1️⃣ Scraping some attributes of jobs from Wuzzuf website Like :

• Job title : Name of job.
• Company name : Name of the company that owns the advertisement.
• Location : Company location.
• Job type : Type of job: is it full time or part time.
• Exp level : Required level is whether senior or manger or ect.
• Exp years : Number of years required to obtain job.
• Skills : Skills required to obtain job.

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2️⃣ Save data in data structure that organizes data into 2-dimensional table:

• Create Data Frame
• save extracted data to Data Frame

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3️⃣ Cleaning :

• See if we have a duuplicated rows.
• Clean up the job_title by removing hyphenated words, text after forward slashes and text within parentheses.
• Remove Egypt from Location column.
• Clean up the Job type by removing forward slashes and get first word of this.
• in Exp year :
  • Remove this part "Yrs of Exp" from each value.
  • Get rid of values as " ", "-".
  • Convert single value to range format by matching them to existing ranges.

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4️⃣ Exploratory data analysis :

• What are the most common Job type?
• What is top 10 jobs?
• What is top 10 districts?
• What level of experience is most required?
• What company name is most in demand for jobs?
• What is top 10 skills?

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5️⃣ Conclusions :

• Most common job types is :
  • Full Time
  • Internship
• Top jobs :
  • Accountant
  • Finance Manager
  • General Accountant
  • Senior Accountant
• Top districts :
  • Cairo
  • New Cairo
• Most common experience level :
  • Experienced
  • Manager
  • Entry Level
• Top company is most in demand for jobs :
  • Confidential
  • AlGammal Contracting
• Top skills :
  • Accounting
  • Finance
  • Analyst
  • Research and Financial Analysis