This specialization is designed for students, business analysts, and data scientists who want to apply statistical knowledge and techniques to business contexts. We recommend that you have some background in statistics, R or another programming language, and familiarity with databases and data analysis techniques such as regression, classification, and clustering.We’ll cover a wide variety of analytics approaches in different industry domains. You’ll engage in hands-on case studies in real business contexts: examples include predicting and forecasting events, statistical customer segmentation, and calculating customer scores and lifetime value. We’ll also teach you how to take these analyses and effectively present them to stakeholders so your business can take action. The third course and the Capstone Project are designed in partnership with Accenture, one of the world’s best-known consulting, technology services, and outsourcing companies. You’ll learn about applications in a wide variety of sectors, including media, communications, public service,etc. By the end of this specialization, you’ll be able to use statistical techniques in R to develop business intelligence insights, and present them in a compelling way to enable smart and sustainable business decisions. You’ll earn a Specialization Certificate from one of the world’s leading business schools and learn from two of Europe’s leading professors in business analytics and marketing.
关于此 专项课程
您将获得的技能
R ProgrammingMarketing AnalyticsPresentationMarketing Performance Measurement And Management
专项课程 的运作方式
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实践项目
每个专项课程都包括实践项目。您需要成功完成这个（些）项目才能完成专项课程并获得证书。如果专项课程中包括单独的实践项目课程，则需要在开始之前完成其他所有课程。
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在结束每门课程并完成实践项目之后，您会获得一个证书，您可以向您的潜在雇主展示该证书并在您的职业社交网络中分享。
此专项课程包含 4 门课程
课程1
企业战略分析基础
4.4
420 个评分
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.
课程2
市场分析基础
4.5
452 个评分
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, in particular in marketing.
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.
Business Analytics, Big Data and Data Science are very hot topics today, and for good reasons. Companies are sitting on a treasure trove of data, but usually lack the skills and people to analyze and exploit that data efficiently. Those companies who develop the skills and hire the right people to analyze and exploit that data will have a clear competitive advantage.
It's especially true in one domain: marketing. About 90% of the data collected by companies today are related to customer actions and marketing activities.The domain of Marketing Analytics is absolutely huge, and may cover fancy topics such as text mining, social network analysis, sentiment analysis, real-time bidding, online campaign optimization, and so on.
But at the heart of marketing lie a few basic questions that often remain unanswered: (1) who are my customers, (2) which customers should I target and spend most of my marketing budget on, and (3) what's the future value of my customers so I can concentrate on those who will be worth the most to the company in the future.
That's exactly what this course will cover: segmentation is all about understanding your customers, scorings models are about targeting the right ones, and customer lifetime value is about anticipating their future value. These are the foundations of Marketing Analytics. And that's what you'll learn to do in this course.
课程3
商业案例分析：ACCENTURE
3.6
132 个评分
Who is this course for ?
This course is RESTRICTED TO LEARNERS ENROLLED IN Strategic Business Analytics SPECIALIZATION as a preparation to the capstone project. During the first two MOOCs, we focused on specific techniques for specific applications. Instead, with this third MOOC, we provide you with different examples to open your mind to different applications from different industries and sectors.
The objective is to give you an helicopter overview on what's happening in this field. You will see how the tools presented in the two previous courses of the Specialization are used in real life projects.
We want to ignite your reflection process. Hence, you will best make use of the Accenture cases by watching first the MOOC and then investigate by yourself on the different concepts, industries, or challenges that are introduced during the videos.
At the end of this course learners will be able to:
- identify the possible applications of business analytics,
- hence, reflect on the possible solutions and added-value applications that could be proposed for their capstone project.
The cases will be presented by senior practitioners from Accenture with different backgrounds in term of industry, function, and country. Special attention will be paid to the "value case" of the issue raised to prepare you for the capstone project of the specialization.
