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Cloud Based Data Science
Cloud Based Data Science (CBDS) is a free online educationalto help anyone who can read, write, and use a computer to move into data science, thenumber one rated job. It is a sequence of 11 MOOCs offered by faculty members in the Johns Hopkins Department of Biostatistics, Bloomberg School of Public Health. Data science jobs are in demand and...
These courses have a total suggested price of $209. Get them now for only $29.00!
About
About the Track
Cloud Based Data Science (CBDS) is a free online educational to help anyone who can read, write, and use a computer to move into data science, the number one rated job. It is a sequence of 11 MOOCs offered by faculty members in the Johns Hopkins Department of Biostatistics, Bloomberg School of Public Health.
Data science jobs are in demand and well regarded, but it is challenging to break into the field. Some of these challenges include:
- Data science training requires expensive computers
- Data science training is expensive
- Data science jobs are centralized in tech centers
- Data science jobs require connections
This sequence of courses teaches you the basics all the way from word processing to basic data analysis and how to network and get a job in data science. The courses are available on a "pay what you want/can" model so that they are accessible to everyone. All proceeds that go to the JHU Data Science Lab will be redirected to help improve training and placement in data science for individuals from under-served backgrounds through the Cloud Based Data Science Plus program.
Learn more about Cloud Based Data Science from the Johns Hopkins Data Science Lab.
Certificate
Verified Certificate
When you successfully complete the Track, you will receive a verified Certificate of Completion from Leanpub, certifying that you have completed the entire Track.
Courses
Courses Included
Introduction to DataTrail
Description
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!
Learning objectives
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.
Google and the Cloud
Description
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!
Learning objectives
After taking this course you will be able to:
- Use Google's web services
- Use popular services like Google Docs, Google Calendar, and Google Slides
- Communicate with Google Hangouts and Slack.
Things you need to do this course
This course is designed for people with no background in data science or cloud computing 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.
Organizing Data Science Projects
Description
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!
Learning objectives
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.
Version Control
Description
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 Organizing Data Science Projects courses. We guide you through the rest!
Learning objectives
After taking this course you will be able to:
- Use version control to keep track of your files
- Use git and GitHub to version control your files
- Contribute to other people's projects through pull requests on GitHub
- Set up a version controlled data science project on GitHub.
Things you need to do this course
This course is designed for people with no background with Chromebooks. It would be helpful if you had already taken our Introduction and Organizing Data Science Projects courses. This should be a great introduction to version control 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.
Introduction to R
Description
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, Organizing Data Science Projects, and Version Control courses. We guide you through the rest!
Learning objectives
After taking this course you will be able to:
- Describe what the R programming language is
- Log in to RStudio Cloud and use the R programming language
- Load and use R packages
- Perform the basic commands you will need to start using R
Things you need to do this course
This course is designed for people with no background with Chromebooks or with R. It would be helpful if you had already taken our Introduction, Organizing Data Science Projects, and Version Control courses. But we don't assume prior programming experience. This should be a great introduction to Rfor 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.
- The appropriate accounts set up in our earlier courses.
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.
Data Tidying
Description
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 Organizing Data Science Projects, Version Control and Introduction to R courses. We guide you through the rest!
Learning objectives
After taking this course you will be able to:
- Explain what raw and tidy data are
- Transform messy data sets into tidy data sets
- Work with strings, factors and dates in R
Things you need to do this course
This course is designed for people with no background with Chromebooks. It would be helpful if you had already taken our Organizing Data Science Projects, Version Control and Introduction to R courses. This should be a great introduction to data tidying 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.
- The accounts that you have set up in previous courses
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.
Data Visualization
Description
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 Organizing Data Science Projects, Version Control, Introduction to R and Data Tidying courses. We guide you through the rest!
Learning objectives
After taking this course you will be able to:
- Explain what makes a plot good or bad
- Create exploratory and explanatory plots in R using the ggplot2 package
- Plot different types of data and make different types of plots
- Convert basic plots into plots that are visually pleasing and communicate information
Things you need to do this course
This course is designed for people with no background with Chromebooks. It would be helpful if you had already taken our Organizing Data Science Projects, Version Control, Introduction to R and Data Tidying courses. This should be a great introduction to data visualization 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.
- The accounts you have set up in previous courses.
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.
Getting Data
Description
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 the previous courses Organizing Data Projects, Introduction to R, Version Control, and Data Tidying. We guide you through the rest!
Learning objectives
After taking this course you will be able to:
- Get data from online, databases, and other resources
- Pull the data from these sources in a variety of formats
- Save them and organize them so you can tidy them for analysis
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.
- The accounts you have set up in previous courses.
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.
Data Analysis
Description
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 the previous courses Organizing Data Projects, Introduction to R, Version Control, Data Tidying and Getting Data. We guide you through the rest!
Learning objectives
After taking this course you will be able to:
- Translate a general question into a data science question
- Identify the type of data science question you are answering
- Use data visualization and linear models to answer descriptive, exploratory, inferential and predictive questions
- Implement your answers in code.
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.
- The accounts you have set up in previous courses.
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.
Written and Oral Communication in Data Science
Description
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 the previous courses Organizing Data Projects, Introduction to R, Version Control, Data Tidying, Getting Data and Data Analysis. We guide you through the rest!
Learning objectives
After taking this course you will be able to:
- Create and write a data science report
- Organize, manage, or attend a data science meeting
- Create and deliver technical and non-technical data science presentations
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.
- The accounts you have set up in the previous courses.
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.
Getting a Job in Data Science
Description
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 the previous courses Organizing Data Projects, Introduction to R, Version Control, Data Tidying, Getting Data, Data Analysis, and Written and Oral Communication in Data Science. We guide you through the rest!
Learning objectives
After taking this course you will be able to:
- Create and maintain your data science resume
- Set up your professional data science website
- Set up and manage your public profiles (Twitter, Github, LinkedIn)
- Know where to look for and apply for data science jobs
- Be able to prepare for a data science interview
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.
- The accounts you have set up in previous courses.
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.
Instructors
About the Instructors
Jeffrey Leek
Jeff is Chief Data Officer, Vice President, and J Orin Edson Foundation Chair of Biostatistics at the Fred Hutchinson Cancer Center. Previously, he was 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 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 a recipient of the Mortimer Spiegelman Award and Committee of Presidents of Statistical Societies Presidential Award.
Episode 19
An Interview with Jeffrey Leek
Aboozar Hadavand
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.
Shannon Ellis
Shannon Ellis is an Associate Teaching Professor in the Cognitive Science Department at UC San Diego.
Leslie Myint
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
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 McClymont
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.
Leonardo Collado Torres
Leonardo Collado-Torres is a Research Scientist at the Lieber Institute for Brain Development. He has been learning and using R & Bioconductor since 2008 and works analyzing genomic data. Leonardo occasionally writes blog posts, is a co-founder of the LIBD rstats club and the Community of Bioinformatics Software Developers (CDSB in Spanish), and is a member of the Bioconductor Community Advisory Board.
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