Did you know data scientists are big data fighter? They clean, polish and arrange a large volume of unstructured and structured, in fact, messy data with their amazing skills in statistics, math and programming.
Then they put all their analytical potential, contextual understanding, industry knowledge and cynicism of existing assumptions to disclose hidden solutions to tackle business challenges.
So, would you like to be the one that I mentioned above? If yes, you need to possess these top competencies to become a data scientist:
Fundamental Tools: It doesn’t matter what kind of organization you’re preparing for, you must have fundamental skills. You should know how to use the tools of the trade, which means a statistical programming language such as R or Python and a database language like SQL at least.
Machine Learning: In case you get posted in a large-scale company with massive data, or working at a company where the product is data driven, you must have familiarity with machine learning procedures. This means k-nearest neighbours, ensemble methods, random forests, all of which are machine learning words. You can implement a lot of these techniques by using R or Python libraries. The most important thing is to know the broad strokes and understand when it is suitable to use various techniques.
Fundamental Statistics: At least fundamental know how of statistics is important for a data scientist. One should be familiar with distributions, statistical tests, highest likelihood estimators, and more. Go back and remember your stats class. Also, this will be state for machine learning, however, one of the most important aspects of one’s statistic skills will be comprehending when dissimilar techniques are or are not a suitable approach.
Remember, statistic is very important for all sorts of companies, specifically for the ones which are data-focused companies and product stakeholders will count on you to make decision and design and design as well as assess experiments.
Know Multivariable Calculus & Linear Algebra: You may be asked by the interviewer to obtain some of the machine learning or statistics results you employ elsewhere. Even if you are not, the interviewer may ask you about fundamental multivariable calculus or linear algebra, since they are the fundamentals of these techniques. You may think what’s the use of knowing these as there are many out of the box applications in sklearn or R? However, the answer is it can become important for a data science team to develop their own implementations in-house.
These may seem most important in firms where the product is described by the data and small improvements in analytical performance or algorithm optimization can add to company’s profit.
Software Engineering: In case you are interviewing at a smaller organization, and are going to be the first data scientist there, you need to have a strong software engineering background. You will be accountable for managing a lot of logging and potentially the building of data-driven products.
So, if you’re interested in choosing data science as your career, do match up a company’s requirements and for that you must possess above said fundamentals first.
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