This course introduces students to the basics of the Python programming language, using the open-source libraries Numpy, Seaborn, and Matplotlib to explain key concepts in data visualization and reporting. Topics include concepts and methods used in graphical representation of data, exploration and reporting of data, and basic linear regression methods. Upon completion, students should be able to effectively use graphical tools to communicate insights about data.
During this course, students will learn the basics of the Python programming language and utilize concepts and methods in the graphical representation of data. Students will also utilize concepts and methods in the exploration and reporting of data. The course will cover conducting basic linear regression methods and applying data visualization concepts to communicate insights about data.
This course is expected to take approximately 40-60 hours to complete. The course is offered through NCLab's learning platform. You will receive an email with login instructions upon registration for the course. You will have access to the course for 6 months from the date of registration.
The course is divided into three Units, and each Unit is composed of five Sections. Each Section consists of seven instructional/practice levels, a quiz, and a master (proficiency) level. You can return to any level or quiz for review. The first two units are in the course titled Intro to Python for Data Science. The third unit is in the course titled Intro to Predictive Data Analytics:
Intro to Python for Data Science
Unit 1 Learn fundamental numeric, text string, list and tuple operations.
Unit 2 Write conditional programs using if-elif-else statements and while loops.
Intro to Predictive Data Analytics
Unit 1 Perform linear regression analysis using Python
As a self-paced class, this learning is flexible in regards to timing, but to keep students engaged in the course, there is a time limit of 6 months to complete the class. The course was made to build on the topics learned and requires commitment to work on the course. Long delays of time between lessons is detrimental to the learning experience.
Prerequisite(s): Knowledge of coding helpful, but not required. High school level math proficiency is recommended.