- Data science workflow
-
What is data science workflow?
-
What are you going to learn?
-
Data science workflow
-
Typical phases
-
Check your knowledge
-
What is data modeling?
-
Machine learning
-
Supervised learning
-
Unsupervised learning
-
Semi-supervised learning
-
Reinforcement learning
-
What part of data science workflow do I like the most?
-
Vegard's approach to data science workflow
-
Check your understanding
-
Key takeaways
-
Feedback
-
Well done
Data Science Workflow
In this course you will learn about the main steps of data science workflow.
Welcome to the course on data science workflow
In this course, we will go through the steps in the data science workflow - which is an iterative process. We commonly reach the best solutions when the data scientist can involve the end-user in the design and development process. The data scientist will continuously collect end-user feedback - and improve accordingly. The workflow contains data collection, preparation, exploration, modeling and visualization. This chapter provides explanations for each of these steps. Next up are some examples of how data scientists have solved industry problems going through this workflow.
By the end of this chapter you will understand:
- the typical data science workflow
Who should take this course?
This course was created for anybody interested to learn more about data science workflow.
This course is independent, but also part of the learning path Data Science Fundamentals. We recommend that you take all courses from the Data Science Fundamentals series and take the final test to receive a certificate of completion.
Estimated duration
20 min
Instructors
The courses are developed by Cognite Academy in collaboration with other industry experts.
Rebecca Seyfarth
Senior ML Engineer in Contextualization at Cognite AS
Alina Astrakova
Senior ML Engineer in Services - Solution Architecture at Cognite AS
Collaborator
Vegard Flovik, Ph.D.
Lead Data Scientist | Associate Professor II in Machine