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@elio.chura: SON DI LUZ #vinto

Elio Chura
Elio Chura
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Sunday 12 April 2026 18:59:39 GMT
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nickj_003
Nico S.M. :
No habrá otros temitas?
2026-04-14 15:33:18
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100 Days → 1 Skill Stack → Real Projects → Job-Ready Portfolio If you’re starting Data Science as a fresher, don’t try to learn everything at once. Follow the sequence. Learn → Practice → Build → Explain. 🗓️ DAYS 1–15 | PYTHON 🐍 Build your programming foundation. → Variables & Data Types → Conditions & Loops → Functions → Lists, Tuples, Sets & Dictionaries → File Handling → Exception Handling → OOP Basics → Modules & Libraries 🎯 Build: Expense Tracker + Data Processing Mini Project 🗓️ DAYS 16–25 | NUMPY + PANDAS Learn to work with real data. → NumPy Arrays → Indexing & Broadcasting → Vectorized Operations → Pandas Series & DataFrames → Filtering & Sorting → GroupBy → Merge & Join → Missing Values → Duplicates → Data Types 🎯 Build: Real Dataset Analysis 🗓️ DAYS 26–35 | STATISTICS 📊 This is where Data Science starts making sense. → Mean, Median & Mode → Variance & Standard Deviation → Percentiles & IQR → Probability → Distributions → Sampling → Correlation & Covariance → Hypothesis Testing → Confidence Intervals 🎯 Practice: Statistical analysis on a real dataset 🗓️ DAYS 36–45 | SQL 🗄️ Learn how to extract data from databases. → SELECT & WHERE → GROUP BY → Aggregations → JOINs → CASE WHEN → Subqueries → CTEs → Window Functions → Date & Time Analysis 🎯 Build: E-Commerce SQL Analytics Project 🗓️ DAYS 46–60 | MACHINE LEARNING 🤖 Learn the core algorithms. → Train/Test Split → Linear Regression → Logistic Regression → Decision Trees → Random Forest → KNN → SVM → K-Means → PCA → Feature Engineering 🎯 Build: 2 ML Projects 🗓️ DAYS 61–70 | MODEL EVALUATION 🎯 A model isn’t useful just because it predicts. → Accuracy → Precision → Recall → F1-Score → ROC-AUC → Confusion Matrix → MAE → MSE → RMSE → Cross-Validation → Hyperparameter Tuning 🎯 Goal: Learn when to use which metric 🗓️ DAYS 71–80 | DEEP LEARNING 🧠 Understand the fundamentals. → Neural Networks → Activation Functions → Forward Propagation → Backpropagation → Loss Functions → Optimizers → CNNs → RNNs → Transfer Learning 🎯 Build: Image or Text Classification Project 🗓️ DAYS 81–90 | MODERN AI ⚡ Move beyond traditional ML. → NLP Fundamentals → Transformers → LLMs → Prompt Engineering → Embeddings → Vector Databases → RAG → AI APIs → Basic AI Agents 🎯 Build: RAG-Based AI Assistant 🗓️ DAYS 91–100 | PORTFOLIO + JOB READY 💼 Turn your knowledge into proof. → Build 3–5 strong projects → Deploy your best projects → Create GitHub READMEs → Build your portfolio → Improve LinkedIn → Prepare your resume → Practice SQL → Practice ML interviews → Practice explaining projects → Start applying 🚀 🔥 YOUR FRESHER STACK Python ↓ NumPy + Pandas ↓ Statistics + Probability ↓ SQL ↓ EDA + Visualization ↓ Machine Learning ↓ Model Evaluation ↓ Deep Learning ↓ GenAI + RAG ↓ Deployment + Portfolio 🎯 THE 100-DAY RULE Don’t spend 100 days collecting certificates. Spend them building evidence that you can solve problems with data. Learn → Code → Break → Debug → Build → Explain → Repeat. By Day 100, you shouldn’t just say: ❌ “I know Data Science.” You should be able to show: ✅ “Here are the problems I solved.” #DataScience #DataScientist #DataScienceRoadmap #creatorsearchinsights #machinelearningengineer
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