Foundations of strategic business analytics (Coursera)

Offered by ESSEC Business School,
Foundations of strategic business analytics (Coursera)

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.

Class Deals by MOOC List - Click here and see Coursera's Active Discounts, Deals, and Promo Codes.

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.
Course 1 of 4 in the Strategic Business Analytics Specialization.

Syllabus

WEEK 1
Introduction to Strategic Business Analytics
In this module, we will introduce you to the course and instructional approach. You will learn that Strategic Business Analytics relies on four distinct skills: IT, Analytics, Business and Communication.
Finding groups within Data
In this module, you will learn how identifying groups of observations enables you to improve business efficiency. You will then learn to create those groups in a business-oriented and actionable way. We will use examples to illustrate various concepts. The assessments will also provide you with opportunities to replicate these examples.

WEEK 2
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.

WEEK 3
Predictions and Forecasting
In this module you will learn more about the importance of forecasting the future.
You will learn through examples from various sectors: first, using the previous examples of credit scoring and HR Analytics, you will learn to predict what will happen. Then, you will be introduced to predictive maintenance using survival analysis via a case discussion. Finally, we’ll discuss seasonality in the context of the first example discussed in this MOOC: using analytics for managing your supply chain and logistics better.

WEEK 4
Recommendation production and prioritization
So far, you’ve learnt to use Business Analytics to glean important information relevant to the success of your business. In this module, you’ll learn more about how to present your Business Analytics work to a business audience. This module is also important for your final capstone project presentation.You’ll learn that it is important to find an angle, and tell a story.Instead of presenting a list of results that are not connected to each other, you will learn to take your audience by the hand and steer it to the recommendations you want to conclude on.You’ll learn to structure your story and your slides, and master the most used visualization tips and tricks. The assessment at the end of this module will provide an opportunity for you to practice these methods and to prepare the first step of the capstone project.

Go to Class
MOOC List is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

Related Courses

Global Statistics - Composite Indices for International Comparisons (Coursera) Coursera
University of Geneva

Global Statistics - Composite Indices for International Comparisons (Coursera)

In this course on global statistics, offered by the University of Geneva jointly with the ETH Zürich KOF, you will learn the general approach of constructing composite indices and some of resulting problems. We will discuss the technical properties, the internal structure (like aggregation, weighting, stability of time series), the primary data used and the variable selection methods. These concepts will be illustrated using a sample of the most popular composite indices. We will try to address not only statistical questions but also focus on the distinction between policy-, media- and paradigm-driven indicators.

Aug 3rd 2026
5-12 Weeks
Making Data Science Work for Clinical Reporting (Coursera) Coursera
Genentech

Making Data Science Work for Clinical Reporting (Coursera)

This course is aimed to demonstrate how principles and methods from data science can be applied in clinical reporting. By the end of the course, learners will understand what requirements there are in reporting clinical trials, and how they impact on how data science is used. The learner will see how they can work efficiently and effectively while still ensuring that they meet the needed standards.

Aug 10th 2026
4 Weeks
Introduction to Accounting Data Analytics and Visualization (Coursera) Coursera
University of Illinois at Urbana-Champaign

Introduction to Accounting Data Analytics and Visualization (Coursera)

Accounting has always been about analytical thinking. From the earliest days of the profession, Luca Pacioli emphasized the importance of math and order for analyzing business transactions. The skillset that accountants have needed to perform math and to keep order has evolved from pencil and paper, to typewriters and calculators, then to spreadsheets and accounting software. A new skillset that is becoming more important for nearly every aspect of business is that of big data analytics: analyzing large amounts of data to find actionable insights. This course is designed to help accounting students develop an analytical mindset and prepare them to use data analytic programming languages like Python and R.

Aug 10th 2026
5-12 Weeks
Data Processing Using Python (Coursera) Coursera
Nanjing University

Data Processing Using Python (Coursera)

This course is mainly for non-computer majors. It starts with the basic syntax of Python, to how to acquire data in Python locally and from network, to how to present data, then to how to conduct basic and advanced statistic analysis and visualization of data, and finally to how to design a simple GUI to present and process data, advancing level by level.

Aug 10th 2026
5-12 Weeks
Advanced Linear Models for Data Science 2: Statistical Linear Models (Coursera) Coursera
Johns Hopkins University

Advanced Linear Models for Data Science 2: Statistical Linear Models (Coursera)

Welcome to the Advanced Linear Models for Data Science Class 2: Statistical Linear Models. This class is an introduction to least squares from a linear algebraic and mathematical perspective. Before beginning the class make sure that you have the following: a basic understanding of linear algebra and multivariate calculus; a basic understanding of statistics and regression models; at least a little familiarity with proof based mathematics; basic knowledge of the R programming language.

Aug 3rd 2026
4 Weeks
Introduction to Neurohacking In R (Coursera) Coursera
Johns Hopkins University

Introduction to Neurohacking In R (Coursera)

Neurohacking describes how to use the R programming language and its associated package to perform manipulation, processing, and analysis of neuroimaging data. We focus on publicly-available structural magnetic resonance imaging (MRI). We discuss concepts such as inhomogeneity correction, image registration, and image visualization.

Aug 10th 2026
4 Weeks
Data Visualization with Tableau Project (Coursera) Coursera
University of California, Davis

Data Visualization with Tableau Project (Coursera)

In this project-based course, you will follow your own interests to create a portfolio worthy single-frame viz or multi-frame data story that will be shared on Tableau Public. You will use all the skills taught in this Specialization to complete this project step-by-step, with guidance from your instructors along the way.

Aug 10th 2026
5-12 Weeks
Practical Time Series Analysis (Coursera) Coursera
The State University of New York

Practical Time Series Analysis (Coursera)

Many of us are "accidental" data analysts. We trained in the sciences, business, or engineering and then found ourselves confronted with data for which we have no formal analytic training. This course is designed for people with some technical competencies who would like more than a "cookbook" approach, but who still need to concentrate on the routine sorts of presentation and analysis that deepen the understanding of our professional topics.

Aug 10th 2026
5-12 Weeks
Psychological First Aid (Coursera) Coursera
Johns Hopkins University

Psychological First Aid (Coursera)

Learn to provide psychological first aid to people in an emergency by employing the RAPID model: Reflective listening, Assessment of needs, Prioritization, Intervention, and Disposition. Utilizing the RAPID model (Reflective listening, Assessment of needs, Prioritization, Intervention, and Disposition), this specialized course provides perspectives on injuries and trauma that are beyond those physical in nature. The RAPID model is readily applicable to public health settings, the workplace, the military, faith-based organizations, mass disaster venues, and even the demands of more commonplace critical events, e.g., dealing with the psychological aftermath of accidents, robberies, suicide, homicide, or community violence.

Aug 10th 2026
5-12 Weeks
Introduction to Statistics (Coursera) Coursera
Stanford University

Introduction to Statistics (Coursera)

Stanford's "Introduction to Statistics" teaches you statistical thinking concepts that are essential for learning from data and communicating insights. By the end of the course, you will be able to perform exploratory data analysis, understand key principles of sampling, and select appropriate tests of significance for multiple contexts. You will gain the foundational skills that prepare you to pursue more advanced topics in statistical thinking and machine learning.

Aug 10th 2026
5-12 Weeks