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.

Об этом курсе
No previous experience required, although prior use of Jupyter Notebooks will be beneficial.
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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.
Приобретаемые навыки
- Data Science
- Methodology
- CRISP-DM
- Data Analysis
- Data Mining
No previous experience required, although prior use of Jupyter Notebooks will be beneficial.
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Программа курса: что вы изучите
From Problem to Approach and From Requirements to Collection
From Understanding to Preparation and From Modeling to Evaluation
From Deployment to Feedback
Рецензии
- 5 stars71,14 %
- 4 stars21,55 %
- 3 stars4,89 %
- 2 stars1,53 %
- 1 star0,87 %
Лучшие отзывы о курсе DATA SCIENCE METHODOLOGY
Great course for understanding data science and data related methodologies. Some parts that included machine learning algorithms confused me a little bit, but a little google search made it clear.
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).
This course covers the end to end cycle suggested framework that can be useful not only in Data Science but also in other Research Projects that manage information to create and deploy a solution.
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.
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