Skills Based Classes
Professional Education
These courses provide skills that are necessary for data science roles within Biotechnology: computing, handling data, and statistics. The primary courses are taught in the R programming language, through the modern tidyverse lens, which provides the easiest onramp to powerful data science techniques, and is widely used in the industry. The courses assume no prior programming experience, and quickly provide training in hands-on and tangible skills. Further courses include, or will include, other programming languages like python and SQL that unlock more advanced data science skills and methodologies essential for fully-rounded fluency in data science.
Skills Based Classes are not eligible for employee tuition reduction.
Python Fundamentals
Unlock the Power of Python Data Analysis!
Are you ready to dive into the world of Python programming and data analysis? Our 4-week Python Data Analysis with Pandas course is designed just for you.Why Choose Our Course?
- Beginner-Friendly: No prior coding experience required! We’ll guide you step by step in this affordable introduction to Python.
- Practical Focus: Learn how to analyze real-world data using the powerful pandas library.
- Flexible Learning: Go at your own pace, anytime, anywhere. No rigid schedules!
- Faculty Support: You’re never alone—our instructors are here to help.
Introduction to R for Data Analysis
Why Choose Our Course?
- Beginner-Friendly: No prior experience required! We’ll guide you step by step in this affordable introduction to R for Data Analysis.
- Flexible Learning: Go at your own pace, anytime, anywhere. No rigid schedules!
- Faculty Support: You’re never alone—our instructors are here to help.
Advanced R for Data Analysis
Take your R code to the next level
This course is designed to take your R programming expertise to the next level, building on the foundations laid in the Introduction to R for Data Analysis course. Dive into advanced techniques and powerful tools that will transform your data workflows and analytical capabilities. In this course, you will learn how to:
- Write your own custom R functions
- Reduce redundancy in your code by learning iteration techniques with the purrr package
- Refactor column variables
- Reshape your data frames
- Join multiple data frames together
Data Cleaning with R
Make data analysis easier with R
Data cleaning is an incredibly important part of the data science process. This course introduces a customizable data cleaning pipeline, focusing on biomedical data from electronic health records and health survey data. In this course, you will learn how to:
- Summarize the data collection process and data dictionaries
- Identify and address missing values using R
- Handle data quality issues (such as invalid and inconsistent values) using R
- Reshape your data into a “tidy” format using R
- Create a custom, reproducible data cleaning function in R
More courses and credentials coming soon
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Class by the Kahlert School of Computing, powered by Professional Education at the University of Utah.
