8 COMMON MISTAKES MADE IN DATA SCIENCE/STATISTICAL MODELLING

#datascience #statistics #machinelearning #AI
Here I discuss about some of the common mistake made by people in data science modelling.
1- not defining the problem statement well
2- Sample is not representative
3- Misuse of p-value
4-Lack of proper data quality checks
5- Improper model validation/cross validation
6- Not checking if model assumptions are valid
7- Not having a proper monitoring strategy
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[@AnalyticsUniversity]

I have made a beginner friendly (yet detailed) course on Quant Finance. For my course on Quantitative Finance contact [e-mail] or WhatsApp me on [phone] or [phone] (do not call, just drop me a message on WhatsApp).

[@avishekmitra2801]

Great.Important points covered.Sir can you upload a short lecture on Design of Experiments and its application in real life highlighting topics like Confounding,Mixed effects model,BIBD???

[@AnalyticsUniversity]

sure