Department of Applied Economics

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Regular version of the site

Events

Apr 25 – Apr 26

7th Conference "Applied Econometrics"

All events

Publications

Book

Academic Star Wars: Excellence Initiatives in Global Perspective
In press

Yudkevich Maria, Altbach P. G., Salmi J.

Cambridge: MIT Press, 2023.

Article

The Impact of Carbon Tax and Research Subsidies on Economic Growth in Japan

Besstremyannaya G., Dasher R., Golovan S.

HSE Economic Journal. 2025. Vol. 29. No. 1. P. 72-102.

Book chapter

Science or industry: Improving the quality of the Russian higher education system

Panova A., Slepyh V.

In bk.: Vocation, Technology & Education. Vol. 1. Iss. 4. Shenzhen Polytechnic University, 2024.

Working paper

Living Standards in the USSR during the Interwar Period

Voskoboynikov I.

Economics/EC. WP BRP. Высшая школа экономики, 2023. No. 264.

All publications

109028, Moscow
Pokrovsky blvd. 11,
Room S-527
Phone: (495) 772-95-99 ext.27502, 27503, 27498

Administration

Department Head – Svetlana B. Avdasheva

Deputy Department Head – Liudmila S. Zasimova

Manager – Maxim Shevelev

Faculty of Economic Sciences

Department of Applied Economics

Delivered by:: Department of Applied Economics

Type:: Elective course

When:: 1 year, 4 module

Instructor

Polyakov, Konstantin L.

Full Syllabus Ask Question

Abstract

In this course, you will learn how to analyze data in Python using multi-dimensional arrays in numpy, manipulate DataFrames in pandas, use SciPy library of mathematical routines, and perform machine learning using scikit-learn!

Learning Objectives

Learn how to analyze data using Python
This course will take you from the basics of Python to exploring many different types of data.

Expected Learning Outcomes

learn how to import data sets
learn how to clean and prepare data for analysis
learn how to manipulate pandas DataFrame
learn how to summarize data
learn how to build machine learning models using scikit-learn
learn how to build data pipelines
learn how to data Analysis with Python is delivered through lecture, hands-on labs, and assignment

Course Contents

Module 1 - Importing Datasets
Learning Objectives Understanding the Domain Understanding the Dataset Python package for data science Importing and Exporting Data in Python Basic Insights from Datasets
Module 2 - Cleaning and Preparing the Data
Identify and Handle Missing Values Data Formatting Data Normalization Sets Binning Indicator variables
Module 3 - Summarizing the Data Frame
Descriptive Statistics Basic of Grouping ANOVA Correlation More on Correlation
Module 4 - Model Development
Simple and Multiple Linear Regression Model Evaluation Using Visualization Polynomial Regression and Pipelines R-squared and MSE for In-Sample Evaluation Prediction and Decision Making
Module 5 - Model Evaluation
Model Evaluation Over-fitting, Under-fitting and Model Selection Ridge Regression Grid Search Model Refinement

Assessment Elements

All Review Questions
The Final Exam
Final scoring based on a cumulative grade from online course ( "Analyzing Data with Python" from edX)

Interim Assessment

Interim assessment (4 module)
0.5 * All Review Questions + 0.5 * The Final Exam

Bibliography

Recommended Core Bibliography

Romano, F. (2015). Learning Python. Birmingham: Packt Publishing. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=nlebk&AN=1133614

Recommended Additional Bibliography

Программирование на PYTHON. Т. 1: ., Лутц, М., 2013
Программирование на PYTHON. Т. 2: ., Лутц, М., 2013

Data Analysis in Python