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Python Developer
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Python Developer
@PythonDvz
A place for all things related to the #python #programming #coding #webdeveloper #webdevelopment #pythonprogramming #ai #ml #machinelearning #datascience ...
United States Joined Haziran 2016
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Unpopular opinion: a clean dashboard hides the hardest part of analytics. amzn.to/43KB8mh
People see a few charts, filters, and KPIs and assume the work was mostly Power BI, Tableau, or Python.
They do not see the weeks spent fixing broken data, aligning metric definitions, and tracing why two teams report completely different numbers for the same KPI.
I see the same mindset all the time:
Learn SQL. Build a dashboard. Become a data analyst.
I understand why it is appealing. Tutorials make the process look linear. Small clean datasets behave nicely.
But that version falls apart inside a real business - where data is fragmented, ownership is unclear, and every metric carries context.
𝗛𝗲𝗿𝗲 𝗶𝘀 𝘄𝗵𝗮𝘁 𝗽𝗲𝗼𝗽𝗹𝗲 𝗼𝘂𝘁𝘀𝗶𝗱𝗲 𝘁𝗵𝗶𝘀 𝘄𝗼𝗿𝗸 𝗿𝗮𝗿𝗲𝗹𝘆 𝘀𝗲𝗲:
-- Data arrives from multiple systems with different formats, refresh cycles, and definitions
-- A "simple" revenue metric changes depending on refunds, currencies, tax, timing, and business unit
-- Analysts spend significant time cleaning, joining, and validating data before any chart exists
-- Governance, access controls, and data quality determine whether the output can be trusted
-- A dashboard has little value when nobody agrees on the metric or knows what action to take
This is the part tutorials never capture.
The difficult work is not placing charts on a screen.
It is understanding the business, defining the right questions, building reliable models, testing assumptions, and making sure the output survives real decision-making.
None of this means dashboards do not matter. They are often the final interface between data and the business.
But the dashboard is only the visible layer.
The real work lives underneath it: ingestion, cleaning, modeling, transformation, testing, governance, monitoring and finally, decisions.
Here is the honest truth:
Building dashboards is easy.
Building analytics people trust and act on is the work.
What part of the analytics process takes the most effort in your experience?
♻️ Repost if this resonates with someone in your network
English
Unpopular opinion: a clean dashboard hides the hardest part of analytics.
People see a few charts, filters, and KPIs and assume the work was mostly Power BI, Tableau, or Python.
They do not see the weeks spent fixing broken data, aligning metric definitions, and tracing why two teams report completely different numbers for the same KPI.
I see the same mindset all the time:
Learn SQL. Build a dashboard. Become a data analyst.
I understand why it is appealing. Tutorials make the process look linear. Small clean datasets behave nicely.
But that version falls apart inside a real business - where data is fragmented, ownership is unclear, and every metric carries context.
𝗛𝗲𝗿𝗲 𝗶𝘀 𝘄𝗵𝗮𝘁 𝗽𝗲𝗼𝗽𝗹𝗲 𝗼𝘂𝘁𝘀𝗶𝗱𝗲 𝘁𝗵𝗶𝘀 𝘄𝗼𝗿𝗸 𝗿𝗮𝗿𝗲𝗹𝘆 𝘀𝗲𝗲:
-- Data arrives from multiple systems with different formats, refresh cycles, and definitions
-- A "simple" revenue metric changes depending on refunds, currencies, tax, timing, and business unit
-- Analysts spend significant time cleaning, joining, and validating data before any chart exists
-- Governance, access controls, and data quality determine whether the output can be trusted
-- A dashboard has little value when nobody agrees on the metric or knows what action to take
This is the part tutorials never capture.
The difficult work is not placing charts on a screen.
It is understanding the business, defining the right questions, building reliable models, testing assumptions, and making sure the output survives real decision-making.
None of this means dashboards do not matter. They are often the final interface between data and the business.
But the dashboard is only the visible layer.
The real work lives underneath it: ingestion, cleaning, modeling, transformation, testing, governance, monitoring and finally, decisions.
Here is the honest truth:
Building dashboards is easy.
Building analytics people trust and act on is the work.
What part of the analytics process takes the most effort in your experience?
♻️ Repost if this resonates with someone in your network
English
🗄️ SQL Essential Commands Every Developer Should Know!
SQL is the language that powers data. Whether you're building websites, mobile apps, or enterprise systems, these commands form the foundation of database management.
✅ SELECT → Retrieve data
✅ INSERT → Add new records
✅ UPDATE → Modify existing data
✅ DELETE → Remove records
✅ WHERE → Filter results
✅ ORDER BY → Sort data
✅ GROUP BY → Aggregate data
✅ JOIN → Combine multiple tables
Master these essentials, and you'll be able to work confidently with almost any relational database. 🚀
#SQL #Database #WebDevelopment #Programming
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Want to get better at Data Structure and Algorithm? Here is a detailed Roadmap
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