Coursebooks

Data science in practice

MGT-415

Lecturer(s) :

Bruffaerts Christopher

Language:

English

Remarque

Special schedule. See the MTE website: http://cdm.epfl.ch/mte/study-plan

Summary

The goal of the course is to introduce students to the main business areas where analytics is used in business. The course is based on use-cases from the financial industry and is meant to give a hands-on experience to students in various domains such as Marketing, Sales, HR, IT, or Compliance.

Content

The different chapters covered in the  scope of this course (may be subject to change):

• General overview/concepts of Analytics in Business
• Customer Analytics
• Wealth Management
• Web Analytics
• Compliance/Fraud Analytics
• Risk Analytics
• HR Analytics

 

Keywords

• Data Science
• Statistics 
• Data Analysis

 

Learning Prerequisites

Important concepts to start the course

• Basic Probability & Statistics
• Machine Learning concepts
• Knowledge of R and/or Python

Learning Outcomes

By the end of the course, the student must be able to:

• Develop a methodology tailored to the problem
• Assess / Evaluate the chosen methodology and approach
• Use programming skills for a given problem
• Identify the adequate analytical methodology to tackle a problem
• Present findings from the analysis
• Formulate a business problem in terms of an analytical one

Transversal skills

• Demonstrate a capacity for creativity.
• Use both general and domain specific IT resources and tools
• Access and evaluate appropriate sources of information.
• Assess progress against the plan, and adapt the plan as appropriate.
• Use a work methodology appropriate to the task.
• Communicate effectively with professionals from other disciplines.
• Demonstrate the capacity for critical thinking

Teaching methods

• First part of the course is dedicated to theoretical concepts, discussion of different use-cases
• Second part of the course consists in applying the knowledge to various problems and datasets using R or Python

Expected student activities

• Attendance and participation in lectures and exercise sessions
• Interactions during class

Assessment methods

• Problem sets accounting for 1/6 of the final grade
• Written exam accounting for 2.5/6 of the final grade
• Group project accounting for 2.5/6 of the final grade

Supervision

Office hours	No
Assistants	No
Forum	No

Resources

Virtual desktop infrastructure (VDI)

Yes

Bibliography

• The elements of Statistical Learning (Hastie, Tibshirani, Friedman)
• Pattern Recognition and Machine Learning (Bishop)
• Data Science from Scratch (Guru)
• Web Scraping with Python (Lawson)
• Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques: A Guide to Data Science for Fraud Detection (Baesens, Van Vlasselaer, Verbeke)
• Python Machine Learning (Raschka)
• Data Science for Business (Provost, Fawcett)

Ressources en bibliothèque

• Data Science for Business / Provost
• Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques: A Guide to Data Science for Fraud Detection / Baesens
• Python Machine Learning / Raschka
• Pattern recognition and machine learning / Bishop
• The elements of Statistical Learning / Hastie, Tibshirani, Friedman
• Data Science from Scratch / Grus
• Web Scraping with Python / Lawson