Masters in Data Science Program Online

10

Lessons

Up to 232 Hrs

Duration

English

Language

Course Details

Nishkam Shivam
Data Scientist at Walmart

Madhu Babu Cherukuri
Sr. Data Scientist at Intel Corporation

Vinodhini R.
Senior Data Scientist at Cisco

$2,600

Please contact us for current promotional rates.

Please contact us for details.

There are no prerequisites for taking up this Data Science graduate program.
  • Instructor-led
  • Self-paced
course features
Career Support:
  • JOB ASSISTANCE

    Assured Interviews
    After 80% of the course completion Assured Interviews upon submission of projects and assignments. Get interviewed by our 500+ hiring partners.

    Exclusive access to Intellipaat Job portal
    After 80% of the course completion Exclusive access to our dedicated job portal and apply for jobs. More than 400 hiring partners’ including top start-ups and product companies hiring our learners. Mentored support on job search and relevant jobs for your career growth.

  • INTERVIEW PREPARATION

    Mock Interview Preparation
    After 80% of the course completion. Students will go through a number of mock interviews conducted by technical experts who will then offer tips and constructive feedback for reference and improvement.

    1 on 1 Career Mentoring Sessions
    After 90% of the course completion Attend one-on-one sessions with career mentors on how to develop the required skills and attitude to secure a dream job based on a learners’ educational background, past experience, and future career aspirations.

  • PROFILE BUILDING

    Career Oriented Sessions
    Over 20+ live interactive sessions with an industry expert to gain knowledge and experience on how to build skills that are expected by hiring managers. These will be guided sessions and that will help you stay on track with your up skilling objective.

    Resume & LinkedIn Profile Building
    After 70% of course completion Get assistance in creating a world-class resume & Linkedin Profile from our career services team and learn how to grab the attention of the hiring manager at profile shortlisting stage

Certificate of Completion

After the completion of the course, you will get certificates from IBM, Microsoft, and Intellipaat.

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About the Masters in Data Science Program Online

Intellipaat’s online master’s in Data Science program lets you gain proficiency in Data Science. You will work on real-world projects in Data Science with R, Hadoop Dev, Admin, Test and Analysis, Apache Spark, Scala, Deep Learning, Tableau, Data Science with SAS, SQL, MongoDB and more. In this program, you will cover 10 courses and 30 industry-based projects with 1 CAPSTONE project. As a part of online classroom training, you will receive five additional self-paced courses co-created with IBM namely Deep Learning with TensorFlow, Build Chatbots with Watson Assistant, R for Data Science, Spark MLlIb, and Python for Data Science. Moreover, you will also get an exclusive access to IBM Watson Cloud Lab for Chatbots course. Enroll now and pursue your MS in Data Science online.

What will you learn in this MS in Data Science program?

In this Data Science graduate program, you will learn about

  • MapReduce and HDFS
  • Real-time analytics with Spark
  • Data Scientist roles and responsibilities
  • Prediction and analysis through clustering
  • Deploying the recommender system
  • SAS advanced analytics and R programming
  • Linear and logistic regression
  • Making sense of NoSQL data
  • Deep Learning model in AI

What are the prerequisites for taking up this master’s in Data Science training course?

There are no prerequisites for taking up this Data Science graduate program.

Why should you take up this best online MS in Data Science course?

  • Data Scientist is the best job of the 21st century – Harvard Business Review
  • Global Big Data market to reach $122 billion in revenue by 2025 – Frost & Sullivan

This best Data Science master’s program has been created keeping in mind the needs of the industry when it comes to the domain of Data Science. Today’s Data Scientists need to have a diverse set of skills which include working with huge volumes of data, parsing that data and converting them into a format that is easily understandable, using which business insights can be derived. This training program lets you play multiple roles in the Big Data and Data Science domains and get hired for top-notch salaries.

Who can apply for the course?

  • Professionals who aspire to be a Data Scientist in top organizations
  • Data Scientists who have a keen interest in upgrading their skills
  • Information Architects
  • Machine Learning professionals
  • Business Intelligence professionals
  • Software Developers
  • Project Managers

What roles does a Data Scientist play?

Data Scientist
Develop high-quality applications, apart from designing and implementing scalable code.

