Source: TheDigitalArtist at pixabay.comPart three of my ongoing series about building a data science discipline at a startup. You can find links to all of the posts in the introduction.Building data pipelines is a core component of data science at a st…

# Category: R

# The quadratic formula and low-precision arithmetic

What could be interesting about the lowly quadratic formula? It’s a formula after all. You just stick numbers into it. Well, there’s an interesting wrinkle. When the linear coefficient b is large relative to the other coefficients, the quadratic formula can give wrong results when implemented in floating point arithmetic. Quadratic formula and loss of precision The […]

# Cognitive Timing for AI Self-Driving Cars

By Lance Eliot, the AI Trends Insider How fast can you think? If I give you a jigsaw puzzle and ask you to assemble it, you would likely take some amount of time to look at the puzzle pieces and mull over in your mind which piece might go where. You might create a kind […]

# Putting Cows on the Internet of Things

While the world is still getting accustomed to spotting wearable devices on fellow humans, IoT enabled applications have ushered in a new era of the Internet of Products.The Internet of Things is commonly associated with smart home control devices, we…

# The Effect of Naming in Data Science Code

Even though there are tools allowing to practice data science without coding, they are far from sufficient. Data scientists will be writing and reading code. Reading code that has poor readability is a horrible experience. This post focuses on the impo…

# What digits should you bet on in Super Bowl squares?

My new office introduced me to a betting game I wasn’t previously familiar with: Super Bowl squares. It’s played with a ten-by-ten grid, like this one from printyourbrackets.com:

Each row and column gets an assortment of digits from 0-9 representing…

# How to Transform Boring and Dry Reports with Data Visualization

Studies show that one of the most fundamental ways to help people today cope with information overload is to visualize it. In layman’s terms, this means drawing it out as a graph, plotting it on a map or even using data to create an interactive diagram…

# Building a Daily Bitcoin Price Tracker with Coindeskr and Shiny in R

Let’s admit it. The whole world has been going crazy with Bitcoin. Bitcoin (BTC), the first cryptocurrency (in fact, the first digital currency to solve the double-spend problem) introduced by Satoshi Nakamoto has become bigger than well-established firms (even a few countries). So, a lot of Bitcoin Enthusiasts and Investors are looking to keep a […]

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# Behavioral Analysis of GitHub and StackOverflow Users

In this blog post, we will take a look at the activity on websites that

became a significant part of development across all areas in, as well as,

outside of Data Science: GitHub and StackOverflow. It doesn’t matter where

developers are from or what their specific focus is, everyone uses these

websites.

# How to perform Logistic Regression, LDA & QDA in R

Classification algorithm defines set of rules to identify a category or group for an observation. There is various classification algorithm available like Logistic Regression, LDA, QDA, Random Forest, SVM etc. Here I am going to discuss Logistic regression, LDA, and QDA. The classification model is evaluated by confusion matrix. This matrix is represented by a […]

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