The Data Scientist’s Toolbox

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Description

When you enroll for courses through Coursera you get to choose for a paid plan or for a free plan

  • Free plan: No certicification and/or audit only. You will have access to all course materials except graded items.
  • Paid plan: Commit to earning a Certificate—it's a trusted, shareable way to showcase your new skills.

About this course: In this course you will get an introduction to the main tools and ideas in the data scientist's toolbox. The course gives an overview of the data, questions, and tools that data analysts and data scientists work with. There are two components to this course. The first is a conceptual introduction to the ideas behind turning data into actionable knowledge. The second is a practical introduction to the tools that will be used in the program like version control, markdown, git, GitHub, R, and RStudio.

Created by:  Johns Hopkins University
  • Taught by:  Jeff Leek, PhD, Associate Professor, Biostatistics

    Bloomberg School of Public Health
  • Taught by:  …

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When you enroll for courses through Coursera you get to choose for a paid plan or for a free plan

  • Free plan: No certicification and/or audit only. You will have access to all course materials except graded items.
  • Paid plan: Commit to earning a Certificate—it's a trusted, shareable way to showcase your new skills.

About this course: In this course you will get an introduction to the main tools and ideas in the data scientist's toolbox. The course gives an overview of the data, questions, and tools that data analysts and data scientists work with. There are two components to this course. The first is a conceptual introduction to the ideas behind turning data into actionable knowledge. The second is a practical introduction to the tools that will be used in the program like version control, markdown, git, GitHub, R, and RStudio.

Created by:  Johns Hopkins University
  • Taught by:  Jeff Leek, PhD, Associate Professor, Biostatistics

    Bloomberg School of Public Health
  • Taught by:  Roger D. Peng, PhD, Associate Professor, Biostatistics

    Bloomberg School of Public Health
  • Taught by:  Brian Caffo, PhD, Professor, Biostatistics

    Bloomberg School of Public Health
Basic Info Course 1 of 10 in the Data Science Specialization Commitment 1-4 hours/week Language English, Subtitles: French, Chinese (Simplified), Greek, Italian, Portuguese (Brazilian), Vietnamese, Russian, Turkish, Hebrew How To Pass Pass all graded assignments to complete the course. User Ratings 4.5 stars Average User Rating 4.5See what learners said Coursework

Each course is like an interactive textbook, featuring pre-recorded videos, quizzes and projects.

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Johns Hopkins University The mission of The Johns Hopkins University is to educate its students and cultivate their capacity for life-long learning, to foster independent and original research, and to bring the benefits of discovery to the world.

Syllabus


WEEK 1


Week 1
During Week 1, you'll learn about the goals and objectives of the Data Science Specialization and each of its components. You'll also get an overview of the field as well as instructions on how to install R.


16 videos, 5 readings expand


  1. Reading: Welcome to the Data Scientist's Toolbox
  2. Reading: Pre-Course Survey
  3. Reading: Syllabus
  4. Reading: Specialization Textbooks
  5. Video: Specialization Motivation
  6. Reading: The Elements of Data Analytic Style
  7. Video: The Data Scientist's Toolbox
  8. Video: Getting Help
  9. Video: Finding Answers
  10. Video: R Programming Overview
  11. Video: Getting Data Overview
  12. Video: Exploratory Data Analysis Overview
  13. Video: Reproducible Research Overview
  14. Video: Statistical Inference Overview
  15. Video: Regression Models Overview
  16. Video: Practical Machine Learning Overview
  17. Video: Building Data Products Overview
  18. Video: Installing R on Windows {Roger Peng}
  19. Video: Install R on a Mac {Roger Peng}
  20. Video: Installing Rstudio {Roger Peng}
  21. Video: Installing Outside Software on Mac (OS X Mavericks)

Graded: Week 1 Quiz

WEEK 2


Week 2: Installing the Toolbox
This is the most lecture-intensive week of the course. The primary goal is to get you set up with R, Rstudio, Github, and the other tools we will use throughout the Data Science Specialization and your ongoing work as a data scientist.


9 videos expand


  1. Video: Tips from Coursera Users - Optional Video
  2. Video: Command Line Interface
  3. Video: Introduction to Git
  4. Video: Introduction to Github
  5. Video: Creating a Github Repository
  6. Video: Basic Git Commands
  7. Video: Basic Markdown
  8. Video: Installing R Packages
  9. Video: Installing Rtools

Graded: Week 2 Quiz

WEEK 3


Week 3: Conceptual Issues



The Week 3 lectures focus on conceptual issues behind study design and turning data into knowledge. If you have trouble or want to explore issues in more depth, please seek out answers on the forums. They are a great resource! If you happen to be a superstar who already gets it, please take the time to help your classmates by answering their questions as well. This is one of the best ways to practice using and explaining your skills to others. These are two of the key characteristics of excellent data scientists.


4 videos expand


  1. Video: Types of Questions
  2. Video: What is Data?
  3. Video: What About Big Data?
  4. Video: Experimental Design

Graded: Week 3 Quiz

WEEK 4


Week 4: Course Project Submission & Evaluation
In Week 4, we'll focus on the Course Project. This is your opportunity to install the tools and set up the accounts that you'll need for the rest of the specialization and for work in data science.


1 reading expand


  1. Reading: Post-Course Survey

Graded: Course Project
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There are no frequently asked questions yet. Send an Email to info@springest.com