### Must Known Algorithms For Every Data Scientist 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.

Hypothesis Testing

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.

Linear Regression

Being a statistical modeling technique, it focuses on the relationship between a dependent variable and an explanatory variable by matching the observed values with the linear equation. Its main use is to depict a relationship between various variables by using scatterplots (plotting points on a graph by displaying two kinds of values). If no relationship is found, that means matching the data with the regression model doesn’t provide any useful and productive model.

Clustering Techniques

It is a type of unsupervised algorithm wherein a dataset is assembled in distinguished and distinct clusters. Since the output of the procedure is not known to the analyst, it is classified as an unsupervised learning algorithm. It means that the algorithm itself will define the result for us and we do not require to train it on any past inputs. Further, the clustering technique is divided into two types: Hierarchical and Partitional Clustering.

Naive Bayes

A simple, yet so powerful algorithmic technique for predictive modeling. This model consists of two kinds of probability to be calculated on the basis of training data. The first probability is each classes’ probability and the second one is that given each value (say ‘x’), the conditional probability is calculated for each class. After the calculations of these probabilities, predictions can be carried out for new data values using Bayes Theorem.

Naive Bayes make an assumption for each input variable to be independent, so it is sometimes also referred to as ‘naive’. Though it is a powerful assumption and not realistic for real data, it is very effectual for large scale of complex problems.