Data Analytics in R

eCornell

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eCornell

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Who should attend

  • Analysts
  • Developers
  • Data scientists
  • Functional managers
  • Consultants
  • Any professional that uses data to make business decisions

About the course

R is now considered one of the most popular analytics tools in the world. In this certificate program you will develop the skill set necessary to perform key aspects of data science efficiently. The courses cover the application of core analytics concepts in the R programming environment to allow a scalable implementation.

You’ll learn techniques for manipulating and visualizing data, describing data through descriptive statistics, and clustering. You’ll extend these basic reporting approaches through classification and predictive analytics using traditional parametric models (regression and logistic regression) as well as machine learning techniques. In addition, you’ll develop linear, nonlinear, and Monte Carlo decision-making models that will allow you to make more informed decisions.

To be successful in this program, it is recommended that students have a background in predictive and prescriptive data analytics, specifically with optimization, modeling, and Monte Carlo simulations, in addition to a familiarity with programming syntax.

Predictive Analytics in R

Data modeling has become a pervasive need in today's business environment. Often the volume of data you need to process goes beyond the capabilities of spreadsheet modeling. When this is the case, the statistical programming language R offers a powerful alternative. With R, you can avoid the cost of standalone statistical packages. Likewise, you don't need a huge investment in learning the structures required to use a more fully featured programming language.

In this course, you will work through the basic methods of predictive analytics, including generating descriptives, visualization, single and multiple regression, and logistic regression. The benefits of using R for logistic regression are significant, and these are explored in detail. When you have completed this course, you will have gained experience developing R code to solve novel problems in which basic predictive methods are required.

Clustering, Classification, and Machine Learning in R

When faced with a large volume of unstructured data, the question quickly arises: what does this all mean? Techniques in machine learning offer the promise of a meaningful answer to that question. Unsupervised machine learning is a powerful tool that is being put to use in many disciplines. In this course, you'll experience machine learning through scripting in the statistical programming language R.

The course focuses on using unsupervised machine learning to bring coherence to unstructured data. Specifically, you'll use different methods to generate clusters within your data set when no dependent variable is specified. Using supervised machine learning approaches, you'll build and evaluate models that allow you to classify your data and understand the marginal impacts of each attribute. And you'll gain experience with powerful tools in R that allow you to efficiently evaluate competing models to find the one that gives you the most accurate results.

Prescriptive Analytics in R

Sometimes the problem you need to solve involves amounts of data or numbers of decisions that go well beyond the capabilities of spreadsheets. You can work around these limitations by replicating spreadsheet methods of simulation and optimization in the script-based programming environment in R. The use of R carries the benefits of flexibility, automation, and expanded set of tools and algorithms.

In this course, you will work through the development and implementation of Monte Carlo simulations. You'll become familiar with the R functions most commonly used for this purpose. You'll also translate optimization problems that have been defined outside R to a form that supports computational solutions in R. You'll work with both linear and nonlinear solution methods.

It is recommended that students have a background in data analytics especially with optimization, modeling, and monte carlo simulations, in addition to a familiarity with programming syntax

KEY COURSE TAKEAWAYS

  • Understand, model and visualize data using R
  • Make predictions for qualitative and quantitative dependent variables using R
  • Efficiently use the full breadth of parametric and non-parametric predictive data models in R
  • Develop models to make complex, large-scale decisions through the use of mathematical approximations such as optimization (linear, nonlinear, dynamic programming) and Monte Carlo simulations using R

Experts

Christopher Anderson

Chris K Anderson is a Professor at the Cornell School of Hotel Administration. Prior to his appointment in 2006, he was on faculty at the Ivey School of Business in London, Ontario Canada. His main research focus is on revenue management and service pricing. He actively works with industry, acros...

Data Analytics in R at eCornell

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