Book Image

Practical Predictive Analytics

By : Ralph Winters
Book Image

Practical Predictive Analytics

By: Ralph Winters

Overview of this book

This is the go-to book for anyone interested in the steps needed to develop predictive analytics solutions with examples from the world of marketing, healthcare, and retail. We'll get started with a brief history of predictive analytics and learn about different roles and functions people play within a predictive analytics project. Then, we will learn about various ways of installing R along with their pros and cons, combined with a step-by-step installation of RStudio, and a description of the best practices for organizing your projects. On completing the installation, we will begin to acquire the skills necessary to input, clean, and prepare your data for modeling. We will learn the six specific steps needed to implement and successfully deploy a predictive model starting from asking the right questions through model development and ending with deploying your predictive model into production. We will learn why collaboration is important and how agile iterative modeling cycles can increase your chances of developing and deploying the best successful model. We will continue your journey in the cloud by extending your skill set by learning about Databricks and SparkR, which allow you to develop predictive models on vast gigabytes of data.
Table of Contents (19 chapters)
Title Page
About the Author
About the Reviewers
Customer Feedback

Skills and roles that are important in Predictive Analytics

So what skills do you need to be successful in predictive analytics? I believe that there are three basic skills that are needed:

  • Algorithmic/statistical/programming skills: These are the actual technical skills needed to implement a technical solution to a problem. I bundle these all together since these skills are typically used in tandem. Will it be a purely statistical solution, or will there need to be a bit of programming thrown in to customize an algorithm, and clean the data? There are always multiple ways of doing the same task and it will be up to you, the predictive modeler, to determine how it is to be done.
  • Business skills: These are the skills needed for communicating thoughts and ideas among groups of the interested parties. Business and data analysts who have worked in certain industries for long periods of time, and know their business very well, are increasingly being called upon to participate in predictive analytics projects. Data science is becoming a team sport and most projects include working with others in the organization, summarizing findings, and having good presentation and documentation skills are important. You will often hear the term domain knowledge associated with this. Domain knowledge is important since it allows you to apply your particular analytics skills to the particular analytic problems of whatever business you are (or wish to) work within. Everyone business has its own nuances when it comes to solving analytic problems. If you do not have the time or inclination to learn all about the inner workings of the problem at hand yourself, partner with someone who does. That will be the start of a great team!
  • Data storage/Extract Transform and Load (ETL) skills: This can refer to specialized knowledge regarding extracting data, and storing it in a relational or non-relational NoSQL data store. Historically, these tasks were handled exclusively within a data warehouse. But now that the age of big data is upon us, specialists have emerged who understand the intricacies of data storage, and the best way to organize it.

Related job skills and terms

Along with the term predictive analytics, here are some terms that are very much related:

  • Predictive modeling: This specifically means using a mathematical/statistical model to predict the likelihood of a dependent or target variable. You may still be able to predict; however, if there is no underlying model, it is not a predictive model.
  • Artificial intelligence (AI): A broader term for how machines are able to rationalize and solve problems. AI's early days were rooted in neural networks.
  • Machine learning: A subset of AI. Specifically deals with how a machine learns automatically from data, usually to try to replicate human decision-making or to best it. At this point, everyone knows about Watson, who beat two human opponents in Jeopardy.
  • Data science: Data science encompasses predictive analytics but also adds algorithmic development via coding, and good presentation skills via visualization.
  • Data engineering: Data engineering concentrates on data extraction and data preparation processes, which allow raw data to be transformed into a form suitable for analytics. A knowledge of system architecture is important. The data engineer will typically produce the data to be used by the predictive analysts (or data scientists).
  • Data analyst/business analyst/domain expert: This is an umbrella term for someone who is well versed in the way the business at hand works, and is an invaluable person to learn from in terms of what may have meaning, and what may not.
  • Statistics: The classical form of inference, typically done via hypothesis testing. Statistics also forms the basis for the probability distributions used in machine learning, and is closely tied with predictive analytics and data science.