Data Analyst

Tip 1 : We need to be clear with basics of differentiation calculus and probability to get a good understanding of ML algorithms.
Tip 2 : Also, we tend to ignore statistics , but statistics should not be skipped at any cost.
Tip 3 : There should be atleast 2-3 good ML projects for which you are fully confident. You can have one project for topics like supervised learning , unsupervised learning, recommendation systems and if possible deep learning project can also be included.
Tip 4 : You should be fair enough with any one visualisation tool like Tableau, Power BI etc.
Tip 5 : Practice as much as you can from kaggle

Tip 1 : Needs to have atleast 2 ML projects.
Tip 2 : Things like Excel, SQL , and Tableau should be mentioned in the resume.
Tip 3 : Certifications for ML and Excel, SQL and Tableau will help you getting shortlisted.
Tip 4 : And last but not the least, any false thing should not be included if you are not at all aware of it.