Explore

Communicating Data Science Results

Approx. 8 hours to complete

Save Course

Go to Course

Course Summary

Learn how to make sense of data and use it to inform decision-making in this comprehensive course. From data collection to analysis and visualization, you will gain practical skills to apply to your professional work.

Key Learning Points

Learn how to collect and clean data for analysis
Master data analysis techniques and tools
Visualize data and communicate insights effectively

Learning Outcomes

Collect and clean data for analysis
Apply data analysis techniques and tools
Visualize data and communicate insights effectively

Prerequisites or good to have knowledge before taking this course

Basic understanding of statistics
Familiarity with Excel or other spreadsheet software

Course Difficulty Level

Intermediate

Course Format

Online self-paced
Video lectures
Quizzes and assignments

Similar Courses

Data Science Essentials
Data Science Methodology
Data Analysis and Presentation Skills: the PwC Approach

Related Education Paths

Notable People in This Field

Nate Silver
Edward Tufte

Related Books

Description

Important note: The second assignment in this course covers the topic of Graph Analysis in the Cloud, in which you will use Elastic MapReduce and the Pig language to perform graph analysis over a moderately large dataset, about 600GB. In order to complete this assignment, you will need to make use of Amazon Web Services (AWS). Amazon has generously offered to provide up to $50 in free AWS credit to each learner in this course to allow you to complete the assignment. Further details regarding the process of receiving this credit are available in the welcome message for the course, as well as in the assignment itself. Please note that Amazon, University of Washington, and Coursera cannot reimburse you for any charges if you exhaust your credit.

While we believe that this assignment contributes an excellent learning experience in this course, we understand that some learners may be unable or unwilling to use AWS. We are unable to issue Course Certificates for learners who do not complete the assignment that requires use of AWS. As such, you should not pay for a Course Certificate in Communicating Data Results if you are unable or unwilling to use AWS, as you will not be able to successfully complete the course without doing so. Making predictions is not enough! Effective data scientists know how to explain and interpret their results, and communicate findings accurately to stakeholders to inform business decisions. Visualization is the field of research in computer science that studies effective communication of quantitative results by linking perception, cognition, and algorithms to exploit the enormous bandwidth of the human visual cortex. In this course you will learn to recognize, design, and use effective visualizations. Just because you can make a prediction and convince others to act on it doesn’t mean you should. In this course you will explore the ethical considerations around big data and how these considerations are beginning to influence policy and practice. You will learn the foundational limitations of using technology to protect privacy and the codes of conduct emerging to guide the behavior of data scientists. You will also learn the importance of reproducibility in data science and how the commercial cloud can help support reproducible research even for experiments involving massive datasets, complex computational infrastructures, or both. Learning Goals: After completing this course, you will be able to: 1. Design and critique visualizations 2. Explain the state-of-the-art in privacy, ethics, governance around big data and data science 3. Use cloud computing to analyze large datasets in a reproducible way.

Outline

Visualization
01 Introduction: What and Why
02 Introduction: Motivating Examples
03 Data Types: Definitions
04 Mapping Data Types to Visual Attributes
05 Data Types Exercise
06 Data Types and Visual Mappings Exercises
07 Data Dimensions
08 Effective Visual Encoding
09 Effective Visual Encoding Exercise
10 Design Criteria for Visual Encoding
11 The Eye is not a Camera
12 Preattentive Processing
13 Estimating Magnitude
14 Evaluating Visualizations

Privacy and Ethics
Motivation: Barrow Alcohol Study
Barrow Study Problems
Reifying Ethics: Codes of Conduct
ASA Code of Conduct: Responsibilities to Stakeholders
Other Codes of Conduct
Examples of Codified Rules: HIPAA
Privacy Guarantees: First Attempts
Examples of Privacy Leaks
Formalizing the Privacy Problem
Differential Privacy Defined
Global Sensitivity
Laplacian Noise
Adding Laplacian Noise and Proving Differential Privacy
Weaknesses of Differential Privacy

Reproducibility and Cloud Computing
Reproducibility and Data Science
Reproducibility Gold Standard
Anecdote: The Ocean Appliance
Code + Data + Environment
Cloud Computing Introduction
Cloud Computing History
Code + Data + Environment + Platform
Cloud Computing for Reproducible Research
Advantages of Virtualization for Reproducibility
Complex Virtualization Scenarios
Shared Laboratories
Economies of Scale
Provisioning for Peak Load
Elasticity and Price Reductions
Server Costs vs. Power Costs
Reproducibility for Big Data
Counter-Arguments and Summary
AWS Credit Opt-in Consent Form

Summary of User Reviews

This course teaches you how to analyze and interpret data to draw meaningful conclusions. The course has received positive reviews for its engaging content and practical approach. Many users found the hands-on exercises to be particularly helpful.

Key Aspect Users Liked About This Course

Hands-on exercises

Pros from User Reviews

Engaging content
Practical approach
Good for beginners
Clear explanations

Cons from User Reviews

Lacks advanced topics
Some sections are too basic
Not enough focus on statistics

Recommended for you

R for Beginners

Introduction to Programming Language R. Tools for Data Science, Data Analysis and Statistical Analysis. RStudio.   Do you use spreadsheets for data cleaning, wrangling, visualization, and data analysis?...

Save Course

Tableau 2019.1 for Data Scientists

Create powerful dashboards and presentations for your data with Tableau 2019. 1 You've just completed an incredible data science or data analytics project....

Save Course

Social Media Marketing - Content Marketing Masterclass 2021

Online Digital Marketing / Social Media Strategy For Beginners - Posts / Blogs / Podcasts / Video / Google Local SEO...

Save Course

Human Resources Analytics

With advances in technology and cloud computing, there are now numerous data sources available to guide decision-making and drive organizational success....

Save Course