Data Science with R

About This Course: This course is about learning data science, or statistical learning – as it is sometimes called, which is a set of cross-disciplinary skills, or techniques, for modelling and understanding complex datasets, using R programming language. Data science is fundamentally an interdisciplinary subject. It comprises three distinct and overlapping fields: statistics, computer science and the domain expertise— that is, a "classical" training in a subject which is necessary both to formulate the right questions in data science and to put their results in context. Applications of data science include election results reporting, stock returns forecasting, identification of microorganisms in microscope photos, and many more. The goal of this course is to give you the ability to apply tools of data science in your chosen subject area. R programming languages are used by many governments around the world for data science applications - for example, in developing models for predicting river flooding or to track water pollution. It is used by offices of political parties, for instance, to identify potential voters. Companies using R for data science include Facebook, Google, Twitter, Microsoft, and IBM.


People with statistical learning skills are in high demand, and this course will concentrate more on the applications of the methods and less on the mathematical details. To provide students with hands-on experience, the course will illustrate how to implement in R programming language to explore many of the data science methods that will be covered.  


This course is appropriate for professionals or advanced undergraduates, master’s or research students in statistics, computer science, engineering, robotics, geographic information systems, economics, mathematical finance, computational biology, computational chemistry, physics or related quantitative fields or for individuals in other disciplines who wish to use statistical learning tools to analyse their data.


Training Cost: ₦ 315,000.00    


Background on R

R is a software environment and powerful programming language for statistical computing and graphics. R programming was created by Ross Ihaka and Robert Gentleman around 1993 at the University of Auckland (New Zealand) and is an implementation of an earlier programming language called S.  With its libraries, R implements a wide range of statistical and graphical tools, including modelling, classification, clustering, time-series, among others. Some of the main advantages of R programming language include the following:

• R is probably the most comprehensive statistical analysis package available.

• It is a programming language developed for statistical analysis by practising statisticians and researchers for use by other statisticians and researchers.

• It is has an outstanding graphical capabilities

• It is free – R is free of cost and open source. It allows everyone to use it and modify it.

• Cross-platform support: R is available for commonly available computer operating systems, including Microsoft Windows, GNU/Linux and Macintosh.

• R has a large library of packages that are available for free downloaded on a wide range of topics including econometrics, bio-informatics, geographic information systems, and so on.


Course Dates: 
Saturday 6 January 2018 – Saturday 10 March 2018


Course Time: Saturdays, 1pm – 4pm, 6 Jan – 10 Mar 2018


Prerequisites: Basic IT skills and basic programming skills in any programming language. Our Scientific Programming Using Python course or Scientific Programming with C++ course would be useful, for instance. Also, a first year undergraduate-level mathematics or background in engineering, physics, mathematics, economics, finance or another applied science with some mathematical content will be helpful.



  Information on how to book one or more of our courses can be found here .


Some Applications of Data Science with R

   •     Business Analytics Marketing Effectiveness





   •     Forecasting Swizerlands GDP with R





   •     Electric Power Station Spatial Data Mining





   •     Conceptually Linking Geographic Space and Data Space





   •     Population census data analysis





   •     Stock Market Price Trend Prediction





   •     Text Mining in Health Care: Identity, Health and Socialism




Availability Type: 
Key Features
Get an overview of the field of statistical learning
Learn essential techniques for making sense of the vast and complex data sets that have emerged in fields ranging from biology to chemistry to medicine to finance to marketing to astrophysics
Be exposed to some of the most important modelling and prediction techniques, along with relevant applications
Statistical Learning
Linear Regression
Resampling Methods
Linear Model Selection and Regularization
Moving Beyond Linearity
Tree-Based Methods
Support Vector Machines
Unsupervised Learning