Analyze Your Spotify Data: A Beginner Analytics Project

1Analyze Your Spotify Data: A Beginner Analytics Project

This is a beginner analytics project where you request your own Spotify listening history, load and clean it, explore your habits, and visualize the patterns — practicing the complete data analysis workflow on data you actually care about. Because the dataset is personal, every insight is genuinely interesting, which makes the learning stick.

You will touch every stage a real analyst goes through: acquiring data, wrangling it into a usable shape, exploring it to find patterns, visualizing those patterns, and interpreting what they mean. And because it is your own listening history, you will immediately know whether a result looks right, which is a great way to catch mistakes.

No prior experience is required. You can do this in a spreadsheet, in Python with pandas, or in a BI tool — the concepts are the same. This guide walks through the whole project step by step and ends with a personal, portfolio-ready piece of work.

2Why This Is a Great First Project

Beginner projects often use generic datasets you have no feel for, which makes it hard to tell whether your analysis is right. Your Spotify history solves that: you know your own habits, so when the data says your most-played artist is someone you rarely listen to, you immediately know something went wrong in your processing.

It is also motivating. Answering questions like which song you played most this year, or how your listening shifts between weekdays and weekends, is genuinely fun. That motivation carries you through the less glamorous parts — cleaning and shaping data — which is exactly the skill employers value most.

3Step 1: Request Your Spotify Data Export

Spotify lets you request a copy of your personal data for free through your account's privacy settings. You ask for your data, and after a waiting period Spotify emails you a downloadable archive. For listening analysis, the extended streaming history is the richest option, though the standard export also works for a first pass.

The export arrives as a set of files. Your streaming history comes as JSON — a structured text format where each play is a record with fields like the track name, artist, the timestamp it was played, and how many milliseconds you listened. Understanding this structure is the first real analyst skill this project teaches: knowing what shape your raw data is in before you touch it.

4Step 2: Load and Combine the Files

Your streaming history usually spans several JSON files, each covering a slice of time. The first task is to load them and combine them into one dataset. In Python with pandas, you read each JSON file into a data frame and concatenate them; in a spreadsheet you would import and stack them; in a BI tool you would append the queries.

Whatever tool you use, the goal is identical: one table where every row is a single play and every column is an attribute of that play. This is the tidy-data principle — one observation per row, one variable per column — and getting your messy multi-file export into that shape is the foundation everything else builds on.

5Step 3: Clean and Prepare the Data

Raw data is never analysis-ready. Cleaning your Spotify history means handling a few predictable issues so your later results are trustworthy. This is where beginners learn that most analytics work is preparation, not fancy modeling.

The most important step is turning the timestamp text into a real date-time value so you can extract the hour, day of week, and month. You will also want to convert the milliseconds-played field into minutes for readability, and decide how to handle very short plays — a track skipped after two seconds probably should not count as a real listen.

• Parse the play timestamp into a proper date-time so you can pull out hour, weekday, and month.
• Convert milliseconds played into minutes for easier reading and charting.
• Filter out very short plays (for example under 30 seconds) if you only want genuine listens.
• Check for and handle missing values in track or artist fields.
• Standardize text so the same artist is not split by inconsistent capitalization or spacing.

6Step 4: Explore and Answer Questions

With a clean table, exploratory analysis begins. This is the fun part: you pose questions and use grouping and aggregation to answer them. Each question maps to a simple operation — group by a column, count or sum, and sort — which is exactly the muscle a working analyst uses every day.

Start with questions you can sanity-check against your own memory, then move to ones that surprise you. The point is not just the answers but practicing the group-aggregate-sort pattern that underlies almost all analysis.

Who are your top ten artists by total minutes listened?
What are your most-played tracks by number of plays?
How does your listening vary by hour of the day and day of the week?
Which months did you listen most, and does it match a life event you remember?
How many unique artists and tracks did you play in total this year?

7Step 5: Visualize Your Listening Patterns

Numbers in a table tell part of the story; a good chart tells it instantly. Choose the visualization that matches each question. A bar chart ranks your top artists cleanly. A line chart shows how your total listening rose and fell across the months. A heatmap of hour versus day of week reveals when you actually listen — the lit-up cells at 8am might be your commute.

Keep visuals honest and clear: label axes, sort bars by value, and avoid chart types that distort, like a pie chart with too many slices. Choosing the right chart for the question is a core analyst skill, and your personal data is a forgiving place to practice it because you can immediately tell when a chart looks wrong.

8Step 6: Interpret and Tell the Story

Analysis is not finished when the chart is drawn — it is finished when you can say what it means. For each finding, write a sentence or two of plain-language interpretation. Your listening spiked in a certain month; why might that be? Your top artist shifted over the year; what changed? This narrative layer is what separates an analyst from someone who just makes charts.

This interpretive step is also what makes the project a strong portfolio piece. When you present it, you are not showing a pile of graphs — you are telling a coherent story about a real dataset, backed by evidence, exactly as you would for a business stakeholder.

9Turning It Into a Portfolio Piece

To make this project count with employers, document it as a short writeup that walks through your process: the questions you asked, how you cleaned the data, the key charts, and what you learned. Employers care as much about your reasoning as your results, so make your thinking visible.

Because the data is personal, you may want to anonymize or keep the raw export private, and that is fine — you can share the code, the approach, and the aggregate charts without exposing sensitive detail. What you are demonstrating is the workflow and the judgment, both of which transfer directly to any analytics job.

10Frequently Asked Questions

How do I get my Spotify listening data? Request it for free in your Spotify account's privacy settings. After a waiting period of a few days, Spotify emails you a downloadable archive; choose the extended streaming history for the richest dataset.

What format is the Spotify data export? Your streaming history comes as JSON files, where each play is a record with fields like track name, artist, timestamp, and milliseconds played. You load and combine these into a single table for analysis.

Do I need to know how to code for this project? No. You can complete it in a spreadsheet, though Python with pandas or a BI tool makes larger histories easier. The analytics concepts — cleaning, grouping, visualizing — are the same in any tool.

What is the hardest part of this project? Usually cleaning and shaping the data — parsing timestamps, converting milliseconds, and combining files into one tidy table. That preparation work is also the most valuable skill you will practice.

Can I use this project in my portfolio? Yes, and it makes a strong one because the data is personal and the story is engaging. Document your process and share the code and aggregate charts, keeping the raw personal export private if you prefer.

How long does this project take? A first version takes a weekend or a few evenings. You can extend it with more questions, better visuals, or a written narrative to make it a deeper portfolio piece.

11Start Analyzing Your Own Music

Analyzing your Spotify data is one of the most enjoyable ways to learn the full analytics workflow, because every step operates on data you genuinely care about. You request and load the export, clean and shape it, explore it with grouping and aggregation, visualize the patterns, and interpret the story — the exact process a professional analyst uses every day.

You can learn each of these skills — data cleaning, exploratory analysis, and visualization — for free on SkillVeris, then apply them to this project end to end. Start with the free data analytics and Python courses and study notes, request your Spotify export today, and build a portfolio piece that is unmistakably yours.