Introduction to Statistical Data Science with R

Course Agenda

You will work through online material (video presentations, notes, interactive quizzes and practical exercises) covering the following topics:

Data Types

You will learn about the different types of data (e.g. numerical/categorical, quantitative/qualitative, discrete/continuous/ordinal/nominal).

Descriptive Statistics

You will learn how to summarise the location, spread and shape of data using simple metrics (e.g. mean, median, variance, etc.).

Graphical Summaries

Charts and graphs can be an excellent tool to summarise and present data. In this topic you will be introduced to different types of graphs (e.g. bar charts, histograms, boxplots, scatter plots), how to interpret them, and when to use them.

Probability Distributions

You will learn how a probability distribution can be used to describe the probability of the outcomes of an experiment or event.

Statistical Testing

Statistical testing is a method of statistical inference that can be used to test a theory/hypothesis. Applications include:

  • Testing for a deviation from a hypothesised value (e.g. a lightbulb manufacturer claims its lightbulbs last an average of 1000 hours, you want to test this claim)
  • Testing for a difference between two or more groups (e.g. is there a difference in the average salaries for men and women?)
  • Testing for a change following an event or intervention (e.g. does a new drug/treatment affect blood pressure?)

    This topic introduces the statistical testing procedure, including important concepts such as p-values. You will learn how to draw conclusions from statistical tests. You will also learn about different types of tests and how to identify the correct test to use.

    Simple Linear Regression

    Simple linear regression is a statistical modelling technique. It can be used to understand the relationship between two variables and make predictions (e.g. how much increase in sales can we expect if we increase advertising expenditure). In this topic you will learn how to interpret the regression model, how to use the model to make predictions, and how to evaluate how well the model reflects reality.

    R for Data Analysis

    This topic will provide you with the coding skills needed to analyse data in R. You will learn: basic programming, data manipulation, data visualisation, statistical testing and linear regression.

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