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

Key Program Highlights

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Batch Starts:

Batch - July 2019

Offered at:

Amity Bangalore Campus, Kormangala

Duration:

6 Months

Effort:

  • 22 Campus Sessions ( 150+ hours )
  • Weekly 8-10 hours

Certification:

Post Graduate certificate in Data Science
awarded by Amity University

Fees:

Rs. 2,00,000

Options- One Time Payment, Interest Free EMI Plans

PROGRAM FORMAT:

    Campus Learning

UNIQUE FEATURES:

  • 24/7 access to study material & video lectures
  • Offline sessions with corporate leaders at Amity University Campus
  • Online live sessions and Doubt Solving sessions
  • Comprehensive focus on tools
  • Dedicated Academic Counsellor
  • Real-world Projects & Case Studies
  • Career guidance and support
  • 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:
  • Regular offline sessions with corporate leaders at Amity University Campus
  • High-quality Recorded video lectures with illustrative presentations
  • Classroom activities with Case studies discussions & Assignments
  • Instructor-led hands-on sessions to learn popular tools & platforms
Our Campus for Contact Sessions
  • Amity Bangalore, Kormangala

Contact Sessions at our Campus

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. 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
  • B.tech - Indian institute of Technology, Kharagpur

Dr. Suresh Varadarajan

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

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

Course Curriculum 6 months | 5 Modules

  • Module I – Introduction to Data Science (4 Campus Sessions)
    • 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 – Supervised Machine Learning Techniques (4 Campus Sessions)
    • 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 – UnSupervised Machine Learning Techniques (4 Campus Sessions)
    • 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 (4 Campus Sessions)
    • 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 (6 Campus Sessions)
    • 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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Program Fees

  • One Time Payment

    Rs. 2,00,000

    • 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 Campus Sessions ?

    • Can't attend Campus 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

Certification

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

Career Assistance

  • Enhance your career aspirations with assistance from our Career partners cutshort, iimjobs and existing Corporate and Alumni network
  • On successful completion of the program, you will equip yourself with sophisticated expertise to apply for jobs in Machine Learning domain in the roles of Data Scientist, Business Analyst, Business Analytics Consultant, Data Specialist, Big Data Analyst and many more.
  • Dedicated career counselling towards the end of the program to strengthen your resume and interview skills.

Frequently Asked Questions

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