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Regular version of the site
Master 2019/2020

Big Data Emerging Technologies

Type: Elective course (Business Informatics)
Area of studies: Business Informatics
Delivered by: Department of Information Systems and Digital Infrastructure Management
When: 1 year, 2, 3 module
Mode of studies: distance learning
Master’s programme: Business Informatics
Language: English
ECTS credits: 5
Contact hours: 2

Course Syllabus

Abstract

Discipline Big Data Emerging Technologies is an elective “blended” course taken in the 3rd module of the Master’s program “Business Informatics”. The course consists of the on-line part provided by www.coursera.org (course title - Big Data Emerging Technologies, https://www.coursera.org/learn/big-data-emerging-technologies) and the off-line part described below. The students are supposed to study the on-line part on their own using the materials available at www.coursera.org. The course is taught in English and worth 3 credits.
Learning Objectives

Learning Objectives

  • The course provides the theoretical background and practical skills of data analysis with the help of the machine learning algorithms.
Expected Learning Outcomes

Expected Learning Outcomes

  • Be able to make predictions with the help of the regression and classification trees
  • Understand how to select the optimal model, be able to use methods of cross-validation and bootstrapping, apply informational criteria (AIC,BIC).
  • Be able to apply method of dimensionality reduction for analyzing multivariate data sets
  • Be able to make use of cluster analysis for obtaining patterns in the data sets
  • Understand basic principles of machine learning, be able to implement the algorithms in practice
Course Contents

Course Contents

  • Decision Trees
  • Linear Model Selection
  • Dimension Reduction Methods
  • Cluster Analysis
  • Machine Learning in Practice
    The importance of good features, examples. Irrelevant and redundant features. Feature pruning and normalization. Combinational feature explosion. Evaluating model performance. Cross validation. Hypothesis testing and statistical significance. Debugging learning algorithms.
Assessment Elements

Assessment Elements

  • non-blocking certificate in Coursera course
  • non-blocking Final Exam
    Экзамен проводится в письменной форме (эссе) с использованием асинхронного прокторинга. Экзамен проводится на платформе Google Forms (https://docs.google.com/forms/), прокторинг на платформе Экзамус (https://hse.student.examus.net). К экзамену необходимо подключиться за 15 минут. На платформе Экзамус доступно тестирование системы. Компьютер студента должен удовлетворять следующим требованиям: https://elearning.hse.ru/data/2020/05/07/1544135594/Технические%20требования%20к%20ПК%20студента.pdf) Для участия в экзамене студент обязан: заранее зайти на платформу прокторинга, провести тест системы, включить камеру и микрофон, подтвердить личность. Во время экзамена студентам запрещено: общаться (в социальных сетях, с людьми в комнате), списывать. Во время экзамена студентам разрешено: пользоваться собственными письменными конспектами (в тетради или на распечатанных листах). Кратковременным нарушением связи во время экзамена считается прерывание связи до 5 минут. Долговременным нарушением связи во время экзамена считается прерывание связи 5 минут и более. При долговременном нарушении связи студент не может продолжить участие в экзамене. Процедура пересдачи аналогична процедуре сдачи.
Interim Assessment

Interim Assessment

  • Interim assessment (3 module)
    0.7 * certificate in Coursera course + 0.3 * Final Exam
Bibliography

Bibliography

Recommended Core Bibliography

  • Husson, F., Lê, S., & Pagès, J. (2017). Exploratory Multivariate Analysis by Example Using R (Vol. Second edition). Boca Raton: Chapman and Hall/CRC. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=nlebk&AN=1516055
  • Knox, S. W. (2018). Machine Learning : A Concise Introduction. Hoboken, New Jersey: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1729639
  • Shmueli, G., Bruce, P. C., Yahav, I., Patel, N. R., & Lichtendahl, K. C. (2017). Data Mining for Business Analytics : Concepts, Techniques, and Applications in R. Hoboken, New Jersey: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1585613

Recommended Additional Bibliography

  • Mailund, T. (2017). Beginning Data Science in R : Data Analysis, Visualization, and Modelling for the Data Scientist. New York: Apress. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1484645
  • Sarangi, S., & Sharma, P. (2020). Big Data : A Beginner’s Introduction. Abingdon, Oxon: Routledge India. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=2168187