✅ Complete Roadmap to Become a Data Scientist

📂 1. Learn the Basics of Programming
– Start with Python (preferred) or R
– Focus on variables, loops, functions, and libraries like numpy, pandas

📂 2. Math & Statistics
– Probability, Statistics, Mean/Median/Mode
– Linear Algebra, Matrices, Vectors
– Calculus basics (for ML optimization)

📂 3. Data Handling & Analysis
– Data cleaning (missing values, outliers)
– Data wrangling with pandas
– Exploratory Data Analysis (EDA) with matplotlib, seaborn

📂 4. SQL for Data
– Querying data, joins, aggregations
– Subqueries, window functions
– Practice with real datasets

📂 5. Machine Learning
– Supervised: Linear Regression, Logistic Regression, Decision Trees
– Unsupervised: Clustering, PCA
– Tools: scikit-learn, xgboost, lightgbm

📂 6. Deep Learning (Optional Advanced)
– Basics of Neural Networks
– Frameworks: TensorFlow, Keras, PyTorch
– CNNs, RNNs for image/text tasks

📂 7. Projects & Real Datasets
– Kaggle Competitions
– Build projects like Movie Recommender, Stock Prediction, or Customer Segmentation

📂 8. Data Visualization & Dashboarding
– Tools: matplotlib, seaborn, Plotly, Power BI, Tableau
– Create interactive reports

📂 9. Git & Deployment
– Version control with Git
– Deploy ML models with Flask or Streamlit

📂 10. Resume + Portfolio
– Host projects on GitHub
– Share insights on LinkedIn
– Apply for roles like Data Analyst → Jr. Data Scientist → Data Scientist

Data Science Resources: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D

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