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This course provides an understanding of the application of analytics for Fraud detection. It includes a case study on fraud detection using logistic regression analysis in the language of SAS.
By the end of this course you will learn about the various types of fraud. You will learn what information to use from the available data to detect different kinds of fraud.
Participants should be familiar with the language of SAS. You should be able to import and manipulate data in the language of SAS. An understanding of the regression approach to predictive modeling is necessary.
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3 Months, access to over 1.5 hours of lectures
5 Hours of virtual lab
Rs. 3,000/-, 5+ lab hours: Rs.4, 000/-
Part time/ Full Time:
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Gaurav has over 10 years of experience in the field of analytics and has worked across multiple verticals including financial services, retail, FMCG, telecom, pharmaceuticals and leisure industries.
Sarita has over 10 years of extensive analytics and consulting experience across diverse domains including retail, health-care and financial services.
An engineering graduate, Jigsaw’s User Experience and Product Management lead, is also an MBA gold medallist from the Sri SathyaSai Institute of Higher Learning.
Sushma has over 9 years of experience in the media, with stints in film, commercial content development, television and event management in companies like Percept, MTV and FILMY.
Sanchita, a graduate of T.A. Pai Management Institute, Manipal, is a passionate writer and blogger and refers to herself as a global nomad!
Ishita has an MBA from the ICFAI Business School, Hyderabad and an additional Post Graduate Certificate in Human Resource Management from XLRI, Jamshedpur.
Pooja is a publicist with over 6 years of experience in connecting brands with their TG, storytelling fervor, managing events, sales and business development.
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[su_tab title = “AV Review”]
This is again a niche offering from Jigsaw for people who are already fraud analysts or want to become one. If you are one of them, this short course might help you come up the curve fast.