Introduction to Data Analytics for Business

2020/2021

Учебный год

ENG

Обучение ведется на английском языке

3

Кредиты

Кто читает:

Департамент бизнес-информатики

Статус:

Курс адаптационный

Когда читается:

1-й курс, 1 модуль

Преподаватель

Сильчев Виталий Артемович

Course Syllabus

Abstract

This MOOC bridging course (https://www.coursera.org/learn/data-analytics-business) will expose you to the data analytics practices executed in the business world. We will explore such key areas as the analytical process, how data is created, stored, accessed, and how the organization works with data and creates the environment in which analytics can flourish.

Learning Objectives

• What you learn in this course will give you a strong foundation in all the areas that support analytics and will help you to better position yourself for success within your organization. You will develop skills and a perspective that will make you more productive faster and allow you to become a valuable asset to your organization. This course also provides a basis for going deeper into advanced investigative and computational methods, which you have an opportunity to explore in future courses of the Data Analytics for Business specialization.

Expected Learning Outcomes

• Data modeling, the process of creating a data model for the data to be stored in a Database.
• Data quality, a measure of the condition of data based on factors such as accuracy, completeness, consistency, reliability and whether it's up to date.
• Data analysis, a process of cleaning, transforming, and modeling data to discover useful information for business decision-making.
• SQL (Structured Query Language)

Course Contents

• Data and Analysis in the Real World
  In this module we’ll learn how to think about analytical problems and examine the process by which data enables analysis & decision making. We’ll introduce a framework called the Information-Action Value chain which describes the path from events in the world to business action, and we’ll look at some of the source systems that are used to capture data. At the end of this course you will be able to: Explain the information lifecycle from events in the real world to business actions, and how to think about analytical problems in that context , Recognize the types of events and characteristics that are often used in business analytics, and explain how the data is captured by source systems and stored using both traditional and emergent technologies, Gain a high-level familiarity with relational databases and learn how to use a simple but powerful language called SQL to extract analytical data sets of interest, Appreciate the spectrum of roles involved in the data lifecycle, and gain exposure to the various ways that organizations structure analytical functions, Summarize some of the key ideas around data quality, data governance, and data privacy
• Analytical Tools
  In this module we’ll learn about the technologies that enable analytical work. We’ll examine data storage and databases, including the relational database. We’ll talk about Big Data and Cloud technologies and ideas like federation, virtualization, and in-memory computing. We’ll also walk through a landscape of some of the more common tool classes and learn how these tools support common analytical tasks.
• Data Extraction Using SQL
  In this module we’ll learn how to extract data from a relational database using Structured Query Language, or SQL. We’ll cover all the basic SQL commands and learn how to combine and stack data from different tables. We’ll also learn how to expand the power of our queries using operators and handle additional complexity using subqueries.
• Real World Analytical Organizations
  In this module we focus on the people and organizations that work with data and actually execute analytics. We’ll discuss who does what and see how organizational structures can influence efficiency and effectiveness. We’ll also look at the supporting rules & processes that help an analytical organization run smoothly, like Data Governance, Data Privacy, and Data Quality.

Assessment Elements

• Coursera Course Certificate
• Online written exam (test)
  Exam format: the exam is taken in writing, remotely (online) on StartExam platform

Interim Assessment

• Interim assessment (1 module)
  0.7 * Coursera Course Certificate + 0.3 * Online written exam (test)

Bibliography

Recommended Core Bibliography

• Malik, U., Goldwasser, M., & Johnston, B. (2019). SQL for Data Analytics : Perform Fast and Efficient Data Analysis with the Power of SQL. Packt Publishing.
• Spalek, S. (2019). Data Analytics in Project Management. Boca Raton, FL: Auerbach Publications. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1898650

Recommended Additional Bibliography

• Nelli, F. (2018). Python Data Analytics : With Pandas, NumPy, and Matplotlib (Vol. Second edition). New York, NY: Apress. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1905344
• Pyne, S., Prakasa Rao, B. L. S., & Rao, S. B. (2016). Big Data Analytics : Methods and Applications. New Delhi, India: Springer. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1281845