If there is a shortcut to becoming a Data Scientist, then learning to think and work like a successful Data Scientist is it. Most of the established data scientists follow a similar methodology for solving Data Science problems. In this course you will learn and then apply this methodology that can be used to tackle any Data Science scenario.

이 강좌에 대하여
No previous experience required, although prior use of Jupyter Notebooks will be beneficial.
직원에게 수요가 높은 기술을 교육하면 회사가 이점을 얻을 수 있습니까?
비즈니스를 위한 Coursera 경험해 보기배울 내용
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.
귀하가 습득할 기술
- Data Science
- Methodology
- CRISP-DM
- Data Analysis
- Data Mining
No previous experience required, although prior use of Jupyter Notebooks will be beneficial.
직원에게 수요가 높은 기술을 교육하면 회사가 이점을 얻을 수 있습니까?
비즈니스를 위한 Coursera 경험해 보기제공자:
학사 학위 취득 시작
강의 계획표 - 이 강좌에서 배울 내용
From Problem to Approach and From Requirements to Collection
From Understanding to Preparation and From Modeling to Evaluation
From Deployment to Feedback
검토
- 5 stars71.15%
- 4 stars21.54%
- 3 stars4.90%
- 2 stars1.53%
- 1 star0.87%
DATA SCIENCE METHODOLOGY의 최상위 리뷰
It was a good course with very easy to understand material and methodology.
In my opinion additional optional reading resources or case study links is required for this to be a 5 star course.
Very interesting course. It shed a light on what the structured approach really is. It's worth to pause for a moment with every step of the methodology and think how to apply it in real life. Thanks!
This is my favourite in the series, the 10 questions to be answered were mind opening. The repetition after every video makes easier for important points to stick to the brain. Very good indeed...
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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