Your First Data Analysis Project: Analyzing Spotify or Netflix Trends

Key Takeaways

A first project works best when it starts with a small question and ends with a clear explanation of what the data can—and cannot—show.

• Choose Spotify or Netflix data based on the pattern you want to explore.

• Define a question, timeframe, and measure before opening a spreadsheet.

• Check data sources, privacy, and limitations before drawing conclusions.

• Clean the records, summarize them, and choose charts that fit the question.

• Document your method and findings so another person can follow your work.

Choose a platform and define your project question

A first data analysis project Spotify listeners can complete does not need advanced code or a grand discovery. The useful work is often quieter: choosing a question, checking whether the data can answer it, and explaining the result without overstating it. Spotify listening records and Netflix viewing records offer different kinds of activity to examine, so start with the pattern that genuinely interests you. A narrow question gives the project a shape and makes it easier to decide what belongs in the final chart.

Compare the kinds of trends Spotify and Netflix data can reveal

Listening and viewing records are event data: each row may describe something played at a particular time. Depending on the fields available, Spotify data might let you explore listening duration or the artists and tracks in a personal history; Netflix records may support a look at viewing activity by date or title. Public datasets can contain different fields, so check the actual columns rather than assuming a particular metric is present. A project based on personal activity asks about your own habits, while a public dataset may describe a broader collection with its own sampling limits.

A quick comparison can help you choose without turning the decision into a research project of its own. The table below matches a possible question to the kind of fields it would need; it is a planning aid, not a promise that every download includes those fields.

Platform or source	Possible question	Fields to look for
Spotify listening history	When do I listen most?	Play timestamp or date
Spotify public dataset	Which tracks appear most often in this sample?	Track or artist name and record count
Netflix viewing history	How does my viewing vary by month?	Viewing date or timestamp
Public viewing dataset	Which titles are most represented?	Title and a clearly defined activity measure

After choosing a direction, confirm that the data actually contains the fields your question needs. A public Spotify project using Python can offer a useful example of how an analyst explores and visualizes music data; see this Spotify analysis project as a reference for the shape of that kind of work, not as a substitute for checking your own source.

Turn a broad interest into a question you can answer with data

“Understand my music taste” is a good curiosity, but it is too broad to analyze in one sitting. Try turning it into a question with a count, comparison, or change over time: “Which weekday had the most recorded listening minutes in this export?” is narrower, provided the export includes timestamps and duration. The question sets the boundaries: it tells you what records to keep, what calculation to make, and what a useful result would look like.

You can use the same discipline for topics far outside entertainment. A source about Kia wreckers, hair supplements, crane truck hire, African safari planning, or Paraguay and Uruguay residency has a different subject and evidence base from a streaming-history project. Those pages are not relevant data for this analysis; they are reminders to match each source to the question it can actually support. For another example of using numbers to test an idea rather than relying only on instinct, this guide on data-informed writing explores the value of interpreting audience data.

Set a timeframe, audience, and clear measure of success

Decide whether you are examining a week, a month, or a longer span, and write down why that window makes sense. A short period can make a tidy first project, but it may capture an unusual week rather than a lasting habit. Also consider the audience: a personal reflection can use first-person language, while a portfolio piece should explain the dataset and method for readers who do not know your routine.

Define success as a completed, reproducible answer—not a dramatic conclusion. For example, you might aim to calculate listening minutes by weekday and make one chart that answers which day had the highest total in the selected period. If you cannot tell what the measure means in one sentence, tighten it before moving on.

Find a dataset and use it responsibly

The dataset shapes the analysis, so finding one is not just a download step. You need to know where the records came from, what each field means, and whether the data is appropriate for your intended use. Personal exports can be useful for a self-study project, while public files can make it easier to share a reproducible example. Either way, keep the source description with the file and avoid treating a dataset as a complete picture of everyone’s behavior.

Explore Spotify listening history and public datasets

If you want to study your own listening, look for an official way to obtain your account data and read the current instructions supplied with it. The available files and fields can vary, so inspect the export rather than assuming it includes every detail you want. Public datasets are another route: their documentation should describe the collection, field definitions, and any restrictions on reuse. A tutorial or notebook can help you understand the general workflow, but it does not verify the quality or licensing of a separate dataset.

Keep a copy of the original data unchanged. When you begin analysis, work from a separate copy and note the source, access date, and any documentation you relied on. That small habit can save confusion later, especially when a result looks surprising and you need to check whether the issue came from your calculation or from the source data.

