Learn how the social, political, economic and institutional factors affect international business environments and global strategies.
In business, data and algorithms create economic value when they reduce uncertainty about financially important outcomes. This course teaches the concepts and mathematical methods behind the most powerful and universal metrics used by Data Scientists to evaluate the uncertainty-reduction – or information gain - predictive models provide. We focus on the two most common types of predictive model - binary classification and linear regression - and you will learn metrics to quantify for yourself the exact reduction in uncertainty each can offer. These metrics are applicable to any form of model that uses new information to improve predictions cast in the form of a known probability distribution – the standard way of representing forecasts in data science.
In addition, you will learn proper methodology to avoid common data-analytic pitfalls when forecasting – such as being “fooled by randomness” and over-fitting “noise” as if it were “signal.”
Uniquely among data-analytics offerings, this course empowers you to understand and apply quite advanced information theory methods – Bayesian Logical Data Analysis - in business practice, without needing any calculus or matrix algebra, or any knowledge of Matlab or R or software programming.
You will be able to answer all homework and quiz questions either by using basic algebra, or with the special custom Microsoft Excel Templates provided. Nor is any prior experience with Excel required; we will cover in detail at the beginning everything you need to know about using Excel to succeed in the course itself. If you already know Excel, you can skip that part.
Be aware that this is not a broad general Excel skills course; it focuses on use of Excel to calculate information-related metrics, and to solve real business problems, such as developing your own predictive analytics model for which credit card applicants a bank should accept and which reject as too risky. Real problems are complicated!
Personally I think learning to solve real problems is also a great way to learn Excel. We use specific tools in the Excel toolbox to build something useful, and you can always go back and learn more tools in the toolbox – more Excel functions – if and when you ever need them.
This course requires some mathematical background: you should already know how to solve for an unknown using algebra; and have a basic familiarity with sigma (summation) notation; the concept of logarithms and working with bases other than base 10 (including base 2, and the natural logarithm and base “e”); and probability theory concepts such as calculating conditional, product, and joint probabilities. These concepts are assumed in the course rather than taught. All the “new” math taught in the course is summarized in a downloadable PDF document - "Mathematical Supplement" – please refer to it to decide if the difficulty level of this course seems right for you.
Mastering Data Analysis in Excel is course 2 of 5 in the Excel to MySQL: Analytic Techniques for Business Specialization.
Formulate data questions, explore and visualize large datasets, and inform strategic decisions. In this Specialization, you’ll learn to frame business challenges as data questions. You’ll use powerful tools and methods such as Excel, Tableau, and MySQL to analyze data, create forecasts and models, design visualizations, and communicate your insights. In the final Capstone Project, you’ll apply your skills to explore and justify improvements to a real-world business process. The Capstone Project focuses on optimizing revenues from residential property, and Airbnb, our Capstone’s official Sponsor, provided input on the project design. Airbnb is the world’s largest marketplace connecting property-owner hosts with travelers to facilitate short-term rental transactions. The top 10 Capstone completers each year will have the opportunity to present their work directly to senior data scientists at Airbnb live for feedback and discussion.