Get updates on Eligibility, Admission, Placements Fees Structure
Quick Facts
| Medium Of Instructions | Mode Of Learning | Mode Of Delivery | Frequency Of Classes |
|---|---|---|---|
| English | Self Study, Virtual Classroom, Campus Based/Physical Classroom | Video and Text Based | Weekends |
Course Overview
The Big Data Analytics certification programme is offered by Intellipaat in collaboration with E&ICT, IIT, Guwahati, and strives to provide comprehensive training in Big Data Analytics concepts such as Spark, Python, Hadoop, Data Warehousing, MongoDB, and more.
The Big Data Analytics programme provides a complete experience to learners when it comes to understanding the concepts, mastering them, and applying them in real life. Moreover, the course also features top subject matter experts from IBM who will share their analytics expertise with you.
With the Big Data Analytics online course, you get access to over 231 hours of instructor-led training and 182 hours of self-paced video lectures. Dedicated career assistance is also offered to help you land your dream job in the analytics domain.
The Big Data Analytics online training also features over a whopping 300 hours of exercises and projects, all of which are designed to help you gain practical expertise and help you apply the analytical concepts learned in real-life scenarios.
The Highlights
- Certification by E&ICT, IIT GUWAHATI
- 300 Hours of Project & Exercises
- 231 hours of Instructor-Led Training
- One-on-one mentor support
- Executive alumni status
- Job assistance
- 182 Hours of Self-paced Videos
Programme Offerings
- Access to IBM Watson
- job assistance
- 24x7 support
- 1:1 mentor support
- Executive alumni status
- 300 Hours of Project & Exercises
- 182 Hours of Self-paced Videos
- 231 Hours of Instructor-Led Training
Courses and Certificate Fees
| Certificate Availability | Certificate Providing Authority |
|---|---|
| yes | IIT Guwahati (IITG) |
Eligibility Criteria
You'll get a certificate from E&ICT Academy IIT, Guwahati, after completing all the Big Data Analytics programme requirements.
What you will learn
By the end of the Big Data Analytics syllabus, you will be proficient in analytical tools like:
- MS Excel
- Python
- PySpark
- SQL
- Java
- Linux
- Apache Spark
- Statistics for Data Analytics
- Data Visualisation
- Scala
- Business intelligence
- Real-time streaming
Who it is for
The Big Data Analytics certification course is ideal for:
- Anyone having a bachelor’s degree in Big Data Analytics
- Anyone having degrees in computer science, statistics, maths, or any similar field
- Product managers/ Project managers looking to up-skill
- Software engineers and analysts with a bachelor’s degree
- Professionals looking to acquire skills in data analytics and data science
Admission Details
Step 1: Visit Intellipaat’s Big Data Analytics online course page.
Step 2: Click ‘Apply Now’ on the page’s top right.
Step 3: A small ‘Contact Us’ will pop up. Enter all the details prompted on the screen.
Step 4: After you have spoken to Intellipaat’s programme advisors, you’ll be required to fill and submit an application form.
Step 5: An admission panel will shortlist your application based on the application and interview.
Step 6: If you’re selected, you’ll be notified within 1-2 weeks.
Application Details
To begin the enrolment process for the Big Data Analytics online programme, you have to fill a ‘Contact Us’ application form. The form requires you to enter your full name, email ID, mobile number, and training needs.
The Syllabus
Python
- Introduction to Python and IDEs
- Python Basics
- Object Oriented Programming
- Hands-on Sessions And Assignments for Practice
- Java programming for MapReduce
- SQL fundamentals
- Linux fundamentals
- Introduction to Python
- Python basic constructs
- OOPs in Python
- NumPy for mathematical computing
- SciPy for scientific computing
- Data manipulation
- Data visualization with Matplotlib
- Implementing statistical algorithms using Python
- Hadoop installation and setup
- Introduction to Big Data and Hadoop
- Understanding HDFS and MapReduce
Deep dive into MapReduce
- Introduction to Hive
- Advanced Hive and Impala
- Introduction to Pig
- Flume and Sqoop
- Scala programming
- Spark framework
- RDD in Spark
- DataFrames and Spark SQL
- Machine Learning using Spark (MLlib)
- Introduction to PySpark
- Who uses PySpark?
- Why Python for Spark?
