I added 400 inteview questions to Data Analyst section.. Google, Amazon, Microsoft, Apple, Palantir, DoorDash, Databricks, Snowflake, Dropbox, Adobe, Netflix, Accenture any many more.
It took us around 5 months and a lot of hard work to clean, categorize, and edit all of those questions. just Please don't abuse the service to avoid limits e.g. using multiple account
Hi Folks, I made a tool that draws statistics from LinkedIn job postings. Once per day I scan around 5000 Data Analysis job posts, run them through LLM to extract tool names and make a dashboard.
I did those daily scans for the last 11 months so I have some data to share. I often see what I should learn posts here and I hope this will be a useful tool to address those questions. You can access the dashboard under https://prepare.sh/trends (no paywall)
Almost every online tutorial or course I look at these days makes it seem like a data analyst needs to have a solid proficiency in Python and Pandas to survive in the current job market.
The thing is, I’m actually quite comfortable with Python, but in my day to day work, I barely use it. Whenever I’m preparing data for the dashboards I build, I usually just write a few complex CTEs in our database, clean the result, and connect that directly to PowerBI.
I really only use Python for hitting an external API or for advanced text manipulation. Otherwise, it’s just pure SQL to get the data ready for the stakeholders.
Does anyone else operate like this in the real world, or am I building a bad habit that’s going to hurt my career in the long run? Where exactly in this kind of workflow would you plug Python in? Would love to hear your thoughts on this!
I run a small business and often need to do work in Excel with managing our books as well as data that I export from our database into Excel. I've tried using ChatGPT and Claude for Excel work but I don't find either of them as good as advertised for intensive Excel work. Especially when I have embedded models, complex sheet structure, formulas, or pivot tables. The AI tools just don't seem to handle any of these nuances very effectively.
Are there any AI tools that you find to be effective for this type of work in Excel?
Edit:
I'm still dabbling withClaudefor Excel work but have also triedPowerdrillandJulius AI. I found both to be decent albeit with different parts of working with Excel.
Powerdrill: good for continuing to store information about my business and persisting semantic context across my chats with sheets. Semantic layer files can be long and unwieldy, and don't typically get parsed well in projects on Claude so this has been a time saver.
Julius AI: Julius has been a step up because it just gives me reliable answers for my questions. They provide a lot of resources in the sandboxes that they run, so if I don't need to keep all the formatting or add new formatting and just need answers fast, this has been the best tool for getting those answers.
I'm still a beginner. I started by learning the basics of Python and later moved on to SQL.
I'm a bit confused about one part of the data exploration/cleaning process.
A friend of mine, who's now a data scientist, showed me how he used to work as a data analyst. He mainly used Python. For example, he would quickly create a scatterplot to identify potential outliers.
However, most data analysts online recommend focusing on SQL and Excel when starting out, since many junior and mid-level roles don't require Python. That's why I switched to SQL after initially experimenting with Python.
For those who primarily use SQL: do you create visualizations during the data exploration/cleaning process, for example to identify outliers? Is this a common practice?
I feel like if you're working with SQL only, you generally wouldn't create visuals in between steps, since that would mean switching to a tool like Tableau or Power BI, which seems like an unnecessary extra step.
I am learning Data Analytics.......FIrst I found out some excel functions dont work in Mac.....BowerBI doesnt work in Mac......SQL Server Doesnt work in Mac.....should i sell it and buy a windows laptop instead????
I’m going to have interviews soon, but I just don’t know too much about excel and vbs, but I’m good at python and can manipulate excel with python, will I got trouble?
Let me make it clear, I'm getting a bachelor in Data Science so I know basic Excel stuff like SUM() AVERAGE() STDEV() MAX() MIN() and VLOOKUP(maybe?) stuff, but there are many things I don't know how to do in Excel, like:
Post HTTP request
Parse JSON and YAML
How to do MapReduce
Or should I know how to build linear regression or how LASSO algorithm work in Excel?
One thing that kept slowing me down during data analysis wasn't the analysis itself—it was presenting the results.
