No one “perfect” method exists for filling in missing data; You can view this one picture as a starting point with some suggestions, rather than an absolute. You may want to decide beforehand if you care about statistical power or uncertainty; If you do, you'll want to lean towards one of the more complex routes (like multiple imputation), rather than a single imputation method--even if your data is linear or follows another trend or distribution shape.More info:Large Enough SampleShapes of DistributionsReferences:Appropriately Handling Missing Values for Statistical Modelling and PredictionSee More

No one “perfect” method exists for filling in missing data; You can view this one picture as a starting point with some suggestions, rather than an absolute. You may want to decide beforehand if you care about statistical power or uncertainty; If you do, you'll want to lean towards one of the more complex routes (like multiple imputation), rather than a single imputation method--even if your data is linear or follows another trend or distribution shape.More info:Large Enough SampleShapes of DistributionsReferences:Appropriately Handling Missing Values for Statistical Modelling and PredictionSee More

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This one picture shows what areas of calculus and linear algebra are most useful for data scientists.If you read any article worth its salt on the topic Math Needed for Data Science, 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. You don't usually need to know exactly how to take derivatives, minimize sums of squares or create clustering algorithms from scratch--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 or what better alternatives might be available.ReferencesMATH7502: Mathematics for Data Science 2 (Linear Algebra and Topics in Multivariable Calculus).How Much Math Do You Need to Become a Data Scientist?Cluster Analysis: Basic Concepts and AlgorithmsThe Mathematics Behind Principal Component AnalysisLossy CompressionFuzzy Relation Calculus in the Compression and Decompression of Fuzzy RelationsSee More

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This one picture shows what areas of calculus and linear algebra are most useful for data scientists.If you read any article worth its salt on the topic Math Needed for Data Science, 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. You don't usually need to know exactly how to take derivatives, minimize sums of squares or create clustering algorithms from scratch--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 or what better alternatives might be available.ReferencesMATH7502: Mathematics for Data Science 2 (Linear Algebra and Topics in Multivariable Calculus).How Much Math Do You Need to Become a Data Scientist?Cluster Analysis: Basic Concepts and AlgorithmsThe Mathematics Behind Principal Component AnalysisLossy CompressionFuzzy Relation Calculus in the Compression and Decompression of Fuzzy RelationsSee More

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