Use Netflix viewing history or a suitable public dataset

For a Netflix-based project, start with viewing history available through your account settings or choose a public dataset whose origin and intended use are explained. The history you can access may not include every detail needed for a particular question, and public datasets can reflect a limited collection rather than a full catalogue or audience. Check what one row represents before you count it: a title, a viewing event, and an episode are not interchangeable units.

A good fit is a dataset with a clear match between the question and the available fields. If your question is about viewing by month, dates must be usable; if you want to compare titles, the records need a consistent title field. If the information is absent or ambiguous, adjust the question instead of filling gaps with assumptions.

Check privacy, licensing, and data collection limitations

Personal viewing and listening histories can reveal private routines. Remove names, account identifiers, and other details that are not needed before sharing a project, and do not publish someone else’s history without permission. For public files, read the license and any stated attribution or reuse conditions. Keep the origin visible even when you have cleaned the data.

Be candid about how the records were collected and what may be missing. An export of one person’s account cannot establish what all listeners or viewers prefer, and a public sample may overrepresent certain titles or periods. Those limits do not make the project useless; they tell readers how far its conclusions can travel.

Choose beginner-friendly tools and organize your files

You do not need to begin with the most technical tool available. A spreadsheet can be enough for a modest dataset and a simple comparison; code becomes useful when you want to repeat a process or handle more involved transformations. Pick a tool you can explain and use carefully, then let the project—not the prestige of the software—determine when to learn something new. The Microsoft Excel masterclass covers spreadsheet organization and data analysis, including PivotTables and charts, if structured learning would help you build those skills.

Decide between Excel, Google Sheets, and Python

Excel or Google Sheets can make rows, filters, formulas, and simple charts visible as you work. Python can be a good choice when you want a repeatable script or need to work through a series of transformations, but it asks you to learn code and a working environment as well. For a first analysis, the tool that helps you finish accurately is usually the right one. Unicademy offers a Microsoft Excel masterclass that covers spreadsheet organization, formulas, PivotTables, and data visualizations; use a course to build the tool skills you need, not as a reason to make the project larger than it is.

Whichever tool you choose, keep the raw file separate from your cleaned data and your final output. A simple folder structure might include the original export, a working spreadsheet or notebook, charts, and a short notes file. Give files descriptive names with dates or versions, so “cleaned-data” does not silently replace the only copy of your source.

Structure your data so each row and column has a clear meaning

For a typical activity table, one row should represent one event, and each column should describe one field: date, title, artist or category, and duration, for example. The exact fields depend on the source. Avoid merged cells, decorative blank rows, and multiple values packed into one cell; those choices make sorting and calculations harder and can disguise errors.

Before you calculate anything, write down the unit represented by each row and the meaning of each measure. If one row is an episode and another is a whole series, a count of rows may not answer a question about titles. A clear structure makes the later summaries easier to check and helps another person understand the file without asking you to translate it.

Keep source files, working files, and notes organized

Organization is part of reproducibility, not housekeeping for its own sake. A compact project folder can preserve the source, the cleaned version, the chart, and a readme that records the question and decisions. For a beginner, this short sequence is often enough to keep the work traceable:

• Save the downloaded or exported source as read-only.

• Make a separate working copy for cleaning and calculations.

• Keep a brief note of field definitions and changes made.

• Store charts and a short project summary with the analysis.

Once those pieces are in place, you can return to an earlier step without guessing which file was edited. That is especially helpful when you notice a duplicate or change the date range and want to repeat the summary consistently.

Clean and prepare the data

Cleaning is where small assumptions can quietly change a result. A spreadsheet that opens without an error is not necessarily ready to analyze: dates may be text, durations may use inconsistent units, and empty cells may have different meanings. Work through the fields that matter to your question, and keep a note of choices that could affect the totals. The aim is not to make the data look perfect; it is to make the analysis understandable and defensible.

Inspect columns, formats, missing values, and duplicates

Start by checking the column names and a sample of rows. Look for dates stored in mixed formats, blank values, unexpected symbols, or repeated records. A duplicate is not always an error—two separate plays of the same song may be genuine events—so decide whether a repeated row is an accidental copy or a valid activity record before removing it.

Count missing values in the fields required by your question. If a timestamp is blank, for instance, that record may not fit a time-of-day summary, but it could still be useful in a title count. State how you handled such cases rather than quietly deleting rows until the chart looks tidy.

