2021/2022
Data Science for Business
Type:
Mago-Lego
Delivered by:
School of Data Analysis and Artificial Intelligence
When:
4 module
Open to:
students of all HSE University campuses
Instructors:
Anvar Kurmukov,
Leonid E Zhukov
Language:
English
ECTS credits:
3
Contact hours:
40
Course Syllabus
Abstract
Data Science discipline formed recently in response to increasing use of data in business. It utilizes data mining, machine learning and statistical methods, but focuses on business applications, solving real world problems and delivering impact on business. This course is using a case-based approach to teaching, i.e. the students will be learning methods and techniques while solving business case problems. Each case will contain a description of a business problem and available data. The goal would be to convert a business problem into analytical and solve it using data with the help of variety of data mining and machine learning methods. The methods will be introduced as needed for each case solution. The course will be hands-on, during the lectures students will learn the approach and implement and solve the case in their home assignments.
Learning Objectives
- Providing students with essential knowledge of data mining methods and algorithms and experience in converting business problems into analytical and solving them.
Expected Learning Outcomes
- Students formulate and solve analytical problems for given business problem.
- Students know basic notation and terminology used in data science.
- Students understand basic principles behind analysis algorithm.
- Students visualize, summarize and analyze datasets.
Course Contents
- Introduction to Data Science for Business
- Dealing with data
- Data mining, machine learning, statistics.
- Case study 1. Customer segmentation
- Case study 2. Customer churn modeling
- Case study 3. Pricing
- Case study 4. Production optimization
- Case study 5. Sales territory design
- Dealing with big and fast data
- Impacting the business
Interim Assessment
- 2021/2022 4th module0.2 * Homework 3 + 0.2 * Homework 5 + 0.2 * Homework 4 + 0.2 * Homework 1 + 0.2 * Homework 2
Bibliography
Recommended Core Bibliography
- James, G. et al. An introduction to statistical learning. – Springer, 2013. – 426 pp.
- Provost, Foster, Fawcett, Tom. Data Science for Business: What you need to know about data mining and data-analytic thinking. – " O'Reilly Media, Inc.", 2013.
Recommended Additional Bibliography
- Siegel, E. Predictive analytics: The power to predict who will click, buy, lie, or die. – John Wiley & Sons, 2016. – 338 pp.