A Practical Overview of Descriptive and Predictive Techniques
This book was originally put together as I taught the the content to two very different audiences. One was a group of 1st-year Executive MBA students. This group was not particularly technical, but very good at applying the concepts taught to their contexts. The other was a group of Master of Information Systems Management students who had gone straight from their undergrad degrees to the MISM program. They were very tech-savvy, but they had a bit more trouble applying the concepts because of their lack of industry experience.
This forced me to develop the book in a way that it could be adapted easily to either audience. As a result, you'll notice that some chapters may not apply to non-techincal audiences, but they will be perfect for IS, IT, or computer science students. Some checkpoint assignments have additional components for technical studnets and even the final project can be adapted to either audience.
Data analytic techniques are changing daily and, thus, requiring academic curriculums to constantly evolve to maintain relevance. Because of this, paper-based books full of text-based instruction are inadequate because they cannot keep up with the rate of change. Therefore, the purpose of this online book is to teach--through practice-based video tutorials--the latest and most common techniques for both descriptive and predictive data analytics. We use currently industry-leading tool, Tableau, to teach dashboard design and story telling which describes the current state of an organization based on measurable data. However, the supreme value of data is in it's ability to predict the future. This is also the most difficult and risky directive. Therefore, we begin by teaching basic methods in Excel for multiple regression and the assumptions of linear regression. Afterward, the bulk of the course is spent covering more advanced algorithms and techniques using an industry-leading tool for predictive analysis: Microsoft Azure Machine Learning Studio. We chose these tools, first, because they are mainstream industry tools that you are likely to use across a variety of industries, but second, because both come with free versions for students :)