Glassdoor - Data jobs Insights

Data Cleaning , Data Preprocessing and Interactive Visualization
Project Overview
In this data analytics project, I embarked on a comprehensive exploration of the Glassdoor Data Jobs dataset obtained from Kaggle. Through meticulous data cleaning, preprocessing, and in-depth exploratory data analysis (EDA), I revealed valuable insights into various data-related job listings, including job titles, company names, and salaries. The visualizations created using Power BI brought the data to life, showcasing dynamic star ratings and interactive bar charts. This project aims to empower data job seekers with meaningful information and enrich their decision-making process in the competitive job market.
My Contributions
Delved into data cleaning and visualization using Power Query and creating interactive dashboard with custom tooltips and visuals, including star ratings and dynamic visuals for required skills area with the help Power Bi and MS Power Point. Thrilled to demonstrate the power of insights through captivating visualizations!
The objective of this report is to present the meticulous process of data cleaning, preprocessing, and EDA performed on the Glassdoor Data Jobs dataset, sourced from Kaggle. The dataset encompasses valuable information related to diverse data-related job listings, including job titles, company names, salaries, and other pertinent attributes.
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‍Data Cleaning:
During the data cleaning phase, a comprehensive review was conducted to identify and rectify inconsistencies, missing values, and errors that could compromise the analysis. The following steps were implemented:
Handling Missing Values: Missing data points were thoughtfully handled through appropriate imputation techniques or eliminated based on their impact on the analysis.
Dealing with Duplicate Entries: To uphold data integrity, duplicate entries were meticulously identified and eliminated.
Addressing Inconsistent Data: Efforts were made to address conflicting information and formatting discrepancies, ensuring data accuracy.
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‍Data Preprocessing:
‍To make the dataset analysis-ready, the data preprocessing stage involved strategic steps:
Feature Engineering: The dataset was enriched by deriving new features from existing ones, allowing us to capture valuable insights effectively.
Scaling and Normalization: Numerical features were subjected to scaling and normalization, eliminating biases in the analysis by bringing variables to a unified range.
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Exploratory Data Analysis (EDA):
‍EDA was thoughtfully conducted to glean invaluable insights and discern patterns and relationships within the data. The following analyses were performed:
Descriptive Statistics: Calculating summary statistics, such as mean, median, minimum, and maximum, offered essential insights into the central tendencies and distributions of numerical attributes.
Data Visualization: Utilizing diverse charts and graphs, such as bar plots, pie charts, facilitated a visual exploration of the data, leading to the identification of trends and outliers.
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‍Conclusion:
The comprehensive analysis conducted on the Glassdoor Data Jobs dataset brings forth valuable insights that hold immense significance for individuals seeking data-related job opportunities. By eliminating inconsistencies and enriching the dataset, we have established a reliable resource that can aid data job seekers in multiple ways:

Informed Decision-Making: The descriptive statistics and data visualizations offer a clear understanding of salary trends, company reputations, and job market demand, empowering job seekers to make informed decisions about potential employers and career paths.

Skills Alignment: Through feature engineering and dynamic visuals highlighting required skills, job seekers can identify the key competencies sought by companies, guiding them in honing their skillset to align with industry demands.

Salary Negotiation: A deeper understanding of salary distributions and trends can equip job seekers with valuable insights during salary negotiations, ensuring fair compensation based on industry standards

Competitive Edge: By leveraging the insights gleaned from EDA, job seekers can distinguish themselves from competitors by presenting data-driven narratives that showcase their understanding of market trends and industry dynamics.

Targeted Applications: Armed with insights into companies' specific requirements, data job seekers can tailor their applications to match the needs of potential employers, increasing their chances of securing relevant positions.

In essence, this analysis is not only a foundation for advanced data modeling but also a powerful tool for data job searching individuals, helping them navigate the job market with greater confidence, knowledge, and strategic advantage.

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