ABOUT THE PROJECT
This project aims to identify high-demand job opportunities, high-paying domains, trending skills, and top recruitment locations for various domains. The project uses data warehousing techniques to analyze job market trends, job postings, and salary information. The results of this analysis provide insights into the most sought-after jobs, the most lucrative domains, and the most in-demand skills. Moreover, the project identifies the locations with the highest recruitment rates for each domain, providing job seekers with valuable information on where to focus their job search. By leveraging these insights, job seekers can make informed decisions about their career paths, increase their chances of finding a high-paying job, and gain a competitive edge in the job market.
TOPIC: Data warehousing to analyze LinkedIn Datasets
EXTRACT TRANSFORM LOAD
EXTRACTION The following are the datasets used in this project, Data Analyst job listings, Profile info, linkedIn job listings extracted from various sources.
TRANSFORMATION Once the data is extracted, the next step is to transform the datasets using python and SQL. Here, the data wrangling is performed where the raw data is re-organized, mapped, and transformed. The transformation process can help to filter out irrelevant data, remove duplicates, handle missing data, and apply data normalization techniques. Data cleaning is done using SQL and python.
LOADING The final step is to load the transformed and cleaned datasets into a data warehouse. A data warehouse is a centralized repository of data that can be easily queried and analyzed. I have used constellation schema for this project.
OBJECTIVES
DATA WAREHOUSING TECHNIQUES:
The project involves using data warehousing techniques to store and organize job market data. This includes designing and building a data warehouse that can store and manage large amounts of data efficiently.
DIMENSIONAL MODELING:
Dimensional modeling is a technique used in data warehousing to organize data into dimensions and measures.
CONSTELLATION SCHEMA:
A constellation schema is a useful schema for modeling complex relationships between data entities in a data warehousing system. It is particularly beneficial when working with datasets that involve many-to-many relationships, such as social media data. To analyze LinkedIn datasets using a constellation schema, one would identify the different entities and relationships within the data. For example, entities could include users, companies, job postings, and skills, and relationships could include users following companies, companies posting job openings, users applying for jobs, and users endorsing each other for skills. Using a constellation schema to model the LinkedIn dataset allows for easy querying of the data to answer complex questions.
VISUALIZATION:
Tableau tool is used for creating interactive and visually appealing data visualizations, dashboards, and reports. In this project, Tableau is used to create various types of charts, graphs, and maps to explore and analyze job market data.