Кто читает:: Институт медиа

Статус:: Курс по выбору

Когда читается:: 2-й курс, 2 модуль

Преподаватель

Уваров Федор Максимович

Full Syllabus Ask Question

Abstract

Despite a large variety of different courses on analytics, the courses that offer a broad overview of the field are rare. From practice of teaching statistics, it became clear that it is difficult for learners to put together a broad field map if they have taken only a few of the different topics on analytical tools. As a result, they do not see the overall picture of everything that the field of data analysis has to offer.This course is designed to fill this gap. It is a survey course on state-of-the-art in interdisciplinary methods of data analysis, applicable to business and academia alike. Unlike other statistical courses, which focus on specific methods, this course will focus on the broader areas within statistics and data analytics. There are five major topics it will cover. It will start with the root of it all - the data – and some of the problems with the data. Then it will move through the contemporary approaches to descriptive, inferential, predictive and prescriptive analytics.Within each broader topic, the course will offer the theoretical foundation behind the methods without focusing too much on the mathematics. It will provide historical references, examples, explanations and case studies to illustrate the main concepts within each broader topic. In doing so, it will introduce the applied, problem-based approach to using specific tools. Then, it will discuss some of the specific of a particular approach. Overall, after taking this course, the students will get a good understanding of the state-of-the-art tools that the field of data analysis currently has to offer.

Learning Objectives

Understand the theoretical foundation behind the methods without focusing too much on the mathematics
Learn the applied, problem-based approach to using specific tools
Get a good understanding of the state-of-the-art tools that the field of data analysis currently has to offer

Expected Learning Outcomes

To ensure that you the most advanced recent approaches to working with data, including approaches to incomplete data, different data sizes, and network data. To provide you with an understanding of the role that probability plays in statistical analysis, including the concepts of statistical significance and confidence.
To provide you with an overview of the most recent advances in network science and applied statistical methods, complex statistical modeling, analysis, and forecasting To familiarize you with the requirements and guidelines of scientific publishing both in Russia and abroad. To help you continue developing your written and oral communication skills.

Course Contents

SESSION ONE: Markov and Chebyshev Inequalities
SESSION TWO: Data issues that go bump in the night
SESSION THREE: Descriptive Analytics
SESSION FOUR: Inferential analytics
SESSION FIVE: Predictive Analytics

Assessment Elements

Test 1
Test 2
Test 3
Test 4
Test 5
Test 6

Interim Assessment

2025/2026 2nd module
Итоговая оценка – средняя арифметическая за все тесты.

Bibliography

Recommended Core Bibliography

Brown, T. A. (2015). Confirmatory Factor Analysis for Applied Research, Second Edition (Vol. Second edition). New York: The Guilford Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=831411
Denis, D. J. (2016). Applied Univariate, Bivariate, and Multivariate Statistics. Hoboken, New Jersey: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1091881

Recommended Additional Bibliography

Khandker, S. R., Koolwal, G. B., & Samad, H. A. (2010). Handbook on Impact Evaluation : Quantitative Methods and Practices. Washington, D.C.: World Bank Publications. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=305052

Authors

LEVENETS ANASTASIIA ALEKSANDROVNA
Uvarov Fedor Maksimovich

Магистерская программа «Интерактивные медиа и цифровые индустрии»

Поступление на программу

Contemporary Data Analysis: Survey and Best Practices