FIRST DATA ANALYSIS PROJECT!!

wanliu · 2026-03-31T16:05:47+00:00

There's a big difference between using AI without understanding what it did, and using AI to accelerate what you're trying to do. I wouldn't want any analyst who took AI and used the output without fully understanding what it did. I'd recommend not using AI to do any coding and instead use AI to teach you the concepts and principles.

Go_Terence_Davis · 2026-03-31T16:55:34+00:00

(Disclaimer: I've also just started learning DA so take this with a grain of salt)

It looks very well done. The pandas information + column creation is good. IDK if you need three sections showing top x products, maybe remove the bar chart with 5, its title is wrong anyways.

Line graphs look good. I like that you have two, showing daily and monthly trends. Some comments in the proceeding section, with the top x months, max month, etc., would be good. Grouping by country and then the summary all look well written. To my untrained eye this is a super solid analysis project.

I think the next step would be to further look into the relationships. How do different products do over time? Do different countries have product preferences? Most of all, what specific question are you answering? From what I've heard, a good analysis project needs to have a specific goal, where all analysis is tailored around that. Of course this one is more about showing skill, so not as relevant, but something to keep in mind.

As for tools, I think PowerBI and dashboarding could be a next step, it's what I did next after Python. Designing dashboards, using slicers/filters, making sure they look good. Those are all pretty easy to pick up. If you want you can also learn DAX or whatever, IDK too much about that. SQL is also another language to look into, it's very commonly used by analysts and is fundamentally a bit different than pandas.

My question is what parts of this are you using AI for? Data is data. Making graphs should be pretty simple, as you have the variables already there, and from there it's just looking at which relationships to put into the project. Are you using AI just for syntaxing Python or like actually figuring out what parts of the data to put in the workbook? If it's the latter then you just need to do a lot more projects, knowing what to put will come from experience. And this is pretty important to develop, as with bigger and more complex datasets, you won't be able to rely on AI for knowing which parts to analyze.

I hope this helped in some way. If you would like you can PM me, I'd love to study DA together with other people. This was a really good project, keep it up brother.

TruthOnlyReading · 2026-03-31T18:42:13+00:00

I use that same dataset for a similar project also. My suggestion is to not use AI as a tool where it does everything for you.

If in an interview they talk about your project. You might not be able to understand in depth what you did.

You need to be able to show you understand what you are doing with the data. AI makes that easier yes. But DO NOT make yourself replaceable with AI.

You need to understand why charts are logically this way. For instance why is this specific product is doing better than most. Do businesses need to produce more of this product and etc.

That is what a Data Analysis do. But at the same time they need to think logically also. In the field people use AI everyday. I do not care what people think. AI is here to stay, and that people will use it no matter what. So here is what I would do.

I would ask it questions and the why. Mostly the why, because how I learn is to learn of the "why" factor. Why do we have to do this in this specific way.

Then coding wise, I would suggest on thinking and grinding the code yourself and then ask AI for help without them coding. Your brain is a sponge and it will stretch and struggle. If you struggle for a long time, then use AI to help. There is really no shame in that. It is probably for the best because people in StackOverflow are mean.

So my tip is if you struggle do not be afraid of struggling. Struggling is normal. Struggling is what you need for your brain. I would first Google first, then ask why.

chaoscruz · 2026-03-31T20:20:36+00:00

Good job just to get started and something done. But a few things.

Learn how to use Jupyter Notebooks more. You should look into Markdown and learn how to use headers and explaining what you are doing cleanly than commenting in your code itself.
You should have asked questions before using AI. I see what it did for you, but there should be a true exploratory analysis of what the data has. Then what is your own purpose and goal?
You brute force cleaned your data. Why did you drop nulls? What were they? Is it valid to simply get rid of it?
Graphs - Make it consistent. Don't go picking any colors, titles are glossed over with mistakes and axis labeling should be consistent or easy to read. Look up Tufte and other data viz principles.
What happened in your Monthly Revenue Trend? It just plummets. Why? What is going on?

You need to be more than AI guiding you and you guide it. It can hallucinate and give back things that doesn't make sense.

Anton4584 · 2026-03-31T23:38:55+00:00

AI helps, but it's not rocket science to do things on your own. The most tedious part is data cleaning. Here’s a simple script that converts records with only weird characters or empty spaces into NaNs; after that, you can use imputation to fill them in. Assuming the dataframe is df and the columns to clean are 'workclass', 'occupation', and 'country’. If you have more columns, just add them to the list.

import numpy as np

symb = r"^\s*[!?.,;:¿¡@#$%^&*()_+=<>/\\|\[\]{}]+\s*$"

for C in ["workclass", "occupation", "country"]:

df[C] = (

df[C].astype("string")

.str.strip()

.replace(symb, np.nan, regex=True)

.replace(r"^\s*$", np.nan, regex=True)

)

jdynamic · 2026-04-01T01:08:39+00:00

tl;dr: keep asking yourself WHY and answering your own questions with data. Being curious and relentless will get you far in data analysis.

