Certificate in Big Data Analytics for Business & Management - Batch05

Program Description and Objectives

Applications of Big Data transcend disciplines. Use of predictive analytics pervades diverse disciplines as oil and gas, marketing and sales, sports, molecular biology, drug-designing, waste management, finance and the list is very long.

How different Sectors/Industry use Big Data

  • Smart cities, for example, are the melting pot where variety of big data technologies mesh with one another to transform a city into a semi-intelligent being.
  • In Marketing and Sales, for example, Big Data is fast emerging as a potent tool to gain deeper insights into Customer behavior and thereby act as a strong driver in spurring innovation.
  • In manufacturing, operations managers are employing advanced analytics on historical process data to identify patterns and relationships among discrete process steps and inputs, and then optimize the factors that prove to have the greatest effect on yield.

Broadly the course has three parts: one the analytics part, second the technological part and third the contemporary Deep-Learning and Computer Vision. The analytics part is about learning machine learning algorithms and implementing them, the technological part is about learning to work in Hadoop and apache Kafka layered-system as also developing skills in NoSQL databases. Computer vision is an interdisciplinary field that deals with how computers can be made for gaining high-level understanding from digital images or videos. From the perspective of engineering, it seeks to automate tasks that the human visual system can do. At the end of this course, given a large dataset from any domain, a participant should:

  1. Be able to clean, transform and visualize the dataset to gain deeper insights and make it ready for analysis
  2. Be able to select a subset of appropriate machine learning algorithms that could be applied to get the desired predictive results
  3. Gain sufficient proficiency in tools necessary to implement algorithms
  4. Put to use relevant tools and techniques to get a reasonable predictive accuracy
  5. Apply the knowledge of image processing and image analysis to a wide array of disciplines such as health, process control, navigation and others.
  6. f. Should be able to himself install, setup and configure and experiment with a complete Hadoop and Kafka ecosystem.
  7. g. Should be able to install, configure and be sufficiently familiar with the variety of NoSQL databases and decide for himself which one to use, when and how.

(e) and (f) are important objectives as they instill a sense of confidence in students in handling and experimenting with open-source technologies.

This course is project oriented: All tools, data and platforms including Hadoop-ecosystem and Kafka-streaming technologies necessary for learning data-analytics are provided to the participants in advance. There is a heavy emphasis on open-source technologies universally used almost throughout the industry. Each participant, at the beginning of the course, receives a Virtual Machine (VM) fully equipped with all the software platforms, tools, packages and data to work on. Assembling such a VM independently and by himself is also an important part of our education; students are able to work at ease with open-source technologies that are central to Analytics. We make the whole process very simple and stress-free. Details of Virtual Machine are more fully described below.

Complete Program is project based. We have experience with several Industrial projects. Students execute these and other projects while implementing techniques learnt and as part of weekly exercises.

Who should attend

Specifically, the course will be useful to:

  • Executives: Ambitious Executives (from Private/Public sectors) looking forward to sharpening their skills in making sense of data in order to innovate and add more value to their organization and to society..
  • Academicians: Lecturers and Professors for extending the horizon of their knowledge through deepening their research skills.
  • Data Scientists/Developers: Techniques taught to them will have applications in a broad array of disciplines.


We strongly believe that a course in data analytics can only be practice-based rather than theory based. As it is a distance course but in real-time, the teaching pedagogy will be like this: First, the algorithm will be conceptually explained without getting into mathematics and then a project will be undertaken to implement the technique. Datasets for implementation will be made available in advance and so also a copy of code we need to execute. The code will be numbered and copiously commented so that long after the lecture has finished, students can go back through the code/comments and refresh their knowledge. During the lecture, we will execute this code, line-by-line and explain the steps. At his end, the student will also execute the same code. Consequently, results will be available at our end as also on Students Laptop/desktop.

Program Duration: 6 Months Approx

Course Schedule

Two Sessions of 2.5 hours per week on Sat-Sun

Class Timing

Saturday - 10:30 AM to 01:00 PM

Sunday - 10:30 AM to 01:00 PM


Based on Performance in Exercises & Projects

Program Delivery

The sessions will be delivered on TechMahindra Growth Factories interactive learning platform.

Course Material

Course material will be shared either through cloud or in hard copy with all the participants at the appropriate time.

Total Fees

Course Fees are payable by Student directly to Tech Mahindra Growth Factories Limited

Course Fee :

Rs. 65,500/- + GST ( Early Bird Discount of INR 5,000/- for limited time period. additional INR 5,000/- discount in case of lumpsum payment.)

Other Fee :

Rs. 5,500/- + GST : Book and Material(Mandatory Fees)

Collection of Fee:

Program Fees (Part A)
Installment Schedule Registration Fees Admission Fees 1st Installment
Installment Amount 10,500/- + GST 32,500/- + GST 22,500/-+ GST
Installment Date At the time of Enrolment Sep 19 Feb 20

Program Content

  • Introductory Business Statistics (18 Hours)
  • Data Mining & Data Analytics (133 Hours)
    • Machine Learning algorithms (51 Hours)
    • Hadoop and Kafka Eco System; Data stream processing and analysis (35 Hours)
    • NoSQL and Graph Databases (12 Hours)
    • Deep learning, AI & Computer vision (35 Hours)
  • Business Analytics Capstone (Python Oriented) (20 Hours)
  • Web Analytics (08 Hours)
  • Student Exercises/Projects

Detailed Course Content may please be seen here.
Download Brochure

Assessments: Based on Performance in Exercises & Projects

Tech Mahindra Growth Factories Ltd.

A - 20, Sector - 60, Noida - 201301


Mobile:+ 91 9975806184

Apply Now


Apply Now

Request Info

Feel free to ask