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How to Get Your First Data Analyst Job Without Experience

Priya Sharma••9 min read
Data AnalyticsCareer ChangeLearning Path
How to Get Your First Data Analyst Job Without Experience

Seven months ago, I was teaching English online for $18/hour with no career prospects. Today, I'm a Data Analyst at a remote-first company earning $88k. No math degree, no bootcamp, no previous analytics experience.

Here's the exact roadmap I followed—every resource, every project, every mistake.

Why Data Analytics?

I chose data analytics because:

  • High demand (every company needs analysts)
  • Remote-friendly (numbers don't care where you sit)
  • Learnable in months, not years
  • Entry-level jobs actually exist (unlike data science)
  • Good salaries even for juniors ($60k-$90k)

Most importantly: you don't need a STEM degree. You need skills.

Month 1: SQL (The Only Must-Have)

Everyone says "learn Python first." Wrong. Learn SQL first. 90% of analyst interviews test SQL. It's your entry ticket.

Resources I used (all free):

  • SQLBolt (interactive, start here)
  • Mode Analytics SQL Tutorial
  • W3Schools SQL exercises
  • HackerRank SQL challenges (easy to medium)

What to learn:

  • SELECT, WHERE, ORDER BY, GROUP BY
  • Joins (INNER, LEFT, RIGHT) - critical!
  • Aggregations (COUNT, SUM, AVG, etc.)
  • Subqueries and CTEs
  • Window functions (RANK, ROW_NUMBER, LAG/LEAD)
  • Date functions (DATETRUNC, DATE_ADD, etc.)

My practice routine:

  • 1 hour every morning before teaching
  • Solved 3-5 HackerRank problems per day
  • Wrote queries by hand first, then tested them
  • Kept a "SQL cheat sheet" notebook

Month 2: Excel & Basic Stats

Yes, Excel. Every job wants it. No, it's not exciting. Yes, you need it.

Excel skills to master:

  • Pivot tables (you'll use these constantly)
  • VLOOKUP/XLOOKUP
  • IF statements and nested logic
  • Basic charting
  • Data cleaning (remove duplicates, find/replace, text-to-columns)

Basic stats to understand:

  • Mean, median, mode (and when to use each)
  • Standard deviation and variance
  • Percentiles and quartiles
  • Correlation vs. causation
  • A/B testing basics

Resources:

  • Khan Academy Statistics (free, excellent)
  • Excel practice datasets from Kaggle
  • YouTube (Leila Gharani for Excel, StatQuest for stats)

Month 3: Visualization Tools

Analysts don't just find insights—they communicate them. You need one visualization tool. I picked Tableau (free with Tableau Public).

What to learn:

  • Basic charts (bar, line, scatter, pie)
  • When to use which chart type
  • Dashboard design principles
  • Filters and parameters
  • Storytelling with data

Resources:

  • Tableau Public gallery (study what good looks like)
  • Makeover Monday challenges (practice with real data)
  • Cole Nussbaumer Knaflic's blog (data viz best practices)

Month 4-5: Build Real Projects

This is where most people fail. They watch tutorials forever and never build anything. Don't be that person.

My Portfolio Projects:

Project 1: E-commerce Sales Analysis

  • Downloaded dataset from Kaggle
  • Cleaned data in Excel
  • Loaded into SQLite, wrote 15+ queries
  • Found insights (sales trends, top products, customer segments)
  • Built Tableau dashboard showing key metrics
  • Wrote a 2-page report with recommendations

Project 2: COVID-19 Data Exploration

  • Used public COVID data (Our World in Data)
  • SQL analysis: cases by country, vaccination rates, death rates
  • Excel: trend analysis and forecasting
  • Tableau: interactive dashboard with filters
  • Published on Tableau Public

Project 3: Netflix Content Analysis

  • Scraped Netflix data (publicly available dataset)
  • Analyzed: content types, release trends, rating distributions
  • Visualized findings
  • Created "insights document" like a real analyst would

Why these worked:

  • Used real data (not toy examples)
  • End-to-end analysis (data collection → insights → viz)
  • Business-focused (answered real questions)
  • Shareable (portfolio website + Tableau Public)

Month 6: Python (Optional But Helpful)

You don't NEED Python for entry-level analyst roles. But it helps, especially at tech companies.

What I learned:

  • Pandas (data manipulation)
  • NumPy (basic calculations)
  • Matplotlib/Seaborn (visualization)
  • Jupyter Notebooks (for analysis)

Resources:

  • Python for Everybody (Dr. Chuck, free on Coursera)
  • Kaggle Learn (Python and Pandas micro-courses)
  • Real Python tutorials

I redid one of my projects in Python to show I could use both SQL and Python.

Month 7: Job Applications & Portfolio

By now I had:

  • Solid SQL skills (could pass interview tests)
  • Excel proficiency
  • Tableau dashboards on Tableau Public
  • 3 portfolio projects with write-ups
  • Basic Python knowledge

My portfolio website (simple, but effective):

  • Homepage: Brief intro, skills list
  • Projects page: 3 projects with screenshots, links, explanations
  • About: My story (career change, self-taught)
  • Contact: LinkedIn, email, GitHub

Built with Wix (free, took 4 hours).

The Application Strategy

I applied to 180 jobs. Here's what worked:

Job titles to search:

  • "Junior Data Analyst"
  • "Data Analyst I"
  • "Business Analyst"
  • "Analytics Analyst"
  • "Data Coordinator"

My approach:

  • Focused on remote-first companies
  • Targeted startups (50-200 employees)
  • Customized resume for each job (matched keywords)
  • Led with portfolio link in cover letter
  • Applied Monday-Thursday mornings

Stats:

  • Applications: 180
  • Responses: 31 (17%)
  • Phone screens: 12
  • Technical tests: 8
  • Final interviews: 4
  • Offers: 2

The SQL Interview Test

Most companies give you a SQL test. Here's what to expect:

  • 2-5 SQL problems
  • 30-90 minutes
  • Medium difficulty (Hacker Rank medium level)
  • Real business questions (not brain teasers)

Example question I got:
"Given tables users, orders, and products, find the top 5 products by revenue in Q4 2024."

Required: joins, aggregation, date filtering, ordering/limiting. If you've practiced, this is straightforward.

My prep:

  • Solved 50+ SQL problems on LeetCode (database section)
  • Did mock interviews with friends
  • Timed myself (practiced under pressure)

The Offer

Got my offer from a SaaS company (120 employees). They liked that:

  • My portfolio showed real analysis
  • I could explain my thinking (not just code)
  • I asked good questions about their data
  • I was self-taught (showed initiative)

Starting salary: $88k, fully remote, great benefits.

The Exact Roadmap

If I were starting over today:

  1. Month 1: SQL (3-4 hrs/day) - SQLBolt + HackerRank
  2. Month 2: Excel + Basic Stats - Khan Academy + practice datasets
  3. Month 3: Tableau - Makeover Monday challenges
  4. Month 4-5: Build 3 portfolio projects - Kaggle datasets
  5. Month 6: Optional Python - Kaggle Learn
  6. Month 7: Applications + interviews - 20-30 per week

Total cost: $0 (all free resources)

Time investment: 3-4 hours/day, 6 days/week

The Reality Check

This isn't easy. It took me 7 months of consistent work. I wanted to quit in Month 5 when I got my first 20 rejections. But I kept going.

If you're willing to put in the work, this path is proven. Data analyst jobs exist. Companies hire self-taught people. You just have to show you can do the work.

Stop reading. Start coding. Your first query is the hardest one.

Go write it.