Standardize dates, categories, and other inconsistent entries

A date written as “03/04/2026” can mean different things depending on the format, so confirm the source convention before converting it. Likewise, capitalization or punctuation differences can split one artist or title into two categories. Standardize only when you have a sound reason to treat entries as the same; keep the original value available if a change is not obvious.

Record each meaningful change in a notes file or a reproducible sequence of steps. This does not need to be elaborate. A brief line saying that dates were converted to a consistent format or that a verified naming variation was normalized is more useful than a polished chart with no account of how it was made.

Create useful fields such as listening time or viewing month

A new field can make the question easier to answer. If the data provides duration, you might convert it into minutes; if it provides timestamps, you might derive a month or weekday. Only create fields from information you actually have, and write down the calculation—for example, whether you grouped by the date an event began or by a different date supplied in the source.

Try one derived field at a time and check a few records by hand. If a duration looks implausible or a month assignment seems off, go back to the original row and inspect the conversion. A transparent transformation is preferable to a clever one that you cannot explain.

Analyze trends and check what the numbers show

With the data prepared, begin with simple summaries before looking for a story. Counts, totals, and averages can show where activity is concentrated, but each one answers a different question. Keep the original project question in view, and make sure the measure you calculate matches its wording. A neat result is only useful if the calculation is sound and its limits are visible.

Summarize activity by time, genre, artist, or title

Group records by the dimension that fits your question: day, month, artist, genre, or title. Depending on the dataset, you might count events or add duration values. Be explicit about which you chose. “Most played” could mean the most event records, while “most listening time” requires a duration measure; the two may point to different results.

A spreadsheet PivotTable can make grouped summaries easier to inspect, and a small Python workflow can do similar repeatable work. The right calculation is the one you can validate against a few source rows. If you are learning the technique alongside the project, Unicademy’s Microsoft Excel masterclass includes analysis with PivotTables, sorting, and filtering; the course content is relevant when that is the tool you have chosen, while the result still depends on your data and decisions.

Compare patterns across days, months, or content categories

A comparison becomes more informative when groups have a fair basis. A month with more recorded days may naturally have a larger total, so you may need to compare average activity per day or describe the unequal coverage. Similarly, a category with only a handful of records should not be treated as directly comparable to one with hundreds without acknowledging the difference.

Look for a pattern, then test whether it remains visible when you check the underlying records or use a slightly different summary. Do not turn a few high-activity days into a claim about a permanent habit. If you write about the result for an audience, explain what the numbers support and what remains interpretation; the audience-data guide offers a related perspective on using quantitative evidence to check an idea.

Check for small samples, outliers, and unsupported conclusions

A spike might be an unusual day, a repeated record, or simply a real event. Inspect it before deciding. Small samples can make percentages jump sharply, and missing periods can make a trend look more complete than it is. A descriptive result such as “this export contains more recorded listening on Saturday” is safer than a claim about why that happened or what listeners generally prefer.

Try to separate observation from explanation. You can say what the data shows, then offer a possible reason as a hypothesis—not as a finding—unless you have additional evidence. That distinction makes a first project more credible, not less interesting.

Choose visualizations that make your findings clear

A chart should make the answer easier to see, not add decoration to an analysis that is still uncertain. Choose the visual form after you know what comparison or change you want to communicate, and keep the display tied to the same measure used in your calculations. A chart that looks polished but hides its units or timeframe asks readers to guess. Good labels and a direct title do much of the explanatory work.

Match chart types to comparisons, changes, and distributions

A line chart can show change over time when the dates are ordered and the intervals are meaningful. A bar chart is often easier for comparing categories such as weekdays or artists. A distribution chart can help show how values are spread rather than reducing everything to one average. These are starting points; the data and question should determine the final choice.

Unicademy’s Microsoft Excel masterclass covers professional charts and data visualizations, alongside spreadsheet tools for analysis. If you use Excel, practice making a chart from a small, verified summary first; choosing a chart type cannot repair a measure that does not answer the question. For a walkthrough of a Spotify analysis using Python and visualization techniques, the Spotify project tutorial can provide another example of a workflow to study critically.

Label charts so readers can understand them without guessing

Give the chart a title that states what is being compared, and label axes with the measure and unit. Include the date range and define terms that could be interpreted differently, such as “play” or “viewing event.” If categories have been grouped or excluded, add a short note so a reader knows what the display represents.

