Post Graduate Diploma in Data Science-Full Time Course


Eleven-Month Diploma course. The total course fee is INR 3,54,000 which has been split into 3 installments of INR 1,18,000 each.

Classroom (1 Batch ) : 40 Students

Fee: INR 3,54,000/-

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Post Graduate Diploma in Data Science (Part Time)- Manipal Academy of Higher Education
Eleven-Month Diploma course. The total course fee is INR 3,54,000 which has been split into 3 installments of INR 1,18,000 each.

Program Highlights


In collaboration with Genpact & Experian

The course is designed in collaboration with industry leaders Genpact, Experian & Big Data University.

Assured Placement Assistance

Assured placement assistance with leading organisations on successful completion of the course.

Tool Based Learning

Hands on learning with tools like R, Excel , SQL, NoSQL, Hadoop, Pig, Hive , Apache Spark & Storm.

Quick Stats

Companies like IBM and HP are making humongous investments in big data and analytics, making it a popular career option.

Data Science professionals are revered in many industries including govt. fields, academia, retail & gaming.

Eligibility

Working professional with at least two years of work experience, having mathematical, computer science or technical background.

Candidates with an MBA (or equivalent Management qualification) will be eligible based on their graduation streams.

Those earned 50% or above in (or is awaiting results of) one of the following - any of the Engineering Degrees (B.E., B.Tech., M.E. or M.Tech.); or BCA/MCA; or Bachelors or Masters in Maths, Physics, Statistics, Economics, or Operational Research.

Candidates from other streams (Chemistry, Biology, Commerce, Arts, Media, and BBA) who have scored 50% or above, will have to take an online Technical Aptitude assessment.

All candidates will have to undertake an English Proficiency test, irrespective of the graduation streams.

Course Outcome

Concepts of Data Science

Learn about Big Data and Machine Learning from the foundation to advanced level.

Program Structure

The first 3 terms have a mix of theory and labs while the last term is a project term. The last term will be a stipend-based internship or project of 2 months.

Big Data Technologies

Become an expert in Big Data Technologies, the most popular and growing technology in the world.

Better Career Path

Find job opportunities as Data Scientists, Data Analysts, Data Engineer, Data Developer etc.

Loans Available

Loans available from Axis Bank, Credila and Avanse Financial Services.

Soft Skills

Soft skills training is also imparted to help you to transition to corporate and accelerate career growth.

Curriculum

Introduction to Software and Operating Systems (OS)

Introduction to programming

Control Structures:

Functions and Algorithms

Complexity

File operations

Introduction to Database Management Systems

Conceptual Database Design

Logical Database Design

Database Design

Database Implementation

Introduction to Statistics

Probability

Sampling and Sampling Distributions

Testing of Hypothesis

Simple Correlation

Introduction to Data Science

Introduction to Data Analysis

Descriptive Analysis

Data cleansing and transformation

Statistical methods applications for dimension reduction

Statistical methods applications for clustering

Introduction to Machine Learning and Data Science

Regression

Classification

Clustering

Recommendation systems

Understanding of measures and their applications

Customer Analytics

Why Data Visualisation?

Story telling through data

Visualization & Communicating using Data Visualisation

Dashboards and Automation

Visualisation Product

Motivation for Big Data

Getting Started with Hadoop Framework - Introducing Hadoop, Components of Hadoop

Getting Started with Hadoop Framework - Hadoop in the Cloud

Understanding HBase

Analysing Data with Hive

Analysing Data with Pig

Time Series and Forecasting

Artificial Intelligence (AI)

Neural Networks

Understanding Spark

Spark programming

Real-time data stream Analytics (Storm)

Transition to Corporate Culture: 4 Hours

Communication

Spoken Communication

Written Communication

Presentation Skills

Team Work

FAQ

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