Highlights
• Gain an overview of Data Management specialties
• Understand the Lifecycle of Data Analytics projects: the Cross-Industry Standard Process for Data Mining (CRISP-DM)
• Data Science project specification using the CRISP-DM framework
• Data Science project de-risking
• Limitations of the CRISP-DM framework
• Overview on modern challenges: DataOps and MLOps
• Governance and GDPR overview
Course Details
Overview on Data Management specialties
Master Data Management
Data Quality
Data Security
Data Governance
Data Warehousing
Lifecycle of Data Analytics projects: the Cross-Industry Standard Process for Data Mining
(CRISP-DM)
Data Science project specification using the CRISP-DM framework
Business understanding:
determine objectives
produce a plan
Data understanding:
collect
describe
explore
initial data
assess data quality
Data preparation:
select
clean
integrate
reformat data
Modelling:
select modelling techniques
generate experimental design
build models
Evaluation:
assess model results
evaluate business criteria
review process
Deployment:
project review
plan deployment
monitoring and maintenance
Data Science project de-risking
Why do Data Science projects fail?
Tools and techniques for Exploratory Data Analysis
Rapid Prototyping for early risk identification
Limitations of the CRISP-DM framework
Overview on modern challenges:
DataOps
MLOps
Governance overview:
Governance
GDPR
Who should attend
Data Scientists, Data Analysts, Data Science Managers and Consultants who want to successfully deliver Data Science projects, gain confidence in their process and improve the output of their team.
Feedback
4.8 out of 5 average
"The course was very comprehensive with clear examples and good exercises. It was a good to understand the capabilities and basics of Python. The material is very clear. I like the fact that additional material has been shared for practice."
EW, Electronic Engineer, Python, January 2021
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