Start Here · Updated September 2026

Start Here: Breaking Into Data Analytics

A concrete, in-order path to build real data analyst skills, plus where a paid course fits in.

What's the job, really?

"Data" covers several different jobs. Data analyst is the accessible entry point; data engineer and data scientist are different, usually later, destinations, not competing starting points.

As an entry-level analyst, your day follows a consistent loop: pulling data with SQL queries, cleaning messy spreadsheets so the numbers actually mean something, building or updating a dashboard in Tableau or Power BI, and meeting with other teams to understand what they actually need or explain what you found. Excel and SQL are the two non-negotiable skills almost every posting expects on day one.

Data Analyst

Surfaces business insights from existing data: cleaning, querying, dashboards, and explaining what the numbers mean to non-technical teams.

Good for

Beginners. The lowest barrier to entry of the three, companies are used to hiring career switchers straight into this role with solid Excel, SQL, and a real portfolio.

Data Analytics / Science Training (Springboard)

Business Intelligence (BI) Analyst

A close cousin of data analyst, focused specifically on building and maintaining recurring dashboards and reports for business decision-making.

Good for

People who like the dashboard and visualization side of analysis more than one-off deep dives.

Data Engineer

Builds and maintains the pipelines and infrastructure that move and store the data analysts and scientists use.

Good for

People who enjoy backend-style systems work more than interpreting the data itself. Usually not a zero-experience starting point, more often a move after some analyst or software experience.

Data Scientist

Builds predictive models instead of explaining what already happened, using Python, statistics, and machine learning.

Good for

People ready to go deeper into math, statistics, and programming. Almost never a realistic first job, most people arrive here after analyst experience or a more technical, quantitative background.

Data Science Training (Springboard)

Pros and cons to actually weigh

Pros

Real, strong entry-level pay for a no-degree path: entry-level data analyst pay has climbed to around $85K-$100K in major markets, and 85% of listings don't even specify required experience.
AI is genuinely freeing analysts from grunt work rather than replacing them: 70% report AI automation enhances their effectiveness, and 87% feel more strategically valuable than before, since AI increasingly handles the routine data-cleaning, freeing time for the higher-value storytelling and business-judgment side of the job.
A real, accessible on-ramp: companies are genuinely used to hiring fresh grads and career switchers straight into analyst roles, and a demonstrable portfolio (real queries, real dashboards) matters more than a specific degree.
Real growth path once you're in: analyst experience is a legitimate stepping stone toward senior analyst, analytics manager, or, with real upskilling, data scientist or data engineer.

Cons

The entry-level market is more competitive than the growth headlines suggest. "23% projected growth" describes the field overall, not how easy any single entry-level req is to land.
The skill bar has risen. Learning Excel and SQL used to be enough to stand out; employers now expect you to interpret trends, communicate insights clearly, and work alongside AI tools on top of the basics.
The roles most exposed to AI are the narrowest ones: if your entire value is writing SQL queries and pulling static reports with no business context, that specific slice of the job is genuinely at risk.
It's more reporting than discovery most of the time. If you're picturing constant, open-ended exploratory analysis, the reality of a lot of analyst work is recurring dashboards and requests, not a research project.

Your free step-by-step path

Every step here is free. Check them off as you go, saved privately in your browser.

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1.Aim at Data Analyst first, not Data ScientistRead the breakdown above. Data Scientist is the job title most beginners fixate on, but it's rarely a realistic first job. Data Analyst is the accessible, well-documented on-ramp, and analyst experience is what actually opens the door to data science or engineering later, if you still want it.
2.Learn SQL for free, interactivelySQL is one of the two non-negotiable skills for almost every entry-level posting. SQLBolt teaches it free, in the browser, one short interactive lesson at a time.SQLBolt
3.Get genuinely fluent in Excel, not just familiarThe other non-negotiable skill. GCFGlobal's free Excel tutorials, built by a nonprofit, cover real business use, not just formulas for their own sake.GCFGlobal: Excel
4.Learn to build dashboards, not just spreadsheetsTableau Public is a free version of the tool most job postings name directly. Build something real with it, not just a tutorial project.Tableau Public (free)
5.Build one real project with a real datasetKaggle has thousands of free, real-world datasets and free short courses. Pick one dataset that genuinely interests you and turn it into a full analysis: cleaning, a few real findings, and a dashboard, that's your portfolio piece.Kaggle
6.Join a community and start asking questionsr/analytics and r/businessintelligence are both free and used to beginners asking exactly the questions you'll have.r/analytics

When Paying Might Make Sense

You don't have to finish the free steps above before considering this. A paid program makes sense whenever you want a packaged, structured way of learning and a designated community to talk through what you're building, whether that's day one or after you've already tried SQLBolt, Excel, and Tableau on your own. We partner with Springboard for that reason, use code THEWARTHENS at enrollment for $1,000 off.

Data Analytics / Science Training (Springboard)

Quick Answers

How do I get started in data analytics with no experience, for free?

In order: learn SQL through SQLBolt's free interactive lessons, get genuinely fluent in Excel through GCFGlobal's free tutorials, learn to build dashboards with the free version of Tableau, then build one real portfolio project using a free Kaggle dataset. Join a community like r/analytics to start asking questions along the way.

Should I aim to become a data analyst, data scientist, or data engineer?

Data Analyst, if you're starting from zero. It's the most accessible entry point, companies are used to hiring career switchers directly into it, and it's the most common path toward data science or data engineering later. Aiming straight for data scientist as a first job isn't realistic for most beginners.

Do I need a degree for data analytics?

No. 85% of data analyst job listings don't even specify required experience, and employers consistently say a real, demonstrable portfolio (actual SQL queries, actual dashboards) matters more than a specific credential.

Ready to level up?

Already in the role? Here's what actually moves you to the next one.

Realistic timeline

There's less industry-wide benchmark data on analyst-to-senior timelines specifically than exists for sales or engineering promotion paths. What's consistent across sources: it's demonstrated business impact, not tenure alone, that respondents across the field say actually drives advancement.

What actually gets you promoted

Moving beyond routine reporting toward interpreting trends, communicating insights clearly, and working effectively alongside AI tools is what current employers say separates a senior analyst from an entry-level one. The analysts most exposed to being automated are the ones whose entire value is running the same static reports, the ones who advance are the ones adding real business judgment on top of the numbers.

Job-hop or stay?

The old advice to job-hop for a raise doesn't hold up anymore. The pay gap between switching jobs and staying has shrunk to just 1.9 percentage points, the smallest on record, and promoted employees now see meaningfully bigger raises than job-switchers. The better question isn't "should I leave for a bump," it's "am I positioned to move up a level, here or somewhere else."

Not sure this is even the right path for you?

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