Analytics and Insights Analyst
Investigate reported problems in the quality of data and come up with solutions to fix them.

AI & ML Engineer
Deploy models in SageMaker and use Lambda functions and API Gateway to integrate Machine Learning models in web applications.

Data Engineer & Data Analyst
Understand the data, data cleansing, data transformation, analyze outcomes, and present the result in the form of reports and dashboards.

Junior Data Scientist
Use advanced statistical techniques and tools to understand operating behavior and create algorithms with advanced prescriptive and descriptive methods.

Applied Scientist
Design and develop various Machine Learning models to help in deriving intelligence for the business products.

 

COURSE Offered

INSTRUCTOR-LED TRAINING COURSES
SELF-PACED LEARNING COURSES

COURSE OUTLINE

42 Hours 15 Module

Module 01 – Introduction to Data Science with R
Module 02 – Data Exploration
Module 03 – Data Manipulation
Module 04 – Data Visualization
Module 05 – Introduction to Statistics
Module 06 – Machine Learning
Module 07 – Logistic Regression
Module 08 – Decision Trees and Random Forest
Module 09 – Unsupervised Learning
Module 10 – Association Rule Mining and Recommendation Engines

Self-paced Course Content

Module 11 – Introduction to Artificial Intelligence
Module 12 – Time Series Analysis
Module 13 – Support Vector Machine (SVM)
Module 14 – Naïve Bayes
Module 15 – Text Mining

39 Hours 14 Module

Module 01 – Introduction to Data Science using Python
Module 02 – Python basic constructs
Module 03 – Maths for DS-Statistics & Probability
Module 04 – OOPs in Python (Self paced)
Module 05 – NumPy for mathematical computing
Module 06 – SciPy for scientific computing
Module 07 – Data manipulation
Module 08 – Data visualization with Matplotlib
Module 09 – Machine Learning using Python
Module 10 – Supervised learning
Module 11 – Unsupervised Learning
Module 12 – Python integration with Spark (Self paced)
Module 13 – Dimensionality Reduction
Module 14 – Time Series Forecasting

32 Hours 9 Module

Module 01 – Introduction to Machine Learning
Module 02 – Supervised Learning and Linear Regression
Module 03 – Classification and Logistic Regression
Module 04 – Decision Tree and Random Forest
Module 05 – Naïve Bayes and Support Vector Machine (self-paced)
Module 06 – Unsupervised Learning
Module 07 – Natural Language Processing and Text Mining (self-paced)
Module 08 – Introduction to Deep Learning
Module 09 – Time Series Analysis (self-paced)

32 Hours 13 Module

Module 01 – Introduction to Deep Learning and Neural Networks
Module 02 – Multi-layered Neural Networks
Module 03 – Artificial Neural Networks and Various Methods
Module 04 – Deep Learning Libraries
Module 05 – Keras API
Module 06 – TFLearn API for TensorFlow
Module 07 – Dnns (deep neural networks)
Module 08 – Cnns (convolutional neural networks)
Module 09 – Rnns (recurrent neural networks)
Module 10 – Gpu in deep learning
Module 11 – Autoencoders and restricted boltzmann machine (rbm)
Module 12 – Deep learning applications
Module 13 – Chatbots

60 Hours 33 Module

Module 01 – Hadoop Installation and Setup
Module 02 – Introduction to Big Data Hadoop and Understanding HDFS and MapReduce
Module 03 – Deep Dive in MapReduce
Module 04 – Introduction to Hive
Module 05 – Advanced Hive and Impala
Module 06 – Introduction to Pig
Module 07 – Flume, Sqoop and HBase
Module 08 – Writing Spark Applications Using Scala
Module 09 – Use Case Bobsrockets Package
Module 10 – Introduction to Spark
Module 11 – Spark Basics
Module 12 – Working with RDDs in Spark
Module 13 – Aggregating Data with Pair RDDs
Module 14 – Writing and Deploying Spark Applications
Module 15 – Project Solution Discussion and Cloudera Certification Tips and Tricks
Module 16 – Parallel Processing
Module 17 – Spark RDD Persistence
Module 18 – Spark MLlib
Module 19 – Integrating Apache Flume and Apache Kafka
Module 20 – Spark Streaming
Module 21 – Improving Spark Performance
Module 22 – Spark SQL and Data Frames
Module 23 – Scheduling/Partitioning

