By now you have definitely heard about data science and big data. In this one-week class, we will provide a crash course in what these terms mean and how they play a role in successful organizations. This class is for anyone who wants to learn what all the data science action is about, including those who will eventually need to manage data scientists. The goal is to get you up to speed as quickly as possible on data science without all the fluff. We've designed this course to be as convenient as possible without sacrificing any of the essentials.
提供方
課程信息
103,377 次近期查看
學生職業成果
26%
完成這些課程後已開始新的職業生涯
27%
通過此課程獲得實實在在的工作福利
10%
加薪或升職
您將獲得的技能
Data ScienceData AnalysisMachine LearningProject
學生職業成果
26%
完成這些課程後已開始新的職業生涯
27%
通過此課程獲得實實在在的工作福利
10%
加薪或升職
100% 在線
立即開始，按照自己的計劃學習。
第 1 門課程（共 5 門）
可靈活調整截止日期
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完成時間大約為5 小時
建議：1 week of study, 4-6 hours
英語（English）
字幕：中文（繁體）, 阿拉伯語（Arabic）, 俄語（Russian）, 土耳其語（Turkish）, 英語（English）, 印地語
教學大綱 - 您將從這門課程中學到什麼
完成時間為 4 小時
A Crash Course in Data Science
This one-module course constitutes the first "week" of the Executive Data Science Specialization. This is an intensive introduction to what you need to know about data science itself. You'll learn important terminology and how successful organizations use data science.
完成時間為 4 小時
11 個視頻 （總計 78 分鐘）, 8 個閱讀材料, 9 個測驗
11 個視頻
What is Data Science?5分鐘
Statistics by example activities11分鐘
Machine learning, the basics13分鐘
Machine learning further reading3分鐘
What is Software Engineering for Data Science?6分鐘
The Structure of a Data Science Project6分鐘
The outputs of a data science experiment6分鐘
The four secrets of a successful data science experiment14分鐘
Data Scientist Toolbox5分鐘
Separating Hype from Value3分鐘
8 個閱讀材料
Specialization Textbook10分鐘
Grading10分鐘
Pre-Course Survey10分鐘
Statistics by example activities10分鐘
Machine learning10分鐘
The outputs of a data science experiment10分鐘
The four secrets of a successful data science experiment10分鐘
Post-Course Survey10分鐘
9 個練習
What is data science?2分鐘
What is statistics good for?10分鐘
Machine learning8分鐘
Quiz: Software Engineering6分鐘
Structure of a Data Science Project8分鐘
The outputs of a data science experiment10分鐘
Defining Success in Data Science8分鐘
Data scientist toolbox8分鐘
Separating hype from value8分鐘
審閱
來自数据科学速成课的熱門評論
由 NG 提供Jan 8, 2018
Questions could have been more difficult. Plus, I'm frustrated that I was taken into a 25-question survey ranking me with other data scientists, but when I clicked out of it I wasn't able to get back.
由 SK 提供Jul 18, 2017
This course introduced me to the concepts of Data Science and the key words mainly. Some lecturers spoke very fast, and I had listen to them two or three times to keep up with their pace of speaking.
由 RA 提供Dec 29, 2019
Enjoyed the pace at which the course moved. Teachers were qualified and relevant. Video Production quality could be better, some of the presentation graph were illegible - thus the lack of 1 star.
由 JX 提供Jun 20, 2016
fairly general concepts of data science, but may be quite helpful l for people totally new to the field of data (e.g. not having any idea), or as material to review the big picture of data science
關於 约翰霍普金斯大学
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.
關於 数据科学管理 專項課程
Assemble the right team, ask the right questions, and avoid the mistakes that derail data science projects.
In four intensive courses, you will learn what you need to know to begin assembling and leading a data science enterprise, even if you have never worked in data science before. You’ll get a crash course in data science so that you’ll be conversant in the field and understand your role as a leader. You’ll also learn how to recruit, assemble, evaluate, and develop a team with complementary skill sets and roles. You’ll learn the structure of the data science pipeline, the goals of each stage, and how to keep your team on target throughout. Finally, you’ll learn some down-to-earth practical skills that will help you overcome the common challenges that frequently derail data science projects.
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