Accounting Data Analytics 특화 과정
Develop Data Analytics Skills for Accountants. This specialization develops students’ skills of data preparation, data visualization, data analysis, data interpretation, and machine learning algorithms and their applications to real-world problems.
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배울 내용
Know how to operate software that will help you create and run Python code.
Execute Python code for wrangling data from different structures into a Pandas dataframe structure.
Run and interpret fundamental data analytic tasks in Python including descriptive statistics, data visualizations, and regression.
Use relational databases and know how to manipulate such databases directly through the command line, and indirectly through a Python script.
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Projects included in this specialization allow learners to apply the skills developed within the data analytics specialization to real-world problems. Learners will be able to articulate the general process of the CRISP-DM framework, demonstrate data analytics skills in data preparation, data visualization, modeling, and model evaluation, and apply data analytics knowledge and skills to real-world problems. For example, in the capstone project, learners will develop a machine learning model in order to predict whether a loan is to be fully paid and construct a loan portfolio with the help of the analysis.
Programming background would be a plus, but not mandatory.
Programming background would be a plus, but not mandatory.
이 전문 분야에는 4개의 강좌가 있습니다.
Introduction to Accounting Data Analytics and Visualization
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.
Accounting Data Analytics with Python
This course focuses on developing Python skills for assembling business data. It will cover some of the same material from Introduction to Accounting Data Analytics and Visualization, but in a more general purpose programming environment (Jupyter Notebook for Python), rather than in Excel and the Visual Basic Editor. These concepts are taught within the context of one or more accounting data domains (e.g., financial statement data from EDGAR, stock data, loan data, point-of-sale data).
Machine Learning for Accounting with Python
This course, Machine Learning for Accounting with Python, introduces machine learning algorithms (models) and their applications in accounting problems. It covers classification, regression, clustering, text analysis, time series analysis. It also discusses model evaluation and model optimization. This course provides an entry point for students to be able to apply proper machine learning models on business related datasets with Python to solve various problems.
Data Analytics in Accounting Capstone
This capstone is the last course in the Data Analytics in Accountancy Specialization. In this capstone course, you are going to take the knowledge and skills you have acquired from the previous courses and apply them to a real-world problem.
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What is the refund policy?
하나의 강좌에만 등록할 수 있나요?
Is financial aid available?
해당 강좌를 무료로 수강할 수 있나요?
이 강좌는 100% 온라인으로 진행되나요? 직접 참석해야 하는 수업이 있나요?
전문 분야를 완료하면 대학 학점을 받을 수 있나요?
전문 분야를 완료하면 대학 학점을 받을 수 있나요?
전문 분야를 완료하는 데 얼마나 걸리나요?
Do I need to take the courses in a specific order?
What will I be able to do upon completing the Specialization?
궁금한 점이 더 있으신가요? 학습자 도움말 센터를 방문해 보세요.