000			a
999			_c30952 _d30952
008			220610b xxu||||| |||| 00| 0 eng d
020			_a9783030870225
082			_a658.4033 _bPAC
100			_aPaczkowski, Walter R.
245			_aBusiness analytics : data science for business problems
260			_bSpringer, _c2021 _aCham :
300			_axxxviii, 387 p. ; _bill., _c25 cm
365			_b109.99 _cEUR _d86.00
504			_aInclude bibliographic references and index.
520			_aThis book focuses on three core knowledge requirements for effective and thorough data analysis for solving business problems. These are a foundational understanding of: 1. statistical, econometric, and machine learning techniques; 2. data handling capabilities; 3. at least one programming language. Practical in orientation, the volume offers illustrative case studies throughout and examples using Python in the context of Jupyter notebooks. Covered topics include demand measurement and forecasting, predictive modeling, pricing analytics, customer satisfaction assessment, market and advertising research, and new product development and research. This volume will be useful to business data analysts, data scientists, and market research professionals, as well as aspiring practitioners in business data analytics. It can also be used in colleges and universities offering courses and certifications in business data analytics, data science, and market research.
650			_aDecision making
650			_aMathematical models
650			_aStrategic planning
650			_aBusiness intelligence
650			_aAccuracy report
650			_a Autoregressive model(AR)
650			_aCross-sectional data
650			_aDecision trees
650			_aDisturbance term
650			_aElasticity
650			_aEconometrics
650			_aFit method
650			_a Frequency table
650			_a Gaussian distribution
650			_a Heatmap
650			_aHypothesis testing
650			_aK-means clustering
650			_a Label Encoding
650			_aMosaic graph
650			_aNaive Bayes
650			_a Outliers
650			_aPanel data set
650			_aStatsmodels
650			_aTraining data set
650			_aUnrestricted model
650			_aZ-transform
650			_aSpatial data
650			_aTime senses analysis
942			_2ddc _cBK