@datascibykashi: Don’t try to learn everything at once. Follow this roadmap step by step 👇 1️⃣ PYTHON 🐍 Start with the language you’ll use throughout your journey. Learn: • Variables & data types • Conditions & loops • Functions • Lists, tuples, sets & dictionaries • OOP • Exception handling • File handling • Modules & packages • Virtual environments 2️⃣ SQL 🗄️ Data Scientists work with databases constantly. Learn: • SELECT & WHERE • GROUP BY & HAVING • JOINs • Subqueries • CTEs • CASE statements • Window functions • Ranking • Date & time analysis 3️⃣ MATHEMATICS 🧮 You don’t need every branch of mathematics. Focus on: • Algebra • Functions • Exponents & logarithms • Vectors • Matrices • Probability • Statistics • Calculus basics • Optimization 4️⃣ DATA ANALYSIS 📊 Learn how to turn raw data into useful information. Master: 🐼 Pandas 🔢 NumPy 📈 Matplotlib 🎨 Seaborn Practice: Cleaning → EDA → Visualization → Insights 5️⃣ STATISTICS 📐 Understand the mathematics behind your data. Learn: • Mean, median & mode • Variance & standard deviation • Probability distributions • Sampling • Confidence intervals • Hypothesis testing • Correlation • Regression • A/B testing 6️⃣ MACHINE LEARNING 🤖 Now start building predictive models. Learn: Supervised Learning • Linear Regression • Logistic Regression • Decision Trees • Random Forest • Gradient Boosting • SVM • KNN Unsupervised Learning • K-Means • Hierarchical Clustering • PCA 7️⃣ MODEL EVALUATION 🎯 Training a model isn’t enough. Understand: • Train / validation / test sets • Cross-validation • Accuracy • Precision • Recall • F1-score • ROC-AUC • MAE / MSE / RMSE • Overfitting & underfitting • Bias-variance tradeoff 8️⃣ FEATURE ENGINEERING ⚙️ Learn how to improve the information given to your model. Practice: • Encoding • Scaling • Feature selection • Transformation • Missing-value handling • Outlier treatment • Feature creation 9️⃣ DEEP LEARNING 🧠 Move beyond traditional ML. Learn: • Neural Networks • Backpropagation • Activation functions • Optimizers • CNNs • RNNs / LSTMs • Transfer Learning • Transformers Tools: PyTorch / TensorFlow 🔟 GENERATIVE AI 🤖✨ Modern Data Science increasingly overlaps with AI engineering. Learn: • LLMs • Prompt Engineering • Embeddings • Vector Databases • RAG • Fine-tuning • AI Agents • Tool Calling • LLM Evaluation • Hallucination & reliability 1️⃣1️⃣ ML ENGINEERING 🚀 Turn your model into a real application. Learn: • Git & GitHub • APIs • FastAPI • Docker • Cloud basics • Model serving • CI/CD • Monitoring • Model drift • MLOps 1️⃣2️⃣ BUILD REAL PROJECTS 🏗️ Don’t wait until you “finish learning.” Build while learning. Beginner: 📊 EDA + Dashboard Intermediate: 🤖 End-to-End ML Project Advanced: 🧠 Deep Learning Project Modern AI: 🔎 RAG Application Production: 🚀 Deployed ML/AI System 🏆 FINAL ROADMAP Python ⬇️ SQL ⬇️ Math + Statistics ⬇️ Pandas + NumPy ⬇️ EDA + Visualization ⬇️ Machine Learning ⬇️ Model Evaluation ⬇️ Feature Engineering ⬇️ Deep Learning ⬇️ Generative AI ⬇️ ML Engineering + MLOps ⬇️ Real Projects ⬇️ Portfolio + Job Applications 💼 🔥 THE MOST IMPORTANT PART Don’t learn: Python → SQL → ML → DL → AI → then start building. Instead: Learn → Build → Break → Debug → Improve → Explain → Repeat. Your portfolio should prove that you can take: DATA → INSIGHT → MODEL → APPLICATION → REAL-WORLD VALUE 📌 Save this roadmap and use it as your Data Science checklist. #DataScience #DataScientist #MachineLearning #creatorsearchinsights #datascientist

Data Scientist | Kashi

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Sunday 13 September 2026 18:17:37 GMT