- Business Analysis
- Model Deployment
- Data Manipulation
- Data Analysis
- Data Mining
- Data-Driven Decision-Making
- Predictive Modeling
- Data Modeling
- Decision Tree Learning
- Jupyter
- Business Requirements
- Data Cleansing
Data Science Methodology
Completed by UKIWO IREKE IREKE
August 25, 2020
7 hours (approximately)
UKIWO IREKE IREKE'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
