If there is a shortcut to becoming a Data Scientist, then learning to think and work like a successful Data Scientist is it. In this course, you will learn and then apply this methodology that you can use to tackle any Data Science scenario. You’ll explore two notable data science methodologies, Foundational Data Science Methodology, and the six-stage CRISP-DM data science methodology, and learn how to apply these data science methodologies. Most established data scientists follow these or similar methodologies for solving data science problems.

Data Science Methodology

Data Science Methodology
This course is part of multiple programs.


Instructors: Alex Aklson +1 more
374,140 already enrolled
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What you'll learn
Describe what a data science 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.
Evaluate which analytic model is appropriate among predictive, descriptive, and classification models used to analyze a case study.
Determine appropriate data sources for your data science analysis methodology.
Skills you'll gain
- Category: Analysis
- Category: Data Manipulation
- Category: Business Analysis
- Category: Model Training
- Category: Data Collection
- Category: Analytical Skills
- Category: Data Preprocessing
- Category: Data Processing
- Category: Data Mining
- Category: Data Modeling
- Category: Model Evaluation
- Category: Data Cleansing
- Category: Data Analysis
- Category: Business Research
- Category: Data Wrangling
- Category: Data Science
Tools you'll learn
- Category: Jupyter
- Category: Model Deployment
Details to know

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- Learn new concepts from industry experts
- Gain a foundational understanding of a subject or tool
- Develop job-relevant skills with hands-on projects
- Earn a shareable career certificate

There are 4 modules in this course
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