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Updated in [June 30th, 2023]
This course, Data Analysis with R, provides an introduction to exploratory data analysis. Students will learn how to use the R programming language to explore and analyze data sets. Topics covered include data visualization, data wrangling, and basic statistical analysis. Additionally, students will gain an understanding of the importance of exploratory data analysis and how it can be used to gain insights into data sets. Supplemental reading material is available in the form of the Exploratory Data Analysis book, although it is not required for the course. This course is also a part of the Data Analyst Nanodegree.
[Applications]
After completing this course, students can apply their knowledge of data analysis with R to explore and analyze data sets. They can use the techniques learned in the course to identify patterns and trends in data, and to develop insights and conclusions. Additionally, students can use the knowledge gained in this course to create visualizations to better understand the data, and to communicate their findings to others. Finally, students can use the resources provided in the course to further their understanding of exploratory data analysis and to continue their learning journey.
[Career Path]
The career path recommended to learners of this course is Data Analyst. Data Analysts are responsible for collecting, organizing, and analyzing data to help inform business decisions. They use a variety of tools and techniques to uncover trends and insights in data sets, and then communicate their findings to stakeholders. Data Analysts must be able to interpret data, identify patterns and trends, and develop actionable insights.
The development trend of Data Analysts is towards more specialized roles. As data sets become larger and more complex, Data Analysts are increasingly expected to have a deep understanding of specific areas, such as machine learning, natural language processing, or predictive analytics. Data Analysts must also be able to work with a variety of data sources, such as structured and unstructured data, and be able to use a variety of tools and techniques to analyze data. Additionally, Data Analysts must be able to communicate their findings to stakeholders in a clear and concise manner.
[Education Path]
The recommended educational path for learners interested in data analysis with R is to pursue a Bachelor's degree in Data Science. This degree program typically includes courses in mathematics, statistics, computer science, and programming, as well as courses in data analysis and visualization. Students will learn how to use R to analyze data, create visualizations, and develop predictive models. They will also gain an understanding of the principles of data science, such as data mining, machine learning, and artificial intelligence.
The development trend of data science degrees is to focus on the application of data science in various industries. This includes courses in data engineering, data management, and data security. Additionally, many programs are now offering courses in data ethics, data privacy, and data governance. These courses provide students with the skills and knowledge needed to work in the rapidly changing field of data science.
Course Syllabus
What is EDA?
Start by learn about what exploratory data analysis (EDA) is and why it is important.R Basics
EDA, which comes before formal hypothesis testing and modeling, makes use of visual methods to analyze and summarize data sets.,R will be our tool for generating those visuals and conducting analyses.,We will install RStudio and packages, learn the layout and basic commands of R, practice writing basic R scripts, and inspect data sets.Explore One Variable
Perform EDA to understand the distribution of a variable and to check for anomalies and outliers.,Learn how to quantify and visualize individual variables within a data set to make sense of a pseudo-data set of Facebook users.,Create histograms and boxplots, transform variables, and examine tradeoffs in visualizations.Explore Two Variables
DA allows us to identify the most important variables and relationships within a data set before building predictive models.,Learn techniques for exploring the relationship between any two variables in a data set.,Create scatter plots, calculate correlations, and investigate conditional means.Explore Many Variables
Learn powerful methods and visualizations for examining relationships among multiple variables.,Reshape data frames and how to use aesthetics like color and shape to uncover more information,Continue to build intuition around the Facebook data set and explore some new data sets as well.Diamonds and Price Predictions
Investigate the diamonds data set alongside Facebook Data Scientist, Solomon Messing.,See how predictive modeling can allow us to determine a good price for a diamond.,As a final project, you will create your own exploratory data analysis on a data set of your choice.
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