All training courses are provided in-house and the courses listed below can be mixed to meet your needs.
Please contact us to discuss what courses would best match your requirements and if you have any questions on specific courses or course categories.
Programming languages go in and out of style. To be a strong programmer, it’s important to understand not just the ins and outs of a particular programming language, but how computer languages and computing infrastructure work more generally. In this course, learners are introduced to some of the core concepts of computer programming, in a language-agnostic way.
The French idiomatic expression “l’habit ne fait pas le moine” cautions analysts and data consumers alike not to fall into the trap laid by pretty pictures: content is more important than style. In a world where stakeholder buy-in and data storytelling is becoming increasingly important, however, there is no denying that great content and good visuals provide a significant upgrade on great content alone. In this course, we introduce dashboards and data storytelling.
Data analysis can’t happen without data, and that data must come from somewhere. Data collection and and data processing typically take up the bulk of the time spent on any data project; how well this is accomplished carries through to the end of the project. In this course, we consider the main elements required to succeed in data collection, as well as the many ways this activity can go awry.
This full-day in-person, hands-on workshop provides participants with information on using R for Data Science. It is intended for people who already have a programming background. The workshop is structured as follows:
Morning – Theory:
Review of R as a programming language:
History and comparison with other languages (e.g. Python)
Programming environment options – R Studio, R Markdown
Overview of key packages: Statistical Packages, Machine Learning Packages, TidyVerse (data processing + visualization), Shiny
Programming basics – conceptual overview
Brief Review of Relevant Statistical Concepts:
Descriptive Statistics
Statistical Modelling
Comparison between Statistics and Machine Learning
Afternoon – Hands-On:
Quick hands-on walk through of R command-line and R-Studio (e.g. running scripts, installing packages)
Getting into R program – with Pre-Worked Examples in R-Markdown
Practice Exercises and Lab Time