About this Specialization
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100% online courses

Start instantly and learn at your own schedule.

Flexible Schedule

Set and maintain flexible deadlines.

Beginner Level

Approx. 3 months to complete

Suggested 8 hours/week

English

Subtitles: English, Spanish, Chinese (Simplified), Mongolian

What you will learn

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    Explain how data is used for recruiting and performance evaluation

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    Model supply and demand for various business scenarios

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    Solve business problems with data-driven decision-making

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    Understand the tools used to predict customer behavior

Skills you will gain

Customer AnalyticsAnalyticsBusiness AnalyticsDecision Tree

100% online courses

Start instantly and learn at your own schedule.

Flexible Schedule

Set and maintain flexible deadlines.

Beginner Level

Approx. 3 months to complete

Suggested 8 hours/week

English

Subtitles: English, Spanish, Chinese (Simplified), Mongolian

How the Specialization Works

Take Courses

A Coursera Specialization is a series of courses that helps you master a skill. To begin, enroll in the Specialization directly, or review its courses and choose the one you'd like to start with. When you subscribe to a course that is part of a Specialization, you’re automatically subscribed to the full Specialization. It’s okay to complete just one course — you can pause your learning or end your subscription at any time. Visit your learner dashboard to track your course enrollments and your progress.

Hands-on Project

Every Specialization includes a hands-on project. You'll need to successfully finish the project(s) to complete the Specialization and earn your certificate. If the Specialization includes a separate course for the hands-on project, you'll need to finish each of the other courses before you can start it.

Earn a Certificate

When you finish every course and complete the hands-on project, you'll earn a Certificate that you can share with prospective employers and your professional network.

how it works

There are 5 Courses in this Specialization

Course1

Customer Analytics

4.5
6,965 ratings
1,484 reviews
Course2

Operations Analytics

4.7
3,495 ratings
643 reviews
Course3

People Analytics

4.5
3,404 ratings
586 reviews
Course4

Accounting Analytics

4.5
2,022 ratings
352 reviews

Instructors

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Noah Gans

Anheuser-Busch Professor of Management Science, Professor of Operations, Information and Decisions
The Wharton School
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Ron Berman

Assistant Professor of Marketing
The Wharton School
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Senthil Veeraraghavan

Associate Professor of Operations, Information and Decisions
The Wharton School
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Peter Fader

Professor of Marketing and Co-Director of the Wharton Customer Analytics Initiative
The Wharton School
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Eric Bradlow

Professor of Marketing, Statistics, and Education, Chairperson, Wharton Marketing Department, Vice Dean and Director, Wharton Doctoral Program, Co-Director, Wharton Customer Analytics Initiative
The Wharton School
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Matthew Bidwell

Associate Professor of Management
The Wharton School
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Martine Haas

Associate Professor of Management
The Wharton School
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Wharton Teaching Staff

Educators
The Wharton School
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Cade Massey

Practice Professor
The Wharton School
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Sergei Savin

Associate Professor of Operations, Information and Decisions
The Wharton School
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Brian J Bushee

The Geoffrey T. Boisi Professor
Accounting
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Christopher D. Ittner

EY Professor of Accounting
Accounting
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Raghu Iyengar

Associate Professor of Marketing
The Wharton School

About University of Pennsylvania

The University of Pennsylvania (commonly referred to as Penn) is a private university, located in Philadelphia, Pennsylvania, United States. A member of the Ivy League, Penn is the fourth-oldest institution of higher education in the United States, and considers itself to be the first university in the United States with both undergraduate and graduate studies. ...

Frequently Asked Questions

  • Yes! To get started, click the course card that interests you and enroll. You can enroll and complete the course to earn a shareable certificate, or you can audit it to view the course materials for free. When you subscribe to a course that is part of a Specialization, you’re automatically subscribed to the full Specialization. Visit your learner dashboard to track your progress.

  • This course is completely online, so there’s no need to show up to a classroom in person. You can access your lectures, readings and assignments anytime and anywhere via the web or your mobile device.

  • This Specialization doesn't carry university credit, but some universities may choose to accept Specialization Certificates for credit. Check with your institution to learn more.

  • Time to completion can vary based on your schedule, but most learners are able to complete the Specialization in about 5-6 months.

  • 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.

  • We recommend taking the courses in the order presented, as each subsequent course will build on material from previous courses.

  • 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.

  • You’ll gain a deeper understanding of how big data and analytics are used in four key areas: marketing (customer analytics), human resources and talent management (people analytics), operations, and finance. You can use this knowledge to create new business strategies using data, participate in conversations about analytics, transition to a new career, or improve your own business. You will also have a strong foundation for further study related to analytics and big data.

  • You will need a full-featured version of Microsoft Excel for some assignments. You should also have a working knowledge of Excel’s basic functions.

  • No previous knowledge or experience in business or analytics is required. This Specialization is designed for anyone interested in understanding how decisions are made using big data.

More questions? Visit the Learner Help Center.