Data Science Methodology
Updated on
Course overview
- Provider
- Coursera
- Course type
- Free online course
- Level
- Beginner
- Deadline
- Flexible
- Duration
- 8 hours
- Certificate
- Paid Certificate Available
- Course author
- Alex Aklson
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Describe what a methodology is and why data scientists need a methodology.
Describe the six stages in the Cross Industry Process for Data Mining (CRISP-DM) methodology including Business Understanding and Data Understanding.
Describe some of the use cases for different analytic models and approaches, such as Predictive, Descriptive, and Classification models.
Explain the importance of identifying the correct sources of data for your data science project.
Description
Despite the recent increase in computing power and access to data over the last couple of decades, our ability to use the data within the decision making process is either lost or not maximized at all too often, we don't have a solid understanding of the questions being asked and how to apply the data correctly to the problem at hand.This course has one purpose, and that is to share a methodology that can be used within data science, to ensure that the data used in problem solving is relevant and properly manipulated to address the question at hand.
Accordingly, in this course, you will learn:
- The major steps involved in tackling a data science problem.
- The major steps involved in practicing data science, from forming a concrete business or research problem, to collecting and analyzing data, to building a model, and understanding the feedback after model deployment.
- How data scientists think!
