Data Science

The Inherent Bias in LinkedIn Featured Skills & Endorsements

A network graph of LinkedIn logosFor anyone who seriously uses LinkedIn, you know that your profile is really important.  Our profile is our modern day resume, and it is how we will be judged by potential clients and employers.  One part that is especially important are our listed skills.   I have been fortunate that many people have endorsed me for many skills without me ever needing to resort to any form of “hacking“.

But I have come to realize that there is an inherent bias in the LinkedIn Featured Skills & Endorsements, and it is one that cannot be easily fixed, because the bias has to do with who knows what you do, and feels competent enough to make a judgement about it.

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The Four Major Activities of Data Science / Machine Learning

Recently there was a post on LinkedIn by Erle Hall, lead for the Information and Communication Technologies (ICT) for the California Department of Education (CDE) with a diagram about machine learning.  That diagram had 6 steps: Select Data, Model Data, Validate Model, Test Model, Use the Model, and Tune Model.    Those 6 steps mostly encapsulate what traditionally has been called the “data mining” phase.  But there are 3 other important phases, which I will call “data surfing”, “data wrangling” and “data artistry”.  (These names were chosen to be easier to understand and more interesting for students, but also go by different names)  I also personally prefer to use the term “algorithm” instead of “model”, because while traditionally in data science, statistical models were used, there are now often times methods like neural networks and other such algorithms that are less like a traditional statistical model.  In the next few posts, I’ll dive into each of these 4 steps, and give a basic explanation of what each step does, and why the step is important.

Data Surfing: The Oft Forgotten First Stage of Discovery

You got to drift in the breeze before you set your sails. It’s an occupation where the wind prevails. Before you set your sails drift in the breeze.” – Paul Simon

Many texts about data science (including machine learning, data mining, and predictive analytics) don’t include much about the very first step of the process, which is the step where you come up with what your goal is for your other steps.  In traditional science, this might be called the step of making your hypothesis.

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Data Wrangling: Gathering the Data You Need in a Form You Can Use

Data! Data! Data!’ I can’t make bricks without clay.” – Sherlock Holmes

Before data science/machine learning/data mining/predictive analytics can be done, you need to have the data you are going to use.  This may see obvious, but in many cases there is more to this step than may first be assumed, and the whole process is what I will call “data wrangling”, although has other names like “data munging”.

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Data Mining: Discovering Gold in your Data

There’s gold in dem dere data!” – Adaptation of the original quote from M. F. Stephenson

After the data has been gathered and in a form that can be used, it can then have an appropriate algorithm used to accomplish the data mining/machine learning/predictive analytics. This is the stage that traditionally has been called “data mining” because it is the part that gets additional value from the data in the form of some type of knowledge (this is why early on, the process was sometimes called “knowledge discovery in data” (KDD).

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Data Artistry: Using and Sharing the Knowledge in an Effective Manner

Can you picture that?” – Dr. Teeth and The Electric Mayhem

The final stage of doing data science/machine learning/data mining/predictive analytics is to use the results, which generally involves some form of communication to one or more types of audiences.  This, I will term “data artistry”. (This is not necessarily a common term used, but it does have some precedence in specific contexts)

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Three Missing Features in Most Student Information Systems (SIS)

There is a paradox:  Humanity’s most developed organizations and systems are based upon what is learned in our education systems; yet, the field of education lags behind nearly all others.  One such area I have seen, is how feature-poor Student Information Systems (SIS) are.  Despite such systems being case studies in many database books, most of these systems do not use any data science methods to improve operations.  Specifically, I have usually not seen active security, predictive analytics, nor even resource optimization as features.  Here is why these are important to have, and my invitation for SIS providers to come into the 21st century.

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Some Initial Thoughts on using Least Median of Absolute Deviation for my Data Mining to Reduce Problems with Outliers

A while back I wrote on this blog a “cry for help” about some different forms of linear regression…  which given the fact that it was a kind of deep topic in statistics and most of my friends and colleagues are not uber statistics nerds, I didn’t really get any replies…  But I have persevered, and continued to dive in on my own, because as Khan Academy puts it, struggling with ideas improves the brain like lifting weights.

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Python Script to Automate Refreshing an Excel Spreadsheet

Excel Logo Plus Python LogoOften I run into situations where it makes sense to do analysis of a lot of database data in an Excel spreadsheet, but due to the amount of processing the spreadsheet requires when updating, it takes a long time for the spreadsheet to “Refresh All”.

One solution to this problem is to automate the spreadsheet so it refreshes every night.  The following is a small Python script that can do this using the Python for Windows Extension:

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