Ementorhub

What you'll 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'll gain

Data Mining

Stakeholder Engagement

Data Cleansing

Data Storytelling

Decision Tree Learning

User Feedback

Predictive Modeling

Data Modeling

Peer Review

Analytical Skills

Business Analysis

Jupyter

Data Quality

Data Science

There are 4 modules in this course

Begin by learning about forming the business/research problem Learn how data scientists obtain, prepare, and analyze data. Discover how applying data science methodology practices helps ensure that the data used for problem-solving is relevant and properly manipulated to address the question. Next, learn about building the data model, deploying that model, data storytelling, and obtaining feedback You’ll think like a data scientist and develop your data science methodology skills using a real-world inspired scenario through progressive labs hosted within Jupyter Notebooks and using Python.

From Understanding to Preparation and From Modeling to Evaluation

From Deployment to Feedback and Final Evaluation

Final Project and Assessment