MODELING TECHNIQUES IN PREDICTIVE ANALYTICS WITH PYTHON AND R: A GUIDE TO DATA SCIENCE

Today, successful firms win by understanding their data more deeply than competitors do. They compete based on analytics. In Modelling Techniques in Predictive Analytics, the Python edition, the leader of North-western University’s prestigious analytics program brings together all the up-to-date concepts, techniques and Python code you need to excel in analytics.
Thomas W. Miller’s balanced approach combines business context and quantitative tools, appealing to managers, analysts, programmers and students alike. This important reference addresses multiple business challenges and business cases, including segmentation, brand positioning, product choice modelling, pricing research, finance, sports, Web and text analytics and social network analysis. He illuminates the use of cross-sectional data, time series, spatial and even spatio-temporal data. For each problem, Miller explains:
Today’s definitive, comprehensive guide to using predictive analytics to overcome business challenges – now updated and reorganized for more effective learning.
Teaches modelling techniques conceptually, with words and figures – and then mathematically, with the powerful Python language. Restructured standalone chapters provide fast access to all the knowledge you need to solve any category of problem. Covers segmentation, brand positioning, product choice modelling, pricing, finance, sports analytics, Web/text analytics, social network analysis and more. Helps you leverage traditional techniques, machine learning, data visualization and statistical graphics.