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Coursebooks
Data science in practice
MGT-415
Lecturer(s) :
Bruffaerts ChristopherLanguage:
English
Remarque
Special schedule. See the MTE website: http://cdm.epfl.ch/mte/study-planSummary
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
In the programs
- SemesterSpring
- Exam formWritten
- Credits
3 - Subject examined
Data science in practice - Lecture
2 Hour(s) per week x 14 weeks - Exercises
1 Hour(s) per week x 14 weeks
- Semester
- SemesterSpring
- Exam formWritten
- Credits
3 - Subject examined
Data science in practice - Lecture
2 Hour(s) per week x 14 weeks - Exercises
1 Hour(s) per week x 14 weeks
- Semester
- SemesterSpring
- Exam formWritten
- Credits
3 - Subject examined
Data science in practice - Lecture
2 Hour(s) per week x 14 weeks - Exercises
1 Hour(s) per week x 14 weeks
- Semester
- SemesterSpring
- Exam formWritten
- Credits
3 - Subject examined
Data science in practice - Lecture
2 Hour(s) per week x 14 weeks - Exercises
1 Hour(s) per week x 14 weeks
- Semester
- SemesterSpring
- Exam formWritten
- Credits
3 - Subject examined
Data science in practice - Lecture
2 Hour(s) per week x 14 weeks - Exercises
1 Hour(s) per week x 14 weeks
- Semester
Reference week
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Under construction
Lecture
Exercise, TP
Project, other
legend
- Autumn semester
- Winter sessions
- Spring semester
- Summer sessions
- Lecture in French
- Lecture in English
- Lecture in German