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Centre for Professional Development and Lifelong Learning

Short Courses

Business and Data Analytics

Data Science for Business using R


The intelligent use of data can power businesses to new levels of competitiveness. This course provides you with the practical, analytical and technical skills to extract useful knowledge and gain insight from small and large datasets via the ‘R’ tool. The course will address predictive modelling, data scraping, sentiment analysis, and text mining. You will also explore how to generate and incorporate data from webpages, pdf documents and internet search engines such as Google Trends. In summary, the course will bridge the gap between theory and abstract statistics and real-world applications through an instructor-led, hands on approach, and provide you with the analytical and technical tools to distinguish themselves in today’s workplace.

Mode of Delivery: Face-to-Face Online

What will I Learn?

On successful completion of the course, delegates will be able to:

  • Plot a range of graphics and visualisations
  • Formulate hypotheses and design predictive questions
  • Implement the simple prediction process
  • Build linear and nonlinear prediction models for regression and classification
  • Evaluate and compare the performance of prediction models
  • Incorporate data from new sources, particularly through web scraping
  • Import, handle and analyze the sentiment of text data

Who Should do this Course

Persons who have successfully completed the Introduction to R course or have had some exposure to the basics to ‘R’; individuals with specific interest in data science for business decision-making, social media analysis and sentiment analysis; Professionals tasked with managing data science-oriented projects, and individuals seeking to pursue advanced degrees in Business Analytics and/or Data Science will also derive significant benefits from taking this course

Important Information

  This course has been offered at a special rate, CLICK HERE to view Terms and Conditions

Students should bring their laptops sessions


For our International/ Regional delegates who have successfully completed the requirements of obtaining their Certification, The Centre for Professional Development and Lifelong Learning would be more than happy to deliver your Certificate via a courier service for which you would be required to bear the cost. This sum must be paid to The UWI, Cave Hill Campus prior to dispatch of the certificate. 

For more details, email and a representative will guide you accordingly.”

At a Glance

  • Admissions Term: 2020/2021 Semester 1
  • Date: November 3rd- 21st, 2020
  • Time: Tuesday, Thursday (5pm-8pm); Saturday 10am-1pm
  • Duration: 9 days (26 hours)
  • Certificate Awarded: Professional Development Certificate of Competence
  • Course Code: PDLL126
  • CEUs: 2.6
  • Capacity: 20
  • Cost: BDS $2,685 (US $1,342.50) BDS$ 500.00 (US$ 250.00)

The following topics will be addressed
  • Building graphics in ggplot2
  • The principles of prediction
  • An introduction to machine learning
  • Linear regression models
  • Classification models
  • Tree-based models
  • Evaluating prediction accuracy
  • Scraping webpages for new data
  • Incorporating Google Trends into predictive models
  • Handling text data and sentiment analysis

  • A first-year undergraduate/CAPE/A-level understanding of statistics is strongly recommended.  Specifically, participants should have taken a comprehensive course in statistics covering descriptive statistics and OLS regression
  • Completion of the Introduction to R for Business course or evidence of thorough working knowledge on skills and techniques listed in above course. Participants who have not completed the Introduction to R for Business course or are unable to provide evidence of thorough working knowledge of R will be required to pass an R proficiency test

The course will be delivered through interactive practical sessions in a computer lab.  Online illustrations and videos may be incorporated.

Mr. Nigel Henry

Related Courses

Introduction to R for Business