Modern Business Analytics

Auteur
Deanne Larson, Ph.D., is a data science practitioner and academic whose passion is helping others be successful in applying analytics to achieve business value. Her research has focused on implementing an enterprise data strategy, applying agile analytics, and data science best practices. Dr. Larson is passionate about teaching and applying analytics. Deanne attended Executive Training at the Harvard Business School focusing on IT leadership, Stanford University focusing on data science, and New York University focusing on business analytics. She has presented at multiple conferences including TDWI, TDWI Europe, IRM UK, PMI, and other academic conferences. She is Principal Faculty at City University of Seattle, has consulted for several Fortune 500 companies, and has authored multiple research articles on data science methodology and best practices. She holds Project Management Professional (PMP), Project Management Agile Certified Practitioner (PMI-ACP), Certified Business Intelligence Professional (CBIP), and Six Sigma certifications.

Texte du rabat

Deriving business value from analytics is a challenging process. Turning data into information requires a business analyst who is adept at multiple technologies including databases, programming tools, and commercial analytics tools. This practical guide shows programmers who understand analysis concepts how to build the skills necessary to achieve business value. Author Deanne Larson, data science practitioner and academic, helps you bridge the technical and business worlds to meet these requirements. You'll focus on developing these skills with R and Python using real-world examples. You'll also learn how to leverage methodologies for successful delivery. Learning methodology combined with open source tools is key to delivering successful business analytics and value. This book shows you how to:

• Apply business analytics methodologies to achieve successful results
• Cleanse and transform data using R and Python
• Use R and Python to complete exploratory data analysis
• Create predictive models to solve business problems in R and Python
• Use Python, R, and business analytics tools to handle large volumes of data
• Commit code to GitHub to collaborate with data engineers and data scientists
• Measure success in business analytics