You're preparing for a data scientist interview. What are some common algorithms you should know?

1 Sorting algorithms

Sorting algorithms are methods of arranging data in a specific order, such as ascending or descending. Sorting is a fundamental task in data science, as it can help with searching, filtering, grouping, and analyzing data. Some of the most common sorting algorithms are bubble sort, insertion sort, selection sort, merge sort, quick sort, and heap sort. Each of them has different advantages and disadvantages in terms of time and space complexity, stability, and adaptability.

2 Searching algorithms

Searching algorithms are methods of finding a specific element or a set of elements in a data structure, such as an array, a list, or a tree. Searching is another essential task in data science, as it can help with finding patterns, anomalies, similarities, and differences in data. Some of the most common searching algorithms are linear search, binary search, breadth-first search, depth-first search, and hash table search. Each of them has different applications and performance depending on the type and size of the data structure and the search criteria.

3 Regression algorithms

Regression algorithms are methods of modeling the relationship between a dependent variable and one or more independent variables. Regression is a widely used technique in data science, as it can help with predicting, estimating, and explaining data. Some of the most common regression algorithms are linear regression, logistic regression, polynomial regression, ridge regression, lasso regression, and support vector regression. Each of them has different assumptions, limitations, and strengths in dealing with different types of data and problems.

4 Classification algorithms

Classification algorithms are methods of assigning a label or a category to an input based on some features or criteria. Classification is another popular technique in data science, as it can help with identifying, grouping, and separating data. Some of the most common classification algorithms are k-nearest neighbors, decision trees, random forests, naive Bayes, support vector machines, and neural networks. Each of them has different approaches, advantages, and disadvantages in handling different types of data and problems.

5 Clustering algorithms

Clustering algorithms are methods of dividing a set of data points into groups or clusters based on some similarity or distance measure. Clustering is a useful technique in data science, as it can help with discovering, exploring, and understanding data. Some of the most common clustering algorithms are k-means, hierarchical clustering, density-based clustering, spectral clustering, and Gaussian mixture models. Each of them has different characteristics, parameters, and challenges in finding the optimal number and shape of clusters.

6 Optimization algorithms

Optimization algorithms are methods of finding the best or the most efficient solution to a problem or a function. Optimization is a crucial technique in data science, as it can help with improving, refining, and evaluating data. Some of the most common optimization algorithms are gradient descent, stochastic gradient descent, Newton's method, conjugate gradient method, and genetic algorithms. Each of them has different strategies, trade-offs, and applications in solving different types of optimization problems.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?