Complete Data Science Roadmap  1. Introduction to Data Science     - Overview and Importance     - Data Science Lifecycle     - Key Roles (Data Scientist, Analyst, Engineer)  2. Mathematics and Statistics     - Probability and Distributions     - Descriptive/Inferential Statistics     - Hypothesis Testing     - Linear Algebra and Calculus Basics  3. Programming Languages     - Python: NumPy, Pandas, Matplotlib     - R: dplyr, ggplot2     - SQL: Joins, Aggregations, CRUD  4. Data Collection & Preprocessing     - Data Cleaning and Wrangling     - Handling Missing Data     - Feature Engineering  5. Exploratory Data Analysis (EDA)     - Summary Statistics     - Data Visualization (Histograms, Box Plots, Correlation)  6. Machine Learning     - Supervised (Linear/Logistic Regression, Decision Trees)     - Unsupervised (K-Means, PCA)     - Model Selection and Cross-Validation  7. Advanced Machine Learning     - SVM, Random Forests, Boosting     - Neural Networks Basics  8. Deep Learning     - Neural Networks Architecture     - CNNs for Image Data     - RNNs for Sequential Data  9. Natural Language Processing (NLP)     - Text Preprocessing     - Sentiment Analysis     - Word Embeddings (Word2Vec)  10. Data Visualization & Storytelling     - Dashboards (Tableau, Power BI)     - Telling Stories with Data  11. Model Deployment     - Deploy with Flask or Django     - Monitoring and Retraining Models  12. Big Data & Cloud     - Introduction to Hadoop, Spark     - Cloud Tools (AWS, Google Cloud)  13. Data Engineering Basics     - ETL Pipelines     - Data Warehousing (Redshift, BigQuery)  14. Ethics in Data Science     - Ethical Data Usage     - Bias in AI Models  15. Tools for Data Science     - Jupyter, Git, Docker  16. Career Path & Certifications     - Building a Data Science Portfolio  Like if you need more content 😄👍