learning with examples
    • Understanding biases associated with any machine learning algorithm
    • Ways of reducing bias and increasing generalisation capabilites
    • Drivers of machine learning algorithms
    • Cost functions
    • Brief introduction to gradient descent
    • Importance of model validation
    • Methods of model validation
    • Cross validation & average error
    • Case Study
  • Module V – Machine Learning (Supervised Learning - I)
    • Generalised Linear Models
      1. Linear Regression
      2. Ridge and Lasso Regression
      3. Logistic Regression
      4. Assignment
    • Classification
      1. Random Forest
      2. Decision Trees
      3. Support Vector Machines
      4. KNN
      5. Naïve Bayes
      6. Usage
      7. Assignment
    • Boosting Algorithms using Python
      1. Concept of weak learners
      2. Introduction to boosting algorithms
      3. Adaptive Boosting
      4. Extreme Gradient Boosting (XGBoost)
      5. Assignment
    • Case Study
  • Module VI – Machine Learning (UnSupervised Learning - II)
    • Clustering
      1. K-Means
      2. K Nearest Neighbours
      3. Association Rule Learning
      4. Assignment & Case Study
    • 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)
      5. Assignment & Case Study
    • Reinforcement Learning
      1. Markov Decision
      2. Monte Carlo Prediction
      3. Assignment & Case Study
  • Module VII – Deep Learning
    • Time Series Forecasting
      1. Assignment & Case Study
      2. Logistic
      3. Time Series (ARIMA)
      4. 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)
      5. Assignment & Case Study (Time Series)
  • Module VIII – 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

download detailed syllabus

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Industry Insights

Industry Insights from our Career Partner CutShort.io-
  • The demand for Data Analytics professionals has doubled in last one year.
  • This demand will grow exponentially as more than 70% of companies surveyed said they plan to start hiring of Data Professionals in next three years.
  • Data analytics and Business Intelligence are popular skills, along with skills such as Data Science, Machine Learning and Data Engineering.
Get exclusive career opportunities in Data Analytics domain via our partner CutShort.io-
Total companies hiring: 5000+ including
Actively hiring in Data Analytics domain: 100+ including

Application Process

  • 1. Fill up the Application Form

  • 2. Screening call from Admission Director's Office basis on Profile

  • 3. Final Application to be shared with Admission Committee for final selection

Program Fees

145000 /- (All-Inclusive)

  • One Time Payment

    Rs. 145000/-
    (All-Inclusive)

    • Zest No Interest EMI Option
    • 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

Learning Outcomes

By the end of the PGD-DS 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

Certification

Earn a Certificate of Post Graduate Diploma in Data Science ( PGD-DS) from Amity University.

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

Case Studies & Assignments

  • Case Studies
    • Get hands-on experience with real-life case studies
    • Work on real life data sets from the industry
  • Assignments
    • Regular assignments after each module to help you apply concepts and understand gaps in your knowledge.

Projects & Quizzes

  • 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.
  • Capstone Project
    • Capstone Project - the main highlight of PGD-DS
    • Get an opportunity to apply tools and techniques learnt in the program on real-world data science problems
    • Have access to continuous guidance from a Project Mentor to execute the project
    • Submit a detailed report along with a recorded presentation to achieve success in the project

Evaluation

Class performance will be based on following components:

  • Data Science projects across various domains
  • Capstone Project
  • Module-based Assignments
  • Quizzes.

Faculty members may choose a few or all above stated components.

Career Assistance

  • Enhance your career aspirations with assistance from our Virtual Job Fair, existing Corporate and Alumni network, and recruitment partners like CutShort.io and many others.
  • On successful completion of the program, equip yourself with sophisticated expertise to apply for jobs in Analytics domain in the roles of Business Intelligence Analyst, Business Analytics Consultant, Data Specialist, Data Mining Expert, Research Analyst, Big Data Analyst and Data Scientist.
  • Get connected with our Career Counsellor towards the end of the program to be interview-ready as per the current industry requirements.

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