https://blog.namln.org/en/topics/ds/Recent content in Data Science on Nam LeHugoen-USWed, 11 Jan 2023 00:00:00 +0000https://blog.namln.org/en/topics/ds/books/Wed, 11 Jan 2023 00:00:00 +0000https://blog.namln.org/en/topics/ds/books/<h2 class="heading" id="the-law---the-mathematical-foundations"> The Law - The mathematical foundations<span class="heading__anchor"> <a href="#the-law---the-mathematical-foundations">#</a></span> </h2><ul> <li><a href="https://www.amazon.com/Statistical-Inference-George-Casella/dp/0534243126">Statistical Inference</a> - Casella &amp; Berger</li> <li><a href="https://foundations-of-applied-mathematics.github.io/">Foundations of Applied Mathematics</a></li> </ul> <h2 class="heading" id="history---foundational-works-that-provide-additional-context-for-more-advanced-concepts"> History - Foundational works that provide additional context for more advanced concepts<span class="heading__anchor"> <a href="#history---foundational-works-that-provide-additional-context-for-more-advanced-concepts">#</a></span> </h2><ul> <li> <p><a href="https://web.stanford.edu/%7Eboyd/cvxbook/">Convex Optimization</a> - Boyd &amp; Vandenberghe</p> </li> <li> <p><a href="https://www.amazon.com/dp/0521592712">Probability Theory: The Logic of Science</a> - Jaynes</p> </li> <li> <p><a href="https://www.amazon.com/Clean-Code-Handbook-Software-Craftsmanship/dp/0132350882?tag=hackr-20&amp;geniuslink=true">Clean Code</a> - Martin</p> </li> </ul> <h2 class="heading" id="poetry---prose-type-works"> Poetry - Prose type works<span class="heading__anchor"> <a href="#poetry---prose-type-works">#</a></span> </h2><ul> <li> <p><a href="https://www.amazon.com/Art-Data-Analysis-Question-Statistics/dp/1118411315">The Art of Data Analysis</a></p> </li> <li> <p><a href="https://www.amazon.com/Signal-Noise-Many-Predictions-Fail-but/dp/0143125087">Why Predictions Fail</a></p> </li> <li> <p><a href="https://www.amazon.com/Weapons-Math-Destruction-Increases-Inequality/dp/0553418815">Weapons of Math Destruction</a></p> </li> </ul> <h2 class="heading" id="major-prophets---seminal-works-on-major-topics"> Major Prophets - Seminal works on major topics<span class="heading__anchor"> <a href="#major-prophets---seminal-works-on-major-topics">#</a></span> </h2><ul> <li> <p><a href="https://www.amazon.com/Applied-Regression-Analysis-Probability-Statistics/dp/0471170828">Applied Regression Analysis</a> - Draper &amp; Smith</p> </li> <li> <p><a href="https://www.amazon.com/Data-Warehouse-Toolkit-Complete-Dimensional/dp/0471200247">The Data Warehouse Toolkit</a> - Kimball</p> </li> <li> <p><a href="http://www.stat.columbia.edu/%7Egelman/book/">Bayesian Data Analysis</a> - Gelman</p> </li> <li> <p><a href="https://otexts.com/fpp3/">Forecasting: Principles and Practices</a> - Hyndman &amp; Athanasopoulos</p> </li> </ul> <h2 class="heading" id="minor-prophets---important-works-but-not-quite-at-the-level-of-the-ds-major-prophets"> Minor Prophets - Important works, but not quite at the level of the DS Major Prophets<span class="heading__anchor"> <a href="#minor-prophets---important-works-but-not-quite-at-the-level-of-the-ds-major-prophets">#</a></span> </h2><ul> <li> <p><a href="https://www.mostlyharmlesseconometrics.com/">Mostly Harmless Econometrics</a></p> </li> <li> <p><a href="https://matheusfacure.github.io/python-causality-handbook/landing-page.html">Causal Inference for the Brave and True</a></p> </li> </ul> <p><a href="https://www.amazon.com/Trustworthy-Online-Controlled-Experiments-Practical/dp/1108724264">Trustworthy Online Controlled Experiments</a></p> <h2 