- Data Mining
- Model Evaluation
- Business Requirements
- Data Analysis
- Data Manipulation
- Data Preprocessing
- Data-Driven Decision-Making
- Model Deployment
- Data Cleansing
- Data Modeling
- Decision Tree Learning
- Business Analysis
Data Science Methodology
Completed by Nataliya Dmitriyevna Shiyanova
January 30, 2020
7 hours (approximately)
Nataliya Dmitriyevna Shiyanova'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