Keep colors consistent and avoid unnecessary effects that draw attention away from the pattern. If the chart relies on a filtered subset or a transformed measure, say so nearby. The reader should be able to understand the basic comparison without opening your working file.

Connect each visualization to your original project question

Before adding a chart to the final project, ask what sentence it helps you answer. If the question is about activity by weekday, a chart of top titles may be interesting but it is not the answer. You can include an extra observation if it adds context, but distinguish it from the primary finding rather than letting the project wander.

A concise explanation can connect question, measure, and result: “I compared recorded listening minutes by weekday for this export; Saturday had the highest total in the selected period.” The sentence is appropriately bounded, and the chart can make that comparison visible. Add a brief note about missing dates or unusual records if those affect how readers should interpret it.

https://www.youtube.com/watch?v=n5PYzQu3HaE

Turn your first data analysis project into a portfolio piece

A portfolio project does not need a complicated model or an extravagant design. It needs a clear question, a trustworthy explanation of the data, and evidence that you can make careful decisions. A reader should be able to follow the path from raw material to chart and understand why you made each choice. That is practical proof of skill, whether the project supports a current role or a new direction.

Explain your process, tools, and key findings

Start with a short project overview: the question, the dataset, the tool, and the measure you used. Then describe the main cleaning decisions and show one or two findings that directly answer the question. If you used a spreadsheet, code, or both, name the tools and explain their role plainly rather than listing software for its own sake.

A project can also demonstrate how you handle uncertainty. Say what surprised you, what you checked, and what you chose not to claim. That thoughtful boundary-setting helps the work feel like analysis rather than a chart pasted into a portfolio. If you want to build practical skills, choose learning that supports the next step you need, then apply it to a project you can explain.

Document data sources, limitations, and steps for reproducing the work

Include where the data came from, the period it covers, and any license or privacy considerations relevant to sharing it. Describe the important transformations and how a reader could repeat the summary, while removing sensitive details from personal exports. If the original file cannot be shared, explain why and provide a small, non-sensitive description of its structure instead.

A reproducible project is not necessarily a public copy of private records. It is a clear account of the method, inputs, and limitations. For a spreadsheet project, this may mean sharing a sanitized working file and notes; for a coded project, it may mean documenting the sequence of steps and the expected input fields.

Identify a follow-up question to guide your next analysis project

Finish by naming one question that arose from the work but could not be answered with the current data. Perhaps you would want a longer period, more complete records, or a different grouping. A follow-up question shows that you understand the boundary of the first project and have a reason to keep learning.

If your next goal is to strengthen spreadsheet analysis, the Microsoft Excel masterclass from Unicademy covers practical spreadsheet skills including formulas, PivotTables, dashboards, and data visualizations. Build from the skill that your project exposed as a gap, rather than trying to learn every tool at once.

Conclusion

A first analysis of Spotify or Netflix trends can be small and still demonstrate real care: ask a question the data can answer, preserve the source, clean only what you can justify, and explain the result with its limits. The portfolio value comes not from making a sweeping claim, but from showing how you moved from records to a clear, honest answer—and what you would investigate next.

Frequently Asked Questions

What is a good first data analysis project?

Choose a dataset with clear fields and ask one focused question, such as how activity varies by weekday or month. A small project with a well-explained method is a stronger start than a broad question that the data cannot resolve.

Should I analyze Spotify or Netflix data first?

Pick the platform whose activity interests you and whose available records contain the fields your question needs. The best choice is the one that lets you complete a clear, manageable analysis.

Do I need to know Python to analyze streaming data?

No. A spreadsheet can be enough for a small dataset and simple summaries. Python may help with repeatable or more involved workflows, but it is not a prerequisite for learning the basics of analysis.

What should I do if my dataset has missing values?

Check which fields are missing and whether those records are necessary for your specific question. Document whether you exclude, retain, or handle them another way, and explain how that decision affects the result.

How can I tell whether a trend is meaningful?

Check the size and coverage of the sample, inspect unusual values, and compare the summary with the underlying records. Describe the pattern only within the period and data you actually analyzed.

Which chart should I use for my project?

Use a line chart for an ordered change over time, a bar chart for category comparisons, or a distribution chart when spread matters. Label the measure, units, and timeframe so the chart can be read without guesswork.

Can I include personal listening or viewing history in a portfolio?

You can share a project built from personal activity if you remove sensitive details and respect applicable terms and privacy concerns. Explain the data source and limitations, and do not present one person’s records as representative of a wider population.

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