7.5K
_PDijkstra’s algorithm is one of the fundamental algorithms for computing the shortest path in a network of graphs.
@_pica02
#Path In Computing
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841.5K
SWIf I was a beginner learning to code, I would use this 90 day roadmap step by step for beginners 💪 #coding #codingforbeginners #learntocode #computerscience #programming
@swerikcodes
610.5K
DEIt’s literally perfect 🫠 #coding #java #programmer #computer #python
@deskmateco
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
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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
8.8K
DCRoadmap of Full stack with AI
#Roadmap #development #coding #programming #softwareengineer
@dcoder75
407.5K
ENReady to navigate your career in the cloud? ☁️
From entry-level designations to advanced certifications, this roadmap has you covered.
Let’s level up together! 🚀
#CloudComputing #TechCareers #Roadmap
@engineeringdigest.in
2.0M
COThe 🐐
His name is Abdul Bari and he has a bunch of playlists on YouTube of his lectures teaching Computer Science and coding! The way he explains things is so clear and easy to understand!
💌SEND this to yourself or save it!
✅FOLLOW for more Computer Science tips!
#computerscience #compsci #programming #coding #computersciencemajor
@compskyy
19.8K
MSComputer technology 💻💻🖥️
#tech #pc #computer #technology #computerscience #windows #laptop #science #college #tips #trick #microsoft #software #hardware
@msofficeee
1.3M
SWIf I was a beginner learning to code, I would use this Python roadmap step by step for beginners 💪 #coding #codingforbeginners #learntocode #codingtips #cs #python #computerscience #usemassive
@swerikcodes
600.8K
MIDo this in high school and you’ll behead of every Computer Science Major! #computerscience #coding #programmer #programming #coding #codeforces #competitiveprogramming
@michaellin250
78.1K
ITthis is one underrated career in tech/ml that is sooo cool!! #techcareer #careergrowthtips #ai #machinelearning #datascientist #coding #machinelearningengineer #informatics
@itsallykrinsky
584.6K
TIWhat exactly is the difference between a CPU, GPU and TPU? Here is it explained simply with real world examples so you never forget it in a meeting, interview or talking to friends.
A CPU like the one in your laptop or phone is a versatile processor with a few powerful cores. It’s great for handling all kinds of tasks: browsing the web, running apps, editing documents.
And while CPUs do support some parallelism like multiple cores and vector instructions they mostly focus on versatility over scale. For AI training, which demands billions of heavy matrix operations, that’s just not enough power.
A GPU, like the one in your gaming PC or PlayStation, is built for a different job.
Originally designed for graphics rendering handling thousands of pixels and textures at once GPUs are masters of parallelism. With thousands of smaller cores, they process massive numbers of calculations at the same time. That makes them perfect for AI training
Then there’s the TPU, Tensor Processing Unit, engineered by Google specifically for AI workloads.
Unlike GPUs, which are general-purpose accelerators, TPUs are laser-focused: they use specialized designs like systolic arrays, which move data through the chip like an assembly line, rapidly multiplying and processing tensors for AI tasks.
You’ll find TPUs inside Google’s data centers, powering services like Google Translate and training massive AI models like Gemini.
#tech #techexplained #techeducation
@tiffintech
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