Course Information
Registration period
Year-round Recruitment
Course Level
Study Mode
Duration
6 Hour(s) 52 Minute(s)
Language
English
Taught by
R-Tutorials Training
Rating
1 views
Course Overview
Learn how to use R to quickly understand and analyze new data and start your data analysis projects with ease
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!
Course Content
- 3 section(s)
- 63 lecture(s)
- Section 1 Introduction
- Section 2 Initial Data Analysis and Data Pre-processing
- Section 3 Exploratory Data Analysis
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
Skills covered in this course
Reviews
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BBayuzen Ahmad
so many missing data
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PPrakhar Mohan
All the downloadable resource has a common file name "misc_data", this is highly unprofessional way of sharing a content. Also, the file type is unknown so it is really difficult to understand how do we read those resource
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PPaul Bennett
Very excellent instructor. He has good communication skills. Not enough opportunity to apply what is learned.
