What is data exploration? How does one analyse the distribution of data? This in-depth statistics course is created to teach the concepts required to understand and analyse datasets. ​​You will begin by defining some commonly used terms in statistical analysis and go on to study the concept of univariate data and how to use visual representation to examine a data set and reveal hard-to-detect patterns. You will discuss how to create charts and other methods for displaying data including frequency tables, dot plots, stem plots, frequency histograms and cumulative frequency plots. 

The course then describes the spread of the data and how it is measured. Learn about the mean, median, gaps, clusters and outliers. There are many shapes of distributions that occur frequently and so you will explore how a shape is classified into categories. This course goes further in highlighting how shapes in a data set do not have a unifying concept for interpretation. You will get to know how the features of a shape in a dataset is just the reflection of the varying relative frequencies over the data range. You will discover the several advantages of using metrics as a method of data interpretation as well as the decision-making process when interpreting a chart. You will be taken through steps on how to measure the amount of ‘variability’ associated with a particular set of data and learn about standard deviation and mean absolute deviation. 

With huge amounts of data being collected constantly, there is an increasing need for the extraction of useful information. The process of cleaning, transforming and modelling data, also known as ‘data analysis’, is therefore crucial. This course in data exploration and distribution is designed to expand your knowledge of this ever-developing field. So if you want to master the major concepts and tools for collecting, analysing and drawing conclusions from data, start this course today.