Becoming a Data Analyst in 2022 - Insights and Advice from Tom

Jul 15, 2024

Becoming a Data Analyst in 2022

Introduction

  • Presenter: Tom, Senior Data Scientist at CareerFoundry
  • Experience: Over 5 years in the data industry
  • Purpose: Share insights on how to approach a career in data analytics in 2022

Key Points

Industry Changes

  • More Online Content: Increase in online resources and communities
  • More People Working in Data: More job opportunities and networking possibilities
  • More Sectors Using Data: Data analytics now applies to various sectors beyond finance and pharma
  • More Cloud Resources: Availability of online tools for data management and visualization
  • Free Machine Learning Techniques: Applications like object recognition, computer vision, and natural language processing
  • Active Online Communities: Platforms for practicing and learning data analytics

Tom's Journey

  • Master's in Computer Science; specialized in Data Science
  • Worked on projects in e-commerce, finance, and education fields

Learning Methods

  • University:
    • Pros: Structured, motivational environment
    • Cons: Time-consuming, expensive
  • Self-Teaching:
    • Pros: Flexibility, exposure to latest trends
    • Cons: Requires self-discipline, easy to get lost
  • Online Schools (Tom's preferred method):
    • Pros: Variety in cost and depth, sometimes offers human support
    • Career support like CV preparation, portfolio building, interview techniques

Learning & Skill Development Strategy

  • Focus on Practical Experience: Prefer real-world experience over lengthy academic courses
  • Portfolio Building: Essential for job applications
    • 1-3 core projects recommended
  • Minimal Roadmap for Data Analyst Skills:
    1. Working with Data: Grouping, summarizing, cleaning (CareerFoundry offers a free short course)
    2. Learning Tools: Start with Excel, then move to SQL and Python
    3. Statistics: Understanding descriptive statistics
    4. Visualizing Results: Use charts and presentations to tell a compelling story
    5. Finding a Passion Area: Choose an industry you're passionate about
  • Timeline: 6 months to 1 year to feel comfortable with essential skills

Personal Advice

  • Defining Objectives: Know what you want to achieve and work backwards to determine needed skills
  • Building Confidence: Start working in the field to build real competence
  • Interest in Math: Necessary but not at an advanced level; focus on problem-solving aspects
  • Tool Mastery: Start small, break learning into manageable steps
  • Learning from Others: Utilize online resources, study others' work on GitHub, attend hackathons and meetups
  • Networking: Essential for growth and opportunities; utilize platforms like LinkedIn and Reddit

Recommended Resources

  • Medium.com: Articles on the latest in machine learning and data analytics
  • Kaggle.com: Open-source datasets for practicing data analytics
  • HackerRank: Coding challenges for honing SQL and other coding skills
  • YouTube: Tutorials and content about data analytics (including CareerFoundry's channel)

Final Thoughts

  • Determined effort for 6-12 months should lead to a junior data analyst role
  • Encouragement to subscribe to the CareerFoundry YouTube channel for more resources and insights

Conclusion

  • Engagement and continuous learning are key. Numerous online tools and communities can facilitate your journey into data analytics.
  • Practical experience and a well-curated portfolio will significantly enhance job prospects.