The first of a two-volume series covering Python, a general-purpose programming language, for accounting and business tasks. This volume covers the concepts of Python and select foundational libraries using examples, codes, and accompanying outputs. Examples are tailored to accounting and business use-cases.

Includes examples and case studies that teach users the tricks for analysing data in Excel.

For those new to data analysis. Takes the reader on a tour of Excel's data analysis tools.

Large corporations like IBM and Oracle are using Excel dashboards and reports as a Business Intelligence tool, and many other smaller businesses are looking to these tools in order to cut costs for budgetary reasons.

This book delivers a powerful analysis of the new technological forces buffeting the accounting profession. Using case studies and practical examples, the author demonstrates how big data, blockchain, robotic process automation and artificial intelligence can help accountants adapt to new realities.

This book will help accountants and financial managers better understand big data and analytics, including its history and current trends. It dives into the platforms and operating tools that will help you measure program impacts and ROI, visualize data and business processes, and uncover the relationship between key performance indicators.

This book hopes to provide the reader with an inventory of basic algorithms which can be easily expanded and customized to fit an auditor's specific challenges. Accessible and comprehensive tools for these additional approaches are covered in this book, as are research approaches to take advantage of the additional explanatory power that these approaches offer to auditing. Coverage of software, methodologies, and fit statistics provide a concise reference for the conduct of audits, helping assure that audit opinions and decisions are defensible, fair, and accurate.

This book takes a thoughtful look at how the digital finance industry is evolving. It explains how to integrate digital finance concepts into existing traditional finance platforms and explores what successful companies are doing to maximize their opportunities in this context.

This article discusses the importance of data management for accountancy firms, focusing on the trends of privacy, security, and artificial intelligence (AI). It highlights the challenges posed by cyberthreats and regulatory requirements, emphasizing the need to prioritize privacy and security measures. The article suggests strategies such as data encryption, cybersecurity measures, and staff training to enhance data management. It also recommends leveraging AI tools for automation, predictive analytics, and data visualization.

Article discusses how accounting firms are increasingly embracing artificial intelligence (AI) and generative AI to improve efficiency and gain a competitive edge. Many firms are exploring the various applications of AI, such as internal virtual agents, client communication and automation, data analysis and anomaly detection, and tax and client services. While there is a growing acceptance of AI within the profession, some accountants still have concerns about work quality, data accuracy, fiscal costs, undetected bias, and ethical issues. It is important for firms to have proper AI guidelines and policies in place to address these concerns and ensure privacy, transparency, and fairness

Article discusses openness and transparency in data collection from customers. The authors argue that companies that are open about what they are doing with customer data and offer something fair in exchange are at an advantage over companies that are not trusted with data.

The article highlights the importance of data management and security in today's digital supply chain, where every enterprise is considered a "data enterprise." As companies transition to digitally integrated supply chains, they must prioritize data stewardship, embed data governance and security into their operations, and adapt to changing practices and regulations.

The article focuses on data classification as a pre-requisite for data protection, security, and compliance, and it explores the tools available for data classification, including offerings from major tech companies as well as open-source options. It mentions these tools use machine learning and artificial intelligence to automate data classification, ensuring accurate data management and governance.

Article discusses how Ai-driven decisions have led to discrimination against women and minorities in recruitment, criminal sentencing, medical diagnosis and credit approval and led to thousand of fines under GDPR. An overview of a robust ethical review is given and the 'Five Ps of ethical data handling'