By Mr. Data Science

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Throughout this article, we will look at how data science can be used in Human Resource Departments (HR). This includes exploratory data analysis and visualization, predictive algorithms, and named entity recognition using NLTK and spacy. Example 2 looks at some of the available options when dealing with unbalanced datasets. Example 3 introduces named entity recognition which is an integral part of any natural language parser.

While the three examples focus on data and problems from HR, the techniques introduced can be applied to many different fields.

Typical HR department responsibilities include recruiting, hiring, onboarding, training, and…

By Mr. Data Science

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Throughout this article, we will look at why food production will become a significant issue for humanity over the next few decades and how data science might help. We’ll also look at how to use the support vector machine (SVM) algorithm, reshape data, and how to write and use bespoke functions to get data into the form we need for our analysis.

The world’s population is projected to reach nine billion by 2050. This increase will require a 70% increase in food production to avoid famine [1].

As with all potential data science solutions, we need…

By Mr. Data Science

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Throughout this article, we will look at how data science can be used to better understand the education sector. We will look at examples of using regression algorithms to predict grade scores and use an unsupervised clustering algorithm, kmeans, to better understand why some students succeed while others fail.

Although educational institutions generate lots of data on students, education has not been an early adopter of data science. According to [1], there is still a lack of comprehensive studies into data’s role in education. It is an area where data science is just beginning to make…

By Mr. Data Science

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Throughout this article, we will look at techniques like cleaning and reshaping data, plotting violin plots, and analyzing text data in the context of job markets. Specifically, we’ll take a look at some differences between the three main “data” intensive jobs: data scientist, data engineer, and data analyst.

In 2012 an article [1] was published in the Harvard Business Review: Data Scientist: The Sexiest Job of the 21st Century. …

By Mr. Data Science

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Throughout this article, we will look at some of the ways data science and analytics techniques can help us better understand how cities work. Every city has problems. These often include traffic congestion, pollution, water and energy consumption and crime. Understanding these issues could be the first step in reducing or fixing the problem, and consequently, building a better city.

Zoning is an essential component of city planning. It is the designation of land for different uses; some areas will be more residential, commercial, or perhaps industrial. Zoning was one of the first parts of city…

By Mr. Data Science

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Throughout this article, we’ll look at some examples of how data science and analytics have been used in sport. We’ll also see examples of some data visualization, including violin plots and correlation heat maps, using existing data and functions to add new columns to a dataframe, and combining dataframes on a common key.

Data analytics and data science are a big part of modern professional sport. Some of the ways it is used include:

  • winning games
  • choosing players in team games
  • helping teams better understand their fan base
  • improve player performance
  • reduce injury risk

In the…

By Mr. Data Science

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Throughout this article, we will look at how data science has changed the financial world. Specifically, the example code looks at

  • how to derive information from raw customer data,
  • how to detect anomalies in data using the isolation forest algorithm
  • how to predict numerical values for assets using a regression algorithm.

Financial organizations were among the early adopters of data science, especially in fraud detection [1] and trading algorithms. While data analysis has been used in finance for decades, often by quantitative analysts [2], the introduction of data science has made it possible to develop autonomous…

By Mr. Data Science

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This article is for those who are just getting started in Data Science and want to build their skills and begin to establish a data science portfolio. The three projects we will discuss in this article introduce critical skills that every data scientist needs to have. Those critical skills include:

Pre-Processing Data: Real-world datasets are almost always imperfect. Data is often missing, there are outliers, not neatly structured, etc. As a data scientist, you need to know how to manipulate data to get it into a format that is useful.

Exploratory Data Analysis: Once your data…

By Mr. Data Science

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Throughout this article, we will analyze some data on UFO sightings. Recent press releases from the Pentagon have sparked new interest in the topic of UFOs/UAPs, so it is a trendy and interesting way to introduce some data science and data analytics concepts. However, we need to be realistic about what we can discover from publically available datasets on this topic. These datasets usually consist of eyewitness accounts; therefore, the data should be considered low quality from a scientific perspective. Science does not put as much faith in eyewitness accounts as the legal system does; reference…

By Mr. Data Science

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In law enforcement, different types of policing exist. There is active policing such as crowd control and traffic control, and there is preventative policing, where the police make themselves visible to deter crime. Finally, there is reactive policing where a crime occures, and the police respond, investigate, and aprehend criminals. In this article, I’ll demonstrate how Data Science can use pattern detection to predict where and when crimes might happen. This capability could enable reactive policing to become more proactive and preventative. Police forces have been plotting crimes on maps to look for crime patterns for…

Mr. Data Science

I’m just a nerdy engineer that has too much time on his hands and I’ve decided to help people around the world learn about data science!

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