A New Age in Productivity or Removing the ‘Human’ from HR?

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Introduction

Deep learning and AI have been drastically changing industries such as healthcare, financial services, and retail with many companies welcoming new technologies. However, Human Resources (HR) departments have been met with more challenges in integrating intelligent systems into their workflows.

HR Departments are tasked with managing the organization’s employees — hiring, firing, resolving disputes, payroll, benefits, and more. Many of these tasks seem ripe for automation with machine learning, however, they are also often subjective, and handing over the reigns poses interesting ethical challenges.

Employee Hiring

The hiring process is laborious and expensive. From reviewing resumes, interviewing, and training new employees, hiring new employees can carry a large cost to organizations outside of the new employee’s salary. This cost is generally worth it, however, because making the wrong decision can cost even more money if the employee must be let go and the process started again. Not only do the costs of hiring need to be incurred a second time, but lost production and the time it takes the new employee to ramp up to full production will also be present. …


Improve your data analysis skills by getting these three key books

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Why Books?

The internet is a treasure-trove of information on a variety of topics. Whether you want to learn guitar through Youtube videos or how to change a tire when you are stuck on the side of the road, the internet allows us to learn skills faster and easier than ever before.

I am a big supporter of using the internet to learn and improve your data analytics skills. There are loads of resources on personal blogs, Youtube, and my favorite site: Towards Data Science! However, I find that books are still an extremely useful medium for learning these skills.

Online resources are fragmented — written from different authors, expecting various levels of previous experience, and contain slight differences between them. This can make it difficult to make connections between these resources when you are first trying to learn analytics. That is why I think books are a great additional resource to use in your education. …


Using Resumes to Find Relevant Job Postings

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Job Searching Sucks

Anyone who has looked for a job can tell you that it is hard work. Searching for relevant positions, updating your resume and Linkedin, applying for jobs, writing cover letters, and interviewing take a large amount of time and effort to complete. A common saying I always hear is: “Job searching is a full-time job.”

One of the most time-consuming and draining aspects of the job hunt is finding relevant job postings. This often entails frequenting online job boards, scanning for relevant job titles, reading the job’s description, and identifying whether or not this job posting is relevant to your qualifications. …


A Complete Beginner’s Guide to Getting Up and Running Making Beautiful Network Graphs

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Preface

This guide is intended to get complete beginners in social network analysis up to speed on terminology and concepts to create and analyze their first network graph.

This is a long article. Feel free to bookmark this article and come back to it from time to time as you are first learning social network analysis. An outline of this article is also provided below:

Part 1: Background

  1. Why Should I Care About Social Network Analysis?
  2. What Does a Social Network Graph Look Like?
  3. What Tools Do I Need To Get Started?

Part 2: Terms and Concepts

  1. Nodes and Edges
  2. Edge Direction
  3. Edge Weight
  4. Centrality Measures
  5. Network-Level Measures
  6. Path-Level…


Using Social Network Analysis and Community Detection to Understand Pro-ISIS Twitter

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Network Graph of Pro-ISIS Twitter Communities

Background

This article is a continuation of a previous article using social network analysis techniques to explore pro-ISIS twitter accounts. That article can be found here.

The Islamic State of Iraq and the Levant (ISIL), also known as the Islamic State of Iraq and Syria (ISIS) is a group that follows a jihadist doctrine of Sunni Islam. ISIS grew to fame in 2014 when it took over key cities in Iraq. Since then, the monitoring and prevention of ISIS influence have been a key goal of the United States and others.

