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

dc.creatorRoman Zykov
dc.date.accessioned2023-02-21T19:34:13Z
dc.date.accessioned2023-03-07T18:49:17Z
dc.date.available2023-02-21T19:34:13Z
dc.date.available2023-03-07T18:49:17Z
dc.date.created2023-02-21T19:34:13Z
dc.identifier10371.pdf
dc.identifier1- GENERAL
dc.identifier9798465129695
dc.identifier10371
dc.identifier6252
dc.identifierCG10371
dc.identifierhttps://repositorio.ccc.org.co/handle/001/1070
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/5892808
dc.languageeng
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rightshttp://purl.org/coar/access_right/c_abf2
dc.rightsAtribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)
dc.rightshttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectChapter 1. How we make Decisions describes the general principles of decision-making and how data affects decisions.
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.
dc.subjectChapter 3. Building Analytics from scratch describes the process of building analytics, from the first tasks to the choice of technology and hiring personnel
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.
dc.subjectChapter 5. Data covers everything you ever wanted to know about data - volume, access, quality and formats.
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.
dc.subjectChapter 7. Data Analysis Tools describes the most popular analytical methods, from Excel spreadsheets to cloud systems.
dc.subjectChapter 8. Machine Learning Algorithms provides a basic introduction to machine learning.
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.
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.
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.
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.
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.
dc.titleROMAN - S DATA SCIENCE. How to monetize your data.
dc.typeLibro


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