Excel - sumifs, rank, lookup, pivot, slicer, match, index SQL - queries on joins, rank, partition, group, order, having clause etc Data Analytics - Modelling techniques, steps, model validation, types of models etc Power BI - Aggregations, modelling, slicers, sorting, filtering Case study - Expense/Cost optimization General - Past experience, data management, data cleansing, ETL, retail concepts, finance concepts
what is your weakness
1. Bias-Variance tradeoff 2. supervised learning algo 3. Random forest working 4. program to check if given number is power of 3.
Mostly about scenarios
Talk about statistical methods such as regression
Experience with SQL Soft skills ....
which kind of clusterization you know?
What's the database you used at your last job?
1st round - Online test (coding + MCQs on data science) 2nd round - Technical interview. Questions related to your past work and projects. 3rd round - Technical interview. Questions on common ML algorithms, parameters, bias-variance, metrics, quant problems, statistics, probability etc.
1) How do you do cross-validation for a time series dataset? 2) What are the challenges compared to the usual k fold cross-validation?
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