📘 Career Compass: Job Titles, Skills & Pay Grades Dataset

📝 Overview

This dataset provides structured information about a wide variety of job roles, including: - Job Titles - Short Descriptions - Key Skills Required - Industry Categories - General Pay Grades

It is ideal for use in applications like: - Career recommendation systems - Labor market trend analysis - Job description classification - NLP model training and fine-tuning

📂 Dataset Structure

💡 Example Entry

1,Software Engineer,Develop and maintain web applications using modern frameworks,Problem Solving Logical Reasoning Attention to Detail Team Collaboration Critical Thinking,Technology,High paying

648,Senior Tax Manager,Manage complex tax planning and compliance for corporations,Tax Strategy; Corporate Tax; Regulatory Compliance; Team Leadership; Financial Planning,Finance,High paying

970,Online Digital Brand Growth Lead,Lead growth for online brands,Growth Strategy; Leadership; Communication; Technology; Analytics,Marketing,High paying

Context :

There is a lot of public data present over the internet related but nothing is centralized. Even if centralized, it’s not much use for citizens of India, as people are not very familiar with data manipulation and extraction. The objective behind this problem is to empower the citizens of India to extract the knowledge or any insights without diving deep into the enormous datasets.

Evaluation:

DATA CHATBOT For the “experimentalists

Let’s make this more interesting for the experimentalists amongst you. Let’s do the same as above, but this time with NLP. We recommend the participants to include NLP Packages in the interface developed for better performance and the relevant information on NLP for this could be found on Rasa.ai but we believe that there are numerous other methods and libraries that participant may resort to, maintaining or building your own look up table/dictionary should also suffice. Few of the sample questions and their answers/approach are listed below:

● “Net enrollment rate of education” should provide a list of states and the value for each state, when clicked on a state it should provide with the districts of that state and their net enrollment rate respectively. ANDAMAN AND NICOBAR : 90.1% ARUNACHAL PRADESH : 90.22% ANDHRA PRADESH : 78.48% . . .

DAMAN AND DIU : 80.05% DIU : 78.8% DAMAN : 81.3%

And so on...

● “%houses in rural areas of Anantpur with mobile phones” must return: ANANTPUR : 0.97%

● “Districts where literacy rate of girls is more than that of boys” must return: JAINTIA HILLS MEGHALYA ANANTPUR ANDHRA PRADESH

● “Percentage of working population in Anantpur” must return:

ANANTPUR : 49.89% Rural: 54.37% Urban: 38.42% While the implementation is important, sharp focus on overall approach and thinking, along with problem solving skills is what we are particularly looking for. Looking forward to some interactive and engaging interfaces.

For more information about this dataset,

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