Lesson 1 Overview

In this introductory session, we will discuss the need for data-driven decision-making in businesses. Because of the digital revolution, businesses now have massive amounts of data: sales, operations, customer profiles, market conditions, environmental factors, consumer sentiments—you name it. Most of this data is generated as a byproduct of an organization’s everyday workflow. For example, employees generate data about their entry and exit times by scanning their badges at work; we all generate volumes of data about our browsing habits—the sites we visit, how much time we spend there, in what order we stream, and so on. A digital footprint of a patient is created every time they visit a physician, go for a laboratory procedure, order a prescription, pay for a visit, and so on.

As much data as we may have, decisions about the future must be taken under varying levels of uncertainty. The success of a new product launch may depend significantly on the economy, a newly discovered drug may have severe side effects, and a supplier may miss the shipment deadline due to a natural calamity, thus jeopardizing the production schedule. Statistics enable managers to make decisions and judgments based on past data and observations. Although we will never be able to perfectly predict the future, statistical methods should help us assess the likelihood of something happening. For example, how confident are we that our product will be successful? How likely is it that the drug may have unintended consequences?

With this in mind, our first task will be to make simple summaries of data. We will analyze whether or not the data has a lot of variability in it (variance and standard deviation), and what the average data point reveals about the nature of the data (mean and median). We will discuss the takeaways from these analyses for decision-making.

Learning Objectives 

After completing this lesson, you should be able to

• identify the different types of variables to capture aspects of business and everyday life;
• describe a set of data using measures of centrality and position;
• analyze the degree of variability in a dataset using measures like variance and standard deviation; and
• construct tables and charts to visualize a dataset.

To review how the content, activities, and assessments align with one another and the course objectives, please visit the Course Map.

Lesson Readings and Activities

By the end of this lesson, make sure you have completed the readings and activities found in the Course Schedule.