Data-Driven Decision Making for Business

Discover how to make better business decisions by applying practical data frameworks—no coding required.

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2 Hours14 Videos46 Exercises2,342 Learners
2600 XP

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Course Description

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.

  1. 1

    Data-Driven Decision-Making Framework

    Free

    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.

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    Welcome!
    50 xp
    Structuring a data-driven problem
    100 xp
    Probing an analysis
    100 xp
    Analysis as a journey
    50 xp
    Analytical maturity
    100 xp
    Common pitfalls
    50 xp
    Applying the methods and objectives
    50 xp
    Identify the method
    50 xp
    Identify the objective
    50 xp
    Classifying the rest of the course
    100 xp
  2. 2

    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.

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  3. 3

    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.

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  4. 4

    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.

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Collaborators

Hadrien LacroixSara Billen

Prerequisites

Machine Learning for Business
Ted Kwartler Headshot

Ted Kwartler

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.
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What do other learners have to say?

I've used other sites—Coursera, Udacity, things like that—but DataCamp's been the one that I've stuck with.

Devon Edwards Joseph
Lloyds Banking Group

DataCamp is the top resource I recommend for learning data science.

Louis Maiden
Harvard Business School

DataCamp is by far my favorite website to learn from.

Ronald Bowers
Decision Science Analytics, USAA