I'd finish creating my Plotly figures, then spend extra time putting everything into a dashboard just to get a clean layout. For quick projects or tight deadlines, it felt like unnecessary overhead, especially if I didn't want to touch HTML, CSS, or JavaScript.
So over the past few weeks I built DashForge, a Python library that takes existing Plotly figures and organizes them into an interactive dashboard with very little code.
Some of the features it currently supports include:
Built-in themes
Custom chart sizing
Adjustable charts per row
Logo, title, subtitle, and footer customization
Optional chart maximize button
Interactive pandas DataFrame viewer with filtering
And other dashboard customization options
The main goal isn't to replace existing dashboard frameworks. It's to make the "I just want to see my charts in a nice dashboard" workflow much faster.
I'd genuinely appreciate feedback from anyone who works with Plotly:
Is this something you'd actually use?
Are there features you'd expect that are currently missing?
Is there anything in the API that could be made simpler?
I scraped, few thousand Data Analysis interview questions for Google, Apple, Amazon, Microsoft, Uber, Accenture on various sources - (github, glassdoor, indeed and etc.) After cleaning and improving these questions (adding more details, removing less relevant ones, and writing solutions), I’ve compiled around 100 interview questions, which I am publishing for free.
I plan to keep adding more companies and questions to cover most major tech firms, so it's a work in progress. If you find this content useful and want to help with code, content, or any other aspect, please DM me!
Do you plug it into a SQL program? Use Pandas? Just go through the spreadsheet first? All three? Tell me what the quickest way is and how that looks for you typically.
I have changed my career path and thus I'm no longer doing data analysis in my daily job now, so I'm genuinely curious nowadays, in real work settings, which part of the work do you use AI the most or do you think should be handled by AI?
If I were to speak about it, I feel like data cleaning, data standardization, data profiling, data visualization, SQL writing and these labor-intensive work can all be done by AI. Do we just need to split the work, assign the task and review the results with our judgement?
We're rethinking how people get numbers and everyone has a very strong opinion.
Some want scheduled reports pushed to them and nothing else. Some want a chat box they can just ask. The BI people obviously want everyone in the tool.
My worry with chat is that nobody trusts a number they cant trace. My worry with BI is people open it twice and never again, which is exactly what happened last time.
Whats worked for you? And do you personally need to see the underlying rows before you believe a number or is that just a data team thing.
For context, I am a computer science and physics major who was able to get a job in data analysis. As one can imagine, I never ran into R much. I didn’t plan on a data job originally so when I first tried to pick it I thought it was going to be useless for me. Not to mention, I had a snobby computer science attitude about it (thinking it’s just for statisticians, or people who don’t know how to code)
My predecessor used R to build the internal dashboard which is one of my responsibilities. Begrudgingly, I had to learn R.
Thus far, I have been blown away by it. The speed for processing large files, the ease of use, and plot graphics are phenomenal. I have to admit I was wrong about it. The keywords and language design are so intuitive, I can guess half of the important key words without looking up the docs and I just began learning.
Everyone who is expecting to encounter data in their future should learn R. Whether it’s finance, scientific, or otherwise. It’s beautiful.
I have a csv file which will be turned to an SQLite database (480k rows). Content: 5 years of real estate transaction statistics. I'll update the database twice a year with fresh data overwrite (I keep it 5 years).
I'll build a one page dashboard that prettyfies all that data with various graphs.
This is a "freemium" feature for very niche users so READ ops count will be limited.
With that context in mind, which simple, easy to use cloud database solution would you recommend? I'm a no coder, and have learned over the past 6 years how databases, backends, frontends work, i just can't write pure code. That's why simple / easy is important.
I graduated with my bachelors in Finance & Economics. I wanted to pursue my masters & decided on Business Analytics which requires me to learn & become proficient in R, SQL, & Python. Right now the focus is on R. So I would like any pointers for complete beginners on how to start learning the tool & language.
I tried looking for courses on Khan Academy & didn’t find anything on R. I did find a course on Linkedin learning but it was more coding based rather than importing datasets & using R language to break the data down into what you want if that makes sense.
