Top 10+ Most Important Algorithms Every Data Scientist Should Know

Top 10+ Most Important Algorithms Every Data Scientist Should Know

Algorithms have become a very essential and basic part of our daily lives and they can be found in virtually any aspect of a business. Some great people call this as algorithmic business and it is changing the way our organizations run. There are all types of different algorithms and for each aspect of your business, there are different algorithms from basic to complex, which you can now buy at an algorithm marketplace nowadays. Algoritmia offers over 800 algorithms to developers in the field of audio and visual processing, machine learning and computer vision, saving the important time and money to developers.

However, algorithms available on the market may not be suitable for your particular needs. After all, you need different algorithms for different conditions and some advance type algorithm which can work like multi-source for one problem. In fact, there are many different variables that determine which algorithms are to be used and how the algorithm will work. These variables include the type and quantity of data, the industry in which the algorithm will be applied, the application which will be used for it and so on.

Therefore, sometimes buying an algorithm from a different marketplace and then modifying it cannot be the best option. Data scientists should still educate themselves in the most important algorithms; How are algorithms developed and which algorithm can you use for that purpose? After researching, we found 10+ most important algorithms, separated as per their application, which should still be in the list of demonstrations of every major data scientist:

Top 10+ Most Important Algorithms Every Data Scientist Should Know

1. Regression
2. Bayesian
3. Regularization
4. Decision Tree
5. Instance Based
6. Clustering
7. Linear Regression
8. Logistic Regression
9. Apriori Algorithm

Top 10+ Most Important Algorithms Every Data Scientist Should Know

10. Dimensionality Reduction
11. Deep Learning
12. Neural Network
13 Associated Rules
14. Ensemble
15. Support Vector Machine Algorithms
16. Random Forest and more.........

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