Introduction to DataTrail (The Course)
This Course is part of the following Tracks:
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. The only pre-requisites are a computer with a web browser and the ability to type and follow instructions. We guide you through the rest!
After taking this course you will be able to:
- Define the field of data science and the goals of this class and program
- Create the relevant accounts for performing data science in the cloud.
- Use cloud-based tools to complete your first data science project.
Things you need to do this course
This course is designed for people with no background in data science and so 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 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 Welcome to DataTrail
- 2 Program Philosophy
- 3 Why Automated Videos
- 4 The Data Science Process
- 5 How To Learn
- 6 Finding Help
- 7 Account Setup
- 8 Google Account Setup
- 9 Other Accounts Setup
- 10 Your First data science project
- 11 Google Sheets
- 12 RStudio Cloud
- 13 Google Docs
- 14 Google Slides
- 15 Swirl
- 16 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.
This course has a private forum for learners who are taking this course.
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