- Predictive Modeling
- Data Mining
- Feature Engineering
- Data Cleansing
- Data Transformation
- Data Analysis
- Decision Tree Learning
- Data Modeling
- Peer Review
- Business Requirements
- Data Processing
- Jupyter
Data Science Methodology
Completed by ABEOKUTA OGUN STATE OMITOOGUN BASEET AKINTUNDE
May 7, 2020
6 hours (approximately)
ABEOKUTA OGUN STATE OMITOOGUN BASEET AKINTUNDE'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
