In today’s technology-enabled world, organizations collect a wealth of information as a part of their business operations. The past decade has seen an explosion in the amount of business data, types and sources. Companies today are grappling with data overload while trying to extract valuable information, trends, and patterns from the organization’s terabytes of data.
Many executives recognize the power and potential of analytics and seek a plan for putting it to work in their organizations. This course leads participants through the process of business analytics beginning with asking the right analytical questions, through data identification, acquisition, visualization, and analysis, and culminating with providing actionable insights and decisions. The emphasis is on the connection between an organization’s business objectives and the data and analyses that can bolster and/or transform decision-making.
Learn more about this course.
There are two options to complete Leading with Analytics
Blend of live virtual class and on-demand coursework
Week by Week
Title: Why Analytics Matter
Overview: Introduces the important concepts of understanding how data can impact decisions in everyday life. Understanding the data, paying attention to the right questions, and harnessing the information to make decisions for the correct problem transforms the approach to everyday problem-solving. Data has been used over time to solve complex problems, but with today’s access to complex information, organizations have the opportunity to “see” more of the big picture and leverage the knowledge to make better decisions.
Title: Asking Connecting Questions
Overview: Delve into different approaches to understand how to effectively use complex data. The creation of the “Data Scientist” team encourages us to define the roles in an organization, and how best to interact across teams. This unit demonstrates the importance of asking the right questions with the data available, and using visualization to illustrate scenarios created from modeling the data, that leads to better decision-making.
Title: Data Acquisition, Quality & Strategy
Overview: In today’s environment, we have access to an abundance of information, that comes from a variety of sources that can be credible and non-credible. Managing the volume can be difficulty; too much data can be hard to synthesize, too little data can leave dangerous gaps. Using information from credible sources, understanding the question you’re trying to answer, and developing a plan for using the data, reduces the likelihood the answers may take you in the wrong direction.
Title: Visualizing Data
Overview: Examines how we can use new, visual tools to work with data. Visualization is an analytical approach that almost everyone can benefit from using right away. We’ll share examples that show how a picture can be worth a thousand words, or thousands of data points. We will go through a four-question framework that helps us to determine and build the right visualizations that will accomplish our goals. The unit discusses different types of visualizations, and you will practice to visualize your own data.
Title: Using Linear Regression
Overview: We will pay special attention to the workhorse of any analyst’s stable of tools: linear regression. Linear regression can be used to make forecasts and predictions, and to uncover insightful relationships in data. We’ll discuss the basics of linear regression, and we’ll work through examples showing how hypothesis testing, scatter plots, and linear regression can be used to answer business questions and identify areas of bias.
Title: Putting it all Together
Overview: Bring together what we learned as we apply it to a given scenario. We will work on a project that aims to turn data into value as we look at how Big Data can help us to address a vexing healthcare challenge. In using all our skills from the class we’ll have a chance to solidify our learnings and prepare to engage in our own analytics’ initiatives. The unit will review the topics we discussed and provide feedback on the results of the exercise, including making sure we are not engaging in biased behavior, and apply linear regression as needed.