Navigating-the-Data-Science-Job-Landscape

Web Scraping by Selenium

Data Science Jobs Scraping Summary:

- Scraped job information, including Job Title, Company Name, Experience, Location, Salary, and Skills.
- Established a DataFrame to organize and store the collected data.
- Visualized the number of jobs per company using a Bar Chart, Distribution of Experience,
  Distribution of Job Title, Top 10 Skills in Demand and Distribution of Joblistings across Locations.

Technical Documentation:

1. Dependencies:
    - Pandas
    - Matplotlib
    - Seaborn

2. User-defined Functions:
    - create_dataframe(titles, companies, experiences, locations, salaries, skills):
      Takes lists of job-related information and returns a DataFrame.

    - visualize_data(df):
      Plots a bar chart of the number of jobs per company using Matplotlib.

3. Visualization Functions:
    - visualize_data(df):
      Bar chart showing the number of jobs per company.

    - plt.figure(figsize=(10, 8))
      sns.histplot(df['Experience'], bins=20, color='green', kde=True)
      Histogram showing the distribution of experience.

    - plt.figure(figsize=(12, 10))
      sns.countplot(y='Job Title', data=df, order=df['Job Title'].value_counts().index, palette='viridis')
      Count plot showing the distribution of job titles.

    - plt.figure(figsize=(12, 8)
      sns.barplot(x=top_skills.values, y=top_skills.index)
      Horizontal Bar Chart showing top 10 demanding skills for Data-Science jobs.

    - plt.figure(figsize=(12, 10))
      sns.countplot(y='Location', data=df, order=df['Location'].value_counts().index, palette='viridis')
      Countplot showing the number of jobs across locations.

4. How to Use:
    - Call create_dataframe() with the lists of job-related information.
    - Call visualize_data() with the created DataFrame for data visualization.