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.

Cours en ligne à 100 %

Commencez dès maintenant et apprenez aux horaires qui vous conviennent.

Approx. 7 heures pour terminer

Recommandé : 1-4 hours/week

English

Sous-titres : English, French, Chinese (Simplified), Greek, Italian, Portuguese (Brazilian), Vietnamese, Russian, Turkish, Hebrew

Ce que vous allez apprendre

Create a Github repository

Explain essential study design concepts

Set up R, R-Studio, Github and other useful tools

Understand the data, problems, and tools that data analysts work with

Compétences que vous acquerrez

GithubData ScienceR ProgrammingData Analysis

Cours en ligne à 100 %

Commencez dès maintenant et apprenez aux horaires qui vous conviennent.

Approx. 7 heures pour terminer

Recommandé : 1-4 hours/week

English

Sous-titres : English, French, Chinese (Simplified), Greek, Italian, Portuguese (Brazilian), Vietnamese, Russian, Turkish, Hebrew

Syllabus - What you will learn from this course

1

Section

2 hours to complete

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 (Total 51 min), 5 readings, 1 quiz

16 videos

Specialization Motivation12m

The Data Scientist's Toolbox5m

Getting Help8m

Finding Answers4m

R Programming Overview2m

Getting Data Overview1m

Exploratory Data Analysis Overview1m

Reproducible Research Overview1m

Statistical Inference Overview1m

Regression Models Overview1m

Practical Machine Learning Overview1m

Building Data Products Overview1m

Installing R on Windows {Roger Peng}3m

Install R on a Mac {Roger Peng}2m

Installing Rstudio {Roger Peng}1m

Installing Outside Software on Mac (OS X Mavericks)1m

5 readings

Welcome to the Data Scientist's Toolbox10m

Pre-Course Survey10m

Syllabus10m

Specialization Textbooks10m

The Elements of Data Analytic Style10m

1 practice exercises

Week 1 Quiz10m

2

Section

1 hour to complete

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 (Total 51 min), 1 quiz

9 videos

Tips from Coursera Users - Optional Video 3m

Command Line Interface16m

Introduction to Git4m

Introduction to Github3m

Creating a Github Repository5m

Basic Git Commands5m

Basic Markdown2m

Installing R Packages5m

Installing Rtools2m

1 practice exercises

Week 2 Quiz10m

3

Section

1 hour to complete

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 (Total 35 min), 1 quiz

4 videos

Types of Questions9m

What is Data?5m

What About Big Data?4m

Experimental Design15m

1 practice exercises

Week 3 Quiz10m

4

Section

2 hours to complete

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, 1 quiz

1 readings

Post-Course Survey10m

Instructors

Jeff Leek, PhD

Associate Professor, Biostatistics

Roger D. Peng, PhD

Associate Professor, Biostatistics

Brian Caffo, PhD

Professor, Biostatistics

About 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....

Frequently Asked Questions

When will I have access to the lectures and assignments?
Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.
What will I get if I pay for this course?
If you pay for this course, you will have access to all of the features and content you need to earn a Course Certificate. If you complete the course successfully, your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. Note that the Course Certificate does not represent official academic credit from the partner institution offering the course.
What is the refund policy?
Is financial aid available?
Yes! Coursera provides financial aid to learners who would like to complete a course but cannot afford the course fee. To apply for aid, select "Learn more and apply" in the Financial Aid section below the "Enroll" button. You'll be prompted to complete a simple application; no other paperwork is required.

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Spécialisation Data Science