Data and Business Analytics with R

R is a free open source programming language used for data analytics and statistical analysis. Learning to build and use these AI applications with R will quickly enable you to develop custom AI apps to deploy within your own organization: applications for predictive modeling, for deep learning, for extracting mission-critical information from reams of text, and more.

Join AI with R to learn how to create AI applications that benefit your organization and enhance your analytics skill set and your career.

Data science is driving the AI market, with organizations looking to leverage AI capabilities for predictive modeling. To leverage these capabilities, organizations need developers trained in developing Artificial intelligence applications using R. Businesses all over the world are looking for smarter tools and applications that help them reduce efforts and maximize profits.

Data and Business Analytics with R

This 2-day course trains business professionals how to analyze vast volumes of data to directly benefit the goals and objectives of their organization. You will receive “hands-on” instruction using R for data analytics, examining a variety of areas: data exploration and visualization; decision trees; k-means and hierarchical clustering; outlier detection; association rule mining; social network analysis; and text mining. Other “hands-on” topics and R implementation examples may also be presented.

Most organizations have been collecting volumes of data for decades, without fully understanding how to use this vast data resource to grow their business and to gain a competitive advantage. Data volumes are growing and the pace of that growth is accelerating. Data relating to inventory, product sales, customer surveys, and social media are just a few of the data sources being widely collected. These and other data sources bring a volume, velocity, and variety of data, challenging the utility of conventional techniques for extracting actionable mission-critical information from this burgeoning resource. Forward-looking organizations are harnessing new prescriptive, predictive and diagnostic data analytics techniques in creative ways to achieve unprecedented value and competitive advantage from this data.

This 2-day course trains business professionals how to analyze vast volumes of data to directly benefit the goals and objectives of their organization. Data analytics is the process of inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information, suggesting conclusions, and supporting decision-making. Data mining is a particular data analysis technique that focuses on modeling and knowledge discovery for predictive rather than purely descriptive purposes, while business intelligence covers data analysis that relies heavily on aggregation, focusing on business information. In statistical applications, data analysis is often divided into descriptive statistics, exploratory data analysis (EDA), and confirmatory data analysis (CDA). EDA focuses on discovering new features in the data and CDA on confirming or falsifying existing hypotheses. Predictive analytics focuses on application of statistical models for predictive forecasting or classification, while text analytics applies statistical, linguistic, and structural techniques to extract and classify information from textual sources, a species of unstructured data. All are varieties of data analytics.

This 2-day course will present “hands-on” instruction using R for data analytics, examining a variety of areas: data exploration and visualization; decision trees; k-means and hierarchical clustering; outlier detection; association rule mining; social network analysis; and text mining. Other “hands-on” topics and R implementation examples may also be presented.

Course Software

The course uses the most current versions of R software and the RStudio IDE for R, both freely-available no cost software suites.

Other Course Material:

This 2-day course assumes a basic familiarity with R software (and with the RStudio IDE). For any participant who requires additional instruction on the basics of using R, we provide this asynchronous on-line course at no additional cost to participants: https://www.udemy.com/comprcourse/

It is recommended that participants have some working knowledge of R as described by the above course material up through the sixth section, User-Defined Functions. However, participants can review this Introduction to R material ‘on their own schedule’ either before, or after, taking the TCNJ 2-day course. Participants will have permanent, lifetime access to the above introductory R course material, as guaranteed by
Udemy.