About The SDS Bootcamp Courses
A solid foundation in statistics, Python and R programming is critical for success in the SDS graduate programs and to prepare for a career in the field of data science. The school has developed three online, self-paced training courses to ensure that students have this foundation to help with their graduate course work. All SDS students enrolled are eligible to take these training courses. Several instructors may require successful completion of the bootcamps as part of specific classes.
Students are now able to request to register for each bootcamp course, in preparation for August 10th start date.
As an asynchronous course, students can register for a bootcamp course between now and Wednesday, September, 30th. Courses must be completed by Friday, October 30th.
Introduction to Python For Data Science
A Self-paced introduction to the basics of programming in Python 3. This course is organized as a series of modules covering Python variables, expressions, statements, including loops and iterations, functions and pandas library for reading and manipulating data sets.
Introduction to R For Data Science
A Self-paced introduction to R programming for data analysis using RStudio environment. The modules covered include R data structures, functions and packages, importing and cleaning data and data visualization in R.
Overview of Statistics for Data Science
A Self-paced course to help prepare students with the base level of statistics knowledge for success in the data science program. The modules in this course cover an introduction to statistics, probability, and probability distributions. Descriptive statistics and hypothesis testing are also covered in this course.
How To Register
All data science bootcamps can be found in Canvas. Students can register for the individual bootcamps using the registration link below. Registration will close on Wednesday, September 30th, to allow students enough time to complete each course before the end of the semester.
Taking Each Course & Time Requirements
The bootcamps are modularized and self contained. The courses are combinations of videos, canvas pages and markdown files with easy-to-follow examples. The modules must be completed in order, however, If a module contains familiar content, students can move forward to proceed with the quiz for that section.
Each course is designed such that students can study at their own pace. Students should be able to complete each course within four weeks, however, there's no implemented time limit within Canvas.
Upon successful completion of each course, students will be issued a statement of accomplishment. This includes a PDF badge that will be shared via email. Each badge serves as a statement of accomplishment, which can be presented in future courses to prevent retaking a bootcamp course.