How much of a data analyst's day is spent writing SQL?

Most of it. On a typical day, 3 to 5 hours go into writing, testing, and fixing SQL queries. That's the bulk of the work, not a side skill. The queries aren't glamorous—you're pulling a table, checking if the numbers match what someone expects, joining it to another table, and formatting it so a non-technical person can read it.

Do data analysts use Python every day?

Not at the entry level, and often not later either. SQL and a spreadsheet tool cover most entry-level and mid-level analyst work. Python shows up more in data engineering, machine learning, or advanced analytics roles, not in the daily grind of a standard analyst seat.

What meetings does a data analyst attend?

A typical week includes a team standup, one or two stakeholder meetings where you present or explain a number, and the occasional call where someone wants to understand a dashboard they can't read on their own. The skill that matters most in these meetings is explaining a number in one sentence to someone who doesn't care how you got it.

Is being a data analyst boring?

Some of it, yes. Most days include stretches of repetitive query writing and data cleanup that nobody would call exciting. The interesting part shows up when a chart finally answers a question someone's been asking for weeks, or when you catch an error before it reaches a director's inbox.

What's the difference between a junior and senior analyst's day?

A junior analyst spends more time on execution: writing the queries, building the dashboard, fixing the data someone else flagged. A senior analyst spends more time deciding what should get built in the first place and pushing back when a request doesn't make sense. Both still write SQL most days. The senior version just spends less time on syntax and more time on judgment.

What tools does a data analyst use daily?

In order of frequency: SQL editor, a BI tool like Tableau or Looker, spreadsheets like Excel or Google Sheets, Slack or email, and Python (occasionally). SQL editor is used most, while Python ranks lower than many expect, especially at the entry level.

Portfolio Project Ideas Using Sports Data

Sports data makes a good portfolio project because it's free, it's clean enough to start fast, and you already know what the numbers mean. The catch is that most sports projects look the same. A project gets noticed when it answers a business-style question, shows your SQL, and ends with a recommendation. This post gives you 8 project ideas, where to find the data, and how to turn each one into something a hiring manager will read.

You don't need all 8. One finished project beats 5 half-built ones, and you can pick the sport you already follow.

Are sports projects good for a data analyst portfolio?
Why sports data works
Where to find sports data
8 sports project ideas
Turn the idea into a business question
Two SQL examples to start with
How to write it up
Mistakes that make sports projects forgettable
When a sports project is the wrong choice
FAQ

I got hired without a portfolio site. I listed 3 project links on my resume: 2 Tableau Public dashboards and 1 Power BI project. None of them were fancy. They were easy to open and easy to follow, and that carried more weight than polish. That's the bar for a project, and sports data clears it easily.

Are sports projects good for a data analyst portfolio?

Yes, if you treat them like business projects. A sports project works when it shows 3 things:

A clear question. "Does home field advantage change by season?" beats "NBA stats dashboard."
Real SQL or analysis. Joins, aggregations, and window functions, not just a chart of the top scorers.
A recommendation. What a team, a fan, or a front office should do differently because of what you found.

Without those, a sports project reads as a hobby. With them, it reads as the same skills a business analyst uses, applied to data you care about. Portfolio projects that don't look like tutorials explains why that difference matters to a hiring manager.

Why sports data works

Sports data has 4 advantages over most free datasets:

It's easy to get. Many leagues have public stats, and plenty of cleaned datasets are free.
You can check your own work. If your result says a team had the best offense and everyone knows they didn't, you've found a bug.
It has the same shape as business data. Games are transactions, teams are customers or products, and seasons are time periods.
You can talk about it. Interviewers can tell when you're interested in your data, and you'll explain it better.

That third point is the one that matters. A team's win streaks look a lot like customer retention. Player performance over time looks like employee or product performance. The skills carry over even when the subject changes.

Where to find sports data

Start with free, public sources and check each one's terms before you scrape or republish anything. These are common starting points:

Kaggle. Many cleaned sports datasets sit here, usually as CSV files.
Basketball Reference, Pro Football Reference, and Baseball Reference. Detailed stats you can browse and often export.
nflverse. Open data for football, including play-by-play.
The Lahman Baseball Database. A long-running free baseball database.
Baseball Savant. Pitch-level baseball data you can export.
football-data.co.uk. Match results and odds for European soccer.

Pick one sport and one source, and stay there for the whole project. Mixing 4 sources adds cleaning work that doesn't make the project better.

8 sports project ideas

Here are 8 ideas, each framed as a question you can answer with data:

Home field advantage over time. Has the home win rate changed across seasons, and does it differ by team?
Win streaks and momentum. After 3 wins in a row, does a team really win the next game more often?
Rest days and performance. Do teams play worse on short rest, and by how much?
Player aging curves. At what age do players in a given position peak, and when does production drop?
Payroll versus wins. Which teams get the most wins per dollar of payroll?
Fourth-down decisions. Do teams go for it when the numbers say they should?
Fantasy draft value. Which draft positions beat their expected points, and which players were overdrafted?
Schedule strength. How much of a team's record is explained by who it played?

