Add Kaggle Industry-Wide Survey Dataset

[PRIYANSHU2026]

Is there an existing issue for this?

• [ ] I have searched the existing issues

Feature Description

Title: Add Kaggle Industry-Wide Survey Dataset

Description:

For the first time, Kaggle conducted an industry-wide survey to establish a comprehensive view of the state of data science and machine learning. The survey received over 16,000 responses, providing valuable insights into who is working with data, what's happening at the cutting edge of machine learning across industries, and how new data scientists can best break into the field.

To share some of the initial insights from the survey, Kaggle has provided an interactive report along with the following dataset files:

1. schema.csv: This CSV file contains the survey schema, including the questions that correspond to each column name in both the multipleChoiceResponses.csv and freeformResponses.csv.
2. multipleChoiceResponses.csv: Contains respondents' answers to multiple-choice and ranking questions. These are non-randomized and thus a single row corresponds to all of a single user's answers.
3. freeformResponses.csv: Contains respondents' freeform answers to Kaggle's survey questions. These responses are randomized within a column, so reading across a single row does not give a single user's answers.
4. conversionRates.csv: Currency conversion rates (to USD) as accessed from the R package "quantmod" on September 14, 2017.
5. RespondentTypeREADME.txt: A schema for decoding the responses in the "Asked" column of the schema.csv file.

Tasks:

• [ ] Upload the dataset files to the repository.
• [ ] Add a README file explaining the dataset and its structure.
• [ ] Create a Jupyter Notebook to demonstrate how to load and explore the dataset.
• [ ] Add examples of basic data analysis and visualization using the dataset.

Resources:

• Kaggle Interactive Report
• Dataset Files

Use Case

Here's how you can describe the features of your method for analyzing the Kaggle Industry-Wide Survey Dataset on Stack Overflow:

---

Title: Key Features of Analyzing the Kaggle Industry-Wide Survey Dataset

Question:

I'm working with the Kaggle Industry-Wide Survey Dataset and I'm seeking advice on the key features and best practices for analyzing this dataset. The dataset includes various files that provide comprehensive insights into the state of data science and machine learning. Below are the key features of my method for utilizing this dataset:

Features of the Method:

1. Loading and Preprocessing the Dataset:

   • Efficiently load large CSV files into memory using Pandas.
   • Handle missing values and standardize data formats for consistency.
   • Merge schema.csv with multipleChoiceResponses.csv and freeformResponses.csv to provide context for each response.

2. Demographic Analysis:

   • Extract and analyze demographic information such as age, gender, education level, and geographic location.
   • Create visualizations (e.g., bar charts, histograms) to display demographic distributions.

3. Industry Trends Identification:

   • Analyze the tools, techniques, and technologies reported by respondents.
   • Use time series analysis to identify trends over time.
   • Cluster similar responses to identify common patterns and practices in the industry.

4. Salary Analysis:

   • Normalize salary data using the conversionRates.csv to convert all salaries to USD.
   • Analyze salary trends based on job role, experience, location, and other factors.
   • Create visualizations such as box plots and scatter plots to compare salaries across different demographics and roles.

5. Skill Gap Analysis:

   • Identify the most common skills among data scientists and machine learning professionals.
   • Compare skill sets of experienced professionals with those of newcomers to identify gaps.
   • Recommend learning paths based on the analysis of skill gaps.

6. Career Pathway Analysis:

   • Map out common career pathways and progression trends in the field of data science and machine learning.
   • Analyze career transitions and the factors influencing these changes.
   • Visualize career pathways using flowcharts or Sankey diagrams.

7. Market Insights Generation:

   • Provide insights into the demand for various data science and machine learning skills across different industries and regions.
   • Analyze the survey data to identify emerging trends and areas of high demand.
   • Create dashboards to present the market insights interactively.

8. Handling Freeform Responses:

   • Implement text analysis techniques to handle and analyze freeform responses.
   • Use natural language processing (NLP) tools to extract meaningful insights from the freeform text data.
   • Visualize common themes and sentiments from freeform responses.

Specific Questions:

1. What are the best practices for loading and preprocessing this dataset for analysis?
2. How can I effectively merge and utilize the schema.csv with the multipleChoiceResponses.csv and freeformResponses.csv files?
3. What techniques can I use to handle the randomized responses in freeformResponses.csv for meaningful analysis?
4. Are there any recommended visualizations or analysis techniques specifically suited for this type of survey data?
5. How can I normalize salary data using conversionRates.csv and perform a comparative salary analysis?

