Exploratory Data Analysis

Statistical analysis is an indispensable aspect of data analysis because it allows us to collect, review and analyse data to draw valuable conclusions in various industries. This is why the market for statisticians is projected to grow in the future. If you want to build your statistics and probability expertise and learn about data visualisation, this short course is a great introduction to statistics as the art of learning from data. With real-life examples, you will explore the differences between data and information to discover the need for statistical models to gain objective and reliable inferences. You will consider what "unbiased" data collection means and explore various examples of data misrepresentation, misconception or incompleteness which will help you to develop statistical intuition and good practice skills. Data visualisation is a sought-after skill. To create graphical and numerical summaries, you’ll learn and practice R software skills working in RStudio for exploratory data analysis. You will develop an intuitive concept of probability by completing probability experiments and computer simulations of binomial trails e.g., tossing a coin or rolling a die. By the end of the course, you will be able to understand the role of statistical models in data analysis, develop numerical and graphical summaries using RStudio, and perform probability experiments in computer simulations. No matter your current mathematics skill level, you will find something of interest in the course that offers many practical and real-life examples of statistics in action. This course is a taster of the Online MSc in Data Science (Statistics) and it can also be completed by learners who want to understand the fundamentals of exploratory data analysis and data visualisation.