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.
Overview
Syllabus
Course 1: A Crash Course in Data Science
- Offered by Johns Hopkins University. By now you have definitely heard about data science and big data. In this one-week class, we will ... Enroll for free.
Course 2: Building a Data Science Team
- Offered by Johns Hopkins University. Data science is a team sport. As a data science executive it is your job to recruit, organize, and ... Enroll for free.
Course 3: Managing Data Analysis
- Offered by Johns Hopkins University. This one-week course describes the process of analyzing data and how to manage that process. We ... Enroll for free.
Course 4: Data Science in Real Life
- Offered by Johns Hopkins University. Have you ever had the perfect data science experience? The data pull went perfectly. There were no ... Enroll for free.
Course 5: Executive Data Science Capstone
- Offered by Johns Hopkins University. The Executive Data Science Capstone, the specialization’s culminating project, is an opportunity for ... Enroll for free.
- Offered by Johns Hopkins University. By now you have definitely heard about data science and big data. In this one-week class, we will ... Enroll for free.
Course 2: Building a Data Science Team
- Offered by Johns Hopkins University. Data science is a team sport. As a data science executive it is your job to recruit, organize, and ... Enroll for free.
Course 3: Managing Data Analysis
- Offered by Johns Hopkins University. This one-week course describes the process of analyzing data and how to manage that process. We ... Enroll for free.
Course 4: Data Science in Real Life
- Offered by Johns Hopkins University. Have you ever had the perfect data science experience? The data pull went perfectly. There were no ... Enroll for free.
Course 5: Executive Data Science Capstone
- Offered by Johns Hopkins University. The Executive Data Science Capstone, the specialization’s culminating project, is an opportunity for ... Enroll for free.
Courses
-
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
-
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
-
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
-
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
-
The Executive Data Science Capstone, the specialization’s culminating project, is an opportunity for people who have completed all four EDS courses to apply what they've learned to a real-world scenario developed in collaboration with Zillow, a data-driven online real estate and rental marketplace, and DataCamp, a web-based platform for data science programming. Your task will be to lead a virtual data science team and make key decisions along the way to demonstrate that you have what it takes to shepherd a complex analysis project from start to finish. For the final project, you will prepare and submit a presentation, which will be evaluated and graded by your fellow capstone participants. Course cover image by Luckey_sun. Creative Commons BY-SA https://flic.kr/p/bx1jvU
Taught by
Brian Caffo, PhD, Jeff Leek, PhD and Roger D. Peng, PhD
Tags
Related Courses
Related articles
Reviews
4.8 rating, based on 4 Class Central reviews
-
I've completed the series and I think it's worth the time invested. "Managing Data Analysis" is the best one, but I recommend the whole specialization to managers of data science teams.
-
As someone who is interested to learn about the field and apply its techniques to my future career, I find this course to be an excellent and systematic introduction to data science. I am fortunate to have taken this Coursera Specialization before formally embarking on academic programs and employment related to data science. Make no mistake, however: the bird's eye view this Specialization provides is conceptual, rather than practical. Taking further courses and perhaps entering educational programs and project-based training are strongly recommended before aiming to be a full-fledged data scientist.
-
Easy to complete, provides foundations for managing a data science team
This course sequence is easy to complete, doesn't take that long, and provides all the foundational information you need to manage a data science team and integrate their work into your organization so that your team benefits the business. Note that this is an explicitly management-focused specialization, so while you'll learn some basics of data science, you won't dive deep into any of the details. The specialization capstone focuses on the management decisions that need to be made during the course of a data science project, and how you select the right staff to pursue the project and guide them to complete it. -
Jacky WaiA data science for non technical business people
This is an excellent course for the data science begins to what is going on about data science. In the course, you will learn about the whole process of data science project and how to make the decision. The last capstone project is the most interesting one I have taken MOOCs. It requires the student to make decisions in a simulation project case.
