Our project analyzes data from Stack Overflow's 2023 survey to visualize tech industry trends, addressing the uncertainties faced by newcomers regarding skills and programming languages amidst the rapid evolution and rise of AI.
You can find Planning documents for ouy group including: group norms, learning goals, constraints, and a communication plan in the following link: Links to Planning Documents:
The tech industry is evolving rapidly, often leaving newcomers uncertain about which skills and programming languages to learn to stay current in the market. The rise of AI has added to these uncertainties, prompting many questions for those entering the field. Many wish they had a group of experienced tech professionals to consult for guidance.
- Which tools, frameworks, and programming languages are most prevalent in the tech industry?
- Which roles in the tech industry offer the highest salaries?
- What are current tech professionals' opinions on the newly introduced AI tools in the market?
For a more detailed explanation of this phase, you can refer to the following link: Problem_Statement
Our project aims to conduct a deep exploratory analysis of the data from Stack Overflow's 2023 survey. We will provide visual representations of this data to help both prospective and current tech industry professionals understand market trends and needs.
During the data cleaning phase, we created three distinct processed datasets from the original data to meet the requirements of our analysis, focusing on different variables. The original dataset, derived from a survey, contained many null values due to optional questions. To address this, we developed three separate datasets, each without null values for the key variables
Data Source: Link to the official survey and Dataset: 2023 Stack Overflow Survey Results
To manage the extensive Stack Overflow Survey dataset effectively, we organized our analysis into three distinct folders, each reflecting the contributions of a specific analyst:
- Analysis_1 Cleaning: Data cleaning
- Analysis_3 Cleaning: Data cleaning
Description: Each folder contains comprehensive data cleaning performed by our team members, focusing on various aspects of the dataset.
By dividing the analysis among team members, we aimed to distribute workload efficiently and ensure thorough coverage of the research questions. Within each analysis folder, you'll find detailed insights, visualizations, and interpretations aimed at addressing specific questions outlined in the main README file.
- Analysis_1 File: Data analysis
- Analysis_2 File: Data analysis
- Analysis_3 File: Data analysis
In the dynamic landscape of the tech industry, understanding the educational backgrounds, skill proficiencies, and preferences of its workforce is crucial. Our extensive survey analysis sheds light on these critical aspects, providing valuable insights into the qualifications and skills that drive success in programming and related fields.
For a more detailed explanation of this phase, you can refer to the following link: Finding