Comprehensive course analysis
Who should attend
The programme will be targeted towards participants and organizations that are looking to build expertise in data science and intend to utilize machine learning tools for their data analysis needs. The cohort will be a mix of junior and middle level participants with at least a bachelors degree. The bachelors or higher degree should have provided sufficient exposure to the participants on mathematics and computing.
About the course
This programme is a mix of data analytics and optimization tools, and primarily covers the recent developments in the area of machine learning. It will have a strong focus on applications and working of the algorithms so that the participants get a thorough insight in the field of data science. The programme takes a practice-based approach for teaching concepts and tools that are needed for making data driven decisions in business. The participants in this course are expected to have a strong quantitative background to be able to finish the course successfully.
The objective of the course is to introduce various methods from the domains of machine learning and optimization that will be useful to make business decisions when faced with large amount of data. The objectives of the course are as follows:
- Train the participants on handling both small and large amount of data and perform tasks such as classification and predictive modeling. The training will be useful in automating business operations decisions with the use of data.
- Train the participants on using important data analysis and optimization libraries that are available off-the-shelf.
- Give an insight to the participants on how data-driven ideas are being used to develop artificial intelligence technologies to enhance human potential and solve challenging problems using machines.
Following are the concepts that will be covered during the programme.
- Introduction to Python
- Classification Techniques
- Clustering Techniques
- Dimensionality Reduction for Large Datasets
- Neural Networks
- Deep Learning
- Text Mining
- Applications of Analytics in Business
Prior preparation: A three-hour Python video tutorial customized for the participants will be shared before the start of the programme.
Applications: Following are the products that the participants will be trained on building during this programme:
- A platform that assesses the credit worthiness of banking customers and automatically makes loan decisions.
- A recommendation engine that identifies customer characteristics based on their past purchases and makes recommendations
- Location clustering for routing of vehicles to efficiently provide service to customers in diverse localities
- Sentiment analysis engine to make buy, sell and hold decisions for stocks based on the business news headlines
- An object detection model capable of identifying objects and persons in images and videos.
The codes/models/datasets for all the above products will be shared with the participants during the programme.
Tathagata Bandyopadhyay joined IIM Ahmedabad as a faculty member in the Production and Quantitative Methods Area in 2005. Prior to joining IIMA, he taught at the Department of Statistics, University of Calcutta, India for around two decades. At IIMA, he has been teaching quantitative techniques ...
Educational Qualifications Fellow (Operations Research and Systems Analysis), IIM Calcutta, India. B.Tech. (Mechanical Engineering), IIT Kharagpur, India. Academic Affiliation 2001 - Present: Production and Quantitative Methods Area, IIM Ahmedabad 2000 - 2001: Department of Econometrics and Op...
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