How To Become A Data Analyst

How To Become A Data Analyst

Uttkarsh Singh
Uttkarsh Singh

Are you tired of the endless, generic advice that doesn't actually help you land your dream job as a data analyst? You're not alone. In this guide, I'll share a practical, step-by-step plan that focuses only on the skills and actions that truly matter. This isn't fluff — it's an actionable roadmap to help you become job-ready in just 40 days.

Having worked at tech giants like Google, Amazon, Uber, and now as a Senior Analytics Engineer at GoDaddy, I know exactly what it takes to break into the analytics field. If you're ready to focus on real, achievable steps, let's dive in! 🚀

Understanding Analytics Roles

Before diving in, it's important to note that analytics roles go by different names depending on the company. For example:

  • Amazon: Business Intelligence Engineer
  • Google: Product Analyst, Trust & Safety Analyst
  • Stripe: Data Analyst
  • GoDaddy: Business Analyst

Don't let job titles confuse you. Always focus on the job description to understand the role's responsibilities and required skills.

What Do You Really Need?

Contrary to what many resources might suggest, you don't need to know everything like Python, advanced statistics, machine learning, or math to land your first analytics role. Based on my experience and reviewing hundreds of job descriptions and interviews, here are the only three core skills you need:

  • SQL
  • Excel/Google Sheets
  • Dashboarding (Tableau)

If you master these, you're already good to go. Let's break down each skill step-by-step.

1. SQL: The Must-Know Language for Analytics

SQL is essential for querying and analyzing data. It's a skill that every data analyst needs to excel at. Here's how to approach it:

Getting Started

  • Watch a beginner-friendly YouTube tutorial or take a free course to understand the basics.
  • Learn the fundamentals: select statements, filtering, joins, aggregations, and subqueries.

Leveling Up

  • Use Mode Analytics SQL Tutorial to learn practical SQL concepts.
  • Mode's interactive SQL editor is perfect for practicing with sample data.
  • Don't just read; practice each concept hands-on.

Practice, Practice, Practice

Solve SQL interview questions on platforms like:

Start solving beginner questions, then move to medium and advanced-level problems. Make SQL a daily habit! Allocate at least 1 hour every day for practicing questions.

Pro Tip: SQL interview performance can make or break your chances of getting hired. Dedicate time to this every single day!

2. Excel/Google Sheets: The Analyst's Best Friend

Excel and Google Sheets are indispensable tools for any data analyst. They're used for ad-hoc analysis, creating reports, and sharing insights across teams.

Getting Started

  • Open Google Sheets and create a personal finance tracker:
    • Log your expenses by category, date, and amount.
    • Create summary reports, such as:
      • Total spend by category
      • Largest spend category
      • Spending trends over time
    • Use functions like: SUMIFS(), COUNTIF(), VLOOKUP(), INDEX().
  • Practice using pivot tables and creating charts to visualize data.

Additional Practice Ideas

  • Sales Analysis: Create a mock sales dataset and analyze trends, such as monthly revenue, best-performing products, and customer segmentation.
  • Marketing Campaign Tracker: Build a tracker for campaign performance (e.g., clicks, conversions, ROI) and generate insights from it.
  • Employee Data: Create HR data with columns for department, salary, and performance scores.

Pro Tip: Create dashboards directly in Google Sheets to practice presenting insights.

3. Dashboarding: Bring Data to Life with Tableau

Dashboards are the backbone of data storytelling. They help you present insights in a visually engaging way.

Getting Started

  • Watch beginner-friendly tutorials on YouTube to understand Tableau basics.
  • Install Tableau Public (it's free!) and explore its interface.

Practice Projects

  • Use the financial tracker you created in Excel/Sheets and load it into Tableau.
  • Create dashboards showing:
    • Monthly spending trends
    • Category breakdowns
  • Experiment with different chart types: bar charts, scatter plots, and heatmaps.

Leveling Up

  • Add interactivity to dashboards: Filters, tooltips, and actions.
  • Use calculated fields to create new metrics and KPIs.
  • Work with public datasets like COVID-19 data or airline delays and create compelling dashboards.

Additional Resources

  • Participate in Makeover Monday:
    Weekly challenges with datasets and dashboard ideas.
  • Explore Tableau Public for inspiration and to share your work.

Pro Tip: A well-designed dashboard can showcase your skills to potential employers. Save and share your best work!

4. Applying for Jobs: The Final Stretch

Once you've built these skills, it's time to start applying. This step requires consistency and persistence.

How to Apply

Use LinkedIn Effectively

  • Search for analytics roles in your location.
  • Filter by jobs posted in the past week.
  • Apply to all relevant roles.
  • Refresh your search daily by filtering for jobs posted in the last 24 hours.

Make it a habit. Dedicate time every day to apply for 20+ jobs.

Remember: Applying for jobs is a numbers game. Don't get discouraged - it's normal to get 1-2 callbacks for every 100 applications.

The 40-Day Plan at a Glance

Days 1-20: SQL (60% of your focus)

  • Learn basics through tutorials.
  • Practice with Mode Analytics and solve SQL problems on LeetCode/ DataLemur.
  • Continue practicing SQL interview questions daily as you move to the next steps.

Days 21-25: Excel/Google Sheets (20% of your focus)

  • Create a finance tracker.
  • Practice Excel functions, pivot tables, and charts.

Days 26-30: Tableau (15% of your focus)

  • Learn basics through tutorials.
  • Build dashboards using your financial data.
  • Participate in Makeover Monday challenges.

Days 31-40: Apply for Jobs (5% of your focus initially; increase as skills improve)

  • Start applying daily.
  • Focus on consistency and persistence.

Final Thoughts

Becoming a data analyst doesn't have to be overwhelming. Stick to these core skills and follow the plan. Remember, this journey requires practice, patience, and persistence. If you follow this guide, I'm confident you'll be job-ready in 40 days. Let's get started — your dream job is just 40 days away! 🌟