Full course description
What you'll learn:
In this workshop, you will accomplish the following goals:
- Examine the use of advanced list manipulation techniques such as list comprehension, lambda functions, map functions and filter functions
- Experiment with creating, using and manipulating data in NumPy
- Outline how to create, use, and manipulate data in Pandas, as well as learn how to accomplish SQL-like operations in Pandas
- Evaluate the use of the SKLearn framework to build machine learning models
- Demonstrate how to use SKLearn libraries to Preprocess data, build supervised and unsupervised models, as well as use advanced SKLearn features such as pipelines, column transformers, cross-validation, and grid search
- Preform text manipulation using two powerful Python libraries: regex and spaCy
- Visualize data using three different Python libraries: Matplotlib, Pandas, and Seaborn
What you'll do:
This workshop consists of ~15 hours of recorded content, 7 exercise sets and 4 (multiple-choice) quizzes. Students should expect to spend 5-10 hours per week for 4 weeks to fully benefit from the course material.
- Please be sure to actually work through the videos and exercises. You cannot learn Programming by just watching the videos. You must practice writing code yourself. In that regard, it is much like learning any other spoken language - the more you practice speaking it yourself, the more you will learn.
- The quizzes are set up to test your knowledge of key concepts in each module. It is therefore recommended you attempt the quizzes without executing the code. This will also help you hone a valuable skill in coding, of reading and interpreting code without executing it.
Who this course is designed for:
Aspiring Business Analysts, Data Analysts, and Data Scientists
What you'll receive:
A certificate of completion
Meet the instructor
Hina Arora - Clinical Assistant Professor and Director of Experiential Analytics, W. P. Carey School of Business at Arizona State University
Hina Arora is a Clinical Assistant Professor and Director of Experiential Analytics in the W. P. Carey School of Business at Arizona State University. At ASU, Hina has taught advanced data mining courses at the undergraduate and graduate levels, held multiple service roles, and consulted. Prior to joining ASU, Hina was a Group Manager at Microsoft, where she led analytics teams in the Windows Services Division and the US Central Marketing Organization. She has also held Software Development positions at IBM and Cognizant, and worked as a Research Scientist at the Center for Excellence in Document Analysis and Recognition at SUNY Buffalo. Hina has a PhD in Information Systems, a Masters’ degree in Electronics Engineering, and an Undergraduate degree in Physics.