A Fine Slice Of SQL Server
Sebastiao Pereira performs matrix math in-database:
There are numerous applications to obtain a Matrix inverse for a given Matrix. Is it possible to do it using only SQL Server? Read on to learn how to build a matrix inverse calculator using a set of SQL Server custom functions.
I expect this to be extremely slow in comparison to GPU-based methods using a language like C, but this approach maximizes style points.
Comments closedSebastiao Pereira implements a function:
Solving linear equations is essential for solving real-world problems in Science, Engineering, Data Analysis, Machine Learning, Economics, Finance, and other areas. Is it possible to have a tool to solve linear equations directly in SQL Server? We will look at how to create a Gauss-Seidel method function for SQL Server.
This is one way to solve a series of linear equations, and it’s a pretty neat implementation.
Comments closedSebastiao Pereira creates a function:
It is very usual to have a set of discrete data points but sometimes it is necessary to estimate values between those points. Is it possible to create a function to do this in SQL Server?
It turns out that the answer is “yes.” Click through to see how.
Comments closedJohn Mount does a fun analysis:
This is a graph of the percentage of Stack Overflow questions tagged with data science terms such as R, Pandas, and so on. It seems to show exploding interest in R and Pandas, and maybe even Tensorflow. Pandas was likely chosen as a proxy for interest in Python for data science (versus a general interest in Python). I’d prefer view counts over question percentages as a proxy of interest, but it is what it is.
Then I thought, let’s see if they have newer data. They do, and it is horrifying (though not unexpected to those of us in the industry).
Click through for the analysis, as well as an important note in the comments.
Comments closedRichard Vale digs into a dataset:
In this post, I would like to draw attention to a very interesting data set collected by Guan, Palma and Wu as part of the replication package for their paper The rise and fall of paper money in Yuan China, 1260-1368. The paper describes inflation, money and prices during the Yuan Dynasty era in China.
First, a little historical background.
Read on for the analysis. H/T R-Bloggers.
Comments closedSebastiao Pereira performs an analysis:
Regression analysis is a statistical technique to identify relationships between a dependent variable (outcome) and one or more independent variables (features). Is it possible to do this in SQL Server without using any external tools?
Read on for the answer. I’d file this under “neat but probably not something I’d want to rely upon.”
Comments closedBhala Ranganathan talks about a powerful algorithm:
Cardinality is the number of distinct items in a dataset. Whether it’s counting the number of unique users on a website or estimating the number of distinct search queries, estimating cardinality becomes challenging when dealing with massive datasets. That’s where the HyperLogLog algorithm comes into the picture. In this article, we will explore the key concepts behind HyperLogLog and its applications.
HyperLogLog is the algorithm that SQL Server users in the APPROX_COUNT_DISTINCT() function to make it so much faster than a regular COUNT(DISTINCT) while still providing correctness guarantees within a fixed percentage error: they guarantee a 2% or lower error rate with a 97% probability.
David Lindelöf asks a question:
I sometimes use this fun interview question for aspiring data scientists:
How are p-values distributed assuming the null hypothesis is true?
Read on for four incorrect answers, followed by the actual answer. I actually got the answer right, but in the sloppiest and laziest way. But hey, it was still the right answer. H/T R-Bloggers.
Comments closedDavid Lindelöf deal with an issue in analyzing classification models:
When you run a binary classifier over a population you get an estimate of the proportion of true positives in that population. This is known as the prevalence.
But that estimate is biased, because no classifier is perfect.
Read on to learn what this means for precision, as well as one technique for tracking prevalence changes over itme.
Comments closedIn this video, I demonstrate how much information we can gain from one sample of a distribution.
Some aspect of this is “that’s a neat parlor trick” but it does speak to the marginal information gain of a small amount of data.
Comments closed