Fundamentals of Data Science 

Fundamentals of Data Science 

In this course, learners will be introduced to the basics of data science through four distinct components: domain expertise, statistics, programming, and communication. Through broad expertise in these four components, the learner will become a proficient data scientist able to competently and confidently solve complex problems in the real world.   

Learners will gain experience solving real-world problems involving data analysis and data science through the use of data science concepts, R programming and the ability to generalize these concepts to other applications, and the ability to define appropriate conclusions based on sound statistical principles.  

Upon course completion, learners will gain a broad skill set and be able to solve problems faced by data-driven businesses across the world.  

Who Should Take This Course

Anyone looking to start a career in data science or interested in receiving a balanced background in data science topics should take this course.   

The examples presented in this course are through a life sciences context, primarily with clinical trial data examples. However, the learner does not need to be currently in or planning to enter the life sciences industry professionally.  

Duration and Prerequisites

An estimated 30 hours are needed to complete this course, varying based on the learner’s experience level and comfort in more technical topics.  

• Education and/or Experience 

A high school degree with exposure to mathematics such as geometry and algebra, including logarithms, exponents, and basic summation and product notation. Some experience with the English language is needed, including composition and writing. Experience in technical writing is preferred.  

A college degree and some preliminary background in statistics and programming experience is preferred but not required.  

• System Requirements 

Installation of R, RStudio, and various R packages. Instructors will help learners install and download.  

• Assessments 

Knowledge checks will be performed at the end of every module. A minimum of 75% is required to pass.