Data mining, analytic modeling, algorithms, artificial intelligence, machine learning … getting from business needs to analytic solutions demands a highly specialized set of skills and the ability to apply those skills in the right ways. Real data science includes the discipline of scientific method.
Predictive analytics is the baseline of advanced analytics and data science. It is a set of techniques used to gain new knowledge from large amounts of raw data by combining data mining, statistics, and modeling. Predictive analytics goes beyond insight (knowing why things happen) to foresight (knowing what is likely to happen in the future).
Analytics encompasses many skills and disciplines. Identifying the problem, choosing the modeling approach, selecting the correct features to model, and evaluating the result are at the heart of analytics. The tendency, however, is to focus primarily on the technology rather than the process. It is important to start by understanding the problem and defer technology until later in the process.
Data mining is an underlying discipline for the solutions to many kinds of data science and analytics problems. R is an open source software environment for statistical computing and graphics. It is popular with data scientists and an effective environment to learn how to apply data mining techniques.
The Enabling Foresight: Skills for Predictive Analytics workshop will cover essential analytics and data science techniques and best practices over three days of in-depth, interactive training.
There are no prerequisites for this course.
Business analysts, data analysts, and data scientists who need to frame analytic problems and choose the most effective ways to solve those problems
Business and technical managers who need to understand the nature of analytics and data science work
BI and analytics developers who work with data scientists
Anyone who aspires to become a data analyst, business analyst, or data scientist
Anyone interested in learning to use data mining techniques to find insights in data and who has at least some statistical and programming experience
Definitions, concepts, and terminology of predictive analytics
To distinguish among various predictive model types and understand the purpose and statistical foundations of each
Understand and classify different types of data science problems
Discern the characteristics of common data science scenarios
Match data science problems to the best-fit models to solve them
Use R as a data mining tool including functions for correlation, covariance, linear regression, logistic regression, and non-linear models