#Data Science Tutorials

Comment roadmap to get sent my free and complete data engineering roadmap!

Comment ‘Projects’ to get 5 Data Scientist Project ideas and a plan 👩🏻‍💻 ♻️ repost to share with friends. Here is how to become a data scientist in 2026 and beyond 📈 the original video was 4 min Andi had to cut it down to 3 because instagram. Should I do a part 3v what are other skills that you would add to the list and let me know what I should cover in the next video 👩🏻‍💻 #datascientist #datascience #python #machinelearning #sql #ai

This is the framework I used to simultaneously prep for SWE and data science internships. It’s based on my personal experience — feel free to adapt it, or copy it entirely. Comment “roadmap” and I’ll DM you the resource list. #tech #fyp #explore

Here’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]

Comment PROJECT to access my step-by-step Python tutorial that anyone can follow to build your very first geospatial dashboard web app! 🌍📊 A good number of portfolio projects is 3–5, and the types of projects you choose should reflect the kind of data role you’re going after. A data analyst portfolio should look very different from a machine learning engineer one. Even within data science, a product/decision data scientist portfolio should focus on A/B testing and metrics storytelling—while an algorithm data scientist portfolio might highlight modeling and experimentation. ✨ Especially if you’re building your very first project, prioritize: 🌱 Real-world messiness (not polished Kaggle sets) 🌱 Business context and decision-making 🌱 Clear documentation (what you did and why) 🌱Visuals to help your work stand out No one’s asking for perfection—they want to see how you think. #datascienceportfolio #dataanalyst #learnpython #codingjourney #techcareers

What if they just want to learn 🥺🥺 #vibecoding #programming

DATA SCIENCE ROADMAP FROM GOOGLE DATA SCIENTISTS . . . #datascience #google #nodaysoff #AI #sql #python #roadmap #cheatsheet

Logistic regression is an algorithm and statistical method used in machine learning for binary classification tasks, where the goal is to predict one of two possible outcomes (for example, yes or no). Logistic regression outputs probabilities that a certain event will occur, using the logistic function to transform the linear output of a model into a probability value from 0 to 1. It’s used mainly for cases where the variable is categorical. The model learns from the data by finding the best set of weights (also the coefficients of the curve) that minimize the error between the model’s predicted and actual values. Logistic regression is useful since it’s simple, easy to interpret, and effective for simple classification problems and serves as a good introduction to machine learning. C: 3 minute data science Join our AI community for more posts like this @aibutsimple 🤖 #datascientist #computerengineering #deeplearning #computerscience #math #mathematics #ml #logisticregression #machinelearning #datascience #education #coding #programming #learning #courses #bootcamp #course

🟢Comment 'X' and I'll send you a short guide on how to join my free data community 🟢

Here’s how you can setup your very own vector database using Pinecone, to build a vertical AI tools on top of. #vibecoding #coding

This is the road to failure

if you wanna get started learning about AI & ML this year, these are my top tips! happy new year everyone! #techcareer #careerdevelopment #technicalproductmanager #ai #upskilling