Data Science Research Methods

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About Course

Data Science Research Methods 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 the data scientist know how to evaluate the quality of information that comes into data analysis. This is especially the case when data are collected specifically for some analysis (e.g., a survey).

In this Data Science Research Methods 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 the data scientist may often play a key part in data analysis, the entire research process must work cohesively for valid insights to be gleaned.

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What Will You 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

Course Content

Data Science Research Methods

  • Goals of Research
    00:00
  • More Goals of Research
    00:00
  • The Circle of Science
    00:00
  • Clarifying the Questions
    00:00
  • The Psychology of Providing Data Part 1
    00:00
  • The Psychology of Providing Data Part 2
    00:00
  • The Psychology of Providing Data Part 3
    00:00
  • Samples vs Populations
    00:00
  • Null Hypothesis
    00:00
  • Discrediting the Null Hypothesis P Values
    00:00
  • Discrediting the Null Hypothesis Confidence Intervals
    00:00
  • Power Part 1
    00:00
  • Power Part 2
    00:00
  • Sample Size Planning
    00:00
  • False Positives and False Negatives
    00:00
  • Questionable Research Practices
    00:00
  • Frequency Claims- A Conceptual Overview
    00:00
  • Frequency Claims- Analysis Considerations
    00:00
  • Frequency Claims- Interpretation
    00:00
  • Association Claims- A Conceptual Overview
    00:00
  • Association Claims- Analysis Considerations
    00:00
  • Association Claims- Analysis Considerations
    00:00
  • Association Claims- Interpretation
    00:00
  • Causal Claims- An Overview
    00:00
  • Causal Claims – Analysis Considerations
    00:00
  • Causal Claims- Interpretations
    00:00
  • Writing Survey Questions- Part 1
    00:00
  • Writing Survey Questions- Part 2
    00:00
  • Two Types of Measurement
    00:00
  • Reliability and Validity Overview
    00:00
  • Reliability
    00:00
  • Validity
    00:00
  • Bivariate Design
    00:00
  • Multivariate Design
    00:00
  • Between Groups Experiment
    00:00
  • Within Groups Experiment
    00:00
  • Factorial Experiment Between Groups
    00:00
  • Factorial Designs Within Subjects
    00:00

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