What do I get?

• Intro to Data Analysis: we cover the basics of R, version control, reproducibility and best practice, reading in data, data types, functions, control flow, SQL, databases, data wrangling and data cleaning, dealing with outliers and missing values, and basic data analysis. We also explore data management, data governance, and data ethics.
• Statistics & Visualisation: we cover statistics, visualisation, communicating data, and start to introduce some specialist topics including time series and spatial analysis. This also includes the basics of hypothesis testing, probability, and simple regression models.
• Predictive Analytics: The final module covers more complicated statistics and specialist topics including power analysis, effect size calculation, text mining and multiple regression. It also covers manual model building, variable engineering, variable reduction, model implementation and performance, cluster analysis, decision tree building, and model testing. It finishes with Python and the option to complete a final project in either Python or R.

What do I need?

• All students must complete the application process
• This is to ensure we can get to know you a little, understand your career experience and expectations and prepare you to start on campus as a confident, productive learner.All students need to complete the pre-work before starting the course
• This is designed to help you get comfortable with the tools you’ll be using throughout your training
• We’ll also use this time to give you a refresher on some of the maths you might not have looked at since you left school
• The pre-course is self-guided and completed at your own pace during the two weeks before you kick off on campus.