Managing Software Development Projects or Applied Data Mining

Managing Software Development Projects: Formula for Success

Author: Neal Whitten

Practical, comprehensive—a complete, no-nonsense guide to better project management...

This no-nonsense troubleshooting guide was written for frontline managers who want results, not rhetoric. Short on theory and long on practical, hands-on advice and guidance, it arms you with proven, easy-to-implement solutions to big ticket problems that plague today's software development projects, including those relating to personnel, quality, project scheduling and tracking, product requirements, product quality and usability, and much more. Written in a straightforward, conversational style and packed with realistic scenarios, Managing Software Development Projects, Second Edition shows you how to:

• Identify, resolve, and avoid most common development problems
• Improve the quality of your products and your customers' satisfaction with them
• Shorten development cycles
• Increase the productivity of your team members

Updated and expanded by over 50 percent to reflect many changes that have occurred in the field over the past four years, this Second Edition of the bestselling original is now, more than ever, an indispensable resource for every project manager or software developer.

See also: Depression or Numb Toes and Aching Soles

Applied Data Mining: Statistical Methods for Business and Industry

Author: Paolo Giudici

Data mining can be defined as the process of selection, exploration and modelling of large databases, in order to discover models and patterns. The increasing availability of data in the current information society has led to the need for valid tools for its modelling and analysis. Data mining and applied statistical methods are the appropriate tools to extract such knowledge from data. Applications occur in many different fields, including statistics, computer science, machine learning, economics, marketing and finance.

This book is the first to describe applied data mining methods in a consistent statistical framework, and then show how they can be applied in practice. All the methods described are either computational, or of a statistical modelling nature. Complex probabilistic models and mathematical tools are not used, so the book is accessible to a wide audience of students and industry professionals. The second half of the book consists of nine case studies, taken from the author's own work in industry, that demonstrate how the methods described can be applied to real problems.

• Provides a solid introduction to applied data mining methods in a consistent statistical framework

• Includes coverage of classical, multivariate and Bayesian statistical methodology

• Includes many recent developments such as web mining, sequential Bayesian analysis and memory based reasoning

• Each statistical method described is illustrated with real life applications

• Features a number of detailed case studies based on applied projects within industry

• Incorporates discussion on software used in datamining, with particular emphasis on SAS

• Supported by a website featuring data sets, software and additional material

• Includes an extensive bibliography and pointers to further reading within the text

• Author has many years experience teaching introductory and multivariate statistics and data mining, and working on applied projects within industry

A valuable resource for advanced undergraduate and graduate students of applied statistics, data mining, computer science and economics, as well as for professionals working in industry on projects involving large volumes of data - such as in marketing or financial risk management.

Data sets used in the case studies are available at

Table of Contents:

	Preface
1	Introduction	1
Pt. I	Methodology	17
2	Organisation of the data	19
3	Exploratory data analysis	33
4	Computational data mining	69
5	Statistical data mining	129
6	Evaluation of data mining methods	187
Pt. II	Business cases	207
7	Market basket analysis	209
8	Web clickstream analysis	229
9	Profiling website visitors	255
10	Customer relationship management	273
11	Credit scoring	293
12	Forecasting television audience	323
	Bibliography	353
	Index	357