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<h1 id="data-science-tutorials-resources-for-beginners-awesome">Data
Science Tutorials &amp; Resources for Beginners <a
href="https://github.com/sindresorhus/awesome"><img
src="https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg"
alt="Awesome" /></a></h1>
<p><em>If you want to know more about Data Science but don’t know where
to start this list is for you!</em> :chart_with_upwards_trend:</p>
<p>No previous knowledge is required but Python and statistics basics
will definitely come in handy. These resources have been used
successfully for many beginners at my local Data Science student group
<a href="http://ml-ka.de/">ML-KA</a>.</p>
<h2 id="what-is-data-science">What is Data Science?</h2>
<ul>
<li><a href="https://www.quora.com/What-is-data-science">‘What is Data
Science?’ on Quora</a></li>
<li><a
href="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">Explanation
of important vocabulary</a> - Differentiation of Big Data, Machine
Learning, Data Science.</li>
<li><a href="https://amzn.to/2voPJUi">Data Science for Business
(Book)</a> - An introduction to Data Science and its use as a business
asset.</li>
<li><a href="https://www.scaler.com/blog/data-science-process/">Data
Science Process: A Beginner’s Comprehensive Guide</a> - Technical Skills
for the Data Science: This emphasizes the practical skills needed
throughout the data science process.</li>
</ul>
<h2 id="common-algorithms-and-procedures">Common Algorithms and
Procedures</h2>
<ul>
<li><a
href="https://stackoverflow.com/questions/1832076/what-is-the-difference-between-supervised-learning-and-unsupervised-learning">Supervised
vs unsupervised learning</a> - The two most common types of Machine
Learning algorithms.</li>
<li><a
href="https://nbviewer.jupyter.org/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/05.05-Naive-Bayes.ipynb">9
important Data Science algorithms and their implementation</a></li>
<li><a
href="https://nbviewer.jupyter.org/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/05.03-Hyperparameters-and-Model-Validation.ipynb">Cross
validation</a> - Evaluate the performance of your algorithm/model.</li>
<li><a
href="https://nbviewer.jupyter.org/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/05.04-Feature-Engineering.ipynb">Feature
engineering</a> - Modifying the data to better model predictions.</li>
<li><a
href="http://www.cs.umd.edu/%7Esamir/498/10Algorithms-08.pdf">Scientific
introduction to 10 important Data Science algorithms</a></li>
<li><a
href="https://www.analyticsvidhya.com/blog/2017/02/introduction-to-ensembling-along-with-implementation-in-r/">Model
ensemble: Explanation</a> - Combine multiple models into one for better
performance.</li>
</ul>
<h2 id="data-science-using-python">Data Science using Python</h2>
<p>This list covers only Python, as many are already familiar with this
language. <a href="https://github.com/ujjwalkarn/DataScienceR">Data
Science tutorials using R</a>.</p>
<h3 id="general">General</h3>
<ul>
<li><a href="https://amzn.to/2GSjjrK">O’Reilly Data Science from Scratch
(Book)</a> - Data processing, implementation, and visualization with
example code.</li>
<li><a
href="https://www.coursera.org/specializations/data-science-python">Coursera
Applied Data Science</a> - Online Course using Python that covers most
of the relevant toolkits.</li>
</ul>
<h3 id="learning-python">Learning Python</h3>
<ul>
<li><a
href="https://www.youtube.com/watch?v=oVp1vrfL_w4&amp;list=PLQVvvaa0QuDe8XSftW-RAxdo6OmaeL85M">YouTube
tutorial series by sentdex</a></li>
<li><a href="http://www.learnpython.org/">Interactive Python tutorial
website</a></li>
</ul>
<h3 id="numpy">numpy</h3>
<p><a href="http://www.numpy.org/">numpy</a> is a Python library which
provides large multidimensional arrays and fast mathematical operations
on them.</p>
<ul>
<li><a
href="https://www.datacamp.com/community/tutorials/python-numpy-tutorial#gs.h3DvLnk">Numpy
tutorial on DataCamp</a></li>
</ul>
<h3 id="pandas">pandas</h3>
<p><a href="http://pandas.pydata.org/index.html">pandas</a> provides
efficient data structures and analysis tools for Python. It is build on
top of numpy.</p>
<ul>
<li><a
href="http://www.synesthesiam.com/posts/an-introduction-to-pandas.html">Introduction
