Prerequisite: Attendees should have some coding experience, basic statistics, and will need to bring a laptop computer with RStudio installed prior to the session. In advance of the class attendees will receive detailed instructions for download and installation of RStudio.
Data mining, analytic modeling, algorithms, artificial intelligence, machine learning—you need highly specialized skills to go from business needs to analytics solutions. Real data science also includes the discipline of the 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 Building the In-Demand Skills for Analytics and Data Science workshop will cover essential analytics and data science techniques and best practices over three days of in-depth, interactive training.
Your Team Will Learn How To
- 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
- 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
- Computers with RStudio installed are necessary for the exercise portions of this content