About this 專項課程
100% 在線課程

100% 在線課程

立即開始,按照自己的計劃學習。
靈活的計劃

靈活的計劃

設置並保持靈活的截止日期。
初級

初級

完成時間(小時)

完成時間大約為7 個月

建議 3 小時/週
可選語言

英語(English)

字幕:英語(English), 韓語, 德語(German)...

您將獲得的技能

StatisticsData AnalysisMachine LearningRegression Analysis
100% 在線課程

100% 在線課程

立即開始,按照自己的計劃學習。
靈活的計劃

靈活的計劃

設置並保持靈活的截止日期。
初級

初級

完成時間(小時)

完成時間大約為7 個月

建議 3 小時/週
可選語言

英語(English)

字幕:英語(English), 韓語, 德語(German)...

How the 專項課程 Works

加入課程

Coursera 專項課程是幫助您掌握一門技能的一系列課程。若要開始學習,請直接註冊專項課程,或預覽專項課程並選擇您要首先開始學習的課程。當您訂閱專項課程的部分課程時,您將自動訂閱整個專項課程。您可以只完成一門課程,您可以隨時暫停學習或結束訂閱。訪問您的學生面板,跟踪您的課程註冊情況和進度。

實踐項目

每個專項課程都包括實踐項目。您需要成功完成這個(些)項目才能完成專項課程並獲得證書。如果專項課程中包括單獨的實踐項目課程,則需要在開始之前完成其他所有課程。

獲得證書

在結束每門課程並完成實踐項目之後,您會獲得一個證書,您可以向您的潛在雇主展示該證書並在您的職業社交網絡中分享。

how it works

此專項課程包含 5 門課程

課程1

Data Management and Visualization

4.4
654 個評分
183 個審閱
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....
課程2

Data Analysis Tools

4.5
320 個評分
72 個審閱
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....
課程3

Regression Modeling in Practice

4.3
223 個評分
43 個審閱
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....
課程4

Machine Learning for Data Analysis

4.2
206 個評分
45 個審閱
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....

講師

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Lisa Dierker

Professor
Psychology
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Jen Rose

Research Professor
Psychology

行業合作夥伴

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關於 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. ...

常見問題

  • 可以!点击您感兴趣的课程卡开始注册即可。注册并完成课程后,您可以获得可共享的证书,或者您也可以旁听该课程免费查看课程资料。如果您订阅的课程是某专项课程的一部分,系统会自动为您订阅完整的专项课程。访问您的学生面板,跟踪您的进度。

  • 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。

  • 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。

  • 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.

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