The typical data science task in industry starts with an “ask” from the business. But few data scientists have been taught what to do with that ask. This book shows them how to assess it in the context of the business’s goals, reframe it to work optimally for both the data scientist and the employer, and then execute on it. Written by two of the experts who’ve achieved breakthrough optimizations at BuzzFeed, it’s packed with real-world examples that take you from start to finish: from ask to actionable insight.
Andrew Kelleher and Adam Kelleher walk you through well-formed, concrete principles for approaching common data science problems, giving you an easy-to-use checklist for effective execution. Using their principles and techniques, you’ll gain deeper understanding of your data, learn how to analyze noise and confounding variables so they don’t compromise your analysis, and save weeks of iterative improvement by planning your projects more effectively upfront.
Once you’ve mastered their principles, you’ll put them to work in two realistic, beginning-to-end site optimization tasks. These extended examples come complete with reusable code examples and recommended open-source solutions designed for easy adaptation to your everyday challenges. They will be especially valuable for anyone seeking their first data science job -- and everyone who’s found that job and wants to succeed in it.
"synopsis" may belong to another edition of this title.
Andrew Kelleher is a staff software engineer and distributed systems architect at Venmo. He was previously a staff software engineer at BuzzFeed and has worked on data pipelines and algorithm implementations for modern optimization. He graduated with a BS in physics from Clemson University. He runs a meetup in New York City that studies the fundamentals behind distributed systems in the context of production applications, and was ranked one of FastCompany's most creative people two years in a row.
Adam Kelleher wrote this book while working as principal data scientist at BuzzFeed and adjunct professor at Columbia University in the City of New York. As of May 2018, he is chief data scientist for research at Barclays and teaches causal inference and machine learning products at Columbia. He graduated from Clemson University with a BS in physics, and has a PhD in cosmology from University of North Carolina at Chapel Hill.
"About this title" may belong to another edition of this title.
FREE shipping within U.S.A.
Destination, rates & speedsSeller: BooksRun, Philadelphia, PA, U.S.A.
Paperback. Condition: Good. 1. Ship within 24hrs. Satisfaction 100% guaranteed. APO/FPO addresses supported. Seller Inventory # 0134116542-11-1
Quantity: 1 available
Seller: Sugarhouse Book Works, LLC, Salt Lake City, UT, U.S.A.
Condition: LikeNew. As pictured. Very slight edge wear. No marks or creases throughout. Nearly new. Carefully packed and promptly shipped. Seller Inventory # 238IF80011RS
Quantity: 1 available
Seller: Better World Books: West, Reno, NV, U.S.A.
Condition: Very Good. Used book that is in excellent condition. May show signs of wear or have minor defects. Seller Inventory # 52230923-75
Quantity: 1 available
Seller: booksdeck, Westlake Village, CA, U.S.A.
Softcover. Condition: New. Brand new book. International edition printed overseas but with similar contents as compared to the US edition. Same day shipping with tracking. Seller Inventory # 105|9780134116549
Quantity: 2 available
Seller: SecondSale, Montgomery, IL, U.S.A.
Condition: Good. Item in good condition. Textbooks may not include supplemental items i.e. CDs, access codes etc. Seller Inventory # 00087485999
Quantity: 1 available
Seller: Goodwill of Silicon Valley, SAN JOSE, CA, U.S.A.
Condition: very_good. Supports Goodwill of Silicon Valley job training programs. The cover and pages are in very good condition! The cover and any other included accessories are also in very good condition showing some minor use. The spine is straight, there are no rips tears or creases on the cover or the pages. Seller Inventory # GWSVV.0134116542.VG
Quantity: 1 available
Seller: Grand Eagle Retail, Fairfield, OH, U.S.A.
Paperback. Condition: new. Paperback. Machine Learning in Production is a crash course in data science and machine learning for people who need to solve real-world problems in production environments. Written for technically competent accidental data scientists with more curiosity and ambition than formal training, this complete and rigorous introduction stresses practice, not theory. Building on agile principles, Andrew and Adam Kelleher show how to quickly deliver significant value in production, resisting overhyped tools and unnecessary complexity. Drawing on their extensive experience, they help you ask useful questions and then execute production projects from start to finish. The authors show just how much information you can glean with straightforward queries, aggregations, and visualisations, and they teach indispensable error analysis methods to avoid costly mistakes. They turn to workhorse machine learning techniques such as linear regression, classification, clustering, and Bayesian inference, helping you choose the right algorithm for each production problem. Their concluding section on hardware, infrastructure, and distributed systems offers unique and invaluable guidance on optimisation in production environments. Andrew and Adam always focus on what matters in production: solving the problems that offer the highest return on investment, using the simplest, lowest-risk approaches that work. Leverage agile principles to maximise development efficiency in production projects Learn from practical Python code examples and visualisations that bring essential algorithmic concepts to life Start with simple heuristics and improve them as your data pipeline matures Avoid bad conclusions by implementing foundational error analysis techniques Communicate your results with basic data visualisation techniques Master basic machine learning techniques, starting with linear regression and random forests Perform classification and clustering on both vector and graph data Learn the basics of graphical models and Bayesian inference Understand correlation and causation in machine learning models Explore overfitting, model capacity, and other advanced machine learning techniques Make informed architectural decisions about storage, data transfer, computation, and communication Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9780134116549
Quantity: 1 available
Seller: bmyguest books, Toronto, ON, Canada
Soft cover. Condition: Very Good. 1st Edition. 256 Pages With An Index. In Very Good Condition. Soft Cover,books are NOT signed. We will state signed at the description section. we confirm they are signed via email or stated in the description box. - Specializing in academic, collectiblle and historically significant, providing the utmost quality and customer service satisfaction. For any questions feel free to email us. Seller Inventory # A9304a
Quantity: 1 available
Seller: Romtrade Corp., STERLING HEIGHTS, MI, U.S.A.
Condition: New. This is a Brand-new US Edition. This Item may be shipped from US or any other country as we have multiple locations worldwide. Seller Inventory # ABNR-25918
Quantity: 1 available
Seller: Basi6 International, Irving, TX, U.S.A.
Condition: Brand New. New. US edition. Expediting shipping for all USA and Europe orders excluding PO Box. Excellent Customer Service. Seller Inventory # ABEJUNE24-417
Quantity: 1 available