Course Summary
This course is designed for individuals who want to learn how to collect and analyze data for their projects. You will learn about various data collection methods, data analysis techniques, and how to create a project plan.Key Learning Points
- Learn how to collect and analyze data for your projects
- Understand various data collection methods and data analysis techniques
- Create a project plan to successfully execute your data collection and analysis
Related Topics for further study
Learning Outcomes
- Understand the importance of data collection and analysis
- Learn various data collection methods and data analysis techniques
- Create a project plan to execute data collection and analysis
Prerequisites or good to have knowledge before taking this course
- Basic knowledge of statistics
- Familiarity with Excel or similar software
Course Difficulty Level
IntermediateCourse Format
- Online
- Self-paced
- Video lectures
- Assignments
Similar Courses
- Data Analysis and Visualization
- Marketing Analytics
Related Education Paths
Notable People in This Field
- Statistician and Founder of FiveThirtyEight
- Data Scientist and TED Speaker
Related Books
Description
In this course you will learn how to use survey weights to estimate descriptive statistics, like means and totals, and more complicated quantities like model parameters for linear and logistic regressions. Software capabilities will be covered with R® receiving particular emphasis. The course will also cover the basics of record linkage and statistical matching—both of which are becoming more important as ways of combining data from different sources. Combining of datasets raises ethical issues which the course reviews. Informed consent may have to be obtained from persons to allow their data to be linked. You will learn about differences in the legal requirements in different countries.
Outline
- Basic Estimation
- Overview
- Basic R examples
- Basic R examples (continued)
- Degrees of Freedom
- Estimating Means
- Multistage samples
- Quantile estimation in R
- Slides
- Slides
- Slides
- Slides
- Slides (continued)
- Slides
- Course 6 Module 1
- Models
- Introduction
- Estimation Method
- Linear Models
- Diagnostics in R
- Linear Models in Stata
- Logistic Models in R
- Odds Ratios
- Logistic Regression in Stata
- Slides
- Slides
- Slides
- Slides
- Slides
- Slides
- Slides
- Slides
- Course 6 Module 2
- Record Linkage
- Why we link records
- Gentle Introduction
- Challenges
- Key Techniques
- Improving Federal Statistics Using Multiple Data Sources
- Longitudinal Employer-Household Dynamics (LEHD)
- Impact of Research on Innovation, Competition and Science
- Slides
- Slides - Introduction
- Technical Overview - Software
- Slides: Challenges
- Slides
- Record Linkage (Herzog/Scheuren/Winkler 2010)
- Febrl - A Freely Available Record Linkage System (Christen)
- Machine Learning and Record Linkage (Winkler 2011)
- Privacy Preserving Record Linkage (Schnell et al. 2009)
- Quiz 3 - Record Linkage
- Ethics
- Privacy and Confidentiality
- Linkage Consent and Consent Bias
- Correlates of Consent
- Bias in Administrative Estimates
- Optimizing Linkage Consent
- Slides
- Assessing the Magnitude of Non-Consent Biases (Sakshaug & Kreuter 2012)
- Placement, Wording and Interviewers (Sakshaug et al.)
- Quiz - Linkage Consent
Summary of User Reviews
Data Collection and Analysis for Project Success course is highly recommended by users. The course provides a great learning experience and is suitable for beginners and professionals alike. One key aspect that many users thought was good is the hands-on approach to learning.Pros from User Reviews
- Great learning experience
- Suitable for beginners and professionals
- Hands-on approach to learning
- Useful practical exercises
- Excellent content
Cons from User Reviews
- Some users found the course material too basic
- Some users felt the course was too short
- Some users experienced technical issues with the course platform
- Some users found the course lacked depth
- Some users found the course to be too theoretical
Richard Valliant, Ph.D.
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