Algorithms are extremely useful techniques for starting any analytical model and every data scientist’s knowledge would be considered incomplete without algorithms. Powerful and advanced techniques like factor analysis and discriminant analysis should be in every data scientist’s arsenal. But for these kinds of advanced techniques, you have to know some of the basic algorithms that are just as useful and productive. Since machine learning is one of the areas where data science is heavily used, knowledge of such algorithms is crucial. Some of the basic and most widely used algorithms that every data scientist should know about are discussed below.
Hypothesis testing
Although not an algorithm, without knowing this, a data scientist would be incomplete. No data scientist should move forward without mastering this technique. Hypothesis testing is a procedure for testing statistical results and verifying whether the hypothesis is true or false on the basis of statistical data. Then, depending on the hypothetical tests, 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 verify if an event that occurs is important or just a mere chance, hypothesis tests are 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 represent a relationship between several variables through the use of scatter plots (plotting points on a graph showing two types of values). If no relationship is found, that means that matching the data to the regression model does not provide any useful and productive model.
Grouping techniques
It is a type of unsupervised algorithm in which a data set is assembled into discrete and distinct groups. Since the result 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 don’t need to train it on any previous input. Furthermore, the clustering technique is divided into two types: hierarchical and partitional clustering.
naive bayesian
A simple yet powerful algorithmic technique for predictive modeling. This model consists of two types of probability that are calculated on the basis of training data. The first probability is the probability of each class and the second is that given each value (say ‘x’), the conditional probability for each class is calculated. After calculations of these probabilities, predictions can be made for new data values using Bayes’ Theorem.
Naive Bayes assumes that each input variable is independent, which is why it is also sometimes called ‘naive’. Although it is a powerful and unrealistic assumption for real data, it is very effective for complex, large-scale problems.
