Master's in Data Science Program Online

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

Tools covered

Python for Data Science

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

Tools covered

Machine Learning

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)

Tools covered

AI & Deep Learning

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

Tools covered

Big Data Hadoop and Spark

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

Tools covered

Tableau Desktop 10

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

Tools covered

Data Science with SAS

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

Tools covered

Advanced Excel

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

Tools covered

MongoDB

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

Tools covered

MS-SQL

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

Tools covered

Free Career Counselling

We are happy to help you 24/7

Project Work

Projects will be a part of your Data Science Architect Master’s program to consolidate your learning. It will ensure that you have real-world experience in Data Science.

Practice 100+ Essential Tools

Get Real-world Experience

Working with NumPy

Get hands-on learning to successfully work with the NumPy library to solve various Python problems. Also, the project requires you to efficiently create 2D arrays and perform simple arithmetic operations on the two arrays.

Visualizing and Analyzing the Customer Churn dataset using Python

Analyze data by building aesthetic graphs to make better sense of it. Also, work with the bar plots and their applications which also includes histogram graphs for data analysis, and box plots and outliers in them.

Deal with Financial Data

The project is included to help learners with hands-on experience in financial data analysis. Analyze global sales numbers and profit data by developing an interactive map, and use map styles and layers for enhanced visualization.

Work with Agricultural Data

As a part of the project, learners will have to display district-wise data to successfully build an interactive treemap with state labels. Also, the districts must be displayed on hovering over the respective states.

Analyzing the Naming Trends Using Python

This project involves the analysis of naming trends using Python. Also, use the Python programming language to understand the applications of data manipulation, extract files with data, and concepts of data visualization.

Performing Analysis on Customer Churn Dataset

As an important part of the project, learners will be required to analyze employment reliability in the Telecom industry, and further work on real-time analysis of data with multiple labels, data visualization for reliability factor.

Movie Recommendation

This movie recommendation project will allow you to use Apache Spark MLlib component and statistical analysis to successfully create collaborative filtering, and also regression, clustering, and dimensionality reductions.

Web Scraping

The project gives a practical exposure to the applications of Python in Web Scraping. Also get a chance to work on various Web Scraping libraries, Beautiful Soup, Navigable String, parser, searching tree deployment, and more.

Twitter Sentiment Analysis

The project lets you use and successfully apply Twitter sentiment analysis to find the reaction of people concerning the demonetization move by India by analyzing their tweets. Also, download the tweets and load them into Pig storage.

Market Basket Analysis

The project requires learners to identify trends in the company’s inventory dataset to increase sales numbers. Moreover, they also need to implement data extraction, data manipulation, etc. for Market Basket Analysis.

Credit Card Fraud Detection

The project lets you perform data analysis on a banking dataset for several parameters. Also, use V4 predictor analysis, v7 predictor analysis, and data visualization techniques to calculate the probability of fraud activities.

Prediction of Loan Approval

The project involves using banking datasets to analyze, clean, process, and visualize the data. Once the data analysis is completed, the project also lets you learn to implement Naive Bayes and Principal Component Analysis.

Analyzing the Trends of COVID-19 With Python

Use the Pandas module to collect data from various files. Also, use Plotly to build interactive visualizations. The project also requires you to use the Prophet library from Facebook to efficiently develop time-series models.

Analyzing IPL T20 Cricket

This interesting project has been included to help you analyze an entire IPL T20 cricket match and get some details of the match. As a next step, load the IPL dataset into HDFS and analyze the data using Apache Pig or Hive.

Career Transition

Ankit Kumar

Data Scientist | Delhi

B.Com Graduate to Data Scientist

After working in non-tech, I wanted to shift to Data Science but it seemed difficult. Thanks to Intellipaat’s rich training, I am a successful Data Scientist. Post the training, I believe Data Science is for non-tech too.

B.com Graduate

Data Scientist

Melwin Rodrigues

Data Scientist | India

Non Tech to Data Scientist

Becoming a Data Scientist from a Customer Service Agent has been possible only due to Intellipaat and the expert guidance by its trainers. Even after working for 10 years in customer care, I am a Data scientist today.

Customer Service Agent

Data Scientist

Shehzin Mulla

Digital Marketing Analyst | India

Salary Hike

Post the training, I was able to shift from a Data Steward Specialist to an Analyst with a 35% salary hike. I gained a deep understanding of technical skills, especially in analytics. I can’t thank you enough, Intellipaat.

Data Steward Specialist

Digital Marketing Analyst

Gaurav Saboo

Senior Technical Associate | India

Salary Hike

I got promoted with a 16% hike after completing this course from Intellipaat. The training taught me new skills and how to implement the same in my current role. Today, I am a Sr. Technical Associate. Thanks, Intellipaat.

Technical Analyst

Senior Technical Associate

D Rajesh Sahoo

Machine Learning Engineer | India

Civil Engg Graduate to Machine Learning Engg.

In search of a better paying and growth-oriented job, I decided to make the transition to the IT industry. Intellipaat made the transition smooth with its round the clock support team, excellent curriculum, and efficient trainers.

B.Tech - Civil Engineering

Machine Learning Engineer

Adarsh Vijay

Data Analyst | India