Dive in to terminology:
Preparedness vs Practise.
Preparedness equips us with the skills and resources to tackle data science challenges, while practice hones our abilities and helps us stay at the forefront of the constantly evolving field.
Preparedness vs Practise.
Preparedness equips us with the skills and resources to tackle data science challenges, while practice hones our abilities and helps us stay at the forefront of the constantly evolving field.
Preparedness refers to being ready or equipped for a certainsituation, task or event. In the context of data science, preparedness means having the necessary skills, knowledge, and resources to tackle a data science project or problem effectively.
This includes having a good understanding of relevant programming languages, data analysis techniques, and machine learning algorithms, as well as access to powerful computing resources.
Practice, on the other hand, refers to the act of repeating a task or behavior in order to improve performance or gain proficiency.
#openDOSM @StatsMalaysia
#openDOSM @StatsMalaysia
In the context of data science, practice involves working on data science projects, experimenting with different techniques, and constantly striving to improve one's skills and knowledge.
It is a key aspect of becoming a proficient data scientist, as the field is constantly evolving and new techniques and tools are being developed all the time. Practicing and constantly learning helps data scientists stay up-to-date and relevant in the field.
• • •
Missing some Tweet in this thread? You can try to
force a refresh
