I have compiled notes for all 9 courses of the Johns Hopkins Unversity/Coursera Data Science Specialization. The notes are all written in R Markdown format and cover all concepts convered in class, as well as additional examples I have compiled from lecture, my own exploration, StackOverflow, and Khan Academy. These documents are intended to be comprehensive sources of reference for future use and they have served me wonderfully in completing the assignments for each course. So I hope you will find them helpful as well.
They are by no means perfect, so feel free to correct, contribute (send a pull request), or use them in anyway you would like.
You will find HTML, PDF, and R Markdown files here in this repository in each of the corresponding folders. You can view the HTML versions here
If you have any questions, drop me an email at [email protected]
- Problem Formulation – First, identify the problem to be solved. This step is easily overlooked. However, many dollars and hours have been spent solving the wrong problems.
- Obtain The Data – Next, collect new data and/or gather the data that already exists. In almost all cases, this data will need to be transformed and cleansed. It is important to note that this stage does not always involve big data or a data lake.
- Analysis – This is the part of the process where insight is to be extracted from the data. Commonly, this step will involve creating and optimizing statistical/machine learning models for prediction, but that is not always necessary. Sometimes, the analysis only contains graphs, charts, and basic descriptions of the data.
- Data Product – The end goal of data science is a data product. The insight from the Analysis phase needs to be conveyed to an end user. The data product might be as simple as a slideshow; more commonly it is a website dashboard, a message, an alert, or a recommendation.