Who is this course for?
This course is designed for students, business analysts, and data scientists who want to apply statistical knowledge and techniques to business contexts. For example, it may be suited to experienced statisticians, analysts, engineers who want to move more into a business role.
You will find this course exciting and rewarding if you already have a background in statistics, can use R or another programming language and are familiar with databases and data analysis techniques such as regression, classification, and clustering.
However, it contains a number of recitals and R Studio tutorials which will consolidate your competences, enable you to play more freely with data and explore new features and statistical functions in R.
With this course, you’ll have a first overview on Strategic Business Analytics topics. We’ll discuss a wide variety of applications of Business Analytics. From Marketing to Supply Chain or Credit Scoring and HR Analytics, etc. We’ll cover many different data analytics techniques, each time explaining how to be relevant for your business.
We’ll pay special attention to how you can produce convincing, actionable, and efficient insights. We'll also present you with different data analytics tools to be applied to different types of issues.
By doing so, we’ll help you develop four sets of skills needed to leverage value from data: Analytics, IT, Business and Communication.
By the end of this MOOC, you should be able to approach a business issue using Analytics by (1) qualifying the issue at hand in quantitative terms, (2) conducting relevant data analyses, and (3) presenting your conclusions and recommendations in a business-oriented, actionable and efficient way.
Prerequisites : 1/ Be able to use R or to program 2/ To know the fundamentals of databases, data analysis (regression, classification, clustering)
We give credit to Pauline Glikman, Albane Gaubert, Elias Abou Khalil-Lanvin (Students at ESSEC BUSINESS SCHOOL) for their contribution to this course design.

De la lección

Factors leading to events

In this module, you will learn why using rigorous statistical methods to understand the relationship between different events is crucial.
We’ll cover two examples: first, using a credit scoring example, you will learn how to derive information about what makes an individual more or less likely to have a strong credit score? Then, in a second example drawn from HR Analytics, you will learn to estimate what makes an employee more or less likely to leave the company.
As usual, we invite you to replicate those examples thanks to the recital
and to use the assessments provided at the end of the module to strengthen your understanding of these concepts.