Following topics will be available only in self-paced mode:

Module 24 – Hadoop Administration – Multi-node Cluster Setup Using Amazon EC2
Module 25 – Hadoop Administration – Cluster Configuration
Module 26 – Hadoop Administration – Maintenance, Monitoring and Troubleshooting
Module 27 – ETL Connectivity with Hadoop Ecosystem (Self-Paced)
Module 28 – Hadoop Application Testing
Module 29 – Roles and Responsibilities of Hadoop Testing Professional
Module 30 – Framework Called MRUnit for Testing of MapReduce Programs
Module 31 – Unit Testing
Module 32 – Test Execution
Module 33 – Test Plan Strategy and Writing Test Cases for Testing Hadoop Application

30 Hours 13 Module

Module 01 – Introduction to Data Visualization and The Power of Tableau
Module 02 – Architecture of Tableau
Module 03 – Charts and Graphs
Module 04 – Working with Metadata and Data Blending
Module 05 – Advanced Data Manipulations
Module 06 – Working with Filters
Module 07 – Organizing Data and Visual Analytics
Module 08 – Working with Mapping
Module 09 – Working with Calculations and Expressions
Module 10 – Working with Parameters
Module 11 – Dashboards and Stories
Module 12 – Tableau Prep
Module 13 – Integration of Tableau with R

Self-paced Courses

22 Hours 17 Module

Module 01 – Introduction to SAS
Module 02 – SAS Enterprise Guide
Module 03 – SAS Operators and Functions
Module 04 – Compilation and Execution
Module 05 – Using Variables
Module 06 – Creation and Compilation of SAS Data Sets
Module 07 – SAS Procedures
Module 08 – Input Statement and Formatted Input
Module 09 – SAS Format
Module 10 – SAS Graphs
Module 11 – Interactive Data Processing
Module 12 – Data Transformation Function
Module 13 – Output Delivery System (ODS)
Module 14 – SAS Macros
Module 15 – PROC SQL
Module 16 – Advanced Base SAS
Module 17 – Summarization Reports

24 Hours 23 Module

Module 01 – Entering Data
Module 02 – Referencing in Formulas
Module 03 – Name Range
Module 04 – Understanding Logical Functions
Module 05 – Getting started with Conditional Formatting
Module 06 – Advanced-level Validation
Module 07 – Important Formulas in Excel
Module 08 – Working with Dynamic table
Module 09 – Data Sorting
Module 10 – Data Filtering
Module 11 – Chart Creation
Module 12 – Various Techniques of Charting
Module 13 – Pivot Tables in Excel
Module 14 – Ensuring Data and File Security
Module 15 – Getting started with VBA Macros
Module 16 – Ranges and Worksheet in VBA
Module 17 – IF condition
Module 18 – Loops in VBA
Module 19 – Debugging in VBA
Module 20 – Dashboard Visualization
Module 21 – Principles of Charting
Module 22 – Getting started with Pivot Tables
Module 23 – Statistics with Excel

24 Hours 9 Module

Module 01 – Introduction to NoSQL and MongoDB
Module 02 – MongoDB Installation
Module 03 – Importance of NoSQL
Module 04 – CRUD Operations
Module 05 – Data Modeling and Schema Design
Module 06 – Data Management and Administration
Module 07 – Data Indexing and Aggregation
Module 08 – MongoDB Security
Module 09 – Working with Unstructured Data

16 Hours 13 Module

Module 01 – Introduction to SQL
Module 02 – Database Normalization and Entity Relationship Model
Module 03 – SQL Operators
Module 04 – Working with SQL: Join, Tables, and Variables
Module 05 – Deep Dive into SQL Functions
Module 06 – Working with Subqueries
Module 07 – SQL Views, Functions, and Stored Procedures
Module 08 – Deep Dive into User-defined Functions
Module 09 – SQL Optimization and Performance
Module 10 – Advanced Topics
Module 11 – Managing Database Concurrency
Module 12 – Programming Databases Using Transact-SQL
Module 13 – Microsoft Courses: Study Material

Project work

The Master's in Data Science Program Online is an online course with industry recognized certification.

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