Big Data and Data Science Master's Course

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

Apache Spark and Scala

24 Hours 23 Module

Scala Course Content

Module 01 – Introduction to Scala
Module 02 – Pattern Matching
Module 03 – Executing the Scala Code
Module 04 – Classes Concept in Scala
Module 05 – Case Classes and Pattern Matching
Module 06 – Concepts of Traits with Example
Module 07 – Scala–Java Interoperability
Module 08 – Scala Collections
Module 09 – Mutable Collections Vs. Immutable Collections
Module 10 – Use Case Bobsrockets Package

Spark Course Content

Module 11 – Introduction to Spark
Module 12 – Spark Basics
Module 13 – Working with RDDs in Spark
Module 14 – Aggregating Data with Pair RDDs
Module 15 – Writing and Deploying Spark Applications
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

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

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

Splunk Developer and Admin

26 Hours 39 Module

Module 1 – Splunk Development Concepts
Module 2 – Basic Searching
Module 3 – Using Fields in Searches
Module 4 – Saving and Scheduling Searches
Module 5 – Creating Alerts
Module 6 – Scheduled Reports
Module 7 – Tags and Event Types
Module 8 – Creating and Using Macros
Module 9 – Workflow
Module 10 – Splunk Search Commands
Module 11 – Transforming Commands
Module 12 – Reporting Commands
Module 13 – Mapping and Single Value Commands
Module 14 – Splunk Reports and Visualizations
Module 15 – Analyzing, Calculating and Formatting Results
Module 16 – Correlating Events
Module 17 – Enriching Data with Lookups
Module 18 – Creating Reports and Dashboards
Module 19 – Getting Started with Parsing
Module 20 – Using Pivot
Module 21 – Common Information Model (CIM) Add-On

Splunk Administration Topics

Module 22 – Overview of Splunk
Module 23 – Splunk Installation
Module 24 – Splunk Installation in Linux
Module 25 – Distributed Management Console
Module 26 – Introduction to Splunk App
Module 27 – Splunk Indexes and Users
Module 28 – Splunk Configuration Files
Module 29 – Splunk Deployment Management
Module 30 – Splunk Indexes
Module 31 – User Roles and Authentication
Module 32 – Splunk Administration Environment
Module 33 – Basic Production Environment
Module 34 – Splunk Search Engine
Module 35 – Various Splunk Input Methods
Module 36 – Splunk User and Index Management
Module 37 – Machine Data Parsing
Module 38 – Search Scaling and Monitoring
Module 39 – Splunk Cluster Implementation

Tools covered

Artificial Intelligence & Deep Learning Course with Tensorflow

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

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

Microsoft Azure Training

30 Hours 10 Module

Module 01 – Introduction to Microsoft Azure
Module 02 – Introduction to ARM & Azure Storage
Module 03 – Introduction to Azure storage
Module 04 – Azure Virtual Machines
Module 05 – Azure App and Container services
Module 06 – Azure Networking – I
Module 07 – Azure Networking – II
Module 08 – Authentication and Authorization in Azure using RBAC
Module 09 – Microsoft Azure Active Directory
Module 10 – Azure Monitoring

Tools covered

AWS

36 Hours 14 Module

Module 01 – Introduction to Cloud Computing & AWS
Module 02 – Elastic Compute and Storage Volumes
Module 03 – Load Balancing, Autoscaling and DNS
Module 04 – Virtual Private Cloud
Module 05 – Storage – Simple Storage Service (S3)
Module 06 – Databases and In-Memory DataStores
Module 07 – Management and Application Services
Module 08 – Access Management and Monitoring Services
Module 09 – Automation and Configuration management
Module 10 – AWS Migration

Self Paced

Module 11 – Architecting AWS – whitepaper
Module 12 – DevOps on AWS
Module 13 – Amazon FSx and Global Accelerator
Module 14 – AWS Architect Interview Questions

Tools covered

Data Science with R

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

Tools covered

Apache HBase

12 Hours 8 Module

Module 01 – HBase Overview
Module 02 – Architecture of NoSQL
Module 03 – HBase Data Modeling
Module 04 – HBase Cluster Components
Module 05 – HBase API and Advanced Operations
Module 06 – Integration of Hive with HBase
Module 07 – File Loading with Both Load Utilities

Tools covered

Apache Cassandra

16 Hours 12 Module

Module 01 – Advantages and Usage of Cassandra
Module 02 – CAP Theorem and No SQL DataBase
Module 03 – Cassandra fundamentals, Data model, Installation and setup
Module 04 – Cassandra Configuration
Module 05 – Summarization, node tool commands, cluster, Indexes, Cassandra & MapReduce, Installing Ops-center
Module 06 – Multi Cluster setup
Module 07 – Thrift/Avro/Json/Hector Client
Module 08 – Datastax installation part,· Secondary index
Module 09 – Advance Modelling
Module 10 – Deploying the IDE for Cassandra applications
Module 11 – Cassandra Administration
Module 12 – Cassandra API and Summarization and Thrift

