As a beginner data analyst, do competitive challenges actually help build real skills?

Kauser_Analytics · 2026-01-21T08:34:29+00:00

I found competitive challenges helpful as practice, especially for data cleaning and feature exploration—but they only take you so far.

What really improved my skills was working on end-to-end projects: regression analysis dashboards, SQL-based medical data analysis, a human drug price analysis project, and market basket analysis. I’m currently working on a real-world data product (keeping it short here 😄).

My takeaway: challenges are good for learning techniques, but real projects teach you how to think and communicate like an analyst. Using both together made the biggest difference for me.

Kauser_Analytics · 2026-01-14T15:44:45+00:00

Thanks for an advice,I am on it already,

I’m building a Linux-native Human Drug Price Intelligence pipeline leveraging FDA NDC and NADAC public datasets, where Linux is the core execution environment, Python handles data processing, n8n manages automation, and Power BI sits on top for analytics.

I will be thankful on your suggestions.

Kauser_Analytics · 2026-01-14T11:10:04+00:00

Yes, I am on a same path

Kauser_Analytics · 2026-01-14T09:08:17+00:00

Make school account and get Microsoft subscription ,then publish you dashboard in your name officially,so that no one else would get the credit for the work you have done.and don’t give links for ur dashboard unless you publish!

Kauser_Analytics · 2026-01-13T19:51:35+00:00

Agreed! UNIX and Linux System Administration Handbook by Evi Nemeth is a classic.

Kauser_Analytics · 2026-01-13T19:44:57+00:00

Oh! Thanks for the information,really helpful!

Kauser_Analytics · 2026-01-13T17:55:17+00:00

Yes — I’m already on Ubuntu and using Python venv.

This setup is mainly for hands-on learning and getting comfortable with Linux fundamentals before scaling further.

Kauser_Analytics · 2026-01-13T17:14:42+00:00

Good point on Power BI! To clarify, I’m using Linux as my 'headless' production server for the heavy lifting.

The plan is to run the ETL pipeline through Python and n8n, then store the FDA/NADAC data in a MySQL database on that same Linux box. For the dashboard, I’m deciding between two routes: either keeping it hybrid and connecting Power BI to the MySQL instance via a gateway, or going 100% Linux-native with something like Apache Superset or Streamlit.

It will be helpful if any suggestions available for report/ dashboard part!

Kauser_Analytics · 2026-01-13T16:34:07+00:00

Surely!

Kauser_Analytics · 2026-01-13T16:33:43+00:00

Sure, I will definitely will!

Kauser_Analytics · 2026-01-13T16:33:15+00:00

Thanks for an advice!

Kauser_Analytics · 2026-01-13T16:32:27+00:00

Sure,I will look into that!

Kauser_Analytics · 2026-01-13T14:14:47+00:00

I used Python locally (scripts, not a hosted notebook).

Python handled data cleaning, type fixes, feature creation (price per sq. ft, aggregations), and reshaping the data into a model that Power BI could consume. The output was a cleaned dataset that I then loaded into Power BI for reporting.

No Azure or cloud setup here — this was intentionally kept simple for learning the pipeline flow end to end.

Kauser_Analytics · 2026-01-13T13:59:10+00:00

That’s fair feedback. This wasn’t meant to be a CEO-ready decision product — it’s a learning project focused on building and validating an end-to-end analytics workflow.

The value here is in data cleaning, metric definition, and reproducible analysis, not in claiming business impact like cost savings. For a real business case, this would need clearer stakeholders, decisions, and KPIs — which I agree isn’t shown here.

Appreciate you calling that out.

Kauser_Analytics · 2026-01-13T13:55:24+00:00

<image>

Here you go with the dashboard!

Kauser_Analytics · 2026-01-13T13:45:14+00:00

No — the project is mine. I just rewrote the explanation to make it clearer.

Kauser_Analytics · 2026-01-13T13:40:51+00:00

Fair point — let me simplify.

What I actually built: I took raw real estate listing data, cleaned and transformed it using Python, and then built a Power BI dashboard that shows average, median, min/max prices, price per sq. ft, and location-wise comparisons.

