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.
Executive Data Science 专项课程
Be The Leader Your Data Team Needs. Learn to lead a data science team that generates first-rate analyses in four courses.
提供方
关于此 专项课程
您将学到的内容有
Become conversant in the field and understand your role as a leader.
Recruit, assemble, evaluate, and develop a team with complementary skill sets and roles.
Navigate the structure of the data science pipeline by understanding the goals of each stage and keeping your team on target throughout.
Overcome the common challenges that frequently derail data science projects.
您将获得的技能
Data ScienceData ManagementData AnalysisCommunicationLeadership
专项课程 的运作方式
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Coursera 专项课程是帮助您掌握一门技能的一系列课程。若要开始学习，请直接注册专项课程，或预览专项课程并选择您要首先开始学习的课程。当您订阅专项课程的部分课程时，您将自动订阅整个专项课程。您可以只完成一门课程，您可以随时暂停学习或结束订阅。访问您的学生面板，跟踪您的课程注册情况和进度。
实践项目
每个专项课程都包括实践项目。您需要成功完成这个（些）项目才能完成专项课程并获得证书。如果专项课程中包括单独的实践项目课程，则需要在开始之前完成其他所有课程。
获得证书
在结束每门课程并完成实践项目之后，您会获得一个证书，您可以向您的潜在雇主展示该证书并在您的职业社交网络中分享。
此专项课程包含 5 门课程
课程1
A Crash Course in Data Science
4.5
4,297 个评分
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.
This is a focused course designed to rapidly get you up to speed on the field of data science. Our goal was to make this as convenient as possible for you without sacrificing any essential content. We've left the technical information aside so that you can focus on managing your team and moving it forward.
After completing this course you will know.
1. How to describe the role data science plays in various contexts
2. How statistics, machine learning, and software engineering play a role in data science
3. How to describe the structure of a data science project
4. Know the key terms and tools used by data scientists
5. How to identify a successful and an unsuccessful data science project
3. The role of a data science manager
Course cover image by r2hox. Creative Commons BY-SA: https://flic.kr/p/gdMuhT
课程2
Building a Data Science Team
4.5
2,142 个评分
Data science is a team sport. As a data science executive it is your job to recruit, organize, and manage the team to success. In this one-week course, we will cover how you can find the right people to fill out your data science team, how to organize them to give them the best chance to feel empowered and successful, and how to manage your team as it grows.
This is a focused course designed to rapidly get you up to speed on the process of building and managing a data science team. Our goal was to make this as convenient as possible for you without sacrificing any essential content. We've left the technical information aside so that you can focus on managing your team and moving it forward.
After completing this course you will know.
1. The different roles in the data science team including data scientist and data engineer
2. How the data science team relates to other teams in an organization
3. What are the expected qualifications of different data science team members
4. Relevant questions for interviewing data scientists
5. How to manage the onboarding process for the team
6. How to guide data science teams to success
7. How to encourage and empower data science teams
Commitment: 1 week of study, 4-6 hours
Course cover image by JaredZammit. Creative Commons BY-SA. https://flic.kr/p/5vuWZz
课程3
Managing Data Analysis
4.5
1,873 个评分
This one-week course describes the process of analyzing data and how to manage that process. We describe the iterative nature of data analysis and the role of stating a sharp question, exploratory data analysis, inference, formal statistical modeling, interpretation, and communication. In addition, we will describe how to direct analytic activities within a team and to drive the data analysis process towards coherent and useful results.
This is a focused course designed to rapidly get you up to speed on the process of data analysis and how it can be managed. Our goal was to make this as convenient as possible for you without sacrificing any essential content. We've left the technical information aside so that you can focus on managing your team and moving it forward.
After completing this course you will know how to….
1. Describe the basic data analysis iteration
2. Identify different types of questions and translate them to specific datasets
3. Describe different types of data pulls
4. Explore datasets to determine if data are appropriate for a given question
5. Direct model building efforts in common data analyses
6. Interpret the results from common data analyses
7. Integrate statistical findings to form coherent data analysis presentations
Commitment: 1 week of study, 4-6 hours
Course cover image by fdecomite. Creative Commons BY https://flic.kr/p/4HjmvD
课程4
Data Science in Real Life
4.4
1,335 个评分
Have you ever had the perfect data science experience? The data pull went perfectly. There were no merging errors or missing data. Hypotheses were clearly defined prior to analyses. Randomization was performed for the treatment of interest. The analytic plan was outlined prior to analysis and followed exactly. The conclusions were clear and actionable decisions were obvious. Has that every happened to you? Of course not. Data analysis in real life is messy. How does one manage a team facing real data analyses? In this one-week course, we contrast the ideal with what happens in real life. By contrasting the ideal, you will learn key concepts that will help you manage real life analyses.
This is a focused course designed to rapidly get you up to speed on doing data science in real life. Our goal was to make this as convenient as possible for you without sacrificing any essential content. We've left the technical information aside so that you can focus on managing your team and moving it forward.
After completing this course you will know how to:
1, Describe the “perfect” data science experience
2. Identify strengths and weaknesses in experimental designs
3. Describe possible pitfalls when pulling / assembling data and learn solutions for managing data pulls.
4. Challenge statistical modeling assumptions and drive feedback to data analysts
5. Describe common pitfalls in communicating data analyses
6. Get a glimpse into a day in the life of a data analysis manager.
The course will be taught at a conceptual level for active managers of data scientists and statisticians. Some key concepts being discussed include:
1. Experimental design, randomization, A/B testing
2. Causal inference, counterfactuals,
3. Strategies for managing data quality.
4. Bias and confounding
5. Contrasting machine learning versus classical statistical inference
Course promo:
https://www.youtube.com/watch?v=9BIYmw5wnBI
Course cover image by Jonathan Gross. Creative Commons BY-ND https://flic.kr/p/q1vudb
讲师
Jeff Leek, PhD
Associate Professor, BiostatisticsBloomberg School of Public Health
Brian Caffo, PhD
Professor, BiostatisticsBloomberg School of Public Health
Roger D. Peng, PhD
Associate Professor, BiostatisticsBloomberg School of Public Health
行业合作伙伴
关于 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.
常见问题
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此课程是 100% 在线学习吗？是否需要现场参加课程？
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。
完成专项课程后我会获得大学学分吗？
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。
How long does it take to complete the Executive Data Science Specialization?
Time to completion can vary based on your schedule, but most learners are able to complete the Specialization in 2 months.
此专项课程中每门课程的开课频率为多久？
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.
What background knowledge is necessary?
A basic understanding of how data can be used in an industry, academic, or government environment.
Do I need to take the courses in a specific order?
We recommend that you take the courses in the following order: Crash Course in Data Science, Building a Data Science Team, Managing Data Analysis, Data Science in Real Life
Will I earn university credit for completing the Executive Data Science Specialization?
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.
What will I be qualified to do upon completing the Executive Data Science Specialization?
Upon completion, you will be qualified to lead a team of data scientists. You will know how to ask the right questions, recruit the right people, and manage the full team as you work through the entire data science pipeline. The skills you learn in this specialization will prepare you to harness the potential of the data scientists in your organization and deliver world-class analyses to your clients and stakeholders.
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