- Data Analysis
- Decision Tree Learning
- Stakeholder Engagement
- Data Processing
- Data Mining
- Data Transformation
- Data Cleansing
- Peer Review
- Data Modeling
- Software Development Methodologies
- Jupyter
- Data Collection
Data Science Methodology
Completed by EMMANUEL GONZALEZ
April 3, 2020
6 hours (approximately)
EMMANUEL GONZALEZ's account is verified. Coursera certifies their successful completion of Data Science Methodology
What you will 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 will gain
