Introduction to Accounting Data Analytics and Visualization

Go to Course: https://www.coursera.org/learn/intro-accounting-data-analytics-visual

Introduction

### Course Review: Introduction to Accounting Data Analytics and Visualization on Coursera **Course Overview:** The "Introduction to Accounting Data Analytics and Visualization" course on Coursera is a comprehensive exploration of how the accounting profession integrates data analytics to enhance decision-making. By highlighting the evolution of accounting practices from manual calculations to modern data-driven methodologies, this course prepares professionals to leverage big data analytics effectively in the business environment. As Luca Pacioli, the father of accounting, emphasized the importance of analytical thinking, this course serves as a modern homage to his legacy, blending traditional accounting principles with innovative data analytic techniques. --- **Syllabus Breakdown:** **Module 1: Introduction to Accounting Analytics** This opening module sets the tone for the course by familiarizing you with the learning environment, your instructor, and fellow participants. You will gain essential technical skills to navigate through the course seamlessly, enabling a smooth transition into more complex topics. **Module 2: Accounting Analysis and an Analytics Mindset** You will delve into the importance of an analytical mindset, exploring how empirical inquiry underpins effective decision-making in accounting. This module helps bridge theoretical knowledge with practical applications, vital for anyone looking to stay relevant in today’s data-driven landscape. **Module 3: Data and its Properties** Focusing on the specific characteristics of data, this module illustrates how understanding these properties is fundamental for effective decision-making. You will gain insights into the types of data available and their implications for accounting. **Module 4 & 5: Data Visualization** The course emphasizes the significance of data visualization. In the first visualization module, you'll grasp the fundamental principles necessary for creating impactful charts using Excel. The second part elevates your skill set by introducing Tableau, an intuitive platform for visual data analysis, empowering you to identify trends and anomalies in datasets with ease. **Module 6 & 7: Analytic Tools in Excel** These modules delve deep into the practical applications of analytics using Excel, instructing you on correlation analysis, regression modeling, and improving your capacity for data interpretation. These practical lessons foster hands-on learning that strengthens analytical skills essential in modern accounting roles. **Module 8: Automation in Excel** The final module covers automation techniques in Excel, enabling you to streamline data handling and analysis processes. Learning how to implement clustering and other advanced functions enhances your efficiency and effectiveness as a data analyst. --- **Course Highlights:** - **Engaging Course Structure**: The course follows a logical flow, from foundational concepts to advanced analytical techniques, accommodating both beginners and experienced practitioners alike. - **Real-World Applications**: Practical case studies and exercises throughout the modules equip you with skills that are immediately applicable in the workplace. - **Interactive Learning**: The course encourages collaboration and networking with peers, an essential aspect of professional growth in the accounting and analytics fields. - **Access to Cutting-Edge Tools**: Learning to use Tableau and advanced Excel features helps you become proficient in visualization and data analysis—valuable skills in today’s job market. --- **Recommendation:** I highly recommend the "Introduction to Accounting Data Analytics and Visualization" course for anyone in the accounting field or related sectors looking to elevate their data analytics skills. The blend of theoretical knowledge and practical application prepares you for a career where data is increasingly becoming central. Whether you're an accountant seeking to enhance your expertise or a business professional interested in data-driven decision-making, this course provides a critical foundation for success in the modern business landscape. Don't miss this opportunity to expand your skill set and stay ahead in the ever-evolving field of accounting. Enroll today on Coursera and take the first step towards mastering the art of accounting data analytics and visualization!

Syllabus

Course Introduction and Module 1: Introduction to Accountancy Analytics

In this module, you will become familiar with the course, your instructor and your classmates, and our learning environment. This orientation module will also help you obtain the technical skills required to navigate and be successful in this course.

In this module, you will learn how the accounting profession has evolved. You will recognize how data analytics has influenced the accounting profession and how accountants have the ability to impact how data analytics is used in the profession, as well as in an organization. Finally, you will learn how data analytics is influencing the different subdomains within accounting.

In this module, you will learn to recognize the importance of making room for empirical enquiry in decision making. You will explore characteristics of an analytical mindset in business and accounting contexts, and link those to your core courses. You will then evaluate a framework for making data-driven decisions using big data.

This module looks at specific characteristics of data that make it useful for decision making.

In this module, you will learn fundamental principles that underlie data visualizations. Using those principles, you will identify use cases for different charts and learn how to build those charts in Excel. You will then use your knowledge of different charts to identify alternative charts that are better suited for directing attention.

In this module, you’ll learn how to use Tableau to do with data what spies do when observing their surroundings: get an overview of the data, narrow in on certain aspects of the data that seem abnormal, and then analyze the data. Tableau is a great tool for facilitating the overview, zoom, then filter details-on-demand approach. Tableau is a lot like a more powerful version of Excel's pivot table and pivot chart functionality.

In this module, you'll be guided through a mini-case study that will illustrate the first three parts of the FACT model, with a focus on the C, or calculations part of the FACT model. First, you will perform a correlation analysis to identify two-way relationships, and analyze correlations using a correlation matrix and scatter plots. You will then build on your knowledge of correlations and learn how to perform regression analysis in Excel. Finally, you will learn how to interpret and evaluate the diagnostic metrics and plots of a regression analysis.

In this module, you’ll learn how the regression algorithm can be applied to fit a wide variety of relationships among data. Specifically, you’ll learn how to set up the data and run a regression to estimate the parameters of nonlinear relationships, categorical independent variables. You’ll also investigate if the effect of an independent variable depends on the level of another independent variable by including interaction terms in the multiple regression model. Another aspect of this module is learning how to evaluate models, regression or otherwise, to find the most favorable levels of the independent variables. For models that explain revenue, the most favorable levels of the independent variables will maximize revenue. In contrast, if you have a model that describes costs, like a budget, then the most favorable levels of the independent variables will minimize costs. Optimizing models can be difficult because there are so many inputs and constraints that need to be managed. In this module, you’ll learn how to use the Solver Add-In to find the optimal level of inputs. For some models, the dependent variable is a binary variable that has only two values, such as true/false, win/lose, or invest/not invest. In these situations, a special type of regression, called logistic regression, is used to predict how each observation should be classified. You’ll learn about the logit transformation that’s used to convert a binary outcome to a linear relationship with the independent variables. Excel doesn’t have a built-in logistic regression tool, so you’ll learn how to manually design a logistic regression model, and then optimize the parameters using the Solver Add-In tool.

The lessons in this module are organized around several useful tasks, including stacking multiple dataframes together into one dataframe, creating multiple histograms to accompany the descriptive statistics, and learning how to perform k-means clustering. After going through this module, you’ll not only gain a foundation to help you understand coding, but you’ll also learn more about analyzing financial data. Along the way, I hope that you’ll also pick up on a few other useful Excel functions.