About the Trainings
Each class session has both interactive Modules and Walkthroughs that you will need to work through after doing the readings and watching a lecture (if applicable). These lessons are a central part of the class—they will teach you how to use R and other packages eventually leading to the tidyverse family.
Interactive training sections are provided as a courtesy by Data Camp1.
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 are a polar personality type when it comes to work (i.e. primarily a perfectionist or mostly careless), then the modules will likely prove to be a challenge. It is highly unlikely that you will be able to comprehend everything by going beyond your limit or that it will just come to you so please work hard but also take breaks, swear2, and ask peers or me for help. Your score is predicated on putting in a solid effort, rather than getting it perfect because everything is probabilistic and nothing is for certain.
Data Camp Schedule
A tentative schedule is given below. The Course and Chapter names represent Data Camp titles3:
| Exploration | Link | Due by | Required | Course or Project Name | Chapters covered |
|---|---|---|---|---|---|
| 1 | Week 1 | 5/22/22 | Introduction to R | Intro to basics, Vectors, Matrices, Factors, Data Frames, Lists | |
| 5/22/22 | Working with Data in the Tidyverse | Explore your data, Tame your data, Tidy your data, Transform your data | |||
| 2 | Week 2 | 5/29/22 | Introduction to the Tidyverse | Data wrangling, Data visualization, Grouping and summarizing, Types of visualizations | |
| 5/29/22 | Foundations of Inference | Introduction to ideas of inference, Completing a randomization test: gender discrimination, Hypothesis testing errors: opportunity cost, Confidence intervals | |||
| 3 | Week 3 | 6/5/22 | Data Manipulation with dplyr | Transforming Data with dplyr, Aggregating Data, Selecting and Transforming Data, Case Study: The babynames Dataset | |
| 6/5/22 | Introduction to Statistics in R | Summary Statistics, Random Numbers and Probability, More Distributions and the Central Limit Theorem, Correlation and Experimental Design | |||
| 4 | Week 4 | 6/12/22 | Introduction to Regression in R | Simple Linear Regression, Predictions and model objects, Assessing model fit, Simple logistic regression | |
| 5 | Week 5 | 6/19/22 | Survey and Measurement Development in R | Preparing to analyze survey data, Exploratory factor analysis & survey development, Confirmatory factor analysis & construct validation, | |
| 6 | Week 6 | 6/26/22 | Analyzing Survey Data in R | Introduction to survey data, Exploring categorical data, Exploring quantitative data, Modeling quantitative data | |
| EC1 | 6/26/22 | Introduction to Data Visualization with ggplot2 | Introduction, Aesthetics, Geometries, Themes |
Need Help?
While I am happy to meet face-to-face, I am not consistently in my office at the moment. It is likely easier to simply schedule Zoom session using the calendar or by notifying me on Slack by adding @Dr. Abhik Roy to your message.