In week 2, we’ll focus on basic statistics. It’s one of the most important components of Data Analytics and it’s crucial to have a clear understanding of all the related concepts to be successful in the data industry. Statistics provide us with a set of tools that offer ways to convert quantitative data and qualitative data into information that we can use to generate insights.

Businesses must constantly strive to offer “better” products and services than their competitors. One of the oldest and time-proven techniques by which we can visualise and think about quality in a methodological way is via normal distributions or bell curves. So in week 3, we’ll start by learning about histograms and the normal curve and then have a look at empirical rule which gives us a quick rough estimate about the spread of the given data. Finally, we’ll learn about the measures that quantify the interrelationships between two data variables. Correlation and covariance are two important measures that quantify the relationship between variables and we’ll study both.

Visualisation is a key technique which can provide answers hidden in data. In this week, you will explore various data visualisations available and how to use them for analysis. These techniques will empower you to create compelling stories and dashboards from your data that the non-analyst community can also understand easily. As a person working in the data industry, you don’t just need to deal with data and solve data-driven problems but the incumbent also needs to convince company executives and government officials of the right decisions to make. These executives/officials may not be well versed in data science, so the incumbent must but be able to present and visualise the data’s story in a way they will understand. And this module will help you achieve that.

This week we learn how to create bar and bullet charts, and dashboards. Data visualization helps to tell stories by curating data into a form easier to understand. A good visualisation tells a story, by removing the noise from data and highlighting the useful information.

This week we’ll look at how, by using predictive modelling, we can generate actionable insights that when implemented will provide businesses with a predictable future outcome. Predictive modeling is a group of methods and algorithms that you can employ to forecast an outcome. Utilising basic predictive modelling techniques, we will also explore consumer demand forecasting.