Data science and analytics at Vinted are fields that are constantly evolving. To excel in this ever-changing landscape, it's imperative to stay informed about the latest trends and tools.Within Data Science &amp; Analytics (DSA) we have three distinct roles. To understand your potential role within DSA context better, here are brief descriptions of each role we have in the department:<ul class="ul1"><li class="li1">Analytics Engineers are responsible for data curation – translating data needs from stakeholders into architecting, building and maintaining efficient &amp; reliable data models and pipelines. &nbsp;</li><li class="li1">Decision Scientists are responsible for actionable insights, identifying and seizing opportunities, and automated tools that increase the quality of product and business decisions by applying statistical methods and data-driven decision making. &nbsp;</li><li class="li1">Data Scientists are responsible for identification of algorithmic opportunities, ensuring those opportunities are addressed in an optimal fashion and design, development and maintenance of production-grade statistical and machine learning algorithms.</li></ul>

Here's a diverse range of recommendations that can help you navigate this journey, but be sure to customize your choices to match your individual interests and objectives within the realm of data science and analytics.&nbsp;&nbsp;About Git &amp; GitHub:<ul class="ul1"><li class="li3"><a href="https://eu.udacity.com/course/how-to-use-git-and-github--ud775">How to Use Git and GitHub</a></li><li class="li3"><a href="https://www.udemy.com/course/github-ultimate/">GitHub Ultimate: Master Git and GitHub - Beginner to Expert</a></li><li class="li3"><a href="https://nvie.com/posts/a-successful-git-branching-model/">A successful Git branching model</a></li><li class="li3"><a href="http://engineering.vinted.com/2015/01/02/proper-git/">Proper Git</a></li><li class="li3"><a href="https://git-scm.com/book/en/v2/Getting-Started-About-Version-Control">What is version control?</a></li></ul>&nbsp;Useful Courses:<ul class="ul1"><li class="li3"><a href="https://scikit-learn.org/stable/">Scikit learn</a></li><li class="li3"><a href="https://pytorch.org/tutorials/beginner/basics/intro.html">Pytorch basics</a></li><li class="li3"><a href="https://course.fast.ai/">Practical Deep Learning</a></li><li class="li3"><a href="https://mode.com/sql-tutorial/introduction-to-sql/">Introduction to SQL</a></li><li class="li3"><a href="https://www.bradyneal.com/causal-inference-course">Introduction to causal analysis</a></li></ul>&nbsp;Books:<ul class="ul1"><li class="li3"><a href="https://www.goodreads.com/book/show/51635906-trustworthy-online-controlled-experiments">Trustworthy Online Controlled Experiments: A Practical Guide to A/B Testing</a></li><li class="li3"><a href="https://d2l.ai/">Dive into Deep Learning</a>&nbsp;</li><li class="li3"><a href="https://www.oreilly.com/library/view/building-machine-learning/9781492045106/">Building Machine Learning Powered Applications </a>by Emmanuel Ameisen</li><li class="li3"><a href="https://www.amazon.com/Clean-Code-Handbook-Software-Craftsmanship/dp/0132350882">Clean Code: A Handbook of Agile Software Craftsmanship</a> by by Robert C. Martin</li><li class="li1"><a href="https://www.deeplearningbook.org/">Deep Learning</a> by Ian Goodfellow, Yoshua Bengio and Aaron Courville</li><li class="li3"><a href="https://www.oreilly.com/library/view/python-for-data/9781449323592/">Python for Data Analysis</a> / Oreilly</li><li class="li3"><a href="https://www.oreilly.com/library/view/thinking-with-data/9781491949757/">Thinking with data</a> / Oreilly</li><li class="li3"><a href="https://www.amazon.com/Storytelling-Data-Visualization-Business-Professionals/dp/1119002257?pd_rd_w=GOBtn&amp;content-id=amzn1.sym.7f0cf323-50c6-49e3-b3f9-63546bb79c92&amp;pf_rd_p=7f0cf323-50c6-49e3-b3f9-63546bb79c92&amp;pf_rd_r=PRDGPBNW9STWB7TQTHQW&amp;pd_rd_wg=v8uFy&amp;pd_rd_r=024055bd-f103-4273-9e7f-100ca4cb5886&amp;pd_rd_i=1119002257&amp;psc=1&amp;linkCode=sl1&amp;tag=swdbooks-20&amp;linkId=309f73640a0cb64e3b676e26561d07a0&amp;language=en_US&amp;ref_=as_li_ss_tl">Storytelling with Data: A Data Visualization Guide for Business Professionals</a></li></ul>&nbsp;Blogs:<ul class="ul1"><li class="li3"><a href="https://huyenchip.com/blog/">Chip Huyen’s</a> blog</li><li class="li3"><a href="https://eugeneyan.com/writing/">Eugenayan.com</a></li><li class="li3"><a href="https://drive.google.com/file/d/1ZOu6yXnpL_duYixQKsXTMEH_sAe4JDKT/view?usp=sharing">Scientific Method &amp; ML Engineering</a></li><li class="li1"><a href="https://www.kaggle.com/">Kaggle</a> - the repository of community-published models, data &amp; code</li></ul>&nbsp;

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List of useful resources for aspiring Data and Science Analysts