Once a matrix has been decomposed, using SVD into the above mentioned vectors, we’re ready to use the singular values (d

Computer programming is probably the most critical part of a data science job. Programming skills are one of the crucial abilities required for data scientists to change turn the raw data into an effective analytics software user experience. That’s why data scientists need to be proficient in more than one programming language.

Per figure 1 below, you might select 2 PC’s based on scree plot elbow with 58% variance explained or 4 PC’s on cumulative sum with 74% of variance explained. The main point to illustrate here is that from the original feature set of 13 features (Boston Housing data), it appears we can reduce down to a much smaller representation (2 - 4 PC’s) while retaining the valuable information using PCA.

I created some easy to use functions that include all the all the combined step-by-step code in my GitHub repo. Hopefully, you now have a more intuitive understanding of PCA and can speak to it intelligently when asked in conversation or your next data science interview!

Well, that was a 5-minute introduction to PCA using SVD. We talked about some applications for PCA and then walked through the steps to do PCA using SVD. As you can see its not at all black box and really only a few steps to transform and reduce your data. The next time you’re working with a high dimensional dataset you can use these steps in your favor.

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irst real signs that there is an appetite for a modern incarnation of C and C is the rising popularity of Go and Rust. We see a lot of typical tools that was commonly written in C or C in the past now being written in Go or Rust. A plethora of command line tools e.g. are popping up, which have been written in either of these langauges. I cover some of these tools here. You see people attempt to write game engines in Rust.

With respect to doing PCA, when you do SVD it will produce n-singular values / n-singular vectors equal to n-features. So if you have 13 features in your data it will produce 13 singular values / vectors. We can select which singular values we want to use based on how much variance they explain.

The emergence of data science in many industries has attracted millions of fresh talents to grow their computer programming and machine learning skills and land a data science job in the past few years. As data science projects are mostly done within the framework of enterprise software projects, software engineering skills are mandatory for data scientists to perform. In this article, we will discuss core software engineering skills that are required for aspiring data scientists:

To be honest this story is a bit half-baked as I realize Zig is just too big of a topic to cover in some grans sweeping story. There are so many other topics I could cover but which would simply make this story excessively long. Instead I will attempt to write multiple smaller articles focused on different aspects of Zig in the future.

This roll-your-own approach can be very powerful. We have seen LISP programmers use this to build object-oriented programming systems in LISP, even creating multiple-dispatch systems akin to Julia.

There is no globally accepted selection threshold for a lower-dimensional space. Selection can be done by plotting the variance explained ratio by singular values (principal components) with a cumulative sum and/or skree plot (Figure 1 below). Once you visualize variance explained you can balance the trade off between the “elbow” in a scree plot and the cumulative sum of variance explained.

I created some easy to use functions that include all the all the combined step-by-step code in my GitHub repo. Hopefully, you now have a more intuitive understanding of PCA and can speak to it intelligently when asked in conversation or your next data science interview!

With Zig you could build C style object-oriented system, something more akin to Go or even object-oriented programming more similar to a more dynamic language like Objective-C.

To be honest this story is a bit half-baked as I realize Zig is just too big of a topic to cover in some grans sweeping story. There are so many other topics I could cover but which would simply make this story excessively long. Instead I will attempt to write multiple smaller articles focused on different aspects of Zig in the future.

In this analysis, I have created a metric that scores each draft pick by comparing them against the players taken after them (the full methodology is available at the end). After applying this over the last 10 years (the drafts from 2010 to 2019), we can now measure which teams have been successful in their drafting and what teams have not.

Drafting is one of the most crucial elements of a successful long-term franchise. Fueled by a series of great draft picks, the San Antonio Spurs went on to make the playoffs for a record-tying 22 seasons while winning five championships in the process. On the opposite end of the spectrum, the Sacramento Kings are currently riding a 14-year playoff drought — the second longest in NBA history. Unsurprisingly, this analysis ranks them among the teams least successful at drafting.