Data Science in Real Life

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

( 186 Reviews )

Created by: Brian Caffo

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Overall Score : 86 / 100

Course Description

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 experience2. Identify strengths and weaknesses in experimental designs3. Describe possible pitfalls when pulling / assembling data and learn solutions for managing data pulls.4. Challenge statistical modeling assumptions and drive feedback to data analysts5. Describe common pitfalls in communicating data analyses6. 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 testing2. Causal inference, counterfactuals, 3. Strategies for managing data quality.4. Bias and confounding5. Contrasting machine learning versus classical statistical inferenceCourse promo:https://www.youtube.com/watch?v=9BIYmw5wnBICourse cover image by Jonathan Gross. Creative Commons BY-ND https://flic.kr/p/q1vudb

Time: 6h

Instructor Details

Brian Caffo

4.3 Rating
186 Reviews

Brian Caffo, PhD is a professor in the Department of statistics at the Johns Hopkins University Bloomberg School of Public Health. He graduated from the Department of Statistics at the University of Florida in 2001. He works in the fields of computational statistics and neuroinformatics and co-created the SMART ( www.smart-stats.org) working group. He has been the recipient of the Presidential Early Career Award for Scientist ( PECASE) and Engineers and Bloomberg School of Public Health Golden Apple and AMTRA teaching awards.