Uncover critical insights. Start making efficient and profitable data-driven business decisions.
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No prior knowledge required
No unnecessary risks
This course is designed for students, business analysts, and data scientists who are interested in applying statistical knowledge and techniques in business contexts. For example, it may be suitable for experienced statisticians, engineering analysts, and data scientists who are interested in moving into business roles.
You will find this course exciting and rewarding if you already have a background in statistics, know how to use R or another programming language, and are familiar with databases and data analysis techniques such as regression, classification, and clustering.
However, the course includes several R Studio exercises and tutorials that will strengthen your skills, allow you to play with data more freely, and explore new statistical content and functions in R.
Through this course, you will get an initial overview of strategic business analytics topics. We will discuss a wide range of business analytics applications – from marketing to supply chain management, credit scoring to human resource analytics, and more. We will focus on different data analysis techniques, each time clarifying how to be relevant to your business.
We will pay special attention to how you can generate compelling, actionable, and effective insights. We will introduce you to different data analysis tools that can be used for different problems.
In this way, we will help you develop four skill sets required to extract value from data: analytics, technology, business, and communication.
At the end of this course, you will be able to approach a business problem using analytics by:
We give credit to Pauline Glickman, Alban Gober, Elias Abu Khalil-Lenouin (students at ESSEC Business School) for their contributions to the design of this course.
This course is designed for students, business analysts and data scientists who are interested in applying statistical knowledge and techniques in business contexts. For example, it may be suitable for experienced statisticians, engineering analysts who are interested in moving into business roles, particularly in marketing.
You will find this course exciting and rewarding if you already have a background in statistics, know how to use R or another programming language, and are familiar with databases and data analysis techniques such as regression, classification, and clustering.
However, the course includes several R Studio exercises and tutorials that will strengthen your skills, allow you to play with data more freely, and explore new statistical content and functions in R.
Business analytics, big data, and data science are hot topics today, and for good reason. Many companies are sitting on a treasure trove of data, but they often lack the skills and people to analyze and utilize that data effectively. Those companies that develop the skills and hire the right people to analyze and utilize that data will have a clear competitive advantage.
This is especially true in one area: marketing. About 90% of the data collected by companies today is related to customer actions and marketing activities. The field of marketing analytics is vast, and it can include fascinating topics such as text prediction, social network analysis, sentiment analysis, real-time suggestions, online campaign optimization, and more.
But at the heart of marketing are some basic questions that often remain unanswered:
That’s exactly what this course will cover: Customer segmentation is the practice of understanding your customers, ranking models help target the right ones, and customer lifetime value focuses on estimating their future value. These are the basics of marketing analytics, and that’s what you’ll learn to do in this course.
This course is intended exclusively for students enrolled in the Strategic Business Analytics specialization in preparation for their final project. In the first two courses, you focused on specific techniques for specific applications. Instead, in this course we offer various examples to open your mind to different applications from different industries and sectors.
The goal is to give you a broad overview of what’s happening in this field. You’ll see how the tools presented in the previous two courses of the special are used in real projects.
We want to ignite your thinking process. So, make the most of Accenture’s cases by watching the course and then independently exploring the concepts, industries, or challenges presented during the videos.
The cases will be presented by senior Accenture professionals with diverse industry, function, and country backgrounds. Special attention should be paid to the “value case” of the problem raised to prepare you for the specials’ final project.
Accenture is a leading global professional services firm, providing a broad range of services and solutions across strategy, consulting, digital, technology and operations. Combined, Accenture brings unrivaled experience and unique skills across more than 40 industries and across business functions – supported by the world’s largest network of services delivery – working at the intersection of business and technology to help clients improve performance and create sustainable value for their stakeholders. With more than 358,000 people serving clients in more than 120 countries, Accenture innovates to improve the way the world works and lives. Visit us at www.accenture.com.
The final project is a personal assignment. Participants decide on the topic they want to research and define the problem they want to solve. Their “field of play” should include different sectoral data (such as agriculture and nutrition, culture, economy and employment, education and research, intelligence and Europe, housing, sustainable development and energy, health and society, society, territories and transport).
Participants are encouraged to integrate the different fields and utilize existing information with open databases (precisely de-identified).
The preliminary preparation phase and problem definition. The goals are to define what, why and how. What problem do we want to solve? Why does this promise value to public authorities, companies, and citizens? How do we want to explore the data provided?
The participant should present the intermediate outputs and adjustments to the analysis framework. The goals are to validate the nature and significance of the initial results.
The participant needs to present the final deliverables and the value case. The goal is to validate the reason. Why it creates value for public authorities, companies and citizens.
Participants will present their results to their peers regularly. An evaluation framework will be provided for participants to assess the quality of others’ deliverables.
