Business Metrics for Data-Driven Companies (Coursera)

Offered by Duke University,
Business Metrics for Data-Driven Companies (Coursera)

In this course, you will learn best practices for how to use data analytics to make any company more competitive and more profitable. You will be able to recognize the most critical business metrics and distinguish them from mere data. You’ll get a clear picture of the vital but different roles business analysts, business data analysts, and data scientists each play in various types of companies. And you’ll know exactly what skills are required to be hired for, and succeed at, these high-demand jobs.

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Finally, you will be able to use a checklist provided in the course to score any company on how effectively it is embracing big data culture. Digital companies like Amazon, Uber and Airbnb are transforming entire industries through their creative use of big data. You’ll understand why these companies are so disruptive and how they use data-analytics techniques to out-compete traditional companies.
Course 1 of 5 in the Excel to MySQL: Analytic Techniques for Business Specialization.

Syllabus

WEEK 1
Introducing Business Metrics
Welcome! This week we will explore business metrics - the critical numbers that help companies figure out how to survive and thrive. Inside every pile of data is a vital metric trying to get out! By the end of this week, you will be able to: distinguish business metrics from mere business data; identify critical business metrics such as cash flow, profitability, and online retail marketing metrics; distinguish revenue, profitability and risk metrics; and distinguish traditional from dynamic metrics. Included in this week’s course materials is a Cash Flow and P&L statement for Egger’s Roast Coffee, as a supplemental document, so be sure to review it carefully and refer to the glossary for key information.

WEEK 2
Working in the Business Data Analytics Marketplace
Welcome! This week, we will meet some great people - all former students of mine - now working at super-interesting and exciting jobs as business analysts, business data analysts, or data scientists. We’ll explore what they do, how their role relates to big data, and the skills they needed to get hired! Our hope is this information will give you a better understanding of the type of data-related job you might apply for once you've completed this specialization, and a sense of the type of company you would find most appealing to work for. By the end of this week, you will be able to: differentiate among different job roles within a company that work with data; identify how each role works with data; and describe the skills required to perform each job role. You will differentiate how different types of companies relate to big data culture, and rank any company according to a 20-item checklist. You will also learn to differentiate how different types of companies relate to big data culture. Included in this week’s materials is a 20-item checklist to rank companies. This week also includes in-video polls so you can see how others are ranking their businesses.

WEEK 3
Going Deeper into Business Metrics
Welcome! This week we’re going to go deeper into the critically-important metrics for web marketing - metrics every type of business needs to understand in order to survive. We’ll dive into the 'vertical' market of financial services - where digital companies are threatening to take away the market from traditional 'brick-and- mortar' companies.By the end of this week, you will be able to: Identify critical business metrics for all companies engaged in web-based marketing; and identify critical business metrics for financial services companies. You’ll find additional website links that expand some of the course materials covered in this week’s video lectures.

WEEK 4
Applying Business Metrics to a Business Case Study
This week contains the final course assignment, a peer assessment in which you will identify business metrics of interest in a case example, describe those metrics, and propose a business process change that could be supported by the metric chosen.

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