TCS iON Industry Honour Certification - Analytics and ML in Fintech

Offered By in collaboration with

Course Details

Credits & LTP
Each course offers 5 credit points
Lecture
3
Tutorial
1
Practical (Hands-on)
1
Credit
5
Recommended For:
Graduation Programme - B.E./B.Tech
Branch - Electronics & Communication Engineering, Computer Science & Information Technology, Electrical Engineering, Mechanical (all Science and Technology/Eng. streams/Mathematics, Physics, Statistics)
Semester -
5 th,  6 th,  7 th  and  8 th

Course Summary

Analytics and ML in Fintech is an algorithmic technology that has the potential to explore and learn from data to enhance or automate decision-making in financial services or processes. With the ability to handle large data sets and resources, Machine Learning (ML) has advanced from being an outlier to becoming the very centre of the technology boom in the finance industry. Machine learning algorithms in the financial services industry can forecast market risks, reduce frauds, and recognise opportunities for the future.

The global Artificial Intelligence (AI) in Fintech market was valued at US$ 6.67 billion in 2019 and is expected to reach US$ 22.6 billion by 2025 with the demand projected to grow at a Compound Annual Growth Rate (CAGR) of 23.37% over the forecast period 2020-2025. The Fintech market has undergone constant change in the investment management sector. By adopting advanced technology and applications, including the use of Big Data, AI and ML companies are able to identify investment opportunities, optimise their investment strategies, and reduce the associated risks.

The Analytics and ML in Fintech course focusses on Statistics, Python Data handling, Machine Learning techniques and model development processes. It emphasises on the use of analytics and machine learning in the Fintech space. The course is structured in accordance with the existing state of the art and industry requirement. It introduces the students to relevant data science techniques and their application.

Hands-On

Python-based analytics and machine learning libraries/packages are used. The hands-on environment is integrated with the learning environment.

  • Students/Institutions need to ensure the availability of the following for industry assignment as well as for Part B of the summative assessment:
    • a. Open source tools like Python programming
  • The above mentioned hands-on configuration will be leveraged by the students to complete the industry assignment as well as to complete Part B of the summative assessment.
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RECOMMENDED PRIOR KNOWLEDGE
A strong understanding of:
  1. Basic to advanced Statistics
  2. Programming skills
  3. Probability
  4. Basic Mathematics
  5. Algorithms in Machine Learning and
  6. Recent developments in the financial world

Course Syllabus

The course syllabus will be delivered through a combination of eLearning resources, digital lectures, community based digital classrooms as applicable.
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Course Components

Digital Learning
Resources
Digital course accessible anytime and anywhere View Sample
Digital
Lectures
Digital lectures delivered by industry and academic experts View Sample
Academic Connect
Community
Focussed on building conceptual clarity View Sample
Industry Connect
Community
Focussed on building industry oriented applied knowledge View Sample
Industry
Assignment
Access to two industry related mini projects View Sample
Practice
Assessment
Access to two practice assessments for self-evaluation View Sample
Summative
Assessment
Test of theoretical and applied knowledge View Sample
Verifiable Digital
Certificate
Verifiable certificate on successful completion View Sample
Internship
Opportunity
For top percentile, subject to vacancies in corporates and their hiring policies View Sample
Job
Opportunity
Visibility to job vacancies with leading corporate recruiters, subject to vacancies and their hiring policies View Sample

Testimonials

!~Testimonials~!