Data-based analysis reveals data science job prospects

With Purdue University’s Data Science Initiative emphasizing on students having the best skills necessary for jobs, here’s a data-driven look at what the job market will look like.

The job description data came from studies by Corey Seliger, Director of Integration and Data Platforms, ITaP and the web scraping service PromptCloud. The data sets that scraped job posting websites were obtained through the Purdue University Research Repository and the machine learning community Kaggle.

Though Purdue does not offer as many courses from their Data Science Initiative when compared to other schools, this is less of a statement about Purdue’s Data Science Initiative and more of what universities classify as “data science” courses. The interdisciplinary nature of Purdue’s Data Science efforts meant that many courses through various other departments were not counted as data science courses. This analysis did not include, for example, courses from biology or neuroscience departments that may be considered intensive with data analysis. 

The Statistics Living-Learning Community’s publication and presentation record since their inception shows the influence the Data Science Initiative has had in getting students’ work published. 

The majority of them are in big cities like Chicago and San Francisco. These cities show where the future data science jobs are likely to be. They can be classified with corresponding metrics of predictability.

More data science jobs require a Ph.D. when compared to other education qualifications of Bachelor’s degrees or Master’s degrees. 16,618 require a Ph.D, 10,695 required a Bachelor’s, and 8,759 required a Master’s. Over half of the jobs listed, 82,726 contained no educational requirements.

The associated wordcloud provides another glimpse into the words these job postings most commonly use. Though these words may be used frequently throughout the job postings, their importance to data science as a whole shows by their usage.

The wordcloud shows relative frequencies of each keyword from the job descriptions. Key phrases like “practical experience,” “complex data,” and “strategic tactical” show the types of skills data science jobs require. That jobs requiring these skills means students seeking data science careers should understand them for their career goals. 

The data was taken from over 90,000 job postings of data science jobs. The job postings contained various information including salary, background, experience, and personal skills relevant to the job. They emphasized specific skills they look for in applicants and the types of experience required. 

Latent Dirichlet allocation (LDA) is a generative statistical model to use unobserved groups that explain why parts of the data are similar to make observations. The LDA analysis of data science job prospects can be found here. With LDA, we observe how keywords or phrases cluster among themselves in groups of principal components. LDA has uses in Natural Language processing and topic modeling for large amounts of text. Modeling texts on various ways their keywords cluster can be used to predict books to recommend to someone based on their previous reading. Publications can model topics to extract key features from articles or cluster articles that are similar close together. 

LDA involves estimating topic assignments for key topics in the text. For each document, an amount of topics is chosen and words are generated according to the Dirichlet distribution. Then the model works backwards to figure out how to find a set of topics and corresponding distribution that could generate the document’s content. 

It determines how document topics vary among themselves, the words associated with each topic, and the phi value. The phi value is how likely a word belongs to a certain topic. The script, written in the python programming language, takes about 20 seconds to extract and analyze data while outputting results nearly 100,000 job descriptions. The speed of the pipeline came through programming techniques to take advantage of the the similarity between the input files. A multi-threaded procedure was used to run these tasks in parallel with one another and a search approach that saves memory by directly converting the file text to manageable data reduced the pipeline’s runtime from several hours to less than a minute.

Through various sampling methods, LDA can learn how to improve its method of determining the document’s content. We can train the LDA on various known sets of data to learn how to extract features and form predictions. 

The process included parsing each file for keywords. This meant removing unnecessary words (such as “and” and “the”) as well as combining similar words into one another (such as “analysis” and “analyze”). 

The LDA models can be further optimized for use in sampling such that predictions may be formed on future job prospects in data science. Tools to extract data science job information would prove beneficial for improving the LDA model. Education curricula can reflect these trends in a fluctuating job market. Future work can include finding more varied sets of data such as including machine learning and artificial intelligence keywords in addition to data sciences ones. 

Using a support vector machine for classification and data-fitting methods training and testing, the models can be used as the basis for forming predictions on the data science job market. The entirety of these techniques can be found here:

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