Most Valued Data Science Skills

1 Introduction

The goal of this project is to investigate which data science skills are most valued in the job market.

Our main research question is: Which data science skills are most frequently requested by employers, and how do these requirements differ across data science roles?

We will analyze job postings to identify the most frequently requested skills and compare skill demand across Data Analyst, Data Engineer, and Data Scientist positions, as well as different job levels.

We will use the frequency of skills mentioned in job postings as our measure of skill demand. This will help us understand which skills employers commonly request.

Group Members: Ozge Gundogan, Antik Salimullah

2 Planned Approach

We will use the “Data Science Job Postings & Skills (2024)” dataset from Kaggle for this project.

Data Source: https://www.kaggle.com/datasets/asaniczka/data-science-job-postings-and-skills

We selected this dataset because it contains information about job postings, required skills, job titles, companies, and locations. The dataset consists of three CSV files: job_postings.csv, job_skills.csv, and job_summary.csv. We will primarily use job_postings.csv and job_skills.csv for our analysis.

We will download the CSV files from Kaggle and load them into R using the read.csv() function. We will then examine the structure of the datasets and identify missing values, empty fields, and duplicate records. We will separate comma-separated skills into individual rows, standardize skill names, and remove duplicate job-skill combinations. We will also examine the number of skills listed in each job posting to identify potential outliers.

We will use SQLite to organize the cleaned data into three related tables:
- Jobs: Stores information about job postings.
- Skills: Stores unique skills.
- Job_Skills: Connects job postings with their required skills.

We will use job_link and skill_id to connect the three tables and represent the many-to-many relationship between jobs and skills. The ER diagram below illustrates our proposed database structure and key relationships.

erDiagram
    Jobs ||--o{ Job_Skills : requires
    Skills ||--o{ Job_Skills : includes

    Jobs {
        string job_link PK
        string job_title
        string company
        string job_location
        string job_level
        string job_type
    }

    Skills {
        int skill_id PK
        string skill_name
    }

    Job_Skills {
        string job_link PK,FK
        int skill_id PK,FK
    }

We will use SQL to identify the ten most frequently requested skills. We will also compare skill demand across different job levels and three job categories: Data Analyst, Data Engineer, and Data Scientist. We will calculate percentages when comparing groups with different numbers of job postings and use the ggplot2 package in R to visualize our findings.

We plan to use Gmail for group communication and GitHub for sharing code, documentation, and version control.

3 Anticipated Challenges

One possible challenge is handling missing or incomplete data. Some job postings may contain empty fields or missing skill information.

Another challenge is cleaning and standardizing skill names. Since multiple skills are stored in a single column, we will need to separate them into individual records, standardize capitalization, and remove duplicate job-skill combinations.