Understanding New Data - Exploratory Analysis in R
What you'll learn
- Identify suitable R libraries for data exploration
- Create suitable data visualizations
- Learn the succession of steps in data exploration
- Use a combination of hypothesis tests, explorations and models
- How to prepare data for exploration
- What to do when problems arise in the initial stages
- Work with the main variable types
- Use time series data
- Basic R programming skills
- A general understanding of statistics and data visualization
- R and RStudio ready on your computer
Are you new to R and data analysis?
Do you ever struggle starting an analysis with a new dataset?
Do you have problems getting the data into shape and selecting the right tools to work with?
Have you ever wondered if a dataset had the information you were interested in and if it was worth the effort?
If some of these questions occurred to you, then this program might be a good start to set you up on your data analysis journey. Actually, these were the question I had in mind when I designed the curriculum of this course. As you can see below, the curriculum is divided into three main sections. Although this course doesn't have a focus on the basic concepts of statistics, some of the most important concepts are covered in the first section of the course.
The two other sections have their focus on the initial and the exploratory data analysis phases respectively. Initial data analysis (or IDA for short) is where we clean and shape the data into a form suitable for the planned methods. This is also where we make sure the data makes sense from a statistical point of view. In the IDA section I present tools and methods that will help you figure out if the data was collected properly and if it is worthy of being analyzed.
On the other hand, the exploratory data analysis (EDA) section offers techniques to find out if the data can answer your analytical questions, or in other words, if the data has a relevant story to tell. This will spare you from investing time and effort into a project that will not deliver the results you hoped for. In an ideal case the results of EDA may confirm that the planned analysis is worth it and that there are insights to be gained from that dataset and project.
If you are interested in statistical methods and R tools that help you bridge the gap between data collection and the confirmatory data analysis (CDA), then this program is for you. Take a look at the curriculum and give this course a try!
Who this course is for:
- Data scientists
- Analysts of all fields
- Researchers working and analyzing data
- Young professionals wanting to switch to data analysis related work
- Students taking data analysis exams
- Everyone interested in analyzing data
- Data exploration is an initial phase of a data analysis project therefore you will need these skills in most of your projects
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We focus on Data Science tutorials. Offering several R courses for every skill level, we are among Udemy's top R training provider. On top of that courses on Tableau, Excel and a Data Science career guide are available.
All of our courses contain exercises to give you the opportunity to try out the material on your own. You will also get downloadable script pdfs to recap the lessons.
The courses are taught by our main instructor Martin – trained biostatistician and enthusiastic data scientist / R user.
Should you have any questions, you are invited to check out our website, you can open a discussion in the course or you can simply drop us a pm.
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