LinkedIn Sr Data Scientist Interview prep

Data science questions...I would presume. To prepare, know basic combinatorics, basic probability and stats (simple discrete and continuous distributions), hypothesis testing, confidence intervals, sampling, some basic understanding of central limit theorem. Basic machine learning methods: regression, classification, clustering. Supervised vs unsupervised learning. For data retrieval, SQL (basic select, insert, update, table joins, (left, right, inner, outer), some subqueries, aggregate functions like sum, max, min, count, etc. Knowing how to handle nulls and duplicates. For basic data wrangling and analysis, R, Python, Pandas, Numpy, Scikit-Learn, maybe SAS, JMP...For presentation, maybe Tableau, Ppt, Matplotlib (Seaborn, Plotly)...there are all kinds of tools out there. Fundamentally, you must know how to explore datasets and derive useful business information from the data and effectively communicate the results. Unlike SWE positions, where Leetcode is pretty much the only way to get through the interviews, there is no such venue for DS roles. You may practice machine learning all day on kaggle, but if one interviewer asks you some basic conditional probability questions (Bayes!) and you mess it up, you may miss the opportunity. Or you may be asked stuff about algorithms, certainly not difficult questions, but maybe outside the comfort zone of a typical aspiring data scientist. Know the basics for sure and know them well, and hope that you don’t face an unfair interviewer.