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Hands-On Statistical Predictive Modeling [Video]

Data Science at your fingertips: build statistical predictive models to make predictions based on data.

Predicting future trends can be the difference between profit and loss for competitive enterprises. Most businesses state that poor data quality leads to bad decision-making. Further, the predictive analytics market is expected to grow by 22% by 2020. As this technology hits the mainstream, now is the time to consider which predictive modeling techniques will produce the best results for your organization.

Hands-On Statistical Predictive Modeling gives you everything you need to bring the power of statistical predictive models into your statistical or data mining work. However, without the right predictive modeling techniques, analytics projects are unlikely to provide actionable insights. This course will show you how these core algorithms underpin the accuracy and relevance of statistical results and drive competitive differentiation. You will be able to anticipate customer behavior, take steps to cultivate customer loyalty, and capture a greater share of the market. You will be aware of the data science forces shaping your future economy and will have mastered how best to use and seize these coming opportunities.

By the end of this course, you will be able to elevate your company’s analytics know-how to enhance its decision-making skills, cost efficiency, and profitability. You will also be able to put these skills to use in your upcoming statistical and data mining projects.

All data files for this course are available on GitHub at https://github.com/PacktPublishing/Hands-On-Statistical-Predictive-Modeling

Style and Approach

This is an application-oriented course and the approach is practical. It discusses situations in which you would use each statistical predictive modeling technique, the assumptions made by the method, how to set up the analysis, and how to interpret the results. No proofs will be derived, but rather the focus will be on the practical aspects of data analysis for the purpose of improving predictive models.

What You Will Learn

  • Differentiate between various types of predictive models
  • Master linear regression
  • Explore the results of logistic regression
  • Understand when to use discriminant analysis
  • Understand the inner workings of your models
  • Maximize your productivity by analyzing your models and interpreting their accuracy in a well-organized manner

Authors

Jesus Salcedo

Jesus Salcedo has a Ph.D. in Psychometrics from Fordham University. He is an independent statistical and data-mining consultant and has been using SPSS products for over 20 years. He is a former SPSS Curriculum Team Lead and Senior Education Specialist; he has written numerous SPSS training courses and trained thousands of users.

 

 

 

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