to pandas</a></li>
<li><a
href="https://www.datacamp.com/courses/pandas-foundations">DataCamp
pandas foundations</a> - Paid course, but 30 free days upon account
creation (enough to complete course).</li>
<li><a
href="https://github.com/pandas-dev/pandas/blob/master/doc/cheatsheet/Pandas_Cheat_Sheet.pdf">Pandas
cheatsheet</a> - Quick overview over the most important functions.</li>
</ul>
<h3 id="scikit-learn">scikit-learn</h3>
<p><a href="http://scikit-learn.org/stable/">scikit-learn</a> is the
most common library for Machine Learning and Data Science in Python.</p>
<ul>
<li><a
href="https://nbviewer.jupyter.org/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/05.02-Introducing-Scikit-Learn.ipynb">Introduction
and first model application</a></li>
<li><a
href="http://scikit-learn.org/stable/tutorial/machine_learning_map/">Rough
guide for choosing estimators</a></li>
<li><a
href="http://scikit-learn.org/stable/user_guide.html">Scikit-learn
complete user guide</a></li>
<li><a
href="http://machinelearningmastery.com/ensemble-machine-learning-algorithms-python-scikit-learn/">Model
ensemble: Implementation in Python</a></li>
</ul>
<h3 id="jupyter-notebook">Jupyter Notebook</h3>
<p><a href="https://jupyter.org/">Jupyter Notebook</a> is a web
application for easy data visualisation and code presentation.</p>
<ul>
<li><a href="https://jupyter.org/install.html">Downloading and running
first Jupyter notebook</a></li>
<li><a
href="https://www.kaggle.com/sudalairajkumar/simple-exploration-notebook-instacart">Example
notebook for data exploration</a></li>
<li><a
href="https://elitedatascience.com/python-seaborn-tutorial">Seaborn data
visualization tutorial</a> - Plot library that works great with
Jupyter.</li>
</ul>
<h3 id="various-other-helpful-tools-and-resources">Various other helpful
tools and resources</h3>
<ul>
<li><a
href="https://github.com/drivendata/cookiecutter-data-science">Template
folder structure for organizing Data Science projects</a></li>
<li><a href="https://www.continuum.io/downloads">Anaconda Python
distribution</a> - Contains most of the important Python packages for
Data Science.</li>
<li><a href="https://spacy.io/">Spacy</a> - Open source toolkit for
working with text-based data.</li>
<li><a href="https://github.com/Microsoft/LightGBM">LightGBM gradient
boosting framework</a> - Successfully used in many Kaggle
challenges.</li>
<li><a href="https://aws.amazon.com/">Amazon AWS</a> - Rent cloud
servers for more timeconsuming calculations (r4.xlarge server is a good
place to start).</li>
</ul>
<h2 id="data-science-challenges-for-beginners">Data Science Challenges
for Beginners</h2>
<p>Sorted by increasing complexity.</p>
<ul>
<li><a
href="https://www.dataquest.io/blog/kaggle-getting-started/">Walkthrough:
House prices challenge</a> - Walkthrough through a simple challenge on
house prices.</li>
<li><a
href="https://www.drivendata.org/competitions/2/warm-up-predict-blood-donations/">Blood
Donation Challenge</a> - Predict if a donor will donate again.</li>
<li><a href="https://www.kaggle.com/c/titanic">Titanic Challenge</a> -
Predict survival on the Titanic.</li>
<li><a
href="https://www.drivendata.org/competitions/7/pump-it-up-data-mining-the-water-table/">Water
Pump Challenge</a> - Predict the operating condition of water pumps in
Africa.</li>
</ul>
<h2 id="more-advanced-resources-and-lists">More advanced resources and
lists</h2>
<ul>
<li><a
href="https://github.com/bulutyazilim/awesome-datascience">Awesome Data
Science</a></li>
<li><a href="https://github.com/ujjwalkarn/DataSciencePython">Data
Science Python</a></li>
<li><a
href="https://github.com/ujjwalkarn/Machine-Learning-Tutorials">Machine
Learning Tutorials</a></li>
</ul>
<h2 id="contribute">Contribute</h2>
<p>Contributions welcome! Read the <a
href="contributing.md">contribution guidelines</a> first.</p>
<h2 id="license">License</h2>
<p><a href="http://creativecommons.org/publicdomain/zero/1.0"><img
src="http://mirrors.creativecommons.org/presskit/buttons/88x31/svg/cc-zero.svg"
alt="CC0" /></a></p>
<p>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.</p>
<p><a
href="https://github.com/siboehm/awesome-learn-datascience">learndatascience.md
Github</a></p>