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.
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课程信息
您将学到的内容有
Describe what a methodology is and why data scientists need a methodology.
Apply the six stages in the Cross-Industry Process for Data Mining (CRISP-DM) methodology to analyze a case study.
Determine an appropriate analytic model including predictive, descriptive, and classification models to analyze a case study.
Decide on appropriate sources of data for your data science project.
您将获得的技能
- Data Science
- Data Mining
- Methodology
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IBM 技能网络
IBM is the global leader in business transformation through an open hybrid cloud platform and AI, serving clients in more than 170 countries around the world. Today 47 of the Fortune 50 Companies rely on the IBM Cloud to run their business, and IBM Watson enterprise AI is hard at work in more than 30,000 engagements. IBM is also one of the world’s most vital corporate research organizations, with 28 consecutive years of patent leadership. Above all, guided by principles for trust and transparency and support for a more inclusive society, IBM is committed to being a responsible technology innovator and a force for good in the world.
授课大纲 - 您将从这门课程中学到什么
From Problem to Approach and From Requirements to Collection
In this module, you will learn about why we are interested in data science, what a methodology is, and why data scientists need a methodology. You will also learn about the data science methodology and its flowchart. You will learn about the first two stages of the data science methodology, namely Business Understanding and Analytic Approach. Finally, through a lab session, you will also obtain how to complete the Business Understanding and the Analytic Approach stages and the Data Requirements and Data Collection stages pertaining to any data science problem.
From Understanding to Preparation and From Modeling to Evaluation
In this module, you will learn what it means to understand data, and prepare or clean data. You will also learn about the purpose of data modeling and some characteristics of the modeling process. Finally, through a lab session, you will learn how to complete the Data Understanding and the Data Preparation stages, as well as the Modeling and the Model Evaluation stages pertaining to any data science problem.
From Deployment to Feedback
In this module, you will learn about what happens when a model is deployed and why model feedback is important. Also, by completing a peer-reviewed assignment, you will demonstrate your understanding of the data science methodology by applying it to a problem that you define.
审阅
- 5 stars71.13%
- 4 stars21.55%
- 3 stars4.88%
- 2 stars1.54%
- 1 star0.88%
来自DATA SCIENCE METHODOLOGY的热门评论
Very informative step-by-step guide of how to create a data science project. Course presents concepts in an engaging way and the quizzes and assignments helped in understanding the overall material.
It is a very important course to understand the procedures and thought processes behind data science. I strongly recommend it to those who are unfamiliar with data science or reserach methodology.
I like the way they provide sample with food preparation on each of the stage of data science methodology. Need to give more sample for the study case to give more insight and understanding.
This was a clear and concise overview of the methodology and using the case study really helped (although sometimes it got a bit advanced considering this comes before actually learning models).
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