Accounting has always been about analytical thinking. From the earliest days of the profession, Luca Pacioli emphasized the importance of math and order for analyzing business transactions. The skillset that accountants have needed to perform math and to keep order has evolved from pencil and paper, to typewriters and calculators, then to spreadsheets and accounting software. A new skillset that is becoming more important for nearly every aspect of business is that of big data analytics: analyzing large amounts of data to find actionable insights. This course is designed to help accounting students develop an analytical mindset and prepare them to use data analytic programming languages like Python and R.
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The University of Illinois at Urbana-Champaign is a world leader in research, teaching and public engagement, distinguished by the breadth of its programs, broad academic excellence, and internationally renowned faculty and alumni. Illinois serves the world by creating knowledge, preparing students for lives of impact, and finding solutions to critical societal needs.
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INTRODUCTION TO THE COURSE
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.
MODULE 1: INTRODUCTION TO ACCOUNTANCY ANALYTICS
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.
MODULE 2: ACCOUNTING ANALYSIS AND AN ANALYTICS MINDSET
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.
MODULE 3: DATA AND ITS PROPERTIES
This module looks at specific characteristics of data that make it useful for decision making.
MODULE 4: DATA VISUALIZATION 1
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.
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INTRODUCTION TO ACCOUNTING DATA ANALYTICS AND VISUALIZATION의 최상위 리뷰
It teaches us the basics of data analytics and it is very progressive. There are assignments to help us understand and practice the methods being taught. This allows us to have first-hand experiences.
Useful for those interesting in learning more about data analytics using Excel! Might be a little content heavy towards week 6 but it was interesting nonetheless with plenty of hands-on assignments.
Excellent content! Instructor was amazing...interesting and comprehensive. Assignments were excellent as well, though peer-review was impossible because no one kept up with me in the course.
Very insightful session on how to get the best picture out of huge data. I certainly like the homework as it gave me time to practice on certain items. I highly recommend to those who take
Accounting Data Analytics 특화 과정 정보
This specialization develops learners’ analytics mindset and knowledge of data analytics tools and techniques. Specifically, this specialization develops learners' analytics skills by first introducing an analytic mindset, data preparation, visualization, and analysis using Excel. Next, this specialization develops learners' skills of using Python for data preparation, data visualization, data analysis, and data interpretation and the ability to apply these skills to issues relevant to accounting. This specialization also develops learners’ skills in machine learning algorithms (using Python), including classification, regression, clustering, text analysis, time series analysis, and model optimization, as well as their ability to apply these machine learning skills to real-world problems.

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