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Over the years, Algorithms has proven to become an important part of our daily lives which is found almost in every area of business. Gartner said this to be the algorithmic business also stated that it is going to change the way in which we manage and run our organizations.

12 Most Important Algorithms Every Professional Data Scientist Should Know

Algorithms Every Professional Data Scientist Should Know

There are so many algorithms today which can play vital roles in different areas of any business. There are various algorithms software/programs which can even be bought at an algorithm marketplace. Algoritmia gives developers more than 801 algorithms in the areas of visual and audio processing, computer vision and machine learning which saves developers money and time.

However, some of the algorithms present on the Algoritmia store/marketplace may not suit some need. Generally, for different cases, we need various types of algorithms, and yes, the same algorithm in a different environment might give mixed or different results. Well, there are some distinct variables which decide which type of algorithm that is to be used the way that the algorithm will work or perform its job. These variables comprise of nature and volume/size of the data, the application it will be used for, the industry the algorithm will be applied to, etc.

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So, often times than none, purchasing an off-the-shelve algorithm and also tweaking it may not be the most suitable or best option. Data scientists must and should still educate/enlighten themselves in the most important and best algorithms; how the algorithms developed and the reason for which you use the algorithm and which to use? An article from Think Big Data put up an infographic displaying 12 Most Important Algorithms Every Professional Data Scientist Should Know, classified by their application intent, which must be in the archive of every big data scientist:

12 Most Important Algorithms Every Professional Data Scientist Should Know-1
12 Most Important Algorithms Every Professional Data Scientist Should Know-1

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