Over the years ISIS has been using social networks to spread its message and recruit advocates for its cause. From pro-ISIS content to graphic materials such as executions, the use of the internet has been a prime tool of the terrorist group. These social networks have even been used to recruit members from Western countries such as the United States, the United Kingdom, and Canada. …


Understanding the Islamic State’s Twitter Network Using Social Network Analytics

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Social Network Graph of Pro-ISIS Twitter Accounts

Introduction

The Islamic State of Iraq and the Levant (ISIL), also known as the Islamic State of Iraq and Syria (ISIS) is a group that follows a jihadist doctrine of Sunni Islam. ISIS grew to fame in 2014 when it took over key cities in Iraq. Since then, the monitoring and prevention of ISIS influence have been a key goal of the United States and others.

Over the years ISIS has been using social networks to spread its message and recruit advocates for its cause. From pro-ISIS content to graphic materials such as executions, the use of the internet has been a prime tool of the terrorist group. …


Snowball Sampling for Dark Web Security Research

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Warning: Accessing the dark web can be dangerous! Please continue at your own risk and take necessary security precautions such as disabling scripts and using a VPN service.

Introduction

To most users, Google is the gateway to exploring the internet. However, the deep web contains pages that cannot be indexed by Google. Within this space, lies the dark web — anonymized websites, often called hidden services, dealing in criminal activity from drugs to hacking to human trafficking.

Conducting security research on the dark web can be difficult. Website URLs on the dark web do not follow conventions and are often a random string of letters and numbers followed by the .onion subdomain. …


Scraping the Dark Web using Python, Selenium, and TOR on Mac OSX

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Warning: Accessing the dark web can be dangerous! Please continue at your own risk and take necessary security precautions such as disabling scripts and using a VPN service.

Introduction

To most users, Google is the gateway to exploring the internet. However, the deep web contains pages that cannot be indexed by Google. Within this space, lies the dark web — anonymized websites, often called hidden services, dealing in criminal activity from drugs to hacking to human trafficking.

Website URLs on the dark web do not follow conventions and are often a random string of letters and numbers followed by the .onion subdomain. These websites require the TOR browser to resolve, and cannot be accessed through traditional browsers such as Chrome or Safari.

Finding Hidden Services

The first hurdle in scraping the dark web is finding hidden services to scrape. If you already know the locations of websites you wish to scrape, you are in luck! The URL’s to these websites are often not searchable and are passed from person to person, either in-person or online. Luckily, there are a couple of methods we can use to find these hidden services. …


How to Implement Your First Machine Learning Algorithm Using Weka

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Source: Weka GUI

Introduction

Whether you are brand new to machine learning or have tried and failed to get models working in Python or other languages, Weka may be the perfect starting point for you.

Weka is a collection of data visualization and algorithm tools built on top of Java. Don’t worry though! You don’t have to know any Java to make use of Weka thanks to its graphical user interface (GUI). Using this GUI, you can run the popular machine learning models on data without writing any code!

Before getting your hands dirty with Weka, however, you will need to learn some basics of machine learning algorithms. …


Topic Modeling of Soundcloud.com Comments using PyCaret

Introduction

Previously, I developed a framework for identifying sellers of illicit narcotics advertising on Soundcloud.com. This framework scraped comments and identified comments that were advertising drugs through a simple keyword search. While this framework did well due to the similar structure of the comments, I wanted to try to improve this framework by using Latent Dirichlet Allocation.

Latent Dirichlet Allocation (LDA) is a natural language processing (NLP) model for learning abstract topics of text, also known as topic modeling. LDA will cluster documents into topics, allowing us to classify comments and find comments that are advertising the sale of narcotics.

Data

The data used in this model was collected previously for analysis in Tableau. The top three songs on Soundcloud at the time were scraped: @MEH by Playboi Carti, Find My Way by Baby Jesus, and Blueberry Faygo by Lil Mosey. The dataset has 17,048 comments with the features comment and isDrugs. The feature comment is the text of the scraped comment and the feature isDrugs contains a 1 for posts advertising the sale of narcotics and a 0 for other posts. More information on the collection of this data can be seen here. …

About

Mitchell Telatnik

Mitchell Telatnik is an MIS graduate from the University of Arizona applying data science to cybercrime.

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