This program scrapes linkedin.com jobs search to find the common programming languages pertaining to popular careers in computer science. This program takes about 10-20 minutes to go through all of the data. Once the data is exported into a CSV file, there is a console program 'plotData.py' which can visually present the data.
- Run 'getData.py' first to get data
- Run 'plotData.py' after getting data
- pandas
- bs4 (BeautifulSoup)
- selenium (Webdriver, Exceptions, Keys, and Chrome)
- time
- os
- sklearn (prepossessing)
Partial CSV file (There are more programming languages and career positions):
| Programming Languages | DATA SCIENCE | SOFTWARE DEV | NETWORKS | DATABASES | WEB DESIGN |
|---|---|---|---|---|---|
| Java | 14 | 70 | 3 | 8 | 20 |
| C++ | 4 | 50 | 30 | 19 | 0 |
| Python | 33 | 102 | 43 | 24 | 9 |
| SQL | 18 | 13 | 40 | 130 | 1 |
| HTML/CSS | 3 | 38 | 0 | 0 | 98 |
- This example does not represent the true data found.
- Data that did not have a job title relating to the position was ignored. With
this being said, we had to scaled the data when working the data with sklearn preprocessing
Partial CSV files with count pertaining to each job:
| Count/Missed Data | DATA SCIENCE | SOFTWARE DEV | NETWORKS | DATABASES | WEB DESIGN |
|---|---|---|---|---|---|
| Count | 124 | 173 | 58 | 21 | 104 |
| Missed Data | 69 | 300 | 38 | 83 | 13 |
- This example does not represent the true data found.
- The count represents the jobs that were accounted for.
- The missed data is the data that raised an error or was skipped because the title did not match job keywords.
Phone: (971) 708-4444
Email: [email protected]
Linkedin: https://www.linkedin.com/in/ericanderson333
Please contact me and send me any questions/advice! Thanks!