About Accenture
Accenture is a leading global professional services company, providing a broad range of services and solutions in strategy, consulting, digital, technology and operations. Combining unmatched experience and specialized skills across more than 40 industries and all business functions—underpinned by the world’s largest delivery network—Accenture works at the intersection of business and technology to help clients improve their performance and create sustainable value for their stakeholders. With more than 358,000 people serving clients in more than 120 countries, Accenture drives innovation to improve the way the world works and lives. Visit us at www.accenture.com.
课程4
毕业项目：利用公开数据创造价值
3.5
22 个评分
The Capstone project is an individual assignment.
Participants decide the theme they want to explore and define the issue they want to solve. Their “playing field” should provide data from various sectors (such as farming and nutrition, culture, economy and employment, Education & Research, International & Europe, Housing, Sustainable, Development & Energies, Health & Social, Society, Territories & Transport). Participants are encouraged to mix the different fields and leverage the existing information with other (properly sourced) open data sets.
Deliverable 1 is the preliminary preparation and problem qualification step. The objectives is to define the what, why & how. What issue do we want to solve? Why does it promise value for public authorities, companies, citizens? How do we want to explore the provided data?
For Deliverable 2, the participant needs to present the intermediary outputs and adjustments to the analysis framework. The objectives is to confirm the how and the relevancy of the first results.
Finally, with Deliverable 3, the participant needs to present the final outputs and the value case. The objective is to confirm the why. Why will it create value for public authorities, companies, and citizens.
Assessment and grading: the participants will present their results to their peers on a regular basis. An evaluation framework will be provided for the participants to assess the quality of each other’s deliverables.
讲师
Nicolas Glady
Associate professor, at ESSEC Business SchoolMarketing Department
Arnaud De Bruyn
Professor at ESSEC Business SchoolMarketing department
行业合作伙伴
关于 ESSEC商学院
For over a century, ESSEC has been developing a state-of-the-art educational program that gives the individual pride of place in its learning model, promoting the values of freedom, openness, innovation and responsibility. Preparing future managers to reconcile personal interests with collective responsibility, giving consideration to the common good in their decision-making, and weighing economic challenges against the social costs are some of the objectives ESSEC has set for itself. Its ultimate goal? To create a global world that has meaning for us all.
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此课程是 100% 在线学习吗？是否需要现场参加课程？
此课程完全在线学习，无需到教室现场上课。您可以通过网络或移动设备随时随地访问课程视频、阅读材料和作业。
完成专项课程后我会获得大学学分吗？
此专项课程不提供大学学分，但部分大学可能会选择接受专项课程证书作为学分。查看您的合作院校了解详情。
How long does it take to complete the Strategic Business Analytics Specialization?
Time to completion can vary based on your schedule, but most learners are able to complete the Specialization in 5 months.
此专项课程中每门课程的开课频率为多久？
Each course in the Specialization is offered on a regular schedule, with sessions starting about once per month. If you don't complete a course on the first try, you can easily transfer to the next session, and your completed work and grades will carry over. The final Capstone Project will be offered 3-4 times per year.
What background knowledge is necessary?
This Specialization is designed for graduate students and professional interested in practical applications of business analytics techniques and big data, with a good IT and statistical background. You’ll need a strong background in R and analytics to complete the coursework, and some experience with machine learning and SQL will also be useful.
Do I need to take the courses in a specific order?
We recommend taking the courses in the order presented, as each subsequent course will build on material from previous courses.
Will I earn university credit for completing the Strategic Business Analytics Specialization?
Coursera courses and certificates don't carry university credit, though some universities may choose to accept Specialization Certificates for credit. Check with your institution to learn more.
What will I be able to do upon completing the Strategic Business Analytics Specialization?
You’ll be a fully accomplished expert in big data management, with a robust understanding of how data can be used to leverage strategic value. You’ll be comfortable qualifying research objectives, combining and manipulating data sets, and interpreting them. You’ll be prepared to present your interpretations and value case to potential stakeholders successfully.
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