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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- 1 star0.77%
來自INTRODUCTION TO ACCOUNTING DATA ANALYTICS AND VISUALIZATION的熱門評論
useful as an intro course to analytics and data visualization. The nature of the content itself is a bit dry but i think the lecturer did not bad and was clear and concise in delivery.
The course enables its learners to get a hands-on experience using tools to accounting data analytics and visualisation.
Instruction was clear and I found all of the information presented to be useful in my current work with Excel spreadsheets. Now I'm keen to learn Python!
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
關於 Accounting Data Analytics 專項課程
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.