•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!
Disclosure: when you buy through links on our site, we may earn an affiliate commission.
Understanding New Data – Exploratory Analysis in R
Learn how to use R to quickly understand and analyze new data and start your data analysis projects with ease
Top Data Analysis courses:
CourseMarks Score® helps students to find the best classes. We aggregate 18 factors, including freshness, student feedback and content diversity.
Course content can become outdated quite quickly. After analysing 71,530 courses, we found that the highest rated courses are updated every year. If a course has not been updated for more than 2 years, you should carefully evaluate the course before enrolling.
New courses are hard to evaluate because there are no or just a few student ratings, but Student Feedback Score helps you find great courses even with fewer reviews.
The top online course contains a detailed description of the course, what you will learn and also a detailed description about the instructor.
Tests, exercises, articles and other resources help students to better understand and deepen their understanding of the topic.
This course contains:
Table of contents
•Are you new to R and data analysis?
You will learn
✓ 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
• A general understanding of statistics and data visualization
• R and RStudio ready on your computer
This course is for
• 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