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
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:
(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:
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
Two Sessions of 2.5 hours per week on Sat-Sun
Saturday - 10:30 AM to 01:00 PM
Sunday - 10:30 AM to 01:00 PM
Based on Performance in Exercises & Projects
The sessions will be delivered on TechMahindra Growth Factories interactive learning platform.
Course material will be shared either through cloud or in hard copy with all the participants at the appropriate time.
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:
|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|
Assessments: Based on Performance in Exercises & Projects
Tech Mahindra Growth Factories Ltd.
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