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About this course
Data scientists are often trained in the analysis of data. However, the goal of data science is to produce a good understanding of some problem or idea and build useful models on this understanding. Because of the principle of “garbage in, garbage out,” it is vital that a data scientist know how to evaluate the quality of information that comes into a data analysis. This is especially the case when data are collected specifically for some analysis (e.g., a survey).
In this course, you will learn the fundamentals of the research process-from developing a good question to designing good data collection strategies to putting results in context. Although a data scientist may often play a key part in data analysis, the entire research process must work cohesively for valid insights to be gleaned.
Developed as a powerful and flexible language used in everything from Data Science to cutting-edge and scalable Artificial Intelligence solutions, Python has become an essential tool for doing Data Science and Machine Learning. With this edition of Data Science Research Methods, all of the labs are done with Python, while the videos are language-agnostic. If you prefer your Data Science to be done with R, please see Data Science Research Methods: R Edition.
What you’ll learn
After completing this course, you will be familiar with the following concepts and techniques:
- Data analysis and inference
- Data science research design
- Experimental data analysis and modeling
To complete this course successfully, you should have:
- A basic knowledge of math
- Some programming experience-Python is preferred.
Estimate Time : 12 hours
Module 1 The Research Process
- The Research Process
- The Psychology of Providing Data
Module 2 Planning for Analysis
- Planning for Analysis
- Power and Sample Size Planning
- Research Practices
Module 3 Research Claims
- Frequency Claims
- Associated Claims
- Casual Claims
Module 4 Measurement
- Survey Design and Measurement
- Reliability and Validity
Module 5 Correlational and Experimental Design
- Bi-variate and Multivariate Designs
- Between and Within Groups Experimental Designs
- Factorial Designs
Sr. Content Developer, Microsoft
Ben is a Sr. Content Developer for Microsoft’s Learning and Readiness team, and is an analytics professional and educator with over 8 years of industry and managerial experience. Prior to joining Microsoft, Ben ran and directed multiple consulting firms, where he also held critical analytics roles in companies as diverse as Juniper Networks, Costco, and T-Mobile. He has taught Data Visualization at The University of Washington, and recently founded Seattle Pacific University’s Analytics Certificate Program.
Assistant Professor of Psychology, Data Science consultant
Seattle Pacific University
Dr. Tom Carpenter is Assistant Professor of Psychology at Seattle Pacific University, and is also a Data Science consultant. His areas of expertise include personality-social psychology, research methods, and statistics. His teaching focuses on introductory and advanced research methods and statistics in psychology as well as social and personality psychology.
Dr. Carpenter’s research focuses on our hypocritical human nature: our propensity to ignore our overt preferences and standards and to transgress against ourselves and others. One line of research in this area focuses on implicit bias, the impulsive thoughts that can undermine our higher reasoning. Dr. Carpenter has developed new software methods for running the Implicit Association Test (IAT) using online survey software (read more here: www.iatgen.wordpress.com).
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