###### DataTrail (The Course)

#### Course Info

This course includes 1 attempt.

DataTrail is a no-cost, paid 14-week educational initiative for young-adult, high school and GED-graduates. DataTrail aims to equip members of underserved communities with the necessary skills and support required to work in the booming field of data science.

DataTrail is a fresh take on workforce development that focuses on training both Black, Indigenous, and other people of color (BIPOC) interested in the data science industry *and* their potential employers.

Offered by the Johns Hopkins Bloomberg School of Public Health, in partnership with local non-profits and Leanpub, DataTrail combines a mutually-intensive learning experience (MILE) with a whole-person ecosystem of support to allow aspiring data scientists and their employers to succeed.

#### Course Material

- About this Course
- 1 Intro: Welcome to DataTrail
- 2 Pre DataTrail Survey
- 3 Finding Help
- 4 Understanding Your Chromebook
- 5 Working Offline vs Online
- 6 Account Setup
- 7 Remote work and task management
- 8 Forming Questions
- 9 The Data Science Process
- 10 Types of Data Science Questions
- 11 How to Learn
- 12 RStudio and Projects
- 13 Your First Data Science Project
- 14 Getting Data
- 15 What is R
- 16 Objects in R
- 17 Working with Logicals
- 18 Data Frames
- 19 Basic Commands in R
- 20 Getting Help in R
- 21 Managing Files
- 22 R Packages
- 23 R Markdown
- 24 Dataset File Types
- 25 Finding Data
- 26 Ethical Data Science
- 27 Google Documents
- 28 Getting Data Project
- 29 Cleaning Data
- 30 What is Tidy Data
- 31 Reshaping Data
- 32 How to Tidy Data
- 33 Joining Data
- 34 Working with Strings
- 35 Working with Factors
- 36 Working with Dates
- 37 Project Organization
- 38 Cleaning Data Project
- 39 Plotting Data
- 40 Good Plots
- 41 Introduction to ggplot2
- 42 Customization in ggplot2
- 43 Saving Plots
- 44 From Exploratory to Explanatory
- 45 Data Tables
- 46 Tables in R
- 47 Multiple Plots in R
- 48 Advanced Data Visualization
- 49 Plotting Data Project
- 50 Getting Statistics
- 51 Translating Questions to Data Science Questions
- 52 Identifying Data
- 53 In Practice Using Stats
- 54 Descriptive Analysis
- 55 Exploratory Analysis
- 56 Inference: Overview
- 57 Inference: Linear Regression
- 58 Inference: Examples
- 59 Inference: Multiple Regression
- 60 Prediction and Machine Learning
- 61 Stats Project
- 62 Sharing Results
- 63 Reproducibility
- 64 Version Control
- 65 GitHub
- 66 Creating a Repository
- 67 Cloning a Repository
- 68 Pushing and Pulling Changes
- 69 Organization with Issues on GitHub
- 70 Setting Up a Project on GitHub
- 71 Pull Requests
- 72 Version Control Help
- 73 Types of Communication
- 74 Data Science Reports
- 75 Google Slides
- 76 How to Give a Presentation
- 77 Projecting from a Chromebook
- 78 How to Present to a General Audience
- 79 How to Present to a Technical Audience
- 80 How to Write a Blog Post
- 81 Participating in Meetings
- 82 How to Have a One-on-One Meeting
- 83 GitHub and Final Data Project
- 84 Building Resumes
- 85 Resumes
- 86 Cover Letters
- 87 Make Your Own Website
- 88 Project Gallery
- 89 Improving Your GitHub Profile
- 90 Improving Your LinkedIn Profile
- 91 Using Twitter for Data Science
- 92 Data Science Job Descriptions
- 93 Where to Look for Data Science Jobs
- 94 Where to Find Remote Data Science Jobs
- 95 Data Science Interviews
- 96 Data Science Meetups
- 97 Create Your Portfolio
- 98 Post DataTrail Survey
- About the Authors

#### Instructors

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