Businesses run on data, and data offers little value without analytics. The ability to process data to make predictions about the behavior of individuals or markets, to diagnose systems or situations, or to prescribe actions for people or processes drives business today. Increasingly many businesses are striving to become “data-driven”, proactively relying more on cold hard information and sophisticated algorithms than upon the gut instinct or slow reactions of humans.
This course is part of the Introduction to Business Analytics and Information Economics Specialization
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Introduction to Business Analytics and Information Economics SpecializationUniversity of Illinois at Urbana-ChampaignAbout this Course
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Course 1 of 4 in the
Intermediate Level
Approx. 17 hours to complete
English
Subtitles: Arabic, French, Portuguese (European), Italian, Vietnamese, German, Russian, English, Spanish
Could your company benefit from training employees on in-demand skills?
Try Coursera for BusinessFlexible deadlines
Reset deadlines in accordance to your schedule.
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Course 1 of 4 in the
Intermediate Level
Approx. 17 hours to complete
English
Subtitles: Arabic, French, Portuguese (European), Italian, Vietnamese, German, Russian, English, Spanish
Could your company benefit from training employees on in-demand skills?
Try Coursera for Business
Offered by
University of Illinois at Urbana-Champaign
The University of Illinois at Urbana-Champaign is a world leader in research, teaching and public engagement, distinguished by the breadth of its programs, broad academic excellence, and internationally renowned faculty and alumni. Illinois serves the world by creating knowledge, preparing students for lives of impact, and finding solutions to critical societal needs.
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This course is part of the 100% online Master of Computer Science from University of Illinois at Urbana-Champaign.
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Syllabus - What you will learn from this course
6 hours to complete
Course Overview & Module 1 Analytics Beyond the Spreadsheet
This first module exposes and explains key data and analytics concepts from Big Data to data warehousing to natural language query, and everything in-between. Next we will explore various analytic techniques, types of visualizations, and types of analytics solutions. The course will continue with identifying and learning about key data and analytics roles and organization structures, including chief data and analytics officers, data scientists, and analytics centers of excellence. Alternatives to direct hiring, such as outsourcing and crowdsourcing, will also be covered. Finally, the course will scrutinize analytic trends and futures.
6 hours to complete
18 videos (Total 98 min), 8 readings, 2 quizzes
3 hours to complete
Module 2 Industry and Business Function Analytics
Over the course of the module, you will also see how data and analytics in each of these organizations can be used in similar ways, in similar business functions. Accordingly, you will appreciate that to be truly data-driven, you need not only look to examples in your own industry, but, also learn and apply analytics concepts from organizations in other fields.
3 hours to complete
11 videos (Total 66 min), 2 readings, 1 quiz
4 hours to complete
Module 3 Staffing and Organizing for Analytics
In this module you will learn a bunch of crucial analytical roles and the emergence of new roles in organizations from the C-suite down to various analyst roles. You will take a brief look at the job descriptions and the responsibilities. You will also put yourself in either a job seeker’s or a recruiter’s shoes to see what kind of skill sets are the most important and which position fits you the best. For example, it will introduce you to the three core skills of the data scientist and the crucial soft skills required to be a successful data scientist.
4 hours to complete
11 videos (Total 74 min), 2 readings, 2 quizzes
3 hours to complete
Module 4 Analytics Success Today and Tomorrow
This module explores telling stories, through data, that connect emotionally with your audience. It will also review examples and figures that make the concept easy to understand. You will learn the major do’s and don’ts of creating dataviz and rules that lead to the clear depiction of your findings. This unit specifically focuses on Dona Wong’s guidelines for good data visualization and charts. The last leg of Module 4 teaches the three tests that help you improve your visualization. In the final step of dataviz execution, you will learn the McCandless Method for presenting visualizations. This five-step process produces the most effective communication of the graphics to your audience.
3 hours to complete
15 videos (Total 57 min), 4 readings, 1 quiz
Reviews
- 5 stars70.91%
- 4 stars23.10%
- 3 stars3.98%
- 2 stars0.79%
- 1 star1.19%
TOP REVIEWS FROM BUSINESS ANALYTICS EXECUTIVE OVERVIEW
by YSJun 25, 2020
Perfect course for getting an Overview, Current Trends and Implementation of Data Analytics practice in Organization.
by JMJun 25, 2022
Liked everything except the peer-reviewed assignment. Four reviews is too many. It took several weeks to get submissions to review after I had finished everthing else.
by JUJan 21, 2020
Overall a good introduction course into how data is becoming an important addition and will continue to grow in making business decisions.
by SJul 29, 2020
A good course that introduces us to the trends / organization roles etc. A few more real-life examples with the actual set up and organization of the data/analytics department would have helped more.
About the Introduction to Business Analytics and Information Economics Specialization
This specialization targets learners who seek to understand the opportunities that data and analytics present for their organization and those interested in the value of and implications for data as an asset to their organization. Individuals who manage data and make decisions about how data can be leveraged in their organization will find this specialization of particular value.
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