These were the 7 most well-known things you will see when you will get interviewed at big companies for data science-related positions.
Programming Languages: You should comprehend a statistical programming language, like R or Python accompanying Numpy and Pandas Libraries, and a database asking language like SQL
Statistics: You should be able to interpret phrases like null hypothesis, P-value, maximum likelihood estimators, and confidence intervals. Statistics is essential to crunch data and to pick out the most important figures out of a huge dataset. This is crucial in the decision-making process and to design experiments.
Machine Learning: You should be able to demonstrate K-nearest neighbors, random forests, and ensemble methods. These methods typically are implemented in R or Python. These algorithms show to organizations that you have exposure to how data science can be used in more practical manners.
Data Wrangling: You should be able to clean up the data. It is all about recognizing corrupt (or impure) data and correcting/deleting them.
Data Visualization: Data scientist is worthless on his or her own. They need to reach their findings to Product Managers to make sure those data are manifesting into real applications. Thus, experience with data visualization tools like ggplot is very important (so you can SHOW data, not just talk about them)
Software Engineering: You should know algorithms and data structures, as they are often essential for creating efficient algorithms for machine learning. Know the use cases and run time of these data structures: Queues, Arrays, Lists, Stacks, Trees, etc.
If you are more into videos then you can check out this video based on interview questions:
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