Becoming a Data Analyst

Jun 5, 2024

Lecture on Becoming a Data Analyst

Speaker's Journey

  • Started with Excel, landed a data analyst job at Heineken in 3 years.
  • Became a freelance data analyst at a European bank after 2 more years.
  • Quit job to travel the world full-time after another 2 years.

Key Points to Become a Data Analyst Faster

  • Avoid wasting time on unproductive YouTube tutorials and ineffective courses.
  • Focus on a streamlined approach to learn data analysis.

Major Mistakes to Avoid

  1. Over-reliance on watching others without practice.
  2. Trying to solve every problem alone.
  3. Quitting too soon.

Recommended Tools and Programming Languages

  1. Excel:
    • Good for beginners.
    • Wide applications and easy to learn.
    • Limitations with large data sets.
  2. SQL:
    • Next tool to learn after mastering Excel.
    • Highly requested skill in job openings.
    • Handles large data sets and has its own easy-to-use programming language.
  3. Visualization Tools:
    • Power BI: Part of Microsoft stack, free version, budget-friendly.
    • Tableau: Extensive data visualization, high demand, higher cost.
    • Qlikview: Fast, responsive, high price, less demand.
  4. Programming Languages:
    • Python: Recommended for its general-purpose use and high demand.
    • R: Focuses on statistical analysis but less versatile than Python.

Learning by Doing

Building Your Portfolio

  • Apply data analytics skills in current job or start a project at home.
  • Create reports or dashboards to showcase your skills.

Optimizing LinkedIn Profile

  • Include 'Data Analyst' in as many sections as possible.
  • Highlight data analysis experiences and skills.

Job Application Strategies

  1. Apply proactively to job postings.
  2. Optimize LinkedIn to attract recruiters.

Final Advice

  • Persistence is key; do not quit.
  • Progressing beyond being a data analyst can lead to more opportunities, such as freelancing or changing careers.

Closing Thoughts

  • The path taken: Excel → SQL → Power BI → Python.
  • Focus on practical experience and resourcefulness.
  • Consider data analysis as a stepping stone to achieving broader career goals.