Data Analysis (The Course)
This Course is part of the following Tracks:
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 the previous courses Organizing Data Projects, Introduction to R, Version Control, Data Tidying and Getting Data. We guide you through the rest!
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.
- 1 The Purpose of Data Science
- 2 Translating Questions to Data Science Questions
- 3 Do I Have The Data I Need?
- 4 Descriptive Analysis
- 5 Exploratory Analysis
- 6 Inference: Overview
- 7 Inference: Linear Regression
- 8 Inference: Multiple Regression
- 9 Inference: Examples
- 10 Inference: Practice
- 11 Prediction and Machine Learning
- 12 Data Analysis Workflow
- 13 Data Analysis Pipelines
- 14 Final Project
- 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.
Shannon Ellis is an Associate Teaching Professor in the Cognitive Science Department at UC San Diego.
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.
This course has a private forum for learners who are taking this course.
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