# awesome-learn-datascience **Repository Path**: frostw/awesome-learn-datascience ## Basic Information - **Project Name**: awesome-learn-datascience - **Description**: No description available - **Primary Language**: Unknown - **License**: Not specified - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2021-06-08 - **Last Updated**: 2024-12-27 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README # Data Science Tutorials & Resources for Beginners [![Awesome](https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)](https://github.com/sindresorhus/awesome) *If you want to know more about Data Science but don't know where to start this list is for you!* :chart_with_upwards_trend: No previous knowledge required but Python and statistics basics will definitely come in handy. These ressources have been used successfully for many beginners at my local Data Science student group [ML-KA](http://ml-ka.de/). ## What is Data Science? - ['What is Data Science?' on Quora](https://www.quora.com/What-is-data-science) - [Explanation of important vocabulary](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) - Differentiation of Big Data, Machine Learning, Data Science. - [Data Science for Business (Book)](https://amzn.to/2voPJUi) - An introduction to Data Science and its use as a business asset. ## Common Algorithms and Procedures - [Supervised vs unsupervised learning](https://stackoverflow.com/questions/1832076/what-is-the-difference-between-supervised-learning-and-unsupervised-learning) - The two most common types of Machine Learning algorithms. - [9 important Data Science algorithms and their implementation](https://nbviewer.jupyter.org/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/05.05-Naive-Bayes.ipynb) - [Cross validation](https://nbviewer.jupyter.org/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/05.03-Hyperparameters-and-Model-Validation.ipynb) - Evaluate the performance of your algorithm / model. - [Feature engineering](https://nbviewer.jupyter.org/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/05.04-Feature-Engineering.ipynb) - Modifying the data to better model predictions. - [Scientific introduction to 10 important Data Science algorithms](http://www.cs.umd.edu/%7Esamir/498/10Algorithms-08.pdf) - [Model ensemble: Explanation](https://www.analyticsvidhya.com/blog/2017/02/introduction-to-ensembling-along-with-implementation-in-r/) - Combine multiple models into one for better performance. ## Data Science using Python This list covers only Python, as many are already familiar with this language. [Data Science tutorials using R](https://github.com/ujjwalkarn/DataScienceR). ### General - [O'Reilly Data Science from Scratch (Book)](https://amzn.to/2GSjjrK) - Data processing, implementation, and visualization with example code. - [Coursera Applied Data Science](https://www.coursera.org/specializations/data-science-python) - Online Course using Python that covers most of the relevant toolkits. ### Learning Python - [YouTube tutorial series by sentdex](https://www.youtube.com/watch?v=oVp1vrfL_w4&list=PLQVvvaa0QuDe8XSftW-RAxdo6OmaeL85M) - [Interactive Python tutorial website](http://www.learnpython.org/) ### numpy [numpy](http://www.numpy.org/) is a Python library which provides large multidimensional arrays and fast mathematical operations on them. - [Numpy tutorial on DataCamp](https://www.datacamp.com/community/tutorials/python-numpy-tutorial#gs.h3DvLnk) ### pandas [pandas](http://pandas.pydata.org/index.html) provides efficient data structures and analysis tools for Python. It is build on top of numpy. - [Introduction to pandas](http://www.synesthesiam.com/posts/an-introduction-to-pandas.html) - [DataCamp pandas foundations](https://www.datacamp.com/courses/pandas-foundations) - Paid course, but 30 free days upon account creation (enough to complete course). - [Pandas cheatsheet](https://github.com/pandas-dev/pandas/blob/master/doc/cheatsheet/Pandas_Cheat_Sheet.pdf) - Quick overview over the most important functions. ### scikit-learn [scikit-learn](http://scikit-learn.org/stable/) is the most common library for Machine Learning and Data Science in Python. - [Introduction and first model application](https://nbviewer.jupyter.org/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/05.02-Introducing-Scikit-Learn.ipynb) - [Rough guide for choosing estimators](http://scikit-learn.org/stable/tutorial/machine_learning_map/) - [Scikit-learn complete user guide](http://scikit-learn.org/stable/user_guide.html) - [Model ensemble: Implementation in Python](http://machinelearningmastery.com/ensemble-machine-learning-algorithms-python-scikit-learn/) ### Jupyter Notebook [Jupyter Notebook](https://jupyter.org/) is a web application for easy data visualisation and code presentation. - [Downloading and running first Jupyter notebook](https://jupyter.org/install.html) - [Example notebook for data exploration](https://www.kaggle.com/sudalairajkumar/simple-exploration-notebook-instacart) - [Seaborn data visualization tutorial](https://elitedatascience.com/python-seaborn-tutorial) - Plot library that works great with Jupyter. ### Various other helpful tools and resources - [Template folder structure for organizing Data Science projects](https://github.com/drivendata/cookiecutter-data-science) - [Anaconda Python distribution](https://www.continuum.io/downloads) - Contains most of the important Python packages for Data Science. - [Spacy](https://spacy.io/) - Open source toolkit for working with text-based data. - [LightGBM gradient boosting framework](https://github.com/Microsoft/LightGBM) - Successfully used in many Kaggle challenges. - [Amazon AWS](https://aws.amazon.com/) - Rent cloud servers for more timeconsuming calculations (r4.xlarge server is a good place to start). ## Data Science Challenges for Beginners Sorted by increasing complexity. - [Walkthrough: House prices challenge](https://www.dataquest.io/blog/kaggle-getting-started/) - Walkthrough through a simple challenge on house prices. - [Blood Donation Challenge](https://www.drivendata.org/competitions/2/warm-up-predict-blood-donations/) - Predict if a donor will donate again. - [Titanic Challenge](https://www.kaggle.com/c/titanic) - Predict survival on the Titanic. - [Water Pump Challenge](https://www.drivendata.org/competitions/7/pump-it-up-data-mining-the-water-table/) - Predict the operating condition of water pumps in Africa. ## More advanced resources and lists - [Awesome Data Science](https://github.com/bulutyazilim/awesome-datascience) - [Data Science Python](https://github.com/ujjwalkarn/DataSciencePython) - [Machine Learning Tutorials](https://github.com/ujjwalkarn/Machine-Learning-Tutorials) ## Contribute Contributions welcome! Read the [contribution guidelines](contributing.md) first. ## License [![CC0](http://mirrors.creativecommons.org/presskit/buttons/88x31/svg/cc-zero.svg)](http://creativecommons.org/publicdomain/zero/1.0) To the extent possible under law, Simon Böhm has waived all copyright and related or neighboring rights to this work. Disclaimer: Some of the links are affiliate links.