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 stars71.14%
- 4 stars21.54%
- 3 stars4.90%
- 2 stars1.53%
- 1 star0.87%
DATA SCIENCE METHODOLOGY의 최상위 리뷰
This was a critical course for me. Understanding the data scientists workflow which includes customer\client interaction has help me in understanding how to proceed in future endeavors.
It's a very good course for getting the basic idea of the methodology of data science. It will help to get grip on how to proceed to a problem in a systematic manner for getting good results.
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 good course, teaching about the general process and life cycle of a data science project. Excellent tips are provided. Overall, I feel it was lacking a bit in content for 3 weeks.
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