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