Organizing Data Science Projects (The Course)
This course includes 1 attempt.
Data science is one of the most exciting and fastest growing careers in the world. The goal of this series is to help people with no background and limited resources transition into data science. It would be helpful to have already taken our Introduction and Google and the Cloud courses. We guide you through the rest!
After taking this course you will be able to:
- Create projects on RStudio Cloud
- Set up the file structure you will use for data science projects
- Name files for data science projects
- Navigate files in the Terminal and in R on RStudio Cloud
Things you need to do this course
This course is designed for people with no background with Chromebooks and no background in data science. So it is a great introduction for high-school students or people looking for a career change into the tech industry. The only requirements are:
- A computer with a web browser and an internet connection
- The ability to type and follow instructions.
How you will be graded
The course has a series of short quizzes, one for each chapter. You will get two attempts at each quiz and your best score for each quiz will count toward your final score. If you receive more than 70% of the points across all quizzes you will pass. If you receive more than 90% of the points across all quizzes you will pass with honors. You get two attempts at the class with each class purchase.
How to report an error
If you find a bug, typo, or issue in the material, feel free to contact us using this form.
- 1 Motivation
- 2 RStudio and Projects
- 3 Setting Up Data Science Project Folders
- 4 Creating and Previewing Markdown Files in RStudio
- 5 Introduction to Markdown
- 6 Managing Files in the Terminal
- 7 Managing Files in R
- 8 Using Paths in Code
- 9 How to Work
- 10 References
- About this Course
- About the Authors
Jeff is a professor of Biostatistics and Oncology at the Johns Hopkins Bloomberg School of Public Health and co-director of the Johns Hopkins Data Science Lab. His group develops statistical methods, software, data resources, and data analyses that help people make sense of massive-scale genomic and biomedical data. As the co-director of the Johns Hopkins Data Science Lab he has helped to develop massive online open programs that have enrolled more than 8 million individuals and partnered with community-based non-profits to use data science education for economic and public health development. He is a Fellow of the American Statistical Association and Mortimer Spiegelman Award recipient.
Aboozar Hadavand is a postdoctoral fellow at Johns Hopkins Bloomberg School of Public Health. His current research involves analyzing MOOC data. He has previously taught at Barnard College (Columbia University), Brooklyn College, and Yeshiva University.
Leslie obtained her PhD in biostatistics from the Johns Hopkins Bloomberg School of Public Health and is currently an Assistant Professor in the Department of Mathematics, Statistics, and Computer Science at Macalester College.
Leah Jager is an Assistant Scientist of Biostatistics at Johns Hopkins Bloomberg School of Public Health, where she teaches biostatistics to students interested in public health.
Sarah is Human Genetics PhD student in the Institute of Genetic Medicine at Johns Hopkins. She studies the role of regulatory variation in neurodegenerative and neuropsychiatric diseases, like Parkinson disease and schizophrenia.
Shannon Ellis is an Associate Teaching Professor in the Cognitive Science Department at UC San Diego.
This course has a private forum for learners who are taking this course.
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