- Data Cleansing
- Data Science
- Business Analysis
- Data Preprocessing
- Peer Review
- Data-Driven Decision-Making
- Software Development Methodologies
- Data Collection
- Model Deployment
- Data Mining
- Model Evaluation
- Classification Algorithms
Data Science Methodology
Completed by Nanayakkara Mahadange Sudeepa Nadeeshan
August 21, 2019
7 hours (approximately)
Nanayakkara Mahadange Sudeepa Nadeeshan'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
