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ROMAN - S DATA SCIENCE. How to monetize your data.

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dc.contributor.authorRoman Zykovspa
dc.date.accessioned2023-02-21T19:34:13Z
dc.date.available2023-02-21T19:34:13Z
dc.description303 p. , Figuresspa
dc.description.otherAGNB February 2022spa
dc.description.tableofcontentsData is everywhere - from Tinder algorithms that match you with suposedly (but not really) random people, to information wars waged by politicians. It is of no surprise to anyone these days that every single thing we do is closely monitores, including your internet search history and whatever you might be up to offline too. Something catch your eye when you were passing that sports store? Just wait for the ads to start appearing on your social network pages. Tell a friend at work what your cat - s been up to and suddenly there - s dry kibble and cat litter all over your feed. This is where the more impressinable of us might become more than just a little paranoid. But it - s not the data that - s to blame. It - s all about whose hands it fall into. There are many myths when it comes to data analysis, and - data scientist - is one of the - sexiest - and most promising professions of the future. My aim with this book is to debunk these myths and tell things how they really are. And I hope that you, the reader, will find yourself on the - light side - of the Force alongside me.spa
dc.identifier.bitstream10371.pdfspa
dc.identifier.collection1- GENERALspa
dc.identifier.isbn9798465129695spa
dc.identifier.local10371spa
dc.identifier.mfn6252spa
dc.identifier.signatureCG10371spa
dc.identifier.urihttps://hdl.handle.net/20.500.14000/1070
dc.language.localengspa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.rights.coarhttp://purl.org/coar/access_right/c_abf2spa
dc.rights.licenseAtribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)spa
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/spa
dc.subjectChapter 1. How we make Decisions describes the general principles of decision-making and how data affects decisions.spa
dc.subjectChapter 2. Lte - s do some Data Analysis introduces general concepts: What artifacts do we deal with when analysing data? In this chapter I also start to raise some organization issues relating to data analysis.spa
dc.subjectChapter 3. Building Analytics from scratch describes the process of building analytics, from the first tasks to the choice of technology and hiring personnelspa
dc.subjectChapter 4. How about some analytical tasks? This chapter is all about tasks. What is a good analytical task? And how can we test it? The technical attributes of such tasks are datasets, descriptive statistics, graphs, pair analysis and technical debt.spa
dc.subjectChapter 5. Data covers everything you ever wanted to know about data - volume, access, quality and formats.spa
dc.subjectChapter 6. Data Warehouses explains why we need data warehouses and what kind of warehouses exist. This chapter also touches upon the popular Big Data systems Hadoop and Spark.spa
dc.subjectChapter 7. Data Analysis Tools describes the most popular analytical methods, from Excel spreadsheets to cloud systems.spa
dc.subjectChapter 8. Machine Learning Algorithms provides a basic introduction to machine learning.spa
dc.subjectChapter 9. The practice of Machine Learning shares life hacks on how to study machine learning and how to work with it for it to be useful.spa
dc.subjectChapter 10. Implementing ML in Real Life: Hypotheses and Experiments describes three types of statistical analysis of experiments (Fisher statistics, Bayesian statistics and bootstrapping) and the use of A/B tests in practice.spa
dc.subjectChapter 11. Data Ethics. I could not ignore this topic. Our field is becoming increasingly regulated by the states. Here we will discuss the reasons why.spa
dc.subjectChapter 12. Challenges and Startups describes the main tasks that I faced in my time in ecommerce, as well as my experience as a co-founder of Retail Rocket.spa
dc.subjectChapter 13. Building a Career is aimed more at beginners - how to look for a job, develop as an analyst and when to move on to something new.spa
dc.titleROMAN - S DATA SCIENCE. How to monetize your data.spa
dc.typeLibrospa
dc.type.coarhttp://purl.org/coar/resource_type/c_2f33spa
dc.type.coarversionhttp://purl.org/coar/version/c_970fb48d4fbd8a85spa
dc.type.contentTextspa
dc.type.driverinfo:eu-repo/semantics/bookspa
dc.type.localColección Generalspa
dc.type.redcolhttp://purl.org/redcol/resource_type/LIBspa
dc.type.versioninfo:eu-repo/semantics/publishedVersionspa
dspace.entity.typePublication
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