About the Lessons
Description
Each class session has an interactive lesson that you will 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.
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 right because there is no such thing in data science.
Data Camp Schedule
A tentative schedule is given below. The Course and Chapter names represent Data Camp titles3:
| Exploration | Lesson Page | Due by | Required | Course or Project Name | Chapters covered |
|---|---|---|---|---|---|
| 1 | Week 1 | 1/27/21 | Introduction to R | Intro to Basics, Vectors, Matrices, Factors, Data Frames, Lists | |
| 2 | Week 2 | 2/3/21 | Introduction to Data in R | Language of Data, Study Types and Cautionary Tales, Sampling Strategies and Experimental Design, Case Study | |
| 2/3/21 | Data Visualization in R | A Quick Introduction to Base R Graphics, Different Plot Types, Adding Details to Plots, How Much is Too Much?, Advanced Plot Customization and Beyond | |||
| 3 | Week 3 | 2/10/21 | Intermediate R | Conditionals and Control Flow, Loops, Functions, The apply Family, Utilities | |
| 4 | Week 4 | 2/17/21 | Exploratory Data Analysis in R | Exploring Categorical Data, Exploring Numerical Data, Numerical Summaries, Case study | |
| 2/17/21 | Introduction to the Tidyverse | Data Wrangling, Data Visualization, Grouping and Summarizing, Types of Visualizations | |||
| 5 | Week 5 | 2/24/21 | Reporting with R Markdown | Getting Started with R Markdown, Adding Analyses and Visualizations, Improving the Report, Customizing the Report | |
| 2/24/21 | Introduction to Data Visualization with ggplot2 | Introduction, Aesthetics, Geometries, Themes | |||
| 6 | Week 6 | 3/3/21 | Intermediate Data Visualization with ggplot2 | Statistics, Coordinates, Facets, Best Practices | |
| 3/3/21 | Visualization Best Practices in R | Proportions of a Whole, Point Data, Single Distributions, Comparing Distributions | |||
| 7 | Week 7 | 3/17/21 | Analyzing US Census Data in R | Census Data in R with tidycensus, Wrangling US Census Data, US Census Geographic Fata in R, Mapping US Census Data | |
| 8 | Week 8 | 3/24/21 | Text Mining with Bag-of-Words in R | Jumping Into Text Mining with Bag Of Words, Word Clouds and More Interesting Visuals, Adding to your tm Skills, Battle of the Tech Giants for Talent | |
| 3/24/21 | Sentiment Analysis in R | Fast & Dirty: Polarity Scoring, Sentiment Analysis the Tidytext Way, Visualizing Sentiment, Case Study: Airbnb Reviews | |||
| 9 | Week 9 | 3/31/21 | Network Analysis in the Tidyverse | The Hubs of the Network, In Its Weakness Lies Its Strength, Connection Patterns, Similarity Clusters | |
| 3/31/21 | Visualizing Geospatial Data in R | Basic Mapping with ggplot2 and ggmap, Point and Polygon Data, Raster Data and Color, Data Import and Projections | |||
| 10 | Week 10 | 4/7/21 | Building Web Applications with Shiny in R | Get Started with Shiny, Inputs, Outputs, and Layouts, Reactive Programming, Build Shiny Apps | |
| 4/7/21 | Case Studies: Building Web Applications with Shiny in R | Shiny Review, Make the Perfect Plot Using Shiny, Explore a Dataset Interactively With Shiny ,Create Your Own Word Cloud in Shiny | |||
| 11 | Week 11 | 4/14/21 | Introduction to Tableau | Getting Started with Tableau, Building and Customizing Visualizations, Digging Deeper, Presenting Your Data | |
| 4/14/21 | Connecting Data in Tableau | Combining and Saving Data, Managing and Connecting Data | |||
| 12 | Week 12 | 4/21/21 | Analyzing Data in Tableau | Preparing for Analysis, Exploring Visualizations, Mapping Analysis, Groups, Sets, and Parameters | |
| 12 | Week 13 | 4/28/21 | Creating Dashboards in Tableau | Getting Started With Dashboards, Sharing Data Insights | |
| EC1 | Week 14 | 5/3/21 | Analyzing Survey Data in R | Introduction to survey data, Exploring categorical data, Exploring quantitative data, Modeling quantitative data | |
| EC2 | Week 14 | 5/3/21 | Communicating with Data in the Tidyverse | Building Static Dashboards, Building Dynamic Dashboards, Customizing Style, Case Study | |
| EC3 | Week 14 | 5/3/21 | Building Dashboards with shinydashboard | Building Static Dashboards, Building Dynamic Dashboards, Customizing Style, Case Study |