000 03189cam a22004817i 4500
001 20922
003 OSt
005 20241204130613.0
008 240607t20232024cauad 001 0 eng d
020 _a9781098146474
_qpaperback
020 _a1098146476
_qpaperback
040 _aYDX
_beng
_erda
_cYDX
_dOCLCO
_dJRZ
_dOCLCO
_dBDX
_dIUL
_dOCLCF
_dOCLCO
_dDLC
_dIQ-MoCLU
082 0 4 _a006.312
_223
_bV365
100 1 _aVaughan, Daniel,
_eauthor.
245 1 0 _aData science: the hard parts :
_btechniques for excelling at data science /
_cDaniel Vaughan.
246 3 0 _aTechniques for excelling at data science
250 _aFirst edition.
264 1 _aSebastopol, CA :
_bO'Reilly Media,
_c2023
264 4 _c©2024
300 _axvi, 237 pages :
_billustrations, charts ;
_c24 cm
336 _atext
_btxt
_2rdacontent
337 _aunmediated
_bn
_2rdamedia
338 _avolume
_bnc
_2rdacarrier
500 _a"November 2023: First Edition"--Title page verso.
500 _aIncludes index.
505 0 0 _tData analytics techniques --
_tSo what? Creating value with data science --
_tMetrics design --
_tGrowth decompositions: understanding tailwinds and headwinds --
_t2x2 designs --
_tBuilding business cases --
_tWhat's in a lift? --
_tNarratives --
_tDatavis: choosing the right plot to deliver a message --
_tMachine learning --
_tSimulation and bootstrapping --
_tLinear regression : going back to basics --
_tData leakage --
_tProductionizing models --
_tStorytelling in machine learning --
_tFrom prediction to decisions --
_tIncrementality: the holy grail of data science? --
_tA/B tests --
_tLarge language models and the practice of data science.
520 _aThis hands-on guide offers a set of techniques and best practices that are often missed in conventional data engineering and data science education. A common misconception is that great data scientists are experts in the "bit themes" of the discipline, namely ML and programming. But most of the time, these tools can only take us so far. In reality, it's the nuances within these large themes, and the ability to impact the business, that truly distinguish a top-notch data scientist from an average one. Taken as a whole, the lessons in this book make the difference between an average data scientist candidate and an exceptional data scientist working in the field. Author Daniel Vaughan has collected, extended, and used these skills to create value and train data scientists from different companies and industries.
650 0 _aElectronic data processing.
650 0 _aBig data.
650 0 _aDatabase management.
650 0 _aData mining.
650 6 _aDonnées volumineuses.
650 6 _aBases de données
_xGestion.
650 6 _aExploration de données (Informatique)
650 7 _aBig data
_2fast
650 7 _aData mining
_2fast
650 7 _aDatabase management
_2fast
650 7 _aElectronic data processing
_2fast
776 0 8 _iOnline version:
_aVaughan, Daniel.
_tData science.
_bFirst edition.
_dSebastopol, [California] : O'Reilly Media, 2023
_z9781098146443
_w(OCoLC)1407278110
910 _aSAJA
942 _2ddc
_cBK
999 _c20922
_d20922