#Data Science Dojo

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

Data Science 4 All is a free training program that makes data science shockingly uncomplicated

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]

📍How to prepare for Data Scientist role in 2026 🚀 CORE SKILLS YOU MUST MASTER: Programming You must be fluent in: ● Python ● NumPy ● Pandas ● Scikit-learn Writing clean, readable, bug free code Data transformations without IDE help Expect: ● Data cleaning ● Feature extraction ● Aggregations ● Writing logic heavy code SQL Almost every Data Science role tests SQL. You should be comfortable with: ● Joins - inner, left, self ● Window functions ● Grouping & aggregations ● Subqueries ● Handling NULLs Statistics & Probability: ● Probability distributions ● Hypothesis testing ● Confidence intervals ● A/B testing ● Correlation vs causation ● Sampling bias Machine Learning Fundamentals. You must know: ● Supervised vs Unsupervised learning ● Regression & Classification ● Bias Variance tradeoff ● Overfitting / Underfitting Evaluation metrics: ● Accuracy ● Precision / Recall ● F1-score ● ROC-AUC ● RMSE FEATURE ENGINEERING & DATA UNDERSTANDING: ● This is where strong candidates stand out. ● Handling missing data ● Encoding categorical variables ● Feature scaling ● Outlier treatment CORE SKILLS YOU MUST MASTER: Programming You must be fluent in: ● Python ● NumPy ● Pandas ● Scikit-learn Writing clean, readable, bug free code Data transformations without IDE help Expect: ● Data cleaning ● Feature extraction ● Aggregations ● Writing logic heavy code SQL Almost every Data Science role tests SQL. You should be comfortable with: ● Joins - inner, left, self ● Window functions ● Grouping & aggregations ● Subqueries ● Handling NULLs Statistics & Probability: ● Probability distributions ● Hypothesis testing ● Confidence intervals ● A/B testing ● Correlation vs causation ● Sampling bias Machine Learning Fundamentals. You must know: ● Supervised vs Unsupervised learning ● Regression & Classification ● Bias Variance tradeoff ● Overfitting / Underfitting Evaluation metrics: ● Accuracy ● Precision / Recall ● F1-score ● ROC-AUC ● RMSE +++ for more look at the comment #datascientist #aiengineer #softwareengineer #datascience #dataengineer

Comment "DATA" for the links. You Will Never Struggle With Data Science Again 📌 Learn the most important foundations with these beginner-friendly resources: 1️⃣ Learn Python for Data Science – FreeCodeCamp’s full beginner course 2️⃣ Essence of Linear Algebra – 3Blue1Brown’s visual, intuitive playlist 3️⃣ Statistics – A Full Lecture (2025) – step-by-step breakdown of core stats concepts Stop feeling overwhelmed by Python, statistics, or linear algebra. These tutorials simplify the fundamentals of Data Science with clear explanations, visuals, and real-world examples. Whether you’re preparing for a career in Data Science, getting into machine learning, or just curious about data analysis, this is the fastest way to finally understand how it all fits together. Save this post, share it, and turn confusion into clarity with Python, Stats, and Linear Algebra for Data Science 📊

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

FREE Data Analytics learning resources. Seriously, start here before paying for any courses. These are FREE & a great introduction for any skill you want to learn. - SQL: https://www.youtube.com/watch?v=7S_tz1z_5bA - Excel: https://www.youtube.com/watch?v=pCJ15nGFgVg - Tableau: https://www.youtube.com/watch?v=aHaOIvR00So - Python: https://www.youtube.com/watch?v=LHBE6Q9XlzI #dataanalytics #dataanalyst #datascience #womenintech #aiengineering #techcareers

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Comment “DATA” for all projects & links! #coding #datascience #machinelearning #university #student

I didn’t come from a technical background. No coding, no deep math. But little by little, these are the steps that helped me break into Data Science & Machine Learning ⬇️ 1. Start small with Python → I focused on the very basics first (loops, functions, simple algorithms). 2. Build up the math slowly → Statistics and probability were way more useful in the beginning than trying to jump straight into deep learning. 3. Do tiny projects early → Cleaning messy datasets, making visualizations, or trying out a simple sentiment analysis taught me more than just reading theory. 4. Use free resources first → FreeCodeCamp, Kaggle, YouTube, and MOOCs gave me a foundation. Later I used platforms like DataCamp once I knew what I needed. 5. Consistency > intensity → I wasn’t grinding 10 hours a day. I just showed up for 1–2 hours almost every day and that’s what really made the difference. 6. Share your progress → Putting projects on GitHub and LinkedIn helped way more than I expected. It’s how people actually saw what I was learning. If you’re not from a tech background: you don’t need to be born with it, you just need to build it one step at a time. #datascience #coding #machinelearning #cs #studygram #motivation #selfimprovement #study #polymath #stem #inspiration #studywithme #success #mindset #grind #learning #studymotivation #finance #university #student #aesthetic

Data Science is art paired with logic 😊. In my soft girl in tech era 💕. Calm lights, chaotic datasets #datascience #stem #womeninstem #study #computerscience #coding #programming #tech #explore #ai #python #dev #tools #study #datascientist #data #design #software #codinglife #programmer #datascience #build #learning #growth #technology #information

The best projects serve a real use case Comment “data” for all the links and project descriptions #tech #data #datascience #ml #explore