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Asked 4 years, 5 months ago. Active 4 years, 4 months ago. Viewed 4k times.
The first chapters should be relatively elementary but then it picks up. Matrix Algebra Cambridge University Press, that has more emphasis on the applications of matrix calculus. Arrigo Benedetti.
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Statistics A good understanding of statistics is vital as a data scientist. You should be familiar with statistical tests, distributions, maximum likelihood estimators, etc. This can mean things like k-nearest neighbors, random forests, ensemble methods, and more. More important is to understand the broad strokes and really understand when it is appropriate to use different techniques. In an interview for a data science role, you may be asked to derive some of the machine learning or statistics results you employ elsewhere.
Or, your interviewer may ask you some basic multivariable calculus or linear algebra questions, since they form the basis of a lot of these techniques.
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You may wonder why a data scientist would need to understand this when there are so many out of the box implementations in Python or R. The answer is that at a certain point, it can become worth it for a data science team to build out their own implementations in house. Some examples of data imperfections include missing values, inconsistent string formatting e. When it comes to communicating, this means describing your findings, or the way techniques work to audiences, both technical and non-technical.
Visualization-wise, it can be immensely helpful to be familiar with data visualization tools like matplotlib, ggplot, or d3.
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Tableau has become a popular data visualization and dashboarding tool as well. It is important to not just be familiar with the tools necessary to visualize data, but also the principles behind visually encoding data and communicating information. How should you, as the data scientist, interact with the engineers and product managers?