About this Course#
The Data Science Labs on Probability#
By Kindyl King, Mireille Boutin, and Christopher Janjigian with Jackson Fields, Prahas Pattem, and Adharsh Sabukumar
Welcome! This is a one-credit course designed to accompany ECE302, MA416, STAT416, or any other introductory course on probability. Throughout this course, you will explore applications of probability to problems in data science. Additionally, you will gain practical experience in programming with Python and learn how to use Arduino sensors and microprocessors.
This course requires no work outside of the lab. There are no homework assignments, quizzes, tests, or exams. All work will be performed during the 150 minutes spent in the lab each week. Our friendly undergraduate instructors will be available to assist you and answer all your questions during lab time.
Prior experience with at least one DS Lab or Python is a prerequisite for taking this course. Additionally, ECE302, MA416, STAT416, or any other introductory course on probability is a co-requisite for this course.
Purdue Students: You have the opportunity to earn Honors credit for ECE302, MA416, or STAT416 by taking this lab. Refer to the syllabus for more details.
More Information and Contact#
For more information regarding this and the other available Data Science Labs, visit the following link: https://engineering.purdue.edu/~mboutin/Data_Science_labs.html
If you have any further questions about this course, feel free to contact Professor Boutin at mboutin@purdue.edu.
How to Get Started#
You are currently reading an interactive Notebook written in the Jupyter language. Jupyter allows you to neatly compile a collection of files into a single web page, like the one you are viewing now. It also enables you to write and execute Python code. This Notebook has been created using JupyterLab, which you will use to interact with and edit the labs.
To get started with this course, click the next arrow at the bottom right of this page to move to the first lab, Lab 0. In Lab 0, you will learn how to interact with the Notebook, including running code, editing cells, and utilizing other useful features available in Jupyter. During the lab, you will be asked to complete some exercises. You should provide your answers directly in the Notebook by editing the lab file.
We recommend downloading the lab file by clicking the download icon in the top right of the screen and saving the .ipynb version of Lab 0. After downloading, open the JupyterLab application. This will bring you to a screen with files and a Table of Contents on the left side, and the screen on the right side. If it’s not already open, click on the folder icon in the top left corner to open the files directory. Navigate to the “Downloads” folder, and double-click on the lab_0.ipynb file. This will open Lab 0 in JupyterLab, where you can follow the directions in the lab to begin.
When it’s time to submit the labs on Gradescope, you must provide the file as a PDF. Further instructions on how to do this will be provided in Lab 0.
If you have any questions or encounter difficulties during the labs, don’t hesitate to ask the instructor for assistance.
Acknowledgements#
The development of this course was supported by Purdue’s Elmore Family School of Electrical and Computer Engineering and the Department of Mathematics. We thank Prof. Alina Alexeenko, Prof. Kristina Bross, Prof. Milind Kulkarni, Prof. Uli Walther, and Dr. Natasha Duncan for their invaluable input and support.