As proficient data analysts, our objective is to delve into the dynamics of the job market within the realm of data analysis. To accomplish this, we have embarked on a comprehensive data-gathering endeavor, harnessing information from job listings on the widely-utilized professional networking platform, LinkedIn, across various countries.
Our primary goal is to derive insightful conclusions pertaining to the global data job market. To this end, we've amassed a substantial dataset through web scraping techniques. The next crucial step involves meticulously validating the integrity of this data, ensuring its accuracy and reliability.
Data Cleaning and Table Creation:
The initial phase entails a meticulous data cleansing process. This involves removing inconsistencies, correcting errors, and enhancing the overall quality of the dataset. Subsequently, we construct new tables utilizing this refined data, optimizing it for analysis. Automation via Stored Procedure (SP):
Our next undertaking involves the implementation of an automated data cleaning mechanism. We intend to create a Stored Procedure (SP) that will run on a daily basis. This SP will execute the data cleaning process and subsequently update the information in the newly generated tables. This automated system will ensure that our data remains current and reflective of the evolving job market landscape. Expected Outcomes: Through the meticulous execution of these steps, we anticipate deriving valuable insights regarding the global data analysis job market. The automation aspect will not only streamline our analysis process but also ensure that our findings are consistently aligned with the latest trends and developments in the industry.
This project is our thorough exploration of the global data analysis job market. We combine data scraping, rigorous validation, and automation techniques to show our dedication to providing accurate, current, and practical insights.