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Stephanie Glen's blog post was featured

Calculus For Data Science: What Do You Really Need to Know?

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…See More

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Lava Kafle liked Stephanie Glen's blog post Predictive Analytics Techniques in One Picture

yesterday

Stephanie Glen posted a blog post

Calculus For Data Science: What Do You Really Need to Know?

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Katrina Dunkley liked Stephanie Glen's blog post Inference vs Prediction in One Picture

Jul 27

Stephanie Glen's blog post was featured

P Value vs Critical Value

P-values and critical values are so similar that they are often confused. They both do the same thing: enable you to support or reject the null hypothesis in a test. But they differ in how you get to make that decision. In other words, they are two different approaches to the same result. This picture sums up the p value vs critical value approaches.…See More

Jul 26

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Stephanie Glen posted a blog post

P Value vs Critical Value

Jul 26

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Stephanie Glen's blog post was featured

You're a Data Artist, not a Data Scientist

"Data Scientist" is 2020's equivalent of the rocket scientist of the 1950's: mysterious, sexy, and well-paid. But are you actually a "scientist"? While “data science” isn't fully defined yet as an academic subject (National Academies of Sciences, Engineering, and Medicine, 2018), more and more evidence seems to point to it being more of an art, rather than a science. Are…See More

Jul 24

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Arnuld liked Stephanie Glen's blog post Math vs. Statistics in One Picture

Jul 17

Stephanie Glen's 2 blog posts were featured

Jul 15

Stephanie Glen posted a blog post

ANOVA vs Regression in One Picture

If you scour the internet for "ANOVA vs Regression", you might be confused by the results. Are they the same? Or aren't they? The answer is that they can be the same procedure, if you set them up to be that way. But there are differences between the two methods. This one picture sums up those differences.References…See More

Jul 15

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Stephanie Glen's blog post was featured

How to Communicate Data

The following graphic is based on Sam Priddy's excellent DSC/Tableau Webinar How to Accelerate and Scale Your Data Science Workflows. Sam covered many interesting points for organizing, analyzing and presenting data--including which graph is best suited for different data types. This graphic is an overview of some of Sam's points. For more…See More

Jul 8

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Duvan Martinez Benitez liked Stephanie Glen's blog post Math vs. Statistics in One Picture

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Stephanie Glen's blog post was featured

Math vs. Statistics in One Picture

Math and statistics are vital components of any data scientist's tool box. While some view statistics as a type of math, the reality is that they are completely different subjects. Math is all about numbers and concrete answers, while statistics is making sense of numbers via educated "guesses." This one picture, based on Rossman et al's essay Some Key Comparisons between…See More

Jun 30

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Stephanie Glen posted a blog post

Math vs. Statistics in One Picture

Jun 29

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Stephanie Glen's 2 blog posts were featured

Jun 18

Stephanie Glen posted a blog post

Misleading Graphs Part 2: Ladders, Spaghetti, and Other Ways to Ruin a Graph

If you've spent any time with modeling data, you'll know that there are many pitfalls to be had when it comes to data presentation (I addressed some common pitfalls in Misleading Graphs Part 1). Misleading graphs can be the result of incorrect data collection, ignorance of the basic "rules" of data presentation (like labeling axes), or even deliberate attempts to mislead. A fourth…See More

Jun 18

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Profile Information

Company:: Andale Publishing, LLC

Job Title:: CEO

Seniority:: Executive

Job Function:: Other

Industry:: Mathematics Education

Short Bio:: Stephanie Glen is Editorial director at DataScienceCentral.com. She is a mathematician and statistician. After working for several years teaching mathematics and statistics at the university level, she created and developed the website StatisticsHowTo.com.

Interests:: Contributing

Stephanie Glen's Blog

Calculus For Data Science: What Do You Really Need to Know?

Posted on July 31, 2020 at 9:09am 0 Comments 0 Likes

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…

Continue

P Value vs Critical Value

Posted on July 26, 2020 at 7:42am 0 Comments 0 Likes

P-values and critical values are so similar that they are often confused. They both do the same thing: enable you to support or reject the null hypothesis in a test. But they differ in how you get to make that decision. In other words, they are two different approaches to the same result. This picture sums up the p value vs critical value approaches.…

Continue

ANOVA vs Regression in One Picture

Posted on July 15, 2020 at 12:13pm 0 Comments 0 Likes

If you scour the internet for "ANOVA vs Regression", you might be confused by the results. Are they the same? Or aren't they? The answer is that they can be the same procedure, if you set them up to be that way. But there are differences between the two methods. This one picture sums up those differences.

References…

Continue

How to Communicate Data

Posted on July 8, 2020 at 9:02am 0 Comments 0 Likes

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At 3:52am on December 11, 2019, Jason Posavad said…: Your article is so relevant Stephanie, as I am 48 and contemplating an online masters in Data Science. My answer has been to do as much as I can through MOOCs, self-driven projects, and other less-expensive learning. If it gets to the point that I have done all of that and proven to myself that I can do Data Science, and that I need a degree to get land a job, I will then do it. Reality is you do need to be more careful and more risk-averse as you age, as the time to recover from mistakes is just not there anymore.