Data science techniques are powerful tools for extracting knowledge from large datasets. Analyzin... more Data science techniques are powerful tools for extracting knowledge from large datasets. Analyzing the job market by classifying online job advertisements (ads) has recently received much attention. Various approaches for multi-label classification (e.g., self-supervised learning and clustering) have been developed to identify the occupation from a job advertisement and have achieved a satisfying performance. However, these approaches require labeled datasets with hundreds of thousands of examples and focus on specific databases such as the Occupational Information Network (O*NET) that are more adapted to the US job market. In this paper, we present a two-stage job title identification methodology to address the case of small datasets. We use Bidirectional Encoder Representations from Transformers (BERT) to first classify the job ads according to their corresponding sector (e.g., Information Technology, Agriculture). Then, we use unsupervised machine learning algorithms and some similarity measures to find the closest matching job title from the list of occupations within the predicted sector. We also propose a novel document embedding strategy to address the issues of processing and classifying job ads. Our experimental results show that the proposed two-stage approach improves the job title identification accuracy by 14% to achieve more than 85% in some sectors. Moreover, we found that incorporating document embedding-based approaches such as weighting strategies and noise removal improves the classification accuracy by 23.5% compared to approaches based on the Bag of words model. Further evaluations verify that the proposed methodology either outperforms or performs at least as well as the state-of-the-art methods. Applying the proposed methodology to Moroccan job market data has helped identify emerging and high-demand occupations in Morocco.
2019 IEEE Global Engineering Education Conference (EDUCON), 2019
Cybersecurity jobs are becoming more and more in demand for companies and governmental organizati... more Cybersecurity jobs are becoming more and more in demand for companies and governmental organizations, especially with the increasing number of cyber-threats in the world. In order to keep up with these changes, a study for the cybersecurity job market needs is required in order to help universities adapt their curricula and train more skilled graduates. However, most of prior work focuses on IT jobs and overlooks the cybersecurity field which makes the analysis of the needs of this field ambiguous. In this paper, we collect job ads from 9 Moroccan job portals from February 2017 to June 2018. After removing duplicates, we result in 409 job ads. Then, we split the data into two categories that are cybersecurity jobs and IT jobs that require cybersecurity. We use text analysis techniques to extract skills and requirements listed in these ads. More specifically, we extract the education level, programming languages, natural languages, years of experience, certifications and types of contract listed in these ads. Our paper finds that skills that are highly requested, such as risk analysis methods and certificates are not emphasized in cybersecurity-specific curriculum, while skills that are less in demand, such as a range of programming languages, are taught more regularly in universities. Additionally, more jobs require some understanding of English than of French, which was surprising given prevalence of French in Morocco. Our results can be used to reshape universities training in order to meet the demand of the job market.
2018 IEEE Global Engineering Education Conference (EDUCON), 2018
The offshore sector creates a large number of job opportunities in Morocco. Analyzing job ads rel... more The offshore sector creates a large number of job opportunities in Morocco. Analyzing job ads related to that sector can help universities adapt their curricula in order to produce more employable graduates. Unfortunately, analyzing these ads is challenging because they are mostly non-structured. Most prior work, however, focuses on analyzing structured and semi-structured job ads through keyword search and regular expressions. In this work, we collect and analyze job ads related to the offshore sector in Morocco over the period February-August 2017. We use a variety of machine learning and text mining techniques to process these ads. We examine the natural languages, programming languages, education level, and years of experience needed. We also examine contract type and salary when available. Our results reveal that French is the most needed language in offshore jobs, but that other foreign languages (English and Spanish) are also needed. We also find that the most needed offshore...
Skill mismatch is worldwide issue as it can be effect job satisfaction, wages and multiple other ... more Skill mismatch is worldwide issue as it can be effect job satisfaction, wages and multiple other factors. Analyzing selft-reported skill mismatch is highly studied in prior work. In this paper we study evidence of skill mismatch between three categories: Professionals, Students and Faculty member. We collect data from a survey. In this paper we use the Chi-Test to find evidence of skill mismatch between Professionals, Faculty members and students. We suppose that the three proportions are equals which means they agree on the importance of skills. If the hypothesis in not true we use the Marascuillo procedure to make comparisons between all pairs of groups Results of our paper shows that categories agree on the importance of skills categories especially programming languages; However, we find evidence of a mismatch between skills types especially Databases and Audit and risk analysis. We also find agreement between groups for some skills such as C++, as participants agree that this skill is not highly used in the cybersecurity job market.
