About this Specialization

Learn SAS or Python programming, expand your knowledge of analytical methods and applications, and conduct original research to inform complex decisions.

The Data Analysis and Interpretation Specialization takes you from data novice to data expert in just four project-based courses. You will apply basic data science tools, including data management and visualization, modeling, and machine learning using your choice of either SAS or Python, including pandas and Scikit-learn. Throughout the Specialization, you will analyze a research question of your choice and summarize your insights. In the Capstone Project, you will use real data to address an important issue in society, and report your findings in a professional-quality report. You will have the opportunity to work with our industry partners, DRIVENDATA and The Connection. Help DRIVENDATA solve some of the world's biggest social challenges by joining one of their competitions, or help The Connection better understand recidivism risk for people on parole in substance use treatment. Regular feedback from peers will provide you a chance to reshape your question. This Specialization is designed to help you whether you are considering a career in data, work in a context where supervisors are looking to you for data insights, or you just have some burning questions you want to explore. No prior experience is required. By the end you will have mastered statistical methods to conduct original research to inform complex decisions.

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100% online courses

Start instantly and learn at your own schedule.
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Flexible Schedule

Set and maintain flexible deadlines.
Beginner Level

Beginner Level

Clock

Approx. 7 months to complete

Suggested 3 hours/week
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English

Subtitles: English, Korean, German...

What you will learn

  • Check
    Access and manage data using either the Python or SAS programming language
  • Check
    Explore patterns and associations among variables
  • Check
    Use machine learning methods to develop predictive algorithms

Skills you will gain

StatisticsData AnalysisMachine LearningRegression Analysis
Globe

100% online courses

Start instantly and learn at your own schedule.
Calendar

Flexible Schedule

Set and maintain flexible deadlines.
Beginner Level

Beginner Level

Clock

Approx. 7 months to complete

Suggested 3 hours/week
Comment Dots

English

Subtitles: English, Korean, German...

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

Data Management and Visualization

4.4
648 ratings
182 reviews
Whether being used to customize advertising to millions of website visitors or streamline inventory ordering at a small restaurant, data is becoming more integral to success. Too often, we’re not sure how use data to find answers to the questions that will make us more successful in what we do. In this course, you will discover what data is and think about what questions you have that can be answered by the data – even if you’ve never thought about data before. Based on existing data, you will learn to develop a research question, describe the variables and their relationships, calculate basic statistics, and present your results clearly. By the end of the course, you will be able to use powerful data analysis tools – either SAS or Python – to manage and visualize your data, including how to deal with missing data, variable groups, and graphs. Throughout the course, you will share your progress with others to gain valuable feedback, while also learning how your peers use data to answer their own questions....
Course2

Data Analysis Tools

4.5
317 ratings
72 reviews
In this course, you will develop and test hypotheses about your data. You will learn a variety of statistical tests, as well as strategies to know how to apply the appropriate one to your specific data and question. Using your choice of two powerful statistical software packages (SAS or Python), you will explore ANOVA, Chi-Square, and Pearson correlation analysis. This course will guide you through basic statistical principles to give you the tools to answer questions you have developed. Throughout the course, you will share your progress with others to gain valuable feedback and provide insight to other learners about their work....
Course3

Regression Modeling in Practice

4.4
221 ratings
43 reviews
This course focuses on one of the most important tools in your data analysis arsenal: regression analysis. Using either SAS or Python, you will begin with linear regression and then learn how to adapt when two variables do not present a clear linear relationship. You will examine multiple predictors of your outcome and be able to identify confounding variables, which can tell a more compelling story about your results. You will learn the assumptions underlying regression analysis, how to interpret regression coefficients, and how to use regression diagnostic plots and other tools to evaluate the quality of your regression model. Throughout the course, you will share with others the regression models you have developed and the stories they tell you....
Course4

Machine Learning for Data Analysis

4.2
202 ratings
45 reviews
Are you interested in predicting future outcomes using your data? This course helps you do just that! Machine learning is the process of developing, testing, and applying predictive algorithms to achieve this goal. Make sure to familiarize yourself with course 3 of this specialization before diving into these machine learning concepts. Building on Course 3, which introduces students to integral supervised machine learning concepts, this course will provide an overview of many additional concepts, techniques, and algorithms in machine learning, from basic classification to decision trees and clustering. By completing this course, you will learn how to apply, test, and interpret machine learning algorithms as alternative methods for addressing your research questions....

Instructors

Lisa Dierker

Professor
Psychology

Jen Rose

Research Professor
Psychology

Industry Partners

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About Wesleyan University

At Wesleyan, distinguished scholar-teachers work closely with students, taking advantage of fluidity among disciplines to explore the world with a variety of tools. The university seeks to build a diverse, energetic community of students, faculty, and staff who think critically and creatively and who value independence of mind and generosity of spirit. ...

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.

  • No, Specializations are a premium product, and learners must pay or apply for financial aid to join them. You can access individual course content for free by searching for the course title in the catalog and choosing the This Course Only option when enrolling. You will not earn a Certificate in the free version of the course, or be able to access the Capstone Project.

  • Time to completion can vary based on your schedule, but most learners are able to complete the Specialization in 6-7 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. The Capstone Project will be offered four times per year on a recurring schedule.

  • 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 will be able to access and manage data using either the Python or SAS programming language, explore patterns and associations among variables, and use machine learning methods to develop predictive algorithms. Additionally, you will have a portfolio of hands-on project work that demonstrates your ability to apply all of these methods to real-world situations.

  • You may choose to use either Python or SAS to complete the assignments. Both of these software packages are being made freely available.

  • This Specialization is appropriate for anyone interested in learning more about data analysis, including those new to the field. Some knowledge of basic programming and familiarity with linear algebra concepts may be helpful, but no specific background is required.

More questions? Visit the Learner Help Center.