Most beginners waste months learning random tools.
Here’s the real sequence that actually lands jobs:
𝟏. 𝐏𝐫𝐨𝐠𝐫𝐚𝐦𝐦𝐢𝐧𝐠 𝐅𝐨𝐮𝐧𝐝𝐚𝐭𝐢𝐨𝐧
Start with Python + SQL. Don’t chase 5 languages.
Example: Write a SQL query to analyze sales data, then transform it into a Pandas dataframe.
Free resource: https://lnkd.in/duzDYxyW
𝟐. 𝐃𝐚𝐭𝐚 𝐌𝐨𝐝𝐞𝐥𝐢𝐧𝐠 & 𝐖𝐚𝐫𝐞𝐡𝐨𝐮𝐬𝐞𝐬
Learn how companies store data. Understand Star Schema vs Snowflake.
Think of Netflix: They model users, shows, watch history so recommendations work fast.
Free resource: https://lnkd.in/gWivy67u
𝟑. 𝐄𝐓𝐋 / 𝐄𝐋𝐓 𝐏𝐢𝐩𝐞𝐥𝐢𝐧𝐞𝐬
Learn how raw messy data becomes clean, structured data. Tools like Airflow and dbt matter more than shiny dashboards.
Free resource: https://lnkd.in/g-Wzxx9s
𝟒. 𝐁𝐢𝐠 𝐃𝐚𝐭𝐚 𝐒𝐲𝐬𝐭𝐞𝐦𝐬
Handle data at scale with Spark, Kafka, Hadoop.
Example: Twitter streams millions of tweets Kafka pipelines process them in real-time.
Free resource: https://lnkd.in/gizvbK3B
𝟓. 𝐂𝐥𝐨𝐮𝐝 𝐃𝐚𝐭𝐚 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠
AWS, Azure, GCP all have their data stacks. Learn one deeply.
Companies don’t hire tool collectors, they hire people who can deliver value.
Free resource: https://lnkd.in/dKHXFDNR
𝟔. 𝐃𝐚𝐭𝐚 𝐆𝐨𝐯𝐞𝐫𝐧𝐚𝐧𝐜𝐞 & 𝐎𝐩𝐬
Logging, monitoring, and security make you senior-level.
Example: An e-commerce site failing to mask customer PII → million-dollar fines.
Free resource: https://lnkd.in/grni-NfF
𝟕. 𝐅𝐢𝐧𝐚𝐥 𝐏𝐫𝐨𝐣𝐞𝐜𝐭 (𝐲𝐨𝐮𝐫 𝐠𝐨𝐥𝐝𝐞𝐧 𝐭𝐢𝐜𝐤𝐞𝐭)
Build an end-to-end pipeline:
Pull data → Clean it → Store it in warehouse → Expose it via API → Dashboard on top.
Free resource: https://lnkd.in/gBGCsnrx
𝐓𝐡𝐢𝐬 𝐢𝐬 𝐧𝐨𝐭 𝐚 𝐭𝐨𝐨𝐥 𝐜𝐡𝐞𝐜𝐤𝐥𝐢𝐬𝐭. 𝐓𝐡𝐢𝐬 𝐢𝐬 𝐡𝐨𝐰 𝐝𝐚𝐭𝐚 𝐞𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐬 𝐚𝐫𝐞 𝐚𝐜𝐭𝐮𝐚𝐥𝐥𝐲 𝐡𝐢𝐫𝐞𝐝.
Save this roadmap. Read it again in 6 months you’ll see the difference.
That's a wrap!!
- Python 🐍
- AI/ML 🤖
- Data Science 🐼
- SW Dev 🛠
- AI Tools 🧰
- Roadmap ❗️
Find me → Arif Alam ✔️
Everyday, I share post on above topics.