Any guidance, example code snippets, or references to similar analyses would be greatly appreciated!

Resources:

• Kaggle Interactive Report
• Dataset Files

Benefits

Here’s a detailed description of the benefits of your method for analyzing the Kaggle Industry-Wide Survey Dataset that you can use for your Stack Overflow post:

---

Title: Benefits of Analyzing the Kaggle Industry-Wide Survey Dataset

Question:

I'm working with the Kaggle Industry-Wide Survey Dataset and I’m interested in understanding the benefits of applying a comprehensive analysis method to this dataset. The dataset consists of multiple files that provide detailed insights into the data science and machine learning fields. Here are the key benefits of using my method for analyzing this dataset:

Benefits of This Method:

1. Comprehensive Demographic Insights:

   • Detailed Analysis: Gain a thorough understanding of the demographics of data science and machine learning professionals, including age, gender, education level, and geographic location.
   • Informed Decisions: Enable organizations and educational institutions to tailor their programs and outreach efforts based on demographic insights.

2. Identification of Industry Trends:

   • Current Practices: Stay updated on the latest tools, techniques, and technologies used in the industry.
   • Future Planning: Help businesses and professionals anticipate and adapt to emerging trends and shifts in the field.

3. Accurate Salary Analysis:

   • Normalized Data: Provide a clear and fair comparison of salary data across different regions and job roles by normalizing salaries using conversion rates.
   • Career Guidance: Assist professionals in making informed career decisions based on accurate salary insights.

4. Skill Gap Identification:

   • Targeted Learning: Identify the most in-demand skills and the gaps that new entrants need to focus on, enabling targeted learning and professional development.
   • Curriculum Development: Help educational institutions design curricula that address the most relevant and required skills in the industry.

5. Understanding Career Pathways:

   • Career Progression: Map out common career paths and progression trends, providing valuable insights for both new and experienced professionals.
   • Strategic Planning: Enable professionals to plan their career trajectories strategically based on common pathways and transitions in the industry.

6. Market Insights and Strategic Decisions:

   • Demand Analysis: Provide insights into the demand for various skills and roles across different industries and regions, helping businesses with strategic workforce planning.
   • Investment Opportunities: Assist investors and policymakers in identifying high-demand areas for investment and policy formulation.

7. Enhanced Data Interpretation:

   • Freeform Response Analysis: Leverage text analysis and NLP techniques to derive meaningful insights from freeform responses, adding depth to the analysis.
   • Thematic Insights: Identify common themes and sentiments, providing a richer understanding of respondents' perspectives and experiences.

8. Effective Visualization:

   • Data Presentation: Use a variety of visualizations (e.g., bar charts, histograms, box plots, scatter plots, flowcharts, Sankey diagrams) to effectively present data and insights.
   • Interactive Dashboards: Create interactive dashboards that allow users to explore data and insights dynamically, enhancing engagement and understanding.

Specific Questions:

1. What are the best practices for loading and preprocessing this dataset for analysis?
2. How can I effectively merge and utilize the schema.csv with the multipleChoiceResponses.csv and freeformResponses.csv files?
3. What techniques can I use to handle the randomized responses in freeformResponses.csv for meaningful analysis?
4. Are there any recommended visualizations or analysis techniques specifically suited for this type of survey data?
5. How can I normalize salary data using conversionRates.csv and perform a comparative salary analysis?

Any guidance, example code snippets, or references to similar analyses would be greatly appreciated!

Resources:

• Kaggle Interactive Report
• Dataset Files

Priority

Medium

Record

• [X] I agree to follow this project's Code of Conduct
• [X] I'm a GSSOC contributor
• [X] I want to work on this issue
• [X] I'm willing to provide further clarification or assistance if needed.

[aryaVishal1706]

@PRIYANSHU2026 can i work on this issue??? is it counted as gsoc ?

[sanjay-kv]

I appreciate this, but at the point repo is heading to analyse only stack over flow for all year and creating a streamlit for visualisation.

[github-actions[bot]]

Hello @PRIYANSHU2026! Your issue #254 has been closed. Thank you for your contribution!