Course Summary
Exploratory Data Analysis is a course that teaches you how to analyze and visualize data sets using R programming language. Through this course, you will gain a deep understanding of data analysis and be able to make informed decisions based on your findings.Key Learning Points
- Learn how to analyze and visualize data using R programming language
- Gain a deep understanding of data analysis
- Make informed decisions based on your findings
Job Positions & Salaries of people who have taken this course might have
- USA: $62,453
- India: ₹5,81,810
- Spain: €30.000
- USA: $62,453
- India: ₹5,81,810
- Spain: €30.000
- USA: $70,000
- India: ₹8,00,000
- Spain: €30.000
- USA: $62,453
- India: ₹5,81,810
- Spain: €30.000
- USA: $70,000
- India: ₹8,00,000
- Spain: €30.000
- USA: $113,309
- India: ₹12,00,000
- Spain: €40.000
Related Topics for further study
Learning Outcomes
- Understand the basic principles of exploratory data analysis
- Learn how to use R for data analysis and visualization
- Apply exploratory data analysis techniques to real-world data sets
Prerequisites or good to have knowledge before taking this course
- Basic knowledge of statistics and programming
- Familiarity with R programming language
Course Difficulty Level
IntermediateCourse Format
- Online
- Self-Paced
Similar Courses
- Data Science Essentials
- Data Analysis and Visualization
- Python Data Analysis
Related Books
Description
This course covers the essential exploratory techniques for summarizing data. These techniques are typically applied before formal modeling commences and can help inform the development of more complex statistical models. Exploratory techniques are also important for eliminating or sharpening potential hypotheses about the world that can be addressed by the data. We will cover in detail the plotting systems in R as well as some of the basic principles of constructing data graphics. We will also cover some of the common multivariate statistical techniques used to visualize high-dimensional data.
Knowledge
- Understand analytic graphics and the base plotting system in R
- Use advanced graphing systems such as the Lattice system
- Make graphical displays of very high dimensional data
- Apply cluster analysis techniques to locate patterns in data
Outline
- Week 1
- Introduction
- Installing R on Windows (3.2.1)
- Installing R on a Mac (3.2.1)
- Installing R Studio (Mac)
- Setting Your Working Directory (Windows)
- Setting Your Working Directory (Mac)
- Principles of Analytic Graphics
- Exploratory Graphs (part 1)
- Exploratory Graphs (part 2)
- Plotting Systems in R
- Base Plotting System (part 1)
- Base Plotting System (part 2)
- Base Plotting Demonstration
- Graphics Devices in R (part 1)
- Graphics Devices in R (part 2)
- Welcome to Exploratory Data Analysis
- Syllabus
- Pre-Course Survey
- Exploratory Data Analysis with R Book
- The Art of Data Science
- Practical R Exercises in swirl Part 1
- Week 1 Quiz
- Week 2
- Lattice Plotting System (part 1)
- Lattice Plotting System (part 2)
- ggplot2 (part 1)
- ggplot2 (part 2)
- ggplot2 (part 3)
- ggplot2 (part 4)
- ggplot2 (part 5)
- Practical R Exercises in swirl Part 2
- Week 2 Quiz
- Week 3
- Hierarchical Clustering (part 1)
- Hierarchical Clustering (part 2)
- Hierarchical Clustering (part 3)
- K-Means Clustering (part 1)
- K-Means Clustering (part 2)
- Dimension Reduction (part 1)
- Dimension Reduction (part 2)
- Dimension Reduction (part 3)
- Working with Color in R Plots (part 1)
- Working with Color in R Plots (part 2)
- Working with Color in R Plots (part 3)
- Working with Color in R Plots (part 4)
- Practical R Exercises in swirl Part 3
- Week 4
- Clustering Case Study
- Air Pollution Case Study
- Practical R Exercises in swirl Part 4
- Post-Course Survey
Summary of User Reviews
Exploratory Data Analysis course on Coursera received positive feedback from many users. It is highly recommended for learners who want to gain insights into data analysis.Key Aspect Users Liked About This Course
The instructors of the course are highly knowledgeable and provide valuable insights to learners.Pros from User Reviews
- The course is well-structured and easy to follow.
- It provides hands-on experience with real datasets.
- The content is informative and engaging.
- The course offers practical tips and techniques for data analysis.
Cons from User Reviews
- Some users found the course content to be too basic.
- The quizzes and assignments can be challenging at times.
- The course material can be overwhelming for beginners.
- Some users experienced technical issues with the platform.
Roger D. Peng, PhD
4.7 Raiting
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