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Complete Data Science Road Map🔥 with resources👇 1.Math and Statistics: • Linear Algebra • Calculus • Probability • Statistics 2.Languages: • Python ( • NumPy, • Pandas, • Matplotlib, • Seaborn ) • R 3. Data skills: • Data Cleaning • Exploratory Data Analysis • Feature Engineering 4. Data Visualization: • Matplotlib • Seaborn • Plotly • Tableau 5.Machine Learning Basics: • Supervised Learning • Unsupervised Learning • Regression • Classification • Clustering 6. ML Libraries: • Scikit-Learn • TensorFlow • Keras • PyTorch 7.Model Evaluation and Validation: • Cross-Validation • Hyperparameter Tuning • Evaluation Metrics 8.Big Data Technologies: • Apache Hadoop • Apache Spark 9.Database: • SQL Basics • MySQL • PostgreSQL 10.Deep Learning: • Neural Networks • CNN • RNN • Transfer Learning 11.Natural Language Processing (NLP): • Tokenization • Named Entity Recognition (NER) • Sentiment Analysis 12.Time Series Analysis: • Time Series Components • Seasonal Decomposition • Forecasting Methods 13.Model Deployment: • Flask (for Python) • Django (for Python) • Docker 14.Version Control: • Git • GitHub 15. Cloud Platforms: • AWS • Azure • GCP 16. Data Ethics and Privacy: • Ethical Considerations • Privacy Protection 17.Communication and Reporting: • Data Storytelling • Reporting Tools e.g. - Jupyter Notebooks - R Markdown 18.Continuous Learning: • Stay Updated with Industry Trends • Participate in Online Communities • Join online Conferences ------------------- END -------------------- Some good resources to learn Data Science Books: • Python for Data Analysis - by Wes McKinney • Hands-On Machine Learning - by Aurélien Géron • The Art of Data Science - by Roger D. Peng and Elizabeth M. • Data Science from Scratch -by Joel Grus Blogs: • Towards Data Science • KDnuggets • R-bloggers • Flowingdata • Analytics Vidhya YouTube Channel ❯ Python ➟ Corey Schafer ❯ SQL ➟ Joey Blue ❯ Excel ➟ ExcelIsFun ❯ PowerBI ➟ Guy in a Cube ❯ Tableau ➟ Tableau Tim ❯ Mathematics ➟ 3Blue1Brown ❯ Statistics ➟ statquest ❯ Data Analyst ➟ AlexTheAnalyst ❯ ML, DL ➟ sentdex Podcasts: • Data Science at Home • Talking Machines • O'Reilly Data Science Podcast • Linear Digressions • DataFramed Community and Forums: Stack Overflow Reddit - r/datascience: Documentation and Guides: 1.Scikit-Learn Documentation: Official documentation for the Scikit-Learn library. 2.Pandas Documentation: Official documentation for the Pandas library.

Websites to Learn Data Analytics for free. Learn Data Analytics for free with these resources: 📶1. Excel: excel-practice-online.com 📶2. Tableau: tableau.com/learn/starter-… 📶3. PowerBi: powerbi.microsoft.com/en-us/learning/ 📶4. SQL: w3schools.com 📶5. Python: freecodecamp.org/

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Reason(with examples): all(): Returns True if all elements of the iterable are true (or if the iterable is empty). If any element is false, it returns False. any(): Returns True if at least one element of the iterable is true. If the iterable is empty or all the false, it returns False.

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