Tools covered

Couchbase

5 Module

Module 01 – Introduction to Couchbase
Module 02 – Single-node Implementation
Module 03 – Couchbase Web Console
Module 04 – Couchbase Multi-node Cluster
Module 05 – Couchbase Command-line Interface

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

Solr

10 Hours 15 Module

Module 01 – Fundamentals of Search Engine and Apache Lucene
Module 02 – Analyzers in Lucene
Module 03 – Exploring Apache Lucene
Module 04 – Apache Lucene Demonstration
Module 05 – Apache Lucene advanced
Module 06 – Advance topics of Apache Lucene (practical)
Module 07 – Apache Solr
Module 08 – Apache Solr Indexing
Module 09 – Solr Indexing continued
Module 10 – Apache Solr Searching
Module 11 – Deep dive into Apache Solr
Module 12 – Apache Solr continued
Module 13 – Extended Features
Module 14 – Multicore
Module 15 – Administration & SolrCloud

Tools covered

Linux

16 Hours 10 Module

Module 01 – Introduction to Linux
Module 02 – File Management
Module 03 – Files and Processes
Module 04 – Introduction to Shell Scripting
Module 05 – Conditional, Looping statements and Functions
Module 06 – Text Processing
Module 07 – Scheduling Tasks
Module 08 – Advanced Shell Scripting
Module 09 – Database Connectivity
Module 10 – Linux Networking

Tools covered

Java

16 Hours 17 Module

Module 01 – Core Java Concepts
Module 02 – Writing Java Programs using Java Principles
Module 03 – Language Conceptuals
Module 04 – Operating with Java Statements
Module 05 – Concept of Objects and Classes
Module 06 – Introduction to Core Classes
Module 07 – Inheritance in Java
Module 08 – Exception Handling in Detail
Module 09 – Getting started with Interfaces and Abstract Classes
Module 10 – Overview of Nested Classes
Module 11 – Getting started with Java Threads
Module 12 – Overview of Java Collections
Module 13 – Understanding JDBC
Module 14 – Java Generics
Module 15 – Input/Output in Java
Module 16 – Getting started with Java Annotations
Module 17 – Reflection and its Usage

Tools covered

Apache Kafka

12 Hours 6 Module

Module 01 – What is Kafka – An Introduction
Module 02 – Multi Broker Kafka Implementation
Module 03 – Multi Node Cluster Setup
Module 04 – Integrate Flume with Kafka
Module 05 – Kafka API
Module 06 – Producers & Consumers

Tools covered

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 Big Data and Data Science Master’s program to consolidate your learning. It will ensure that you have real-world experience in Big Data and 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 include 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.

Work with the Customer Data of the UK Bank

This real-life based project has been included to let learners analyze and create a dashboard to gain insight from the UK bank’s customer data. Build an asymmetric drop-down consisting of the region and the corresponding user details.

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.

Hadoop Web Log Analytics

The Hadoop Web Log Analytics project requires you to successfully derive insights from web log data. Also, aggregate log data and implement Apache Flume for data transportation, and process data to generate analytics.

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.

Career Transition

Yogesh Kumar

Senior Software Engineer | Gurgaon

Consultant to Tech Profile

This program helped me gain the right skills to make a career switch from a consultant to a Senior Software Engineer. The knowledge of Hadoop and the right tools was the main reason for my transition.

Associate Consultant

Senior Software Engineer

Gaurav Saboo

Senior Technical Associate | India

Salary Hike

I got promoted to Sr. Technical Associate with a 60% hike after completing this course from Intellipaat. The training taught me new skills and how to implement the same in my current role.

Technical Analyst

Senior Technical Associate

Ankit Kumar

Data Scientist | Delhi

B.Com Graduate to Data Scientist

Intellipaat has given me the confidence that anyone who aspires can become a Data Scientist with its rich course and expert guidance. I managed to switch from a non-tech education background to a Data Scientist.

B.com Graduate

Data Scientist

Melwin Rodrigues

Data Scientist | India

Non-Tech to Data Scientist

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

Customer Service Agent

Data Scientist

Shehzin Mulla

Marketing Data Analyst | India

Salary Hike

Thanks to Intellipaat, I was able to shift from a Data Analyst to a Marketing Data Analyst with a 35% salary hike and gained a deep understanding of Analytics.

Data Analyst

Marketing Data Analyst

Nishchay Agrawal

Data Engineer | Bengaluru

Got 30 LPA Package | Fresher to Data Engineer

Intellipaat helped me to acquire a solid job in the third year of BTech. I received seven job offers, with 30 LPA as the highest CTC. Thanks to Intellipaat for making my career successful.

Fresher

Data Engineer

D Rajesh Sahoo

Machine Learning Engineer | India