class="heading" id="the-gospels---the-fulfillment-of-the-ds-law"> The Gospels - The fulfillment of the DS Law<span class="heading__anchor"> <a href="#the-gospels---the-fulfillment-of-the-ds-law">#</a></span> </h2><ul> <li> <p><a href="https://www.statlearning.com/">Introduction to Statistical Learning</a></p> </li> <li> <p><a href="https://hastie.su.domains/ElemStatLearn/">The Elements of Statistical Learning</a></p> </li> <li> <p><a href="https://www.deeplearningbook.org/">Deep Learning</a> - Goodfellow</p> </li> </ul> <h2 class="heading" id="history-pt-2---data-science-goes-to-the-gentiles-non-dsexecs"> History Pt. 2 - Data science goes to the Gentiles (non-DS/execs)<span class="heading__anchor"> <a href="#history-pt-2---data-science-goes-to-the-gentiles-non-dsexecs">#</a></span> </h2><ul> <li> <p><a href="https://www.amazon.com/Data-Science-Executives-Leveraging-Intelligence/dp/1544511256">Data Science for Executives</a></p> </li> <li> <p><a href="https://www.amazon.com/dp/1119002257">Storytelling with Data: a Guide to Data Visualization</a></p> </li> </ul> <h2 class="heading" id="letters---further-explanation-and-interpretation-of-the-ds-gospel"> Letters - Further explanation and interpretation of the DS Gospel<span class="heading__anchor"> <a href="#letters---further-explanation-and-interpretation-of-the-ds-gospel">#</a></span> </h2><ul> <li> <p><a href="https://probml.github.io/pml-book/">Machine Learning: a Probabilistic Perspective</a> - Murphy</p> </li> <li> <p><a href="https://r4ds.had.co.nz/index.html">R for Data Science</a></p> </li> <li> <p><a href="https://www.amazon.com/Python-Machine-Learning-scikit-learn-TensorFlow-ebook/dp/B0742K7HYF">Python Machine Learning</a></p> </li> </ul>https://blog.namln.org/en/topics/ds/github/Wed, 11 Jan 2023 00:00:00 +0000https://blog.namln.org/en/topics/ds/github/<ol> <li>The Data Engineering Cookbook, <a href="https://github.com/andkret/Cookbook?tab=readme-ov-file">Github</a></li> <li>A curated list of data engineering tools for software developers, <a href="https://github.com/igorbarinov/awesome-data-engineering">Github</a></li> <li>Data Engineering Zoomcamp, <a href="https://github.com/DataTalksClub/data-engineering-zoomcamp">Github</a></li> <li>Python Data Science Handbook: full text in Jupyter Notebooks, <a href="https://github.com/jakevdp/PythonDataScienceHandbook">Github</a></li> <li>Data Science for Beginners - A Curriculum, <a href="https://github.com/microsoft/Data-Science-For-Beginners">Github</a></li> <li>Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines. <a href="https://github.com/donnemartin/data-science-ipython-notebooks">Github</a></li> <li>Papers &amp; tech blogs by companies sharing their work on data science &amp; machine learning in production. <a href="https://github.com/eugeneyan/applied-ml">Github</a></li> <li>An awesome Data Science repository to learn and apply for real world problems. <a href="https://github.com/academic/awesome-datascience">Github</a></li> <li>List of Data Science Cheatsheets to rule the world, <a href="https://github.com/FavioVazquez/ds-cheatsheets">Github</a></li> <li>Data science interview questions and answers, <a href="https://github.com/alexeygrigorev/data-science-interviews">Github</a></li> <li>A curated list of applied machine learning and data science notebooks and libraries across different industries, <a href="https://github.com/firmai/industry-machine-learning">Github</a></li> <li>A curated list of data science blogs, <a href="https://github.com/rushter/data-science-blogs">Github</a></li> </ol>