29.2K
SUData Engineer vs Data Scientist: do you you know the difference?
#datascientist #dataengineer
@sundaskhalidd
#Data Engineer Vs Data Scientist
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22.4K
ASData Scientist vs Security Engineer - which is the better career?
#datascience #cybersecurity
@askdatadawn
1.8M
SHConfused between becoming a Data Scientist or an AI Engineer?
Both roles are powerful—but require different skills, tools, and thinking.
Comment “Roles” and I’ll send you a detailed roadmap for both 🚀
Got questions or feeling stuck? Drop your doubts in the comments—I’ll personally help you get clarity and move forward on your journey.
#datascientist #datascience #ai #aiengineer #careergrowth
@shailjamishra__
913.3K
SASoftware Engineer Vs Data Science in 2025 👀 #swe #datascience #tech #fyp
@sajjaad.khader
2.4M
SU🎯 Software Engineer vs. Data Scientist Edition!
#SoftwareEngineer #bigtech #DataScientist #DataScience #faang #SWE #dayinlife
@sundaskhalidd
14.4K
MYData Scientist vs AI Engineer — same field, very different paths 👀
One works with data to find insights 📊
The other builds AI tools and products using those insights 🤖
Both are high-paying, both are in demand —
it just depends on what you enjoy doing.
Comment “data science” and we’ll send you the roadmap 🚀
#DataScience #AIEngineer #CareerInTech #TechCareers #AIJobs DataJobs CareerSwitch DigitalSkills FutureOfWork LearnAI
@mycaptainofficial
39.0K
MRData Engineers work tirelessly behind the scenes to build the infrastructure for data projects. However, their efforts often remain invisible to business users, who focus on the end product and reward Data Scientists and Analysts with more recognition!
#dataengineering #azure #pyspark #dataengineer #azuredataengineer #data #aws #gcp #azuredatabricks #dataanalyst #datascientist #datascience
@mrk_talkstech
14.2K
TEAfter working as a data engineer, here are 5 things I wish I knew earlier:
1. It’s not just SQL or Python
Data engineering isn’t about syntax
It’s about moving data reliably between systems and transforming it correctly along the way
2. Testing data is surprisingly hard
Testing backend code is straightforward → input vs expected output
In data engineering → massive datasets, multiple columns, edge cases… validating correctness is a real challenge
3. It gets harder as you grow
Junior role → write SQL / PySpark pipelines.
Senior role → design architecture, ensure data governance, manage scalability, reliability, and costs.
4. “Pipelines once built are done” — wrong
Data pipelines break. Schemas change. Upstream systems fail.
Maintenance and monitoring are ongoing responsibilities, not one-time work.
5. “More tools = better engineer” — myth
Knowing 10 tools doesn’t matter.
Understanding fundamentals (data modeling, distributed systems, trade-offs) is what actually scales your career.
If you focus only on coding, you’ll plateau early.
If you understand data systems, you’ll grow fast.
💾 Save this for when the role starts feeling more complex than expected
💬 Comment if you’ve felt this shift already
🔁 Follow to keep your thinking sharp as you grow in data engineering
@techwithprateek
1.2M
THHere’s a roadmap to help you go from a software engineer to a data scientist 👩💻 👇
If you’re tired of writing vanilla apps and want to build ML systems instead, this one’s for you.
Step 1 – Learn Python and SQL (not Java, C++, or JavaScript).
→ Focus on pandas, numpy, scikit-learn, matplotlib
→ For SQL: use LeetCode or StrataScratch to practice real-world queries
→ Don’t just write code—learn to think in data
Step 2 – Build your foundation in statistics + math.
→ Start with Practical Statistics for Data Scientists
→ Learn: probability, hypothesis testing, confidence intervals, distributions
→ Brush up on linear algebra (vectors, dot products) and calculus (gradients, chain rule)
Step 3 – Learn ML the right way.
→ Do Andrew Ng’s ML course (Deeplearning.ai)
→ Master the full pipeline: cleaning → feature engineering → modeling → evaluation
→ Read Elements of Statistical Learning or Sutton & Barto if you want to go deeper
Step 4 – Build 2–3 real, messy projects.
→ Don’t follow toy tutorials
→ Use APIs or scrape data, build full pipelines, and deploy using Streamlit or Gradio
→ Upload everything to GitHub with a clear README
Step 5 – Become a storyteller with data.
→ Read Storytelling with Data by Cole Knaflic
→ Learn to explain your findings to non-technical teams
→ Practice communicating precision/recall/F1 in simple language
Step 6 – Stay current. Never stop learning.
→ Follow PapersWithCode (it's now sun-setted, use huggingface.co/papers/trending, ArXiv Sanity, and follow ML practitioners on LinkedIn
→ Join communities, follow researchers, and keep shipping new experiments
-------
Save this for later.
Tag a friend who’s trying to make the switch.
[software engineer to data scientist, ML career roadmap, python for data science, SQL for ML, statistics for ML, data science career guide, ML project ideas, data storytelling, becoming a data scientist, ML learning path 2025]
@the.datascience.gal
555.2K
CHThe best projects serve a real use case
Comment “data” for all the links and project descriptions
#tech #data #datascience #ml #explore
@chrisoh.zip
880.5K
SU🎯 Software Engineer vs. Data Scientist!
ㅤ
#SoftwareEngineer #bigtech #DataScientist #DataScience #faang #SWE #dayinlife
@sundaskhalidd
138.5K
FR📍Data Scientist vs Data Engineer 💥
Data scientist is the most general job title encompassing all the knowledge and skills you need to have if coming from a data science background.
Data engineers are data scientists focused mainly on one particular aspect of data science: handling the raw data and data infrastructure.
👑 Subscribe for daily Interview Questions with answers on Data Science concepts:
✅ Probability, Statistics, SQL, Python, Data Science, Machine Learning, Deep Learning etc..🚀
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@freakz.ai
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