
How to Do Business Math & Statistics with Excel – Calculate Mean, Median & Mode
This video from codebasics explains how to do basic statistical analysis in Excel.. Mean, median and mode are basic statistical formulas that businesses use on daily basis to make data-informed decisions. In this video, I will give you a very simple explanation of these concepts using a few business use cases. We will also use […]
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But what is the Central Limit Theorem?
3Blue1Brown provides a visual introduction to probability’s most important theorem.
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How to implement Naive Bayes from scratch with Python
- Frank
- September 17, 2022
- Bayes
- Python
In the 6th lesson of the Machine Learning from Scratch course, learn how to implement the Naive Bayes algorithm. You can find the code here: https://github.com/AssemblyAI-Examples/Machine-Learning-From-Scratch
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Kristen Kehrer on Statistics, Data Poetry, and Dunkies
In this episode of Data Driven, I interview Kristen Kehrer, developer advocate at CometML and host of the Cool Data Projects Show. In case, you’re wondering where is Andy. He was unable to make the recording due to a last minute schedule change. The show must go on, after all. Learn more about CometML https://www.comet.com/?utm_source=kristen&utm_medium=referral&utm_campaign=online_kristen_2022 Related […]
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How To Update Your Beliefs Systematically – Bayes’ Theorem
Here’s a great video that explains Bayes’ theorem with examples and implications for life. I didn’t say it explicitly in the video, but in my view the Bayesian trap is interpreting events that happen repeatedly as events that happen inevitably. They may be inevitable OR they may simply be the outcome of a series of […]
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Statistics in Julia
Statistics is a domain where some early stage development of packages, and some early applications, have come about in Julia. From Ajay Shah at JuliaCon 2022 We think of this mini-symposium as a combination of (a) Report on many interesting recent developments in this field and (b) Offer a birds eye view to the people […]
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Statistics and Probability for Data Science
- Frank
- June 8, 2022
- data science edureka
- data science probability and statistics
- data science statistics
- Data Science Training
- edureka
- probability
- Probability and Statistics
- Statistics
- statistics and probability
- statistics and probability course
- statistics and probability for data science
- statistics and probability for machine learning
- statistics and probability tutorial
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- statistics edureka
- Statistics for Data Science
- Statistics Tutorial
A lot of people ask me about breaking into the fields of AI and data science. Many are surprised to hear that the core of it all revolves around statistics more so than “just code.” This session on Statistics And Probability will cover all the fundamentals of stats and probability. Topics Covered : 00:00:00 Introduction […]
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Statistics & Probability for Data Science
- Frank
- January 3, 2022
- data science edureka
- Data Science Tutorial
- edureka
- mathematics and statistics for data science
- probability and statistics using r
- r programming language tutorial
- statistics edureka
- Statistics for Data Science
- statistics for data science beginners
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- statistics for data science python
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- Statistics Tutorial
- statistics using r
- statistics using r tutorial
I’m often asked about the basics of data science and how to break into the field. I tell folks that it’s 80% statistics and 20% everything else. This session on Statistics And Probability will cover all the fundamentals of stats and probability concepts used in Data Science. Topics covered in this video: 00:00:00 Introduction 00:00:33 […]
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Is Bayesian thinking a sham?
Here’s a thought provoking video by Julia Galef There’s a common criticism that people who claim to practice “Bayesian thinking” aren’t actually doing anything special — they’re just using a fancy term to make their opinions seem more objective than they really are. In this video I explain why that criticism is misguided.
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Principal Component Analysis (PCA) Explained
Principal component analysis (PCA) is a workhorse algorithm in statistics, where dominant correlation patterns are extracted from high-dimensional data. Steve Brunton explains it in this great video.
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