All 8 can be done with SQL and a dashboard. Ideas 1, 2, and 3 are the best starting points because they need only game results, which are the easiest data to find.

Turn the idea into a business question

Pick a stakeholder and write the question the way they'd ask it. That one step changes how the whole project reads.

A few examples of the reframing:

Instead of "analyze home field advantage," ask "should a team's ticket pricing and travel planning change based on how much home games help them?"
Instead of "player aging curves," ask "how long should a team keep paying a player at this position?"
Instead of "payroll versus wins," ask "which spending decisions gave a team the most return?"

The data is the same. The framing tells a hiring manager you think about decisions, not just numbers. If you want help getting the structure of a project right, how many portfolio projects you need covers how to scope the work.

Two SQL examples to start with

Both of these use a simple table of games. They show the kind of SQL a hiring manager wants to see.

First, home win rate by season, which uses an aggregation:

SELECT season, COUNT(*) AS games, AVG(CASE WHEN home_score > away_score THEN 1.0 ELSE 0 END) AS home_win_rate FROM games GROUP BY season ORDER BY season;

Second, a running count of consecutive wins, which uses a window function. Start by flagging each game as a win or loss, then count the streak in a second step:

SELECT team, game_date, result, SUM(CASE WHEN result = 'W' THEN 1 ELSE 0 END) OVER (PARTITION BY team ORDER BY game_date ROWS BETWEEN 2 PRECEDING AND CURRENT ROW) AS wins_last_3 FROM team_games;

When wins_last_3 equals 3, the team is on a 3-game win streak. You can then compare how often those teams win the next game. If the aggregation feels shaky, the GROUP BY traps are worth reading first, and SQL window functions explained covers the second query.

How to write it up

A project without a write-up is just a file. Spend as much time on the explanation as on the analysis, because that's what a hiring manager actually reads.

A good write-up covers these 6 parts:

The question. One sentence, framed for a stakeholder.
The data. Where it came from, how many rows, and what you cleaned.
The method. The SQL or steps, with a short snippet of the key query.
The finding. The answer in plain language, with 1 or 2 charts.
The caveats. What the data can't tell you.
The recommendation. What the stakeholder should do next.

Keep it to about 1 page. How to write a project README has the structure, and where to host your data analyst portfolio covers where to put it. A public dashboard link plus a short write-up is plenty.

Mistakes that make sports projects forgettable

Most sports projects fail the same way. They show numbers and stop. These are the mistakes to avoid:

The top-scorers chart. Everyone makes it, and it answers no question.
No stated question. If the reader can't tell what you were trying to learn, the project has no point.
Overbuilt models. A fancy prediction model on weak data impresses nobody. A clear SQL analysis does.
Ignoring caveats. Small samples and lucky seasons can fake a pattern. Say so.
Too much jargon. A recruiter who doesn't follow your sport should still understand the finding.

That last one is easy to miss. Explain any sport-specific term the first time you use it. If someone has to know the sport to understand your result, the project works against you.

When a sports project is the wrong choice

Skip a sports project if the jobs you want sit in a specific industry. A healthcare analyst role is better served by a healthcare dataset, and a retail role by retail data. The industry guides cover those, like portfolio projects for retail and ecommerce.

It's also fine to skip it if you don't follow sports. Interest shows up in the work, and a project you force yourself through reads flat. Pick data you can talk about for 5 minutes without notes.

The 90 days at Analyst Hive include project work with a clear structure, so you finish with something worth linking on your resume.

What people ask about sports data portfolio projects

Are sports data projects good for a data analyst portfolio?

Yes, when they answer a business-style question and show real SQL or analysis. A sports project that stops at a chart of top scorers looks like a hobby. One that ends with a clear recommendation shows the same skills as a business project.

What sports data should I use for a portfolio project?

Use one sport and one source you can understand without much explanation. Kaggle datasets and public reference sites are common starting points. Check each source's terms before you scrape or republish anything, and stay with that source for the whole project.

How many sports projects do I need?

One finished project is a good start. A second one that uses a different skill, like a dashboard after a SQL analysis, makes the portfolio stronger. A pile of similar projects adds little. Quality and a clear write-up matter more than the count.

Do I need Python for a sports analytics project?

No. SQL and a BI tool like Tableau or Power BI are enough for most of these ideas. Python helps if you pull data from an API or run a statistical test, and it's optional for an analyst portfolio.

Will employers care that my project is about sports?

They care about the skills, not the subject. A hiring manager looks for a clear question, correct SQL, and a useful conclusion. Sports works when you explain it so someone outside the sport can follow, and it works less well when it relies on insider knowledge.

How long should a portfolio project take?

A focused project takes a few weekends. Spend the first on the data and SQL, the second on the dashboard, and the third on the write-up. Stretching it much longer usually means the question was too broad, so narrow it down.

If you want a day-by-day plan for building projects like this, join Analyst Hive and work through it in order.