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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 heures14 vidéos46 exercices21 521 apprenantsTrophyDéclaration de réalisation

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Apprécié par les apprenants de milliers d'entreprises

Description du cours

Explore the Basics of Data Literacy

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.

Apply Data-driven Decision-making

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.
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  1. 1

    Data-Driven Decision-Making Framework

    Gratuit

    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.

    Jouez Au Chapitre Maintenant
    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.

    Jouez Au Chapitre Maintenant
    Marketing examples
    50 xp
    Finding the best model
    50 xp
    Focus on growth
    50 xp
    Focus on efficient acquisition
    50 xp
    Focus on profitability
    50 xp
    Ad arbitrage
    50 xp
    ID arbitrage examples
    100 xp
    Identify the arbitrage opportunity
    50 xp
    Calculate the profit
    50 xp
    Data-driven product forecasting
    50 xp
    Choose the right P, Q & M analogs
    50 xp
    Diffusion modeling dashboard
    50 xp
  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.

    Jouez Au Chapitre Maintenant
    Traditional investing: risk vs reward
    50 xp
    What good is the risk-free rate?
    50 xp
    Use a CAPM-like chart to choose your investment
    50 xp
    Other traditional asset investing
    50 xp
    Calculate gross rental income
    50 xp
    Review the best investment by cap rate
    50 xp
    Select the best investment opportunity
    50 xp
    Non-traditional investing: Magic the Gathering
    50 xp
    Defining a simulation
    50 xp
    Find the maximum price to pay
    50 xp
    Calculate the expected profit
    50 xp
  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.

    Jouez Au Chapitre Maintenant
    Total addressable market
    50 xp
    TAM: top-down market sizing
    50 xp
    TAM: bottom up market sizing
    50 xp
    Demand curve: meals and drinks
    50 xp
    Fast (& furious) food demand
    50 xp
    Scrutinizing a forecast
    50 xp
    Supply curve: servers for your restaurant
    50 xp
    Reviewing the Erlang-C
    100 xp
    Make a data driven scheduling decision!
    50 xp
    Customer input to improve your operation
    50 xp
    Data to focus effort and training (1)
    50 xp
    Data to focus effort and training (2)
    50 xp
    Wrap up
    50 xp
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collaborateurs

Collaborator's avatar
Hadrien Lacroix
Collaborator's avatar
Sara Billen

prérequis

Machine Learning for Business
Ted Kwartler HeadshotTed Kwartler

Adjunct Professor, Harvard University

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