- Platform
- Coursera
- Provider
- ESSEC Business School
- Effort
- 2-3 hours/week
- Length
- 4 weeks
- Language
- English
- Credentials
- Paid Certificate Available
- Part of
- Course Link
Overview
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.
Taught by
Nicolas Glady
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.
Syllabus
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.
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.
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.
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.
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.
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.
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.
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.
Taught by
Nicolas Glady