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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Université de l'Illinois à Urbana-Champaign
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Meilleurs avis pour INTRODUCTION TO ACCOUNTING DATA ANALYTICS AND VISUALIZATION
I really enjoyed the course and the lecture was very interactive, clear & precise in such a way i felt i was in a classroom.
It provides a lot of hands on example that really help with the explanation of the concepts taught
This is a really helpful course! I have learned not only about using Excel for accounting, but also the many functions of Excel.
The course enables its learners to get a hands-on experience using tools to accounting data analytics and visualisation.
À propos du Spécialisation Accounting Data Analytics
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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