BUSINESS ANALYTICS
In the first module, you will learn how to interpret data correctly, how to ask the right questions and generate insights from data.
This module aims to help avoiding common mistakes when looking at data. Adopt a critical framework when presented a report or a nice chart in a presentation
STATISTICAL THINKING FOR DECISION MAKING
In order to make right business decisions, you do not need to be a statistician. However, grasping the fundamental concepts of statistics can make you ask the right question when challenging analysis and trying to take actions from data insights.
BIG DATA AND DATA TEAMS
Asking the right questions and knowing how to interpret data is nice, but this can be of little help if we don't have access to the right data or the right infrastructure to process it. In this third module you’ll learn which are the critical levers to take into account when setting up a data infrastructure and data teams
PREDICTION AS A BLACKBOX
This module will treat data science as a 'black-box', focusing on the key contact points of data science with the business that should never been neglected, also seeing in practice how the same machine learning model can take different shapes depending on changing business needs.
MACHINE LEARNING IN THE BUSINESS DOMAIN
This module will guide you to build machine learning models to tackle commonly encountered business problems, such as customer segmentation, customer churn prediction and sales forecasts.
You will use a combination of Excel and pre-built python template code to customize, with no coding experience needed.
FINAL PROJECT
This last module is an independent work to apply the skills learned so far in a real-world problem. You will have the opportunity to choose your own topic, depending on your interests, but you will guided throughout the process, both in the project definition and delivery
LESSON STRUCTURE
Each lesson lasts 2 hours and a typical lessons is composed as follows:
Group discussion of the key questions for the lesson (30-45 mins approx): discussion and brainstorming. Gathering of personal experiences.
Theoretical understanding (30-45 mins approx): understanding the theoretical background which underpins the issues discussed
Practical exercise (30-45 mins approx): Most classes will have a hands-on problem to solve it independently. Cases will be done in Excel, online tools or using Python template code (no coding skills required)
MENTORING
A final 1-hour individual mentoring session to review further educational plans or career advice.
Take this opportunity to identify your further learning needs depending on your goals.
