First steps in data analysis
Revealing the logic and strengths of R for working with data
Topic: Data
Are you interested in data science but intimidated by R? Maybe you want to start working with data but have no idea where to begin. If that sounds familiar, this is the course for you.
Expert Rick Scavetta offers an introduction to programming and data science concepts, aimed at absolute beginners. You'll discover the power of R as you explore the language's three core strengths: data manipulation, statistics, and data visualization. Join in to learn how to use R to make your data analysis steps efficient, transparent, and reproducible—the hallmarks of all good scientific practice.
What you'll learnand how you can apply it
By the end of this live online course, you’ll understand:
 Base R and tidyverse syntax
 How to use basic functions in the tidyverse to process raw data for typical data analysis questions
 The most common data structures in R (classes and types) and how they relate to each other
 How to use logical expressions and indexing to ask specific questions of data
 Common pitfalls with vectorization and indexing
And you’ll be able to:
 Complete a basic data analysis workflow
 Calculate groupwise descriptive statistics
 Define basic linear models and calculate ANOVAs
 Draw appropriate and typical visualizations of bivariate data
 Apply the case study on a different dataset to build on the examples
This training course is for you because...
 You have a dataset to analyze but have only used GUIbased software so far.
 You're interested in learning data science but are intimidated by R and have no idea where to start.
 You want to improve your skills to better your future career prospects.
 You're a business professional who wants insight into the work of your data science team.
Prerequisites
 Basic knowledge of data analysis questions and scenarios (e.g., Given a dataset, what questions would you ask, as either the generator or recipient of the data?)
 An RStudio account (You'll be provided RStudio Cloud projects during the course.)
Recommended followup:
 Take Inferential Statistics Using R (live online training course with Rick Scavetta)
 Take Data Analysis Paradigms in the tidyverse (live online training course with Rick Scavetta)
About your instructor

Rick Scavetta has worked as an independent data science trainer since 2012. Operating as Scavetta Academy, Rick has a close and recurring presence at primary research institutes all over Germany, including many Max Planck Institutes and Excellence Clusters, in fields as varied as primatology, earth sciences, marine biology, molecular genetics, and behavioral psychology.
Schedule
The timeframes are only estimates and may vary according to how the class is progressing
Introduction (20 minutes)
 Group discussion: Common questions and tasks in data analysis
 Lecture: What is R?
 Q&A
R fundamentals (20 minutes)
 Lecture: Basic R syntax; navigating RStudio
 Group discussion: Approaching our first data set
 Handson exercise: Apply knowledge in R
 Q&A
 Break (5 minutes)
Case study I (60 minutes)
 Lecture: Descriptive and inferential statistics; data visualization
 Handson exercise: Apply the class’s solutions
 Q&A
 Break (5 minutes)
Case study II (60 minutes)
 Group discussion: The second dataset; analytical questions
 Handson exercise: Apply previous knowledge to the second dataset
 Lecture: Further analysis using the second dataset
 Q&A
Wrapup and Q&A (10 minutes)