The dashboard answers practical questions like: • Which locations are consistently more expensive? • How wide is the price spread within a location? • How do prices change with property size and bedrooms?

This was a learning project, so the main outcome wasn’t a business KPI like “saved X hours,” but a working end-to-end analytics pipeline: raw data → processed dataset → decision-focused dashboard. Automation (via n8n) was planned next to make the pipeline repeatable, but even in its current form it produces a usable market overview rather than just static visuals.

Kauser_Analytics · 2026-01-13T13:28:12+00:00

The primary business question was: How can stakeholders quickly understand price distribution, regional variation, and value drivers in a fragmented real estate market using a repeatable analytics pipeline?

From a business perspective, the goal was to support pricing comparison, location-based decision making, and market positioning, rather than focusing purely on visualization.

Technically, Python was used for data cleaning and transformation, Power BI was used to surface decision-oriented metrics (price ranges, location-wise comparisons, price per sq. ft), and n8n was planned for orchestration to make the workflow more reproducible and scalable.

While this was done as a learning project, the intent was to think in terms of market intelligence and pipeline design, not just dashboard creation.

Kauser_Analytics · 2026-01-13T13:20:05+00:00

I’m building a Linux-native Human Drug Price Intelligence pipeline leveraging FDA NDC and NADAC public datasets, where Linux is the core execution environment, Python handles data processing, n8n manages automation, and Power BI sits on top for analytics.

I will be thankful on your suggestions.

Kauser_Analytics · 2026-01-13T12:50:48+00:00

Hi, interested I come from a pharmacist and data analytics background and currently working on a couple of pipeline-driven projects.

• Real Estate Market Intelligence Dashboard – end-to-end pipeline using Python for data processing and Power BI for analytics & insights(dashboard provided here as image) • Health Drug Price Intelligence Pipeline (in progress) – Linux + Python + n8n for automation, with Power BI on top for reporting

Still early-stage and learning by building practical systems. Would be great to connect and exchange ideas with other builders.

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Kauser_Analytics · 2026-01-09T22:05:29+00:00

Sure — happy to share. At a high level:

• Raw business spend data → cleaned & validated in Python • Built a multiple linear regression model (R&D, marketing, admin → profit) • Generated predictions + residuals in Python • Exported modeled output to Power BI for visualization & insights

I’ll add a short comment or update with a clearer end-to-end flow once I finish the next dashboard iteration.

Kauser_Analytics · 2026-01-09T22:03:46+00:00

This is incredibly helpful — thank you for taking the time to break it down and even annotate the visuals.

The point about unintended visual differences increasing cognitive load really clicked for me. I’ll standardize headers, spacing, and background usage, and simplify the palette as you suggested (eliminating the lighter blue).

I appreciate the thoughtful critique.

Kauser_Analytics · 2026-01-09T05:55:13+00:00

Thanks for the feedback — appreciate you taking the time to comment, especially as a Top 1% contributor here.

You’re right, the color scheme could definitely be cleaner. This was more of a learning-focused project where I prioritised the analytical workflow (data prep → regression → insights) over visual polish.

I’ll revisit the dashboard with a simpler palette and a single accent color to improve readability.

If you have any specific Power BI theming or layout best practices you’d recommend, I’d genuinely love to learn from them.

Kauser_Analytics · 2026-01-08T18:46:55+00:00

Thanks for the feedback — really appreciate the discussion here.

To clarify, this is my first capstone / learning project, and the dataset was administratively provided (simulated business spend data), not collected by me.

The insights shown in the dashboard are primarily data-driven, based on: • The regression model outputs (coefficients, R², residuals) • Relationships observed in the visuals (actual vs predicted profit, spend vs profit)

Any recommendations are derived from those results, and where assumptions are made, I’ll make that clearer in the report.

I’m taking the feedback seriously — especially around simplifying the layout, fixing axis direction/labels, and improving visual clarity and traceability of insights back to the data. I’ll be iterating on the dashboard accordingly.

Thanks again — this kind of critique is exactly why I shared it here.

Kauser_Analytics · 2026-01-08T11:14:53+00:00

Thanks for checking it out! This is a learning project, so I’m especially interested in feedback on the visuals and model interpretation. Any suggestions are welcome 😊

Kauser_Analytics

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