- Values, Types, Variables
- Operands and Expressions
- Conditional Statements
- Loops
- Numbers
- Python files I/O Functions
- Strings and associated operations
- Sets and associated operations
- Lists and associated operations
- Tuples and associated operations
- Dictionaries and associated operations
- Functions
- Lambda Functions
- Global Variables, its Scope, and Returning Values
- Standard Libraries
- Object-Oriented Concepts
- Modules Used in Python
- The Import Statements
- Module Search Path
- Package Installation Ways
- Introduction to Spark Streaming
- Features of Spark Streaming
- Spark Streaming Workflow
- StreamingContext Initializing
- Discretized Streams (DStreams)
- Input DStreams, Receivers
- Transformations on DStreams
- DStreams Output Operations
- Describe Windowed Operators and Why it is Useful
- Stateful Operators
- Vital Windowed Operators
- Twitter Sentiment Analysis
- Streaming using Netcat server
- WordCount program using Kafka-Spark Streaming
Hands-On
- Twitter Sentiment Analysis
- Streaming using Netcat server
- WordCount program using Kafka-Spark Streaming
- Spark-flume Integration
- Demonstrating Loops and Conditional Statements
- Tuple – related operations, properties, list, etc.
- List – operations, related properties
- Set – properties, associated operations
- Dictionary – operations, related properties
- Lambda – Features, Options, Syntax, Compared with the Functions
- Functions – Syntax, Return Values, Arguments, and Keyword Arguments
- Errors and Exceptions – Issue Types, Remediation
- Packages and Modules – Import Options, Modules, sys Path
- Spark Components & its Architecture
- Spark Deployment Modes
- Spark Web UI
- Introduction to PySpark Shell
- Submitting PySpark Job
- Writing your first PySpark Job Using Jupyter Notebook
- What is Spark RDDs?
- Stopgaps in existing computing methodologies
- How RDD solve the problem?
- What are the ways to create RDD in PySpark?
- RDD persistence and caching
- General operations: Transformation, Actions, and Functions
- Concept of Key-Value pair in RDDs
- Other pair, two pair RDDs
- RDD Lineage
- RDD Persistence
- WordCount Program Using RDD Concepts
- RDD Partitioning & How it Helps Achieve Parallelization
- Passing Functions to Spark
Hands-On
- Building and Running Spark Application
- Spark Application Web UI
- Loading data in RDDs
- Saving data through RDDs
- RDD Transformations
- RDD Actions and Functions
- RDD Partitions
- WordCount program using RDD’s in Python
- Introduction to Machine Learning- What, Why and Where?
- Use Case
- Types of Machine Learning Techniques
- Why use Machine Learning for Spark?
- Applications of Machine Learning (general)
- Applications of Machine Learning with Spark
- Introduction to MLlib
- Features of MLlib and MLlib Tools
- Various ML algorithms supported by MLlib
- Supervised Learning Algorithms
- Unsupervised Learning Algorithms
- ML workflow utilities
Hands-On
- K- Means Clustering
- Linear Regression
- Logistic Regression
- Decision Tree
- Random Forest
- Apache Flume and Apache Kafka
- Spark Streaming
- Case Study: Spark vs Kafka and when to use them
- Introduction to Big Data and Data Collection
- Introduction to Cloud Computing & AWS
- Elastic Compute and Storage Volumes
- Virtual Private Cloud
- Storage – Simple Storage Service (S3)
- Databases and In-Memory DataStores
- Data Storage
- Data Processing
- Data Analysis
- Data Visualization and Data Security
Power BI Basics
- Introduction to PowerBI, Use cases and BI Tools, Data Warehousing, Power BI components, Power BI Desktop, workflows and reports, and Data Extraction with Power BI.
- SaaS Connectors, Working with Azure SQL database, Python and R with Power BI
- Power Query Editor, Advance Editor, Query Dependency Editor, Data Transformations, Shaping and Combining Data, M Query, and Hierarchies in Power BI.
DAX
- Data Modeling and DAX, Time Intelligence Functions, DAX Advanced Features
Data Visualization with Analytics
- Slicers, filters, Drill Down Reports
- Power BI Query, Q & A and Data Insights
- Power BI Settings, Administration and Direct Connectivity
- Embedded Power BI API and Power BI Mobile
- Power BI Advance and Power BI Premium
Hands-on Exercise
- Creating a dashboard to depict actionable insights in sales data.
- Marketing, Web, and Social Media Analytics
- Fraud and Risk Analytics
- Supply Chain and Logistics Analytics
- HR Analytics
- Job Search Strategy
- Resume Building
- LinkedIn Profile Creation
- Interview Preparation Sessions by Industry Experts
- Mock Interviews
- Placement opportunities with 400+ hiring partners upon clearing the Placement Readiness Test.
Instructors
IIT Guwahati (IITG) Frequently Asked Questions (FAQ's)
If you miss a class, you will be provided with a recording of the lesson within the next 12 hours.
Intellipaat offers industry-grade projects to work on with the Big Data Analytics online training.
You need to attend live online classes and complete assignments and projects for nine months.
This course is entirely online.
You need to give at least 6 hours per week to complete this programme on time.