COURSE UNIT TITLE

: DATA SCIENCE FOR ECONOMISTS

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
IKT 5129 DATA SCIENCE FOR ECONOMISTS ELECTIVE 3 0 0 6

Offered By

Economics

Level of Course Unit

Second Cycle Programmes (Master's Degree)

Course Coordinator

ASSOCIATE PROFESSOR FIRAT GÜNDEM

Offered to

Economics

Course Objective

This course will be an introduction to machine learning techniques and how to use them to help solve economic and social problems. This course is designed for economics and social science students who are interested in learning modern, scalable, computational data analysis methods (include machine learning, data science, big data, AI), and apply them to social and policy problems. This course will teach students:
1) What role Machine Learning can play in designing, implementing, evaluating, and improving Social Policy.
2) How Machine Learning methods work, how to use them, and how to building machine learning pipelines/systems.
3) How to tackle economic problems using Machine Learning methods and tools
This is a hands-on course where students will be expected to use RStudio to implement solutions to various policy problems. Prior experience with RStudio is better but not necessary. We will cover supervised and unsupervised learning algorithms and will learn how to use them for social problems in areas such as sustainability, economic development, and growth.

Learning Outcomes of the Course Unit

1   Understand Data Science Project Lifecycle
2   Create Data Products for Economic Applications
3   Get the basics of Unsupervised Machine Learning Techniques
4   Understand the basics of Supervised Regression Techniques
5   Understand the basics of Supervised Classification Techniques
6   Get introduced to Big Data Architecture

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Introduction to Data Science
2 Introduction to R
3 Online Data Sources, Data Gathering, and Data Min
4 Data Wrangling and Data Visualization
5 Linear Regression
6 Data Classification and Regulation
7 Supervised Data Clustering 1
8 Midterm
9 Midterm
10 Supervised Data Clustering 2
11 Unsupervised Data Clustering
12 Machine Learning 1
13 Machine Learning 2
14 Proposal Presentations

Recomended or Required Reading

James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning (Vol. 112, p. 18). New York: springer.

Taddy, M. (2019). Business data science: Combining machine learning and economics to optimize, automate, and accelerate business decisions. McGraw Hill Professional.

Dhar, V. (2013). Data science and prediction. Communications of the ACM, 56(12), 64-73.

Provost, F., & Fawcett, T. (2013). Data science and its relationship to big data and data-driven decision making. Big data, 1(1), 51-59.

Athey, S., & Imbens, G. W. (2019). Machine learning methods that economists should know about. Annual Review of Economics, 11, 685-725.

Yu-Wei, C. D. C. (2015). Machine learning with R cookbook. Packt Publishing Ltd.

Xie, Y., Dervieux, C., & Riederer, E. (2020). R markdown cookbook. Chapman and Hall/CRC.

Planned Learning Activities and Teaching Methods

There will be theoretical and applied parts of the class. Computer labs will be used in order to improve data skills via open-source programs such as R.

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA
1 MTE MIDTERM EXAM
2 STT TERM WORK (SEMESTER)
3 FIN FINAL EXAM
4 FCG FINAL COURSE GRADE MTE * 0.20 + STT* 0.40 + FIN* 0.40
5 RST RESIT
6 FCGR FINAL COURSE GRADE (RESIT) MTE * 0.20 + STT * 0.40 + RST* 0.40


Further Notes About Assessment Methods

None

Assessment Criteria

To be announced.

Language of Instruction

Turkish

Course Policies and Rules

To be announced.

Contact Details for the Lecturer(s)

To be announced.

Office Hours

Will be determined during the semester.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 14 3 42
Tutorials 8 3 24
Preparations before/after weekly lectures 14 2 28
Preparation for midterm exam 1 10 10
Preparing assignments 1 15 15
Preparation for final exam 1 30 30
Midterm 1 2 2
Final 1 2 2
TOTAL WORKLOAD (hours) 153

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9
LO.111
LO.2111
LO.311111
LO.411
LO.51111
LO.61111