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.

Welcome!

Structuring a data-driven problem

Probing an analysis

Analysis as a journey

Analytical maturity

Common pitfalls

Applying the methods and  objectives

Identify the method

Identify the objective

Classifying the rest of the course

Data-Driven Decision-Making Framework

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 examples

Finding the best model

Focus on growth

Focus on efficient acquisition

Focus on profitability

Ad arbitrage

Arbitrage Examples?

Not arbitrage

Arbitrage

ID arbitrage examples

Identify the arbitrage opportunity

Calculate the profit

Data-driven product forecasting

Choose the right P, Q & M analogs

Diffusion modeling dashboard

Applying Data to Inform Marketing 

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 reward

What good is the risk-free rate?

Use a CAPM-like chart to choose your investment

Other traditional asset investing

Calculate gross rental income

Review the best investment by cap rate

Select the best investment opportunity

Non-traditional investing: Magic the Gathering

Defining a simulation

Find the maximum price to pay

Calculate the expected profit

Spotting Finance Opportunities With Data

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 market

TAM: top-down market sizing

TAM: bottom up market sizing

Demand curve: meals and drinks

Fast (& furious) food demand

Scrutinizing a forecast

Supply curve: servers for your restaurant

Reviewing the Erlang-C

Make a data driven scheduling decision!

Customer input to improve your operation

Data to focus effort and training (1)

Data to focus effort and training (2)

Wrap up

Data-Driven Business Operations

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.

<h2>Explore the Basics of Data Literacy</h2>
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. 
<br><br>
<h2>Apply Data-driven Decision-making</h2>
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.

Machine Learning for Business

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

Data-Driven Decision Making for Business

Buying a book to study data science to get a higher paying job.

Purchasing a painting at a art show to sell it to a gallery.

Borrowing from a bank at 1% interest to invest it in another bank's savings account with a 2% return.

ID arbitrage examples

Data-Driven Decision Making for Business

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