# Welcome to Unifying Data Science!¶

Hello, and welcome to the course site for IDS 690.04 (listed in some places as “Practicing Data Science II”)!

The aim of the course is to two-fold. First, it aims to provide students with a conceptual framework for understanding the relationship between the many tools that are currently taught under the “data science” umbrella. This course takes the view that data science is fundamentally about answering questions with data, and so is organized around helping students identify different classes of questions (descriptive, causal, and predictive). Over the course of the semester, we will explore each of these types of questions in turn, learning which tools are appropriate for each, and what what pitfalls are common to efforts to answer each type of question.

Second, it aims to provide students with experience both developing and actually answering real questions. Data science is a fundamentally applied field, and so while it is important to have the conceptual framework described above to aid your work, there is no substitute for learning to put these principles into action through practice.

To achieve this second learning goal, over the course of the semester students will develop their own data science projects in small teams. These projects will be developed incrementally over the course of the semester with instructor guidance. By the end of the semester, students will have picked a topic area, developed a (tractable) question, decided what an answer to that question would actually look like, developed a work plan for generating that answer, and executed and presented their project, and then iterated the project based on feedback from their initial presentation.

As this course is primarily designed for students in the MIDS program, it will assume familiarity with statistical modeling (basic statistics, linear regression, logistitic regression, model selection) and the basics of both supervised and unsupervised machine learning. The goal of this course will not be to teach these topics, but rather to help *contextualize* them.

Of the three types of questions we will cover, methods for answering causal questions will receive the greatest attention. This course assumes no familiarity with causal inference, and will cover everything from the basic problem of causal inference to experiments, and to the range of tools available for making causal inferences from observational data.

## Pre-Requisites¶

This course will assume that enrolled students have a good grasp of inferential statistics, statistical modelling, and have experience with machine learning (or be concurrently enrolled in an applied machine learning course).

This course will also assume students are comfortable manipulating real-world data in either Python or R. The substantive content of this course is language-independent, but because students will be required to work on their projects in teams, comfort with one of these two languages will be required to facilitate collaboration (MIDS students are, generally, “bilingual” in R and Python). Where code examples are provided in class, they will use Python (pandas), but both the instructor and TA are also capable of providing support in R.

Finally, students will also be expected to be comfortable collaborating using git and github. If you meet the other requirements for this course but are not familiar with git and github, this is a skill you should be able to pickup on your own in advance of the course without too much difficulty. You can read more about git and github here. The Duke Center for Data and Visualization Science also hosts git and github workshops if you are a Duke student.

## Syllabus¶

To learn more about the course, please read the PRELIMINARY course syllabus available here. It does not yet include all the details of class schedule or grading breakdowns, but should give you a good sense of the material we will cover.