Data science techniques are powerful tools for extracting knowledge from large datasets. Analyzin... more Data science techniques are powerful tools for extracting knowledge from large datasets. Analyzing the job market by classifying online job advertisements (ads) has recently received much attention. Various approaches for multi-label classification (e.g., self-supervised learning and clustering) have been developed to identify the occupation from a job advertisement and have achieved a satisfying performance. However, these approaches require labeled datasets with hundreds of thousands of examples and focus on specific databases such as the Occupational Information Network (O*NET) that are more adapted to the US job market. In this paper, we present a two-stage job title identification methodology to address the case of small datasets. We use Bidirectional Encoder Representations from Transformers (BERT) to first classify the job ads according to their corresponding sector (e.g., Information Technology, Agriculture). Then, we use unsupervised machine learning algorithms and some similarity measures to find the closest matching job title from the list of occupations within the predicted sector. We also propose a novel document embedding strategy to address the issues of processing and classifying job ads. Our experimental results show that the proposed two-stage approach improves the job title identification accuracy by 14% to achieve more than 85% in some sectors. Moreover, we found that incorporating document embedding-based approaches such as weighting strategies and noise removal improves the classification accuracy by 23.5% compared to approaches based on the Bag of words model. Further evaluations verify that the proposed methodology either outperforms or performs at least as well as the state-of-the-art methods. Applying the proposed methodology to Moroccan job market data has helped identify emerging and high-demand occupations in Morocco.
2019 IEEE Global Engineering Education Conference (EDUCON), 2019
Cybersecurity jobs are becoming more and more in demand for companies and governmental organizati... more Cybersecurity jobs are becoming more and more in demand for companies and governmental organizations, especially with the increasing number of cyber-threats in the world. In order to keep up with these changes, a study for the cybersecurity job market needs is required in order to help universities adapt their curricula and train more skilled graduates. However, most of prior work focuses on IT jobs and overlooks the cybersecurity field which makes the analysis of the needs of this field ambiguous. In this paper, we collect job ads from 9 Moroccan job portals from February 2017 to June 2018. After removing duplicates, we result in 409 job ads. Then, we split the data into two categories that are cybersecurity jobs and IT jobs that require cybersecurity. We use text analysis techniques to extract skills and requirements listed in these ads. More specifically, we extract the education level, programming languages, natural languages, years of experience, certifications and types of contract listed in these ads. Our paper finds that skills that are highly requested, such as risk analysis methods and certificates are not emphasized in cybersecurity-specific curriculum, while skills that are less in demand, such as a range of programming languages, are taught more regularly in universities. Additionally, more jobs require some understanding of English than of French, which was surprising given prevalence of French in Morocco. Our results can be used to reshape universities training in order to meet the demand of the job market.
2018 IEEE Global Engineering Education Conference (EDUCON), 2018
The offshore sector creates a large number of job opportunities in Morocco. Analyzing job ads rel... more The offshore sector creates a large number of job opportunities in Morocco. Analyzing job ads related to that sector can help universities adapt their curricula in order to produce more employable graduates. Unfortunately, analyzing these ads is challenging because they are mostly non-structured. Most prior work, however, focuses on analyzing structured and semi-structured job ads through keyword search and regular expressions. In this work, we collect and analyze job ads related to the offshore sector in Morocco over the period February-August 2017. We use a variety of machine learning and text mining techniques to process these ads. We examine the natural languages, programming languages, education level, and years of experience needed. We also examine contract type and salary when available. Our results reveal that French is the most needed language in offshore jobs, but that other foreign languages (English and Spanish) are also needed. We also find that the most needed offshore...
Skill mismatch is worldwide issue as it can be effect job satisfaction, wages and multiple other ... more Skill mismatch is worldwide issue as it can be effect job satisfaction, wages and multiple other factors. Analyzing selft-reported skill mismatch is highly studied in prior work. In this paper we study evidence of skill mismatch between three categories: Professionals, Students and Faculty member. We collect data from a survey. In this paper we use the Chi-Test to find evidence of skill mismatch between Professionals, Faculty members and students. We suppose that the three proportions are equals which means they agree on the importance of skills. If the hypothesis in not true we use the Marascuillo procedure to make comparisons between all pairs of groups Results of our paper shows that categories agree on the importance of skills categories especially programming languages; However, we find evidence of a mismatch between skills types especially Databases and Audit and risk analysis. We also find agreement between groups for some skills such as C++, as participants agree that this skill is not highly used in the cybersecurity job market.
Uploads
Papers by Rahhal Brahim