https://github.com/preciousclement/data-survey-breakdown
https://github.com/preciousclement/data-survey-breakdown
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- Host: GitHub
- URL: https://github.com/preciousclement/data-survey-breakdown
- Owner: Preciousclement
- Created: 2024-08-21T09:22:04.000Z (10 months ago)
- Default Branch: main
- Last Pushed: 2024-08-21T11:11:49.000Z (10 months ago)
- Last Synced: 2024-08-21T12:36:08.328Z (10 months ago)
- Size: 211 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Data Survey Analysis
## Table of Content
- [Project Overview](#project-overview)
- [Data Source](#data-source)
- [Tools Used](#tools-used)
- [Objectives](#objectives)
- [Steps Taken](#steps-taken)
- [Key Insights and Report](#key-insights-and-report)
### Project Overview
This project focuses on analyzing a comprehensive dataset gathered from professionals in the data industry.
The survey aimed to capture various aspects of their roles, career transitions, job satisfaction, compensation, and demographics.
### Data Source
The primary data set used for this analysis is "Data Survey Analysis.xlsx" file gotten from YouTube
The dataset includes the following key variables:
- Unique ID, Email, Date & Time Taken
- Duration each participant spent on the survey.
- Current Role & Career Switch
- Salary Information
- Industry & Programming Language
- Job Satisfaction Metrics
- Career Challenges
- Job Search Priorities
### Tools Used
- MS Excel - Data cleaning
- Power BI - Data visualization and reporting
- [Download Here](https://www.microsoft.com/en-us/download/details.aspx?id=58494)
### Objectives:
1. Understanding the distribution of roles within the data industry and the frequency of career switches.
2. Examining salary trends based on role, industry, and geographical location.
3. Identifying key factors contributing to job satisfaction and areas needing improvement.
4. Analyzing demographic data to uncover trends in education, age, and ethnicity within the data community.
5. Investigating the challenges faced by professionals in breaking into the data field and what they value in potential new roles.
### Steps Taken
#### 1. Excel - Data Cleaning:
- The dataset was first reviewed to identify any inconsistencies, missing values, or formatting issues.
- Standardized date and time formats, cleaned up text fields, and ensured consistency in categorical variables.
- Applied appropriate methods to handle missing data, such as imputation or removal of incomplete records where necessary.
- Converted relevant columns to suitable data types, such as numerical or categorical, to facilitate accurate analysis.
- Cross-checked the dataset for accuracy and consistency after cleaning.
#### 2 Power BI - Visualization:
- Imported the cleaned dataset into Power BI using Power Query.
- Used Power Query to further clean and prepare the data
- Loaded the transformed data into Power BI's data model for visualization.
- Developed key measures using DAX for dynamic calculations,
- Designed an interactive dashboard that visualizes key insights from the survey data.
**Dashbord**
### Key Insights and Report
The survey included a total of 630 participants from various backgrounds and roles within the data industry.
This diverse group provides a broad perspective on the current state of the industry and the experiences of those within it.
**Average Age of Participants:**
The average age of the respondents was 29.87 years. This suggests that the data industry attracts a relatively young workforce, with many individuals possibly in the early to mid-stages of their careers.
**Gender Distribution:**
Male: 49.2%
Female: 50.8%
This nearly equal gender distribution indicates a balanced representation of males and females in the data industry.
**Happy With Work/Life Balance:**
The average satisfaction score for work-life balance was 5.74. This indicates a moderate level of contentment among participants with the balance they maintain between their professional and personal lives.
**Happy With Salary:**
The average satisfaction score for salary was 4.27. This lower score compared to work-life balance suggests that salary satisfaction may be a significant area of concern for many respondents.
**Average Salary Distribution by Gender:**
- Male: 49.2%
- Female: 50.8%
This distribution reveals a slight difference in salary satisfaction between genders, with female participants slightly more represented in higher salary ranges.
**Average Salary by Job Title:**
Data Scientists occupied the top position in terms of average salary followed by Data Engineers, Data Architects, Data Analysts, Database Developers
**Programming Language:**
The most preferred programming language among participants, reflecting its widespread use and popularity in the data industry was Python.
While Python dominates, other programming languages like C++, JavaScript, and Java still hold substantial importance.
**Difficulty in Breaking into Tech:**
- The largest group of participants (269) reported that breaking into the tech industry was neither easy nor difficult.
- A significant number (156) found it very difficult to enter the industry.
- 134 participants found the process easy.
- 27 participants reported a very easy experience.
- 44 participants found it extremely difficult.
These findings highlight that while a considerable number of respondents experienced challenges in entering the tech industry, a sizable portion found it relatively manageable.