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{"slug": "siboehm--awesome-learn-datascience", "title": "Learn Datascience", "description": ":chart_with_upwards_trend: Curated list of resources to help you get started with Data Science", "github_url": "https://github.com/siboehm/awesome-learn-datascience", "stars": "637", "tag": "Computer Science", "entry_count": 37, "subcategory_count": 8, "subcategories": [{"name": "What is Data Science?", "parent": "", "entries": [{"name": "'What is Data Science?' on Quora", "url": "https://www.quora.com/What-is-data-science", "description": ""}, {"name": "Explanation of important vocabulary", "url": "https://www.quora.com/What-is-the-difference-between-Data-Analytics-Data-Analysis-Data-Mining-Data-Science-Machine-Learning-and-Big-Data-1?share=1", "description": "Differentiation of Big Data, Machine Learning, Data Science."}, {"name": "Data Science for Business (Book)", "url": "https://amzn.to/2voPJUi", "description": "An introduction to Data Science and its use as a business asset."}, {"name": "Data Science Process: A Beginner\u2019s Comprehensive Guide", "url": "https://www.scaler.com/blog/data-science-process/", "description": "Technical Skills for the Data Science: This emphasizes the practical skills needed throughout the data science process."}, {"name": "Supervised vs unsupervised learning", "url": "https://stackoverflow.com/questions/1832076/what-is-the-difference-between-supervised-learning-and-unsupervised-learning", "description": "The two most common types of Machine Learning algorithms."}, {"name": "9 important Data Science algorithms and their implementation", "url": "https://nbviewer.jupyter.org/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/05.05-Naive-Bayes.ipynb", "description": ""}, {"name": "Cross validation", "url": "https://nbviewer.jupyter.org/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/05.03-Hyperparameters-and-Model-Validation.ipynb", "description": "Evaluate the performance of your algorithm/model."}, {"name": "Feature engineering", "url": "https://nbviewer.jupyter.org/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/05.04-Feature-Engineering.ipynb", "description": "Modifying the data to better model predictions."}, {"name": "Scientific introduction to 10 important Data Science algorithms", "url": "http://www.cs.umd.edu/%7Esamir/498/10Algorithms-08.pdf", "description": ""}, {"name": "Model ensemble: Explanation", "url": "https://www.analyticsvidhya.com/blog/2017/02/introduction-to-ensembling-along-with-implementation-in-r/", "description": "Combine multiple models into one for better performance."}]}, {"name": "General", "parent": "Data Science using Python", "entries": [{"name": "O'Reilly Data Science from Scratch (Book)", "url": "https://amzn.to/2GSjjrK", "description": "Data processing, implementation, and visualization with example code."}, {"name": "Coursera Applied Data Science", "url": "https://www.coursera.org/specializations/data-science-python", "description": "Online 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creation (enough to complete course)."}, {"name": "Pandas cheatsheet", "url": "https://github.com/pandas-dev/pandas/blob/master/doc/cheatsheet/Pandas_Cheat_Sheet.pdf", "description": "Quick overview over the most important functions.", "stars": "42k"}]}, {"name": "scikit-learn", "parent": "Data Science using Python", "entries": [{"name": "Introduction and first model application", "url": "https://nbviewer.jupyter.org/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/05.02-Introducing-Scikit-Learn.ipynb", "description": ""}, {"name": "Rough guide for choosing estimators", "url": "http://scikit-learn.org/stable/tutorial/machine_learning_map/", "description": ""}, {"name": "Scikit-learn complete user guide", "url": "http://scikit-learn.org/stable/user_guide.html", "description": ""}, {"name": "Model ensemble: Implementation in Python", "url": "http://machinelearningmastery.com/ensemble-machine-learning-algorithms-python-scikit-learn/", "description": ""}]}, {"name": "Jupyter 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"https://spacy.io/", "description": "Open source toolkit for working with text-based data."}, {"name": "LightGBM gradient boosting framework", "url": "https://github.com/Microsoft/LightGBM", "description": "Successfully used in many Kaggle challenges.", "stars": "16k"}, {"name": "Amazon AWS", "url": "https://aws.amazon.com/", "description": "Rent cloud servers for more timeconsuming calculations (r4.xlarge server is a good place to start)."}, {"name": "Walkthrough: House prices challenge", "url": "https://www.dataquest.io/blog/kaggle-getting-started/", "description": "Walkthrough through a simple challenge on house prices."}, {"name": "Blood Donation Challenge", "url": "https://www.drivendata.org/competitions/2/warm-up-predict-blood-donations/", "description": "Predict if a donor will donate again."}, {"name": "Titanic Challenge", "url": "https://www.kaggle.com/c/titanic", "description": "Predict survival on the Titanic."}, {"name": "Water Pump Challenge", "url": "https://www.drivendata.org/competitions/7/pump-it-up-data-mining-the-water-table/", "description": "Predict the operating condition of water pumps in Africa."}, {"name": "Awesome Data Science", "url": "https://github.com/bulutyazilim/awesome-datascience", "description": "", "stars": "24k"}, {"name": "Data Science Python", "url": "https://github.com/ujjwalkarn/DataSciencePython", "description": "", "stars": "5.1k"}, {"name": "Machine Learning Tutorials", "url": "https://github.com/ujjwalkarn/Machine-Learning-Tutorials", "description": "", "stars": "15k"}]}], "name": ""} |