We publish dozens of top-notch articles every day. To help you find some of the very best ones, we’ve created several collections organized around specific topics and post types.<\/p>\n
For example, you might be looking for insights on landing a machine learning job; you can find our recommended articles on that topic by going to our Office Hours<\/a> column. If you’re in the mood for a long, thoughtful read, you can browse around our Deep Dives<\/a> page, where we gather some of our more substantive contributions. And so on.<\/p>\n If you’re a new or aspiring TDS contributor<\/a>, browsing our columns will give you a solid idea of the kinds of posts our team finds especially compelling. Happy reading!<\/p>\n If you are new to data science, start here! Our Getting Started column includes a selection of articles that will help you get started in many data science and machine learning domains.<\/p>\n Posts we feature as Editors’ Picks represent the very best of TDS: insightful, engaging writing on fresh and thought-provoking topics. Some are very technical, others might be more speculative and even opinionated. They’re all must-reads.<\/p>\n From patient explainers that make complex concepts accessible to step-by-step guides with generous amounts of code examples, this collection brings together the articles that really deserve your time and full attention (they’re typically at least 12-minute reads).<\/p>\n Are you interested in getting a job in data science? Do you want to know how to prepare yourself for job interviews or find out what a typical day as a data scientist looks like? These are just some of the topics covered in our Office Hours column.<\/p>\n Every month, our team publishes a selection of some of our best articles on a particular topic. You can find all of our monthly editions here!<\/p>\n Explore all of our podcast episodes and listen to great conversations about data science and machine learning. \ud83c\udfa7<\/p>\n Our Opinion column is where you will find alternate points of view, controversial opinions, new angles on existing issues and more. We encourage our writers to participate in intelligent discussion, and we’d be thrilled to see your thoughts here as well.<\/p>\n Are you looking for inspiration for your next machine learning project? Are you trying to find a tutorial that will teach you the best practices in programming and data science? Would you like to see examples of top-notch portfolio projects to help you stand out in your job search? You can find all of these and more in our Hands-on Tutorials column.<\/p>\n If you’re interested in analyzing and handling big data, you’ll want to take a look at this column. It features advanced tutorials in data engineering, best practices in model deployment, corporate case studies, and more.<\/p>\n We believe that data science can help make the world a better place. We think that the proper use and interpretation of data can improve our lives. To find more articles that discuss the power and importance of data, visit our Data for Change column.<\/p>\n What comes after Python 101? How does object-oriented design work? If you’re looking to boost your programming skills beyond the basics, you’ll want to bookmark this column. It’ll feature articles that teach advanced coding syntax, tips for clean code, and introduce tools that will level up your coding performance.<\/p>\n Are you a news junkie? Check out this column for pieces that combine strong visuals and analytic rigour. Data journalists interpret data and present their findings in engaging narratives that shed light on underreported issues.<\/p>\n Data science and machine learning are two of the fastest growing research fields. Here, we curate research breakthroughs from academics, motivated students, and industry experts.<\/p>\n What does data science look like in practice? We’re interested in learning from practitioners what tools they find most useful in industry use cases. This column will feature case studies across applications from machine learning techniques to managing cloud infrastructures.<\/p>\n We have also created a few columns where<\/a> we gather stories that can help ensure that data science and machine learning technologies evolve in a way that benefits humanity (you can learn more about this here):<\/p>\n Columns on TDS are carefully curated collections of posts on a particular idea or category of articles.<\/p>\n","protected":false},"author":18,"featured_media":3534,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"is_member_only":false,"sub_heading":"Columns on TDS are carefully curated collections of posts on a particular idea or category of articles.","footnotes":""},"categories":[44,22],"tags":[448,446,480,488],"sponsor":[],"coauthors":[],"class_list":["post-3533","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-data-science","category-machine-learning","tag-data-science","tag-machine-learning","tag-tds-explore","tag-towards-data-science"],"yoast_head":"\n
\nGetting Started<\/a><\/h3>\n
\nEditors’ Picks<\/a><\/h3>\n
\nDeep Dives<\/a><\/h3>\n
\nOffice Hours<\/a><\/h3>\n
\nMonthly Edition<\/a><\/h3>\n
\nOur Podcast<\/a><\/h3>\n
\nOpinion<\/a><\/h3>\n
\nHands-on Tutorials<\/a><\/h3>\n
\nMaking Sense of Big Data<\/a><\/h3>\n
\nData for Change<\/a><\/strong><\/h3>\n
\nTips and Tricks<\/a><\/strong><\/h3>\n
\nData Journalism<\/a><\/strong><\/h3>\n
\nThoughts and Theory<\/a><\/strong><\/h3>\n
\nNotes from Industry<\/a><\/strong><\/h3>\n
\nEmerging Problems in Data Science and Machine Learning<\/h3>\n
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