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Discovering Trends in US Jobs Using Dice.com Data

Analyzing Job Titles and Skill Requirements

By PromptCloud [source]

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About this dataset

> This dataset provides an in-depth analysis into the US job market, taken as a subset of a larger dataset consisting of over 4.6 million job listings from Dice.com, a well-known US-based technology job board. Through analyzing the job description with respect to the job title and skills, we can gain valuable insight into both current and future trends in the tech industry. This data was constructed by PromptCloud's web crawling service to give researchers, students and professionals alike access to data that offers unprecedented accuracy and scale when exploring this topic area. By diving deep into this information, we can build models for predicting hiring trends or identify top online recruiters for finding the perfect role for you!

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How to use the dataset

> How to use this Dataset > ======================== > The Dice.com job market dataset can be used to analyze the trends in US jobs and gain insights about the current job market. This dataset enables you to look into different aspects of jobs, such as job title, job location, skills required and descriptions, types of employment and even advertising URL. > > The dataset is made up of various columns like advertiserurl which holds the URL of the advertiser for a job listing; company which holds the name of company advertising a particular job; employmenttype_jobstatus which keeps track on type if employment with respective status; postdate contains date when it was posted publicly etc. Our focus will mainly be on these specific attributes while analyzing data points in this operational table. > > Before diving into further analyze please make sure that your environment is set up properly with python modules pandas, matplotlib and plotly pre-installed in your system’s libraries for tabular representation comparison and plot generation respectively for all data analysis process.. > > First step is read CSV(Comma separated Value) from local machine memory by using Pandas’ library pd ‘read_csv()’ function . Now you should have all csv content transformed into python’s data structure i-e DataFrame for further manipulation or say analyze part can begin now . > > It does not matter where analyses start , not from Question part , so let's jump straight away onto Analyses parts .We can initially find out total number rows present in datasets by using Dataframe's shape method or describe to get summary description (Min/Max/Average or counts) on all columns (integer/floats feature) except string types said earlier! Uniqunesses within string columns reveal info supported uniques terms available under features mentioned earlier ! Visualization coming right after this prior before full deployable model needs setup having necessary packages already has been mentioned earlier at start [1] section footnote.... > > Using Matplotlib library one can generate various charts like bar chart , pie chart histograms etc through last two parameters bar graph often used while vs other column while scatter works better with coordinate axis plotting..... Both Matplotlib / Plotly interchangeable library used depend upon requirement & confidence over interfaces setup [2] section footnote ...To achieve contextual plots please refer guide !! Mastering axis labels helps lot reading those analyse otherwise no use reacting those Bar graphs without know real value behind it actual x things refers ! Make sure there are capital terms

Research Ideas

> - Analyzing which skills are most in-demand by employers in the US job market, allowing job seekers to target their resume and prepare for interviews accordingly. > - Performing sentiment analysis to understand how companies describe job roles, assessing the attractiveness of different positions in the job market relative to one another. > - Comparing the average wage and benefits offered by employers across different regions of the US, allowing candidates to identify areas with more lucrative opportunities and provisionally plan a potential move if required

Acknowledgements

> If you use this dataset in your research, please credit the original authors.

> Data Source > > > ### License > > > License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication > No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: dice_com-job_us_sample.csv

Column name	Description
advertiserurl	The URL of the advertiser's website. (String)
company	The name of the company posting the job. (String)
employmenttype_jobstatus	The type of employment and job status (e.g. full-time, part-time, contract, etc.). (String)
jobdescription	The job description. (String)
joblocation_address	The address of the job location. (String)
jobtitle	The title of the job. (String)
postdate	The date the job was posted. (Date)
shift	The shift the job is for (e.g. day, night, etc.). (String)
site_name	The name of the website the job was posted on. (String)
skills	The skills required for the job. (String)

Acknowledgements

> If you use this dataset in your research, please credit the original authors. > If you use this dataset in your research, please credit PromptCloud.