This one picture shows what areas of calculus and linear algebra<\/strong> are most useful for data scientists.<\/p>\n If you read any article worth its salt on the topic Math Needed for Data Science<\/em>, you’ll see calculus mentioned. Calculus (and it’s closely related counterpart, linear algebra) has some very narrow (but very useful) applications to data science. If you have a decent algebra background (which I’m assuming you do, if you’re a data scientist!) then you can learn all of the calculus you need in a few hours of study. <\/p>\n You don’t usually need to know exactly how to take derivatives<\/a>, minimize sums of squares<\/a> or create clustering algorithms from scratch<\/a>–there are calculators for that! But if you have a general idea of what’s working in the background you’ll be able to recognize when results don’t make sense<\/strong> or what better alternatives<\/strong> might be available.<\/p>\n MATH7502: Mathematics for Data Science 2 (Linear Algebra and Topics in Multivariable Calculus).<\/a><\/p>\n How Much Math Do You Need to Become a Data Scientist?<\/a><\/p>\n Cluster Analysis: Basic Concepts and Algorithms<\/a><\/p>\n The Mathematics Behind Principal Component Analysis<\/a><\/p>\n Lossy Compression<\/a><\/p>\n Fuzzy Relation Calculus in the Compression and Decompression of Fuzzy Relations<\/a><\/p>\n<\/div>\n Go to Source<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<\/a><\/p>\n<\/p>\n
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