000			03323cam a22003137i 4500
008			211007s2022 nyu fo 001 0 eng d
020			_a9780367760489 _qpaperback _cRM141.82
040			_aTYFRS _beng _erda _epn _cTYFRS _dTYFRS _dOCLCF _dOCLCQ _dOCLCO _dUKAHL _dUKMGB _dEBLCP _dYDX _dCSA _dOCLCO _dOCLCQ _dZCU _dOCLCQ _dDLC _dUKM _erda
090			_aT58.6 _b.R354 3
100	1		_aJugulum, Rajesh, _eauthor.
245	1	0	_aCommon data sense for professionals : _ba process-oriented approach for data-science projects / _cRajesh Jugulum.
264		1	_aNew York, NY : _bRoutledge/Productivity Press, _c2022.
300			_axvii, 100 pages _billustrations _c 22cm.
336			_atext _2rdacontent
337			_aunmediated _2rdamedia
338			_avolume _2rdacarrier
504			_aIncludes bibliographical references and index.
520			_aData is an intrinsic part of our daily lives. Everything we do is a data point. Many of these data points are recorded with the intent to help us lead more efficient lives. We have apps that track our workouts, sleep, food intake, and personal finance. We use the data to make changes to our lives based on goals we have set for ourselves. Businesses use vast collections to determine strategy and marketing. Data scientists take data, analyze it and create models to help solve problems. You may have heard of companies having data management teams, or Chief Information Officers (CIO) or Chief Analytics Officers (CAO), etc. These are all people that work with data, but their function is more related to vetting data and preparing it for use by data scientists. The jump from personal data usage for self-betterment to mass data analysis for business process improvement often feels bigger to us than it is. In turn, we often think big data analysis requires tools held only by advanced degree holders. Though an advanced degrees are certainly valuable, this book illustrates how it is not a requirement to adequately run a data science project. Because we are all already data users, with some simple strategies and exposure to basic statistical analysis software programs, anyone who has the proper tools and determination can solve data science problems. The process presented in this book will help empower individuals to work on and solve data- related challenges. The goal for this book is to provide a step-by-step guide to the data science process so that you can feel confident in leading your own data science project. To aid with clarity and understanding, the author presents a fictional restaurant chain to use as a case study -- it illustrates how the various topics discussed can be applied. Essentially, this book helps traditional business people to solve data related problems on their own without any hesitation or fear. The powerful methods are presented in the form of conversations, examples, and case studies. The conversational style is engaging and provides clarity.
650		0	_aBusiness _xData processing.
650		0	_aDecision making _xData processing.
650		0	_aBig data.
650		0	_aBusiness intelligence.
907			_a.b17003465 _b2024-03-18 _c2023-09-25
942			_c01 _n0 _kT58.6 .R354 3
949			_o600000894
990			_azsz
991			_aFakulti Sains Teknologi dan Maklumat
998			_al _b2023-09-25 _cm _da _feng _gnyu _y0 _z.b17003465
999			_c667129 _d667129