About the Trainings

Most class sessions have both interactive Modules courtesy of Data Camp¹ and Walkthroughs created by me that you will need to work through after doing the readings and reviewing the corresponding content (if applicable). The lessons are a central part of the class and are focused on using the tidyverse family of packages, though the approaches are certainly not the only ways to wrangle, clean, analyze, and visualize data in R.

Advice

Carve out some time everyday to go through these. If you try to complete everything in one sitting, it will probably be overwhelming! However if you have familiarity with some modules, please feel free to work ahead.

Grading

The ultimate point of Data Camp is to get you familiarized with an environment that you likely have never seen or been exposed to. While you should absolutely go through each module, there is certainly no expectation that you will get everything right. In fact, the points that you incur don’t mean anything as far as how you are assessed so please use hints as needed! As with any things data science, you’ll learn by doing. If you have a polar personality type as it pertains to work (i.e. primarily a perfectionist or mostly careless), then the modules will likely prove to be a challenge. The chance that you will be able to comprehend everything by going beyond your limit or conversely assuming it will just come to you is low so please work hard but also take breaks, swear², look on the Internet, ask peers, or reach out for help. Your score is predicated on putting in a solid effort, rather than getting it perfect because that’s not realistic when it comes to data.

Data Camp Schedule

A tentative schedule is given below. The Course and Chapter names represent Data Camp titles³:

Required

Modules that do

not require a corresponding task will be assessed only on the successful completion of the data camp course

require a task will be assessed on both the successful completion of the data camp course and corresponding assessment to be submitted via eCampus

Link	Due	Required	Task	Module	Chapters
Week 1	8/30/22			Introduction to R	Intro to basics
					Vectors
					Matrices
					Factors
					Data Frames
					Lists
Week 1	8/30/22			Introduction to R	Intro to basics
					Vectors
					Matrices
					Factors
					Data Frames
					Lists
Week 2	9/6/22			Introduction to the Tidyverse	Data wrangling
					Data visualization
					Grouping and summarizing
					Types of visualizations
Week 2	9/6/22			Introduction to Data Visualization with ggplot2	Explore your data
					Tame your data
					Tidy your data
					Transform your data
Week 2	9/6/22			Introduction to Data Visualization with ggplot2	Explore your data
					Tame your data
					Tidy your data
					Transform your data
Week 3	9/20/22			Intermediate Data Visualization with ggplot2	Statistics
					Coordinates
					Facets
					Best Practices
Week 3	9/20/22			Intermediate Data Visualization with ggplot2	Statistics
					Coordinates
					Facets
					Best Practices
Week 4	10/4/22			Visualization Best Practices in R	Proportions of a whole
					Point data
					Single distributions
					Comparing distributions
Week 4	10/4/22			Visualization Best Practices in R	Proportions of a whole
					Point data
					Single distributions
					Comparing distributions
Week 5	10/18/22			Unsupervised Learning in R	Unsupervised Learning in R
					Hierarchical clustering
					Dimensionality reduction with PCA
					Putting it all together with a case study
Week 6	11/1/22			Introduction to Text Analysis in R	Wrangling Text
					Visualizing Text
					Sentiment Analysis
					Topic Modeling
Week 7	11/15/22			Communicating with Data in the Tidyverse	Custom ggplot2 themes
					Creating a custom and unique visualization
					Introduction to Rmarkdown
					Customizing your RMarkdown report
Week 8	11/29/2022			Analyzing Social Media Data in R	Understanding Twitter data
					Analyzing Twitter data
					Visualize Tweet texts
					Network Analysis and putting Twitter data on the map

Recommended

The following module is optional but highly recommended

Required	Task	Module	Chapters
		Intermediate R	Conditionals and Control Flow
			Loops
			Functions
			The apply family
			Utilities

Extra Credit

The following modules are optional and may count as extra credit contingnet on the successful completion of the data camp course and corresponding assessment to be submitted via eCampus. Please note that each subsequent module is dependent on the previous one.

Due	Required	Task	Module	Chapters
12/9/22			Network Analysis in the Tidyverse	The hubs of the network
				In its weakness lies its strength
				Connection patterns
				Similarity clusters
12/9/22			Supervised Learning in R: Classification	k-Nearest Neighbors (kNN)
				Naive Bayes
				Logistic Regression
				Classification Trees
12/9/22			Predictive Analytics using Networked Data in R	Introduction, networks and labelled networks
				Homophily
				Network Featurization
				Putting it all together

R Tasks

In some weeks you will be expected to complete an additional R task which are indicated by a in the table above. Collectively these serve as the R Data EDA noted on the syllabus.

Working Ahead

By no means do you have to wait for a particular module to be assigned. If you wish to enroll in a training - one that is assigned or otherwise - simply search for the name of that course on the Data Camp site. For those modules assigned for this course, you will receive credit after the due date has passed.

Need Help?

While I am happy to meet face-to-face, it is just as easy to schedule a Zoom session using the calendar or by notifying me on Slack by adding @Dr. Abhik Roy to your message.