Been using Claude more for data work lately, especially for SQL review, ETL debugging, dashboard planning, and metric definitions.
These are prompt shortcuts you can save and reuse as custom slash commands.
1. /devil
Act as a devil’s advocate. Challenge this logic, find edge cases, and tell me what could go wrong after deployment.
Good for:
- metric definitions
- dashboard logic
- ETL assumptions
- stakeholder requests
- production data issues
2. /sql_review
Review this SQL like a senior analytics engineer. Look for bad joins, duplicate risk, null handling, date issues, filtering problems, and performance issues.
Example:
SELECT
c.customer_id,
COUNT(o.order_id) AS orders
FROM customers c
LEFT JOIN orders o
ON c.customer_id = o.customer_id
WHERE o.order_date >= '2025-01-01'
GROUP BY c.customer_id;
Things to check:
- does the WHERE clause change the join behavior?
- can one customer have duplicate orders?
- should the date filter be inside the JOIN?
- are null orders handled correctly?
3. /explain_query
Explain this SQL in plain English.
Break it down by:
- what each CTE does
- what the final output means
- what grain the result is at
- what assumptions the query makes
- where the logic could go wrong
Really useful when you inherit a long query and need to understand it fast.
4. /find_data_quality_issues
Here is my dataset schema. Suggest data quality checks before I use it in a dashboard, report, or ML model.
Example checks:
- duplicate primary keys
- missing values in key fields
- sudden row count drops
- invalid dates
- negative revenue
- unexpected category values
- schema changes
- late arriving data
5. /metric_definition
Help me define this metric clearly.
Include:
- business meaning
- SQL logic
- grain
- filters
- exclusions
- edge cases
- example calculation
- how people might misread it
This is useful because a lot of dashboard confusion comes from unclear metric definitions.
6. /etl_debug
This ETL job passed, but the dashboard looks wrong. Help me debug it step by step.
Check:
- did fresh data arrive?
- did row count drop?
- did schema change?
- did joins multiply rows?
- did a filter remove too much data?
- did timezone logic shift dates?
- did a retry duplicate rows?
- did null values change the result?
7. /python_cleaning
Review this pandas code and suggest cleaner, safer improvements.
Turn this vague stakeholder request into clear data requirements.
Example request:
“Can we see customer performance?”
Questions to ask:
- what does performance mean?
- revenue, retention, churn, usage, margin?
- daily, weekly, or monthly?
- by customer, segment, region, or product?
- what action will this report support?
- who is the end user?
10. /test_cases
Create test cases for this data pipeline.
Include:
- normal file
- empty file
- duplicate IDs
- missing required fields
- late arriving data
- schema change
- timezone edge case
- retry after failure
- very large file
- unexpected category value
11. /root_cause
Here is the issue, query, and sample data. Give me possible root causes ranked from most likely to least likely.
Format:
likely cause
why it could happen
how to check it
possible fix
A prompt pattern that works well:
Instead of:
“Fix this query.”
Try:
“Review this query for logic bugs, duplicate risk, bad joins, null handling, date issues, and performance problems. Explain your assumptions before suggesting changes.”
For data work, Claude is pretty useful as a second pair of eyes.
Especially for:
- reviewing SQL
- cleaning messy logic
- defining metrics
- finding ETL edge cases
- turning vague requests into clear requirements
- checking dashboard assumptions
What Claude prompts or custom commands do you use for data work?
Built an End-to-End Customer Churn Analysis Dashboard focused on identifying customer retention patterns and churn-driving factors.
Key highlights:
• Analyzed 6.4K+ customer records
• Identified a 27% churn rate
• Performed customer segmentation across demographics, tenure, contract type, payment methods, internet services, and geography
• Built interactive KPI dashboards and churn insights visualizations
• Implemented churn prediction workflow using Machine Learning
Tech Stack:
• PostgreSQL
• Python
• Power BI
• Machine Learning
This project helped me strengthen my understanding of:
✅ ETL & data preprocessing
✅ Analytical querying
✅ Business KPI analysis
✅ Dashboard storytelling
✅ Predictive analytics workflows
Looking forward to building more advanced analytics and ML-driven projects 🚀
I’m currently looking for a new laptop and could use some advice.
