關於此專項課程

Ask the right questions, manipulate data sets, and create visualizations to communicate results.

This Specialization covers the concepts and tools you'll need throughout the entire data science pipeline, from asking the right kinds of questions to making inferences and publishing results. In the final Capstone Project, you’ll apply the skills learned by building a data product using real-world data. At completion, students will have a portfolio demonstrating their mastery of the material.

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Globe

100% 在線課程

立即開始,按照自己的計劃學習。
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靈活的計劃

設置並保持靈活的截止日期。
Beginner Level

初級

You should have beginner level experience in Python. Familarity with regression is recommended.
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完成時間大約為9 個月

建議 5 小時/週
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English

字幕:English, French, Chinese (Simplified), Greek, Italian, Portuguese (Brazilian), Vietnamese, Russian, Turkish, Hebrew, Japanese

您將學到的內容有

  • Check
    Build models based on new data types, experimental design, and statistical inference
  • Check
    Create products that can be used to tell stories about data to a mass audience
  • Check
    Formulate context-relevant questions and hypotheses to drive data scientific research
  • Check
    Utilize tools that transform and interpret large-scale datasets

您將獲得的技能

GithubMachine LearningR ProgrammingRegression Analysis
Globe

100% 在線課程

立即開始,按照自己的計劃學習。
Calendar

靈活的計劃

設置並保持靈活的截止日期。
Beginner Level

初級

You should have beginner level experience in Python. Familarity with regression is recommended.
Clock

完成時間大約為9 個月

建議 5 小時/週
Comment Dots

English

字幕:English, French, Chinese (Simplified), Greek, Italian, Portuguese (Brazilian), Vietnamese, Russian, Turkish, Hebrew, Japanese

專項課程的運作方式

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 10 門課程

1課程

The Data Scientist’s Toolbox

4.5
15,933 個評分
3,308 個審閱
In this course you will get an introduction to the main tools and ideas in the data scientist's toolbox. The course gives an overview of the data, questions, and tools that data analysts and data scientists work with. There are two components to this course. The first is a conceptual introduction to the ideas behind turning data into actionable knowledge. The second is a practical introduction to the tools that will be used in the program like version control, markdown, git, GitHub, R, and RStudio....
2課程

R Programming

4.6
12,033 個評分
2,546 個審閱
In this course you will learn how to program in R and how to use R for effective data analysis. You will learn how to install and configure software necessary for a statistical programming environment and describe generic programming language concepts as they are implemented in a high-level statistical language. The course covers practical issues in statistical computing which includes programming in R, reading data into R, accessing R packages, writing R functions, debugging, profiling R code, and organizing and commenting R code. Topics in statistical data analysis will provide working examples....
3課程

Getting and Cleaning Data

4.5
5,151 個評分
826 個審閱
Before you can work with data you have to get some. This course will cover the basic ways that data can be obtained. The course will cover obtaining data from the web, from APIs, from databases and from colleagues in various formats. It will also cover the basics of data cleaning and how to make data “tidy”. Tidy data dramatically speed downstream data analysis tasks. The course will also cover the components of a complete data set including raw data, processing instructions, codebooks, and processed data. The course will cover the basics needed for collecting, cleaning, and sharing data....
4課程

Exploratory Data Analysis

4.7
3,938 個評分
588 個審閱
This course covers the essential exploratory techniques for summarizing data. These techniques are typically applied before formal modeling commences and can help inform the development of more complex statistical models. Exploratory techniques are also important for eliminating or sharpening potential hypotheses about the world that can be addressed by the data. We will cover in detail the plotting systems in R as well as some of the basic principles of constructing data graphics. We will also cover some of the common multivariate statistical techniques used to visualize high-dimensional data....

講師

Jeff Leek, PhD

Associate Professor, Biostatistics
Bloomberg School of Public Health

Roger D. Peng, PhD

Associate Professor, Biostatistics
Bloomberg School of Public Health

Brian Caffo, PhD

Professor, Biostatistics
Bloomberg School of Public Health

關於 Johns Hopkins University

The mission of The Johns Hopkins University is to educate its students and cultivate their capacity for life-long learning, to foster independent and original research, and to bring the benefits of discovery to the world....

常見問題

  • 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 3-6 months.

  • Each course in the Specialization is offered monthly.

  • Some programming experience (in any language) is recommended. We also suggest a working knowledge of mathematics up to algebra (neither calculus or linear algebra are required).

  • Begin by taking The Data Scientist's Toolbox and Introduction to R Programming, in order. The other courses may be taken in any order, and in parallel if desired.

  • You’ll have a foundational understanding of the field and be prepared to continue studying data science.

  • Yes, you can access the course for free via www.coursera.org/jhu. This will allow you to explore the course, watch lectures, and participate in discussions for free. To be eligible to earn a certificate, you must either pay for enrollment or qualify for financial aid.

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