Algorithms are extremely useful techniques to initiate any analytical model and every data scientist’s knowledge would be considered incomplete without the algorithms. The powerful and advanced techniques like Factor Analysis and Discriminant Analysis should be present in every data scientist’s arsenal. But for this type of advanced techniques, one must know some of the basic algorithms that are equally useful and productive. Since machine learning is one of the aspects where data science is used greatly, therefore, the knowledge of such algorithms is crucial. Some of the basic and most used algorithms that every data scientist must know are discussed below.
Though not an algorithm, without knowing this, a data scientist would be incomplete. No data scientist must move forward without mastering this technique. Hypothesis testing is a procedure for testing statistical results and checking if the hypothesis is true or false on the basis of statistical data. Then, depending on the hypothetical testing, it is decided whether to accept the hypothesis or simply reject it. Its importance lies in the fact that any event can be important. So, to check whether an event occurring is important or just a mere chance, hypothesis testing is carried out.