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Data literacy is an essential skill for every role within an organization—not just data scientists and analysts. As companies collect more data than ever before, it’s critical that everyone can read and analyze that data efficiently. In this course, you'll learn the basics of data-driven decision-making and get to apply these skills to three real-life examples from the world of finance, marketing, and operations. You’ll also discover how to uncover new insights and opportunities by applying supply and demand, cost and benefit, and risk and rewards frameworks—gaining practical skills to help you thrive in the new data-driven world.
Data-Driven Decision-Making FrameworkFree
In this chapter, you’ll get familiar with the data-driven decision framework. You’ll learn more about different types of data analysis and their objectives. By the end, you’ll be able to see where each decision fits within the framework.
Applying Data to Inform Marketing
Let’s apply the data-driven decision in a marketing context. You'll use data to optimize ad spend, identify arbitrage opportunities for website traffic, and even how to forecast new product launches with limited but relevant data.Marketing examples50 xpFinding the best model50 xpFocus on growth50 xpFocus on efficient acquisition50 xpFocus on profitability50 xpAd arbitrage50 xpID arbitrage examples100 xpIdentify the arbitrage opportunity50 xpCalculate the profit50 xpData-driven product forecasting50 xpChoose the right P, Q & M analogs50 xpDiffusion modeling dashboard50 xp
Spotting Finance Opportunities With Data
Learning how to use data to inform decisions in finance can be satisfying and profitable. In this chapter, you'll review investment opportunities using data in consumer credit, real estate, and even in a non-traditional market of collectibles.Traditional investing: risk vs reward50 xpWhat good is the risk-free rate?50 xpUse a CAPM-like chart to choose your investment50 xpOther traditional asset investing50 xpCalculate gross rental income50 xpReview the best investment by cap rate50 xpSelect the best investment opportunity50 xpNon-traditional investing: Magic the Gathering50 xpDefining a simulation50 xpFind the maximum price to pay50 xpCalculate the expected profit50 xp
Data-Driven Business Operations
In the final chapter, you'll apply data to create a total addressable market calculation for a startup, learn about supply and demand curves used for staffing, and how to spot customer-driven areas for improvement. This will provide you with practical experience of how to identify optimization opportunities and support existing business operations.Total addressable market50 xpTAM: top-down market sizing50 xpTAM: bottom up market sizing50 xpDemand curve: meals and drinks50 xpFast (& furious) food demand50 xpScrutinizing a forecast50 xpSupply curve: servers for your restaurant50 xpReviewing the Erlang-C100 xpMake a data driven scheduling decision!50 xpCustomer input to improve your operation50 xpData to focus effort and training (1)50 xpData to focus effort and training (2)50 xpWrap up50 xp
In the following tracksData Skills for Business
PrerequisitesMachine Learning for Business
Adjunct Professor, Harvard University
Ted Kwartler is the VP, Trusted AI at DataRobot. At DataRobot, Ted sets product strategy for explainable and ethical uses of data technology in the company's application. Ted brings unique insights and experience utilizing data, business acumen and ethics to his current and previous positions at Liberty Mutual Insurance and Amazon. In addition to having 4 DataCamp courses he teaches graduate courses at the Harvard Extension School and is the author of Text Mining in Practice with R.