Study Learn Grow
Exploratory Data Analysis With R

Exploratory Data Analysis With R

The greatest number of mistakes and failures in data analysis comes from not performing adequate Exploratory Data Analysis (EDA). Lack of EDA knowledge can expose you to the great risk of drawing incorrect, and potentially harmful, conclusions from your data analysis.

Overview

Description
Harness the skills to analyze your data effectively with EDA and R.

The greatest number of mistakes and failures in data analysis comes from not performing adequate Exploratory Data Analysis (EDA). Lack of EDA knowledge can expose you to the great risk of drawing incorrect, and potentially harmful, conclusions from your data analysis.

In this course, you will learn how EDA helps you draw conclusions to make better sense of your data and implement correct techniques. We'll begin with a brief introduction to EDA, its importance, and advantages over BI tools. Using R libraries like dplyr and ggplot2, we will generate insights and formulate relevant questions for investigation and communicate the results effectively using visualizations. You will learn how to spot missing data and errors, validate assumptions, and identify the patterns for understanding the problem. Based on this, you’ll be able to select a correct ML model to use for your data.

By the end of the course, you will be able to quickly get know and interpret various kinds of data sets you will be presented with, and easily understand how to handle and work with them in order to make them ready for further modeling activities.

Here's the link to the GitHub repo to this course: https://github.com/PacktPublishing/Exploratory-Data-Analysis-with-R

Please note that basic knowledge of R and R Studio, together with some knowledge of descriptive statistics, are key to getting the best out of this course.

About the Author

Andrea Cirillo is a Senior Audit Quantitative Analyst at Intesa Sanpaolo Banking Group. He works daily with copious volumes of "messy" data for the purpose of auditing credit risk models. This has prompted him to develop the key skills needed to succeed in Exploratory Data Analysis (EDA). Andrea is also an active contributor to the R community with well-received packages like updateR and paletteR. He recently focused resolving some of his R-related pain-points by helping R users draw the most out of their data through effective data visualization tools like the dataviz bot Vizscorer.
Basic knowledge
Basic knowledge of R and R Studio, together with some knowledge of descriptive statistics, are key to getting the best out of this course

Course Information

Basic knowledge of R and R Studio, together with some knowledge of descriptive statistics, are key to getting the best out of this course

Set up your data and code to avoid mistakes and ensure reproducibility
Really understand the structure and content of your data
Build clear plots to evaluate the distribution of your data with ggplot
Construct summaries of your variables with dplyr
Implement data cleaning and validation tasks to get your data ready for data mining activities
Test a hypothesis or check assumptions related to a specific model
Estimate parameters and figure the margins of error

This course is for those seeking to enhance their data analysis skills by harnessing the benefits of R.

• Lifetime Access to Each Course
• Certificate on Completion of Course
• No Extra Charges Or Admin Fees
• Easy Access to Courses
• High Priority Support After Sales.
• Big Discounts on Individual Courses

Course Specifications

IT and Computing courses are available to study on our learning platform. 

See All Courses

Adult education is the non-credential activity of gaining skills and improved education. 

See All Courses

Online education is electronically supported learning that relies on the Internet for teacher/student interaction. 

See All Courses

A short course is a learning programme that gives you combined content or specific skills training in a short period of time. Short courses often lean towards the more practical side of things and have less theory than a university course – this gives you a more hands-on experience within your field of interest.

See All Courses

Course duration is 24 hours.

See All Courses

Study Learn Grow

Related Jobs