By Mr. Data Science

Throughout this article, we will look at what computer vision is, how we are using it now, and how we might use it in the future. Example one will look at identifying hand-written digits using PyTorch. Example 2 will explore the use of histogram of oriented gradients and support vector machines to identify if an image contains a ship. Finally, example 3 will use segmentation to find objects in an image. …

By Mr. Data Science

Throughout this article, we will look at some of the ways data science is changing political campaigning. We’ll look at how opinion polling struggles to predict election results, how to segment groups based on factors such as income, age, and gender, and how we can use a Naive Bayes algorithm to do some sentiment analysis.

Brief Background On data science and politics:

Politics has used data analysis for many years to study voting and polling trends; the field even has a name, Psephology. Data science has enabled other uses for data in recent years. …

By Mr. Data Science

Throughout this article, we will look at some of the ways data science has changed the field of advertising. We will look at preparing data for predictive models, measure word frequency in text using NLTK and visualize correlations and distributions within customer data.

Brief Background On Data Science and Advertising:

Before social media and the world wide web, a lot of advertising depended on things like TV, radio, billboards, and magazines. One problem with these mediums is that they offer little control over who sees the advertisements. Data provides the ability to focus the advertising on a specific target audience. Anyone who has…

By Mr. Data Science

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.

Brief Background On Data Science and Human Resources:

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

By Mr. Data Science

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.

Brief Background On Data Science and Food Production:

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

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.

Brief Background On Data Science in Education:

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

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.

Brief Background On Data Jobs:

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

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.

Brief Background On The Use Of Data Science In Cities:

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…