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Regular version of the site
Bachelor 2020/2021

Data-driven Decision Making

Type: Elective course (Business Informatics)
Area of studies: Business Informatics
When: 4 year, 2 module
Mode of studies: distance learning
Open to: students of one campus
Instructors: Andrey Dmitriev
Language: English
ECTS credits: 4

Course Syllabus

Abstract

Welcome to Data-driven Decision Making. In this course you'll get an introduction to Data Analytics and its role in business decisions. You'll learn why data is important and how it has evolved. You'll be introduced to “Big Data” and how it is used. You'll also be introduced to a framework for conducting Data Analysis and what tools and techniques are commonly used. Finally, you'll have a chance to put your knowledge to work in a simulated business setting. This course was created by PricewaterhouseCoopers LLP with an address at 300 Madison Avenue, New York, New York, 10017. https://www.coursera.org/learn/decision-making
Learning Objectives

Learning Objectives

  • In this course you'll get an introduction to Data Analytics and its role in business decisions. You'll learn why data is important and how it has evolved. You'll be introduced to “Big Data” and how it is used. You'll also be introduced to a framework for conducting Data Analysis and what tools and techniques are commonly used. Finally, you'll have a chance to put your knowledge to work in a simulated business setting.
Expected Learning Outcomes

Expected Learning Outcomes

  • to introduce you to a framework for data analysis and tools used in data analytics
  • to learn the value data analytics brings to business decision-making processes
  • to learn the basics of data analytics and how businesses use to solve problems
  • to make the course project
  • to identify a variety of tools and languages used and consider when those tools are best used
  • to learn tools for data analytics and some of the key technologies for data analysis
  • to introduce you to PwC's perspective on big data and explain the impact of big data on businesses
  • to learn about different types of data
Course Contents

Course Contents

  • Data analysis techniques and tools
    In this module we will describe some of the tools for data analytics and some of the key technologies for data analysis. We will talk about how visualization is important to the practice of data analytics. Finally we will identify a variety of tools and languages used and consider when those tools are best used.
  • Introduction to Data Analytics
    In this module, you'll learn the basics of data analytics and how businesses use to solve problems. You'll learn the value data analytics brings to business decision-making processes. We’ll introduce you to a framework for data analysis and tools used in data analytics. Finally, we’re going to talk about careers and roles in data analytics and data science. Note: Video transcripts are auto generated and may contain spelling and punctuation errors.
  • Data-driven decision making project
    The course project will give you an opportunity to practice what you have learned. You will participate in a simulated business situation in which you will select the best course of action. You will then prepare a final deliverable which will be evaluated by your peers. Additionally, you will have the opportunity to provide feedback on your peer's submissions.
  • Technology and types of data
    This module is an introductory look at big data and big data analytics where you will learn the about different types of data. We’ll also introduce you to PwC's perspective on big data and explain the impact of big data on businesses. Finally we will name some of the different types of tools and technologies used to gather data.
Assessment Elements

Assessment Elements

  • non-blocking Практическое упражнение к теме №1, выполненное в ходе изучения онлайн курса на платформе Coursera
  • non-blocking Практическое упражнение к теме №2, выполненное при прохождении онлайн курса на платформе Coursera
  • non-blocking Практическое упражнение к теме №3, выполненное при прохождении онлайн курса на платформе Coursera
  • non-blocking Практическое упражнение к теме №3, выполненное при прохождении онлайн курса на платформе Coursera
  • non-blocking Практическое упражнение к теме №4, выполненное при прохождении онлайн курса на платформе Coursera
  • non-blocking Экзамен
    Перезачет оценки, полученной по итогам прохождения онлайн курса на платформе Coursera. Перевод оценки в итоговую оценку в десятибалльной шкале производится путем деления оценки, полученной по итогам прохождения онлайн курса на платформе Coursera, на 10 и округления полученного результата по правилам математического округления.
Interim Assessment

Interim Assessment

  • Interim assessment (2 module)
    0.247 * Практическое упражнение к теме №1, выполненное в ходе изучения онлайн курса на платформе Coursera + 0.247 * Практическое упражнение к теме №2, выполненное при прохождении онлайн курса на платформе Coursera + 0.248 * Практическое упражнение к теме №3, выполненное при прохождении онлайн курса на платформе Coursera + 0.248 * Практическое упражнение к теме №4, выполненное при прохождении онлайн курса на платформе Coursera + 0.01 * Экзамен
Bibliography

Bibliography

Recommended Core Bibliography

  • Big data analytics : turning big data into big money, Ohlhorst, F., 2013
  • Blum, D., Spears, M., Page, J., & Granderson, J. (2018). When Data Analytics Meet Site Operation: Benefits and Challenges. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edssch&AN=edssch.oai%3aescholarship.org%2fark%3a%2f13030%2fqt9842x2ds
  • Elliott, A. C., & Woodward, W. A. (2016). SAS Essentials : Mastering SAS for Data Analytics (Vol. Second edition). Hoboken, New Jersey: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1051725
  • Guller, M. (2015). Big Data Analytics with Spark : A Practitioner’s Guide to Using Spark for Large Scale Data Analysis. [Berkeley, CA]: Apress. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1174460

Recommended Additional Bibliography

  • Big Data analytics with R : utilize R to uncover hidden patterns in your Big Data, Walkowiak, S., 2016
  • Data science foundations : geometry and topology of complex hierarchic systems and big data analytics, Murtagh, F., 2018
  • Fraud data analytics methodology : the fraud scenario approach to uncovering fraud in core business systems, Vona, L. W., 2017