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Версия для слабовидящихЛичный кабинет сотрудника ВШЭПоиск
Магистратура 2018/2019

Перспективные методы анализа данных и Большие данные в бизнес-интеллекте

Статус: Курс обязательный (Системы больших данных)
Направление: 38.04.05. Бизнес-информатика
Когда читается: 1-й курс, 1-3 модуль
Формат изучения: Full time
Преподаватели: Горбунов Александр Андреевич, Марков Николай Владимирович
Прогр. обучения: Системы больших данных
Язык: английский
Кредиты: 5

Course Syllabus

Abstract

Advanced Data Analysis and Big Data for Business Intelligence is the study of the techniques for analyzing big data and big data technologies. Big data is the term for a collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools or traditional data processing applications. The challenges include capture,сleaning, storage, search, sharing, transfer, analysis and visualization.Course is focused on understanding the role of big data analysis for business intelligence. Course content includes techniques for analyzing big data and big data technologies.
Learning Objectives

Learning Objectives

  • Formation of the theoretical knowledge and practical basic skills in the collection, storage, processing and analysis of large data. Develop skills and practical skills to analyze large data to tackle a wide range of applications, including analysis of corporate data, financial data from the data warehousing world markets, modeling data storage and processing, prediction of complex indicators.
Expected Learning Outcomes

Expected Learning Outcomes

  • Formation of the theoretical knowledge and practical basic skills in the collection, storage, processing and analysis of large data.
  • Develop skills and practical skills to analyze large data to tackle a wide range of applications, including analysis of corporate data, financial data from the data warehousing world markets, modeling data storage and processing, prediction of complex indicators.
Course Contents

Course Contents

  • Python basics
  • Python advanced level: comprehensions and generators
  • Introduction to functional programming: lambdas, map, reduce, filter, zip
  • Probability Theory and Mathematical Statistics
  • Data analysis with Python: numpy, pandas
  • Visualizing Big Data: Matplotlib
  • Machine learning models and its applications
  • Analysis of social media: Exploring Twitter API
  • Dealing with Twitter Big Data: multithreading in python
  • Apache Spark Machine Learning on Big Data
  • Business intelligence systems: Traditional Business analytics vs Big Data Analytics
  • Preprocessing Big Data: Tableau Prep
  • Introduction to Tableau
  • GIS - geoinformation systems
  • Big Data and Qlik Sense
Assessment Elements

Assessment Elements

  • non-blocking Control work module 1
  • non-blocking Control work module 2
  • non-blocking Control work module 3
  • non-blocking Oral exam
Interim Assessment

Interim Assessment

  • Interim assessment (3 module)
    0.2 * Control work module 1 + 0.2 * Control work module 2 + 0.2 * Control work module 3 + 0.4 * Oral exam
Bibliography

Bibliography

Recommended Core Bibliography

  • Idris, I. (2016). Python Data Analysis Cookbook. Birmingham, UK: Packt Publishing. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1290098

Recommended Additional Bibliography

  • Kirk, M. (2015). Thoughtful Machine Learning with Python : A Test-Driven Approach. Sebastopol: O’Reilly Media. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1455642
  • Weiming, J. M. (2019). Mastering Python for Finance : Implement Advanced State-of-the-art Financial Statistical Applications Using Python, 2nd Edition (Vol. Second edition). Birmingham, UK: Packt Publishing. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=2116431