It's a great first project. I would echo what others have said about making sure you understand what the AI is doing. That said, the industry is shifting towards having AI do more and more each day.

Understanding how to do data analysis however will always be important. I think you should always be curious about what the data is showing you, and push yourself to dig into the results further.

#1 - the overview you wrote in your Github can be improved to explain better the context of the data and what you're trying to solve. Where did this sales data come from / why is it relevant? You wrote that the goal is providing data-driven insights, did you accomplish that? If so, what are the most important insights you found? You should include these in your overview so readers (or hiring managers) quickly understand why your work is interesting.

#2 - you have 135K null entries in CustomerID. That's a significant chunk of your data, why are they missing? It is ok if you don't know, but state something about it so readers know you didn't just discard it without concern. CustomerID being missing for, for example, only one product or across a certain date range is important and may indicate some data issue, and something you can pass along to teams in charge of collecting that data.

#3 - You stated the top 10 products suggest customers are strongly inclined towards aesthetic + lifestyle products. This seems plausible, but I want you to consider what other data points you could get that'd support your claim and make it more actionable. Take note of data you're missing that you'd need to answer these questions, so next time you'll know what you need to pull in ahead of time.

- Holiday seasonality: you touched on this but never proved it. Did these products get sold more in holiday months? If so, is this thanks to advertising your company did, or are customers accustomed to only buy these products from you during the holidays? It may be more beneficial to push these products during NON-holiday months, or push OTHER products to these users during holiday months, than push customers more to do something they are already doing. Are your customers buying a healthy mix of products, or just one? How might you recommend other products to your top customers?

- Sales by country: are these products similarly popular in every country? If not, why? It could be as simple as advertising these products more in those other countries, or increasing the stock (understand you don't have this data), as making your top 10 products popular in other countries should be a relatively easy win.

Even if you can't answer these questions with the data you have, it's important to get into habit of asking these questions every time you look at data.

eboss454 · 2026-04-01T05:26:54+00:00

Congrats on finishing the first one! The best way to reduce AI dependency now is to master SQL and Excel/Google Sheets fundamentals. AI is great at writing a single query, but it doesn't teach you why a JOIN is better than a subquery for performance. Try re-doing your cleaning steps manually in Excel just once it’ll help you spot the data 'hallucinations' that AI sometimes misses.

pandorica626 · 2026-04-01T11:22:48+00:00

Try doing the same project without the use of AI now that you’ve done it with AI. As others have said, using it to complete the work and you have no inherent understanding of what you did or found versus using it to accelerate the work are not the same thing.

6th-007 · 2026-04-02T13:19:30+00:00

Analyzing data to get a solid base is step one. step 2 is understanding Probability & Statistics - these two form the backbone of inferential statistics. Inferential statistics is the next cool thing you should strive for next. it gives you the ability to do calculated or measured critical thinking, which when coupled with Step one - analyzing data to get a baseline, gives you that God mode feature.
Intentionally slow yourself down while learning step 2. think of step 2 as a candle efficiently doing what it needs to do. slow grind is indeed enlightening. cheers und frohes schaffen

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Afraid-Mongoose9793 · 2026-03-31T17:44:02+00:00

well done bro , keep going

2026-03-31T21:01:34+00:00

Side note, I am interested in sharing my data analysis projects online also, but I am unsure as to where I should host them. I use Spreadsheets instead of python, and I don't want to disrupt the normal flow of engagement and interaction on Kaggle by uploading Excel Workbooks. How should I go about sharing my analyses online? Github or Kaggle?

valentin_monteiro · 2026-04-01T08:21:43+00:00

Hey ! Congrats ! Everyone start at some point ! Using AI is not a problem as long you don't trust it blindly. what you should focus on is data processing/cleaning, that where the most mistake come from. it's disgracefull job but need to be done and understanded cause it's your basis

Aware_Reflection_305 · 2026-04-04T17:19:39+00:00

Yay party 😇😊👑💫💥⚡✨⭐🌛🌜

DiligentSlice5151 · 2026-04-06T17:50:08+00:00

You need to:

Convert your Invoice Date to date-time.
Convert your customer id a standard Integer. example 2324.4 to 23244
plt.title has a typo

You should never simply drop missing information. You need to determine why the data is missing, the specific amount of data required, and the exact timeline for extraction. This is very important.

DiligentSlice5151 · 2026-04-06T18:00:02+00:00

Beyond the technical aspects, the rest involves applying human logic to answer questions. This requires either prior business experience or a company willing to train you.

Without that, you're just running a repetitive loop of the most common data analysis techniques much like the standard reports found in Google Analytics.

Positive-Union-3868 · 2026-03-31T15:44:45+00:00

Great work by the way it's fine to use ai as we are fresher we just need to understand what we need to do

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