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
Related Topics for further study
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
IntermediateCourse 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.
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 exercisesPros 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