10 Algorithms Every Programmer Should Know – and When to Use Them

@tachyeonz : Programmers love algorithms. What’s an algorithm? Good question! In my academic days, we would have said, “An algorithm is a well-defined, self-contained process or set of rules to be followed in a data processing system.”

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A Visual Guide To Statistics

@tachyeonz : Statisticians are one of the fastest growing jobs in the country—and for good reason.

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Bayes Theorem for Computer Scientists

@tachyeonz : Few topics have given me as much trouch as Bayes’ theorem over the past couple of years. I graduated with an undergraduate degree in EE (where calculus reins supreme) and was thrown into probability theory late into my MS coursework.

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Overview of ‘online’ algorithm using Standard Deviation example

@tachyeonz : Here at Logentries we are constantly adding to the options for analysing log generated data. The query language ‘LEQL’ has a number of statistical functions and a recent addition has been the new Standard Deviation calculation.

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5 EBooks to Read Before Getting into A Machine Learning Career

@tachyeonz : Note that, while there are numerous machine learning ebooks available for free online, including many which are very well-known, I have opted to move past these “regulars” and seek out lesser-known and more niche options for readers.

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Practical advice for analysis of large, complex data sets

@tachyeonz : To answer those questions, I put together a document shared Google-wide which I optimistically and simply titled “Good Data Analysis.” To my surprise, this document has been read more than anything else I’ve done at Google over the last eleven years.

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The 7 Epiphanies Needed to Intuitively Grasp Statistical Significance

@tachyeonz : There is only one danger more deadly to an online marketer than ignorance, and that danger is misplaced confidence. Whenever a marketer omits regular statistical significance testing, they risk infecting their campaigns with dubious conclusions that may later mislead them.

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Data Visualization Advice for Scientists

@tachyeonz : Editor’s Note: The following is a guest post from Robert Simmon, a data visualization engineer at Planet Labs, and previously the lead visualizer at NASA’s Earth Observatory. At heart, design is the discipline of clear visual communication.

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How signal processing can be used to identify patterns in complex time series

@tachyeonz : The trend and seasonality can be accounted for in a linear model by including sinusoidal components with a given frequency. However, finding the appropriate frequency for each sinusoidal component requires a little more digging.

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Understanding Independent and Identically Distributed Data

@tachyeonz : Sensor data collected from various sources, be it from products or processes can exhibit a tendency to vary over time. This is because sensors often measure system states over time, as data are continuously collected from these products and processes.

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Becoming a Data Scientist

@tachyeonz : This blogpost is an excerpt of Springboard’s free guide to data science jobs and originally appeared on the Springboard blog. Most data scientists use a combination of skills every day, some of which they have taught themselves on the job or otherwise. They also come from various backgrounds.

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Quick Introduction to Logistic Regression in Exploratory

@tachyeonz : We have added an easier way for you to build, predict, and evaluate some of the well known regression models like Linear Regression, Logistic Regression, and GLM with v3.0. In this post, I’m going to use Logistic Regression as an example to demonstrate how that works at high level.

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