Business Analytics

A Practical Guide with Applications in Python
Erscheinungsjahr 2027
ISBN: 978-3-032-47468-1
Verlag: Springer

Buch, Englisch, Format (B × H): 155 mm x 235 mm

provides an accessible introduction to using Python for data analysis and business intelligence. This book covers essential topics ranging from basic Python programming to advanced machine learning and time series forecasting techniques, all within a business context. The book begins with an introduction to Python and data manipulation using Pandas, then progresses through statistical analysis, machine learning, and time series forecasting. It covers key concepts in business analytics, including customer segmentation, sales forecasting, and fraud detection. Each chapter includes hands-on examples and case studies using real-world datasets to reinforce learning. This book distinguishes itself through its focus on business applications and does not assume readers have prior coding experience. The emphasis is on the applications of techniques, not the mathematics. The book is intended for business analysts, data scientists, and students who want to apply Python to real-world business problems. Its hands-on approach, with numerous coding exercises and practical examples, makes it suitable for both beginners and those with some programming experience looking to expand their skills in business analytics.

Zielgruppe

Professional/practitioner

Autoren/Hrsg.

Jones, Kyle

Fachgebiete

• Wirtschaftswissenschaften Wirtschaftswissenschaften Wirtschaftswissenschaften: Allgemeines
• Mathematik | Informatik Mathematik Stochastik
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Data Mining
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Wissensbasierte Systeme, Expertensysteme

Inhaltsverzeichnis

Chapter 1 Introduction to Python for Data Analysis.- Chapter 2 Four Python Ideas for Business Analytics.- Chapter 3 Data Manipulation with Pandas.- Chapter 4 Data Analysis in Practice with Python.- Chapter 5 Visualizing the normal Distribution with Python and Matplotlib.- Chapter 6 Introduction to Statistics for People Who Do Business Analytics.- Chapter 7 Comparing Means Using T-Tests with Python.- Chapter 8 Chi-squared Test for Independence for Business Analytics.- Chapter 9 Introduction to Anova.- Chapter 10 Introduction to Machine Learning.- Chapter 11 Introduction to Pandas for Data Analysis.

Über Autor(innen)

Kyle Jones is Director of Data and Analytics at Halliburton Landmark and an adjunct faculty member at the University of Oklahoma. He has held technical and leadership roles at Amazon Web Services and Databricks, with experience spanning data analytics, cloud computing, software architecture, and large-scale data systems. He holds a Doctor of Engineering from George Washington University and a Master in Public Policy from Harvard University. His work focuses on practical methods for applying data, analytics, and software to business and technical problems.