Linear Regression Assumptions, Boosting and Bagging. Regularisation, Feature Selection Methods, Forecasting
Data Scientists Interview Questions
54,225 data scientists interview questions shared by candidates
Price optimization
Explain the difference between type 1 and type 2 error.
Go through resume and many case study.
Describe a time when things didn't go your way in the workplace and how you dealt with this.
Have used HPLC/MS or are familiar with techniques
Make sure to study recommendation engines, since this is the bread and butter of their work. They don't really have any interest in your work experience, like many tech interviews.
Lots of coding questions and data analysis tasks.
Given a dataset which we cannot even see properly on hackerrank, build 2 ML models and answer insight related questions within 2 hours. It made no sense. The dataset was in raw form, had to be studied and converted into features and target, split for training/testing and report accuracy. This is not how a data science task can be completed.
Expectation that you can give clear examples of past decisions that make you a good behavioral fit with the organization. These examples are often asked in a question format about past decisions.
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