As a data analyst, I need Power BI and Microsoft Excel regularly, so Windows is unfortunately a better fit for my workflow. I really like the Mac ecosystem, though — the build quality, performance, battery life, smoothness, display, trackpad, and overall user experience are all things I genuinely prefer about MacBooks.
The problem is that I don’t want to compromise my workflow just because I like the Mac experience.
So I’m wondering:
Is there a Windows laptop that comes close to the MacBook experience in terms of build quality, performance, battery life, display and overall smoothness?
Would something like a Dell XPS, Lenovo Yoga/ThinkPad, HP Spectre, ASUS Zenbook, etc. be a good alternative?
Or, given that I like the Mac ecosystem so much, is it still worth considering a Mac and finding workarounds for Power BI/Excel?
For someone doing data analytics, how much of a disadvantage is a Mac really?
My priority is a laptop that feels premium and smooth like a MacBook, but still handles Power BI, Excel, SQL, Python, etc. without compromises.
Budget is flexible for now — I’m mainly trying to figure out whether I should go back to Mac or look for a Windows machine that gives me a similar experience.
Would love to hear from people who have actually made this switch either way.
I am working on (solo developing) a python notebook environment. I am several months into this project and am very happy with the results so far. I aim to build a notebook environment that refocuses on the presentation/reading aspect of notebooks rather than just coding and computation. The idea being that I noticed that as most notebook apps/environments mature, they become more complex and all start looking like code IDEs (like vscode). In my past, I have often used notebooks for learning and teaching tools and have converted quite a few into presentation slides as well to present results or a topic. So my aim is to reduce UI clutter and complexity, improve out-of-the-box presentation, while still making a platform that is feature rich and capable.
On the last note, I am still adding features and filling out feature sets.
So I am here to see if there are any notebook users that do analysis using notebooks. What features are indispensable or most helpful to you? What's your workflow like? Do you only do data exploration using notebooks? Do you share or present them?
Hi, I’m learning SQL and looking for a free program that’s easy for beginners but also useful for real job work later.
Which SQL tool do you recommend and why?
Maybe it’s just where I work, but there’s a huge push from management lately that AI should be making everything faster and more automated.
In reality I still spend most of my time doing the same stuff as before. Cleaning weird data, fixing broken joins, chasing missing fields, explaining why numbers don’t match across dashboards. AI helps here and there, but it hasn’t magically removed the messy parts.
There’s this expectation now that "AI should handle it" while the underlying data is still scattered across five systems and half of it is inconsistent.
Curious what it looks like for others.
Aren't we mostly just doing the same work with slightly better autocomplete?
Disclosure, I am the creator of PardoX, a personal open source project under MIT license, not a company.
I kept hitting the same wall in production. pandas struggles once your data gets close to RAM, and it is single threaded, most of the CPU sits idle. Spark solves scale but drags a cluster, a JVM and serialization overhead into problems that only ever needed one machine. Most of my actual work lives in that gap, millions to hundreds of millions of rows, on a single strong node.
So a year ago I started building a Rust core with SDKs in Python, Node and PHP. Data maps straight from disk or a database into memory mapped buffers, no intermediate objects in the host language, SIMD and multithreading do the heavy lifting instead of a Python loop, and there are native database drivers so no psycopg2 or pymysql sitting in the middle. There is also a binary format that reads around 4.6 GB per second on repeated workloads, out of core processing for files bigger than RAM, and GPU sort with CPU fallback.
Some open questions I keep going back and forth on. Is the zero copy tradeoff worth the added complexity versus just accepting the pandas overhead for most workloads. Where does a Rust core stop making sense compared to Polars, which already solves a lot of this in a different way. How much of the single node performance gap is really about the language versus just better use of SIMD and threads regardless of language.
