80 Days of Tax: Alternatives to Data Scientists
What you'll learn
- You will master U.S. taxation by building 150 knowledge points over 100 days.
- You will learn why any data scientist needs tax or accounting knowledge.
- You will be able to pass all three IRS SEE exams to be an enrolled agent.
- Create a professional tax portfolio to apply for data scientist jobs
- Ability to apply tax or accounting knowledge for data science
- Complete the program to become a data scientist who thinks like a business pro
Requirements
- Some knowledge in data science
- No experience needed; you will learn everything you could pass the exam
Description
Data are omnipresent these days and that what is scarce are the talent … needed to make sense of these data. It is an interesting time to be a data scientist. Because an intellectual pursuit for an educated citizen in the digital age is important, even if one does not necessarily aspire to become a data scientist. But the job requires a person, who is open-minded, easy to communicate enough to analyze, interpret, and present data in meaningful, understandable ways across domains – a wide span involves science, business, education, government agencies, and industries of all variety.
In this course you will learn data scientist alternative skills in taxation by passing the IRS (U.S. Taxes; Internal Revenue Service) Special Enrollment Examination (EES) to become an Enrolled Agent (EA). EAs, like attorneys and certified public accountants (CPAs), are unrestricted as to which taxpayers they can represent, what types of tax matters they can handle, and which IRS offices they can practice before. The 3 IRS EES exams are below.
Part 1 – Individual Tax
Part 2 – Business Tax
Part 3 – Representation, Practice and Procedures
Don’t worry if you have no experience in tax or accounting knowledge, you will learn everything you could pass the exam with a real question bank.
I hope this course can help you in your data scientist journey. And you will receive a certification from both Udemy and IRS (if your enrollment got approved).
Who this course is for:
- People interested in being a data scientist with accounting knowledge
- Companies that wish to help employees continually develop their skills
Instructor
![Chunlei Tang](data:image/svg+xml,%3Csvg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 64 64"%3E%3C/svg%3E){kind=avatar}
- 4.0 Instructor Rating
- 4 Reviews
- 1,910 Students
- 6 Courses
My name is Chunlei Tang. Currently, I am a research associate at Harvard. Prior to Harvard, I worked as a revenue officer in Shanghai, China, for over ten years, responsible for corporate tax-related management. I also serve as the global Treasurer Co-Chair for ACM-W (Association for Computing Machinery's Council on Women).
My background is in computer science, and I graduated from Shanghai Jiao Tong University with a bachelor's degree. I then got a master's degree in software engineering and a Ph.D. in Computer and Software Theory at Fudan University.
My research occupies a unique place in data science because (1) I have experience in both business operations and revenue management, and (2) I conduct my research across fields (e.g., stock markets and healthcare) instead of staying in computer academics. Compared with my peers, I can easily hear the needs and requirements of stakeholders in other domains.
Also, I spent some time on basic research in data science. In my mind, data is resources, assets, and capital. A complete picture consists of three elements: "data, technology, and application," which all work toward augmenting people's decision-making by following data as it crosses borders. I wrote two books independently in 2015 and 2020, respectively. I am the author of The Data Industry: The Business and Economics of Information and Big Data (Wiley; 2016) and Data Capital: How Data is Reinventing Capital for Globalization (Springer; 2021). I am excited to see that Springer's book Data Capital is selling very well, reaching nearly 5,000 digital copies now.
I'm very interested in converting data-driving forces into productivity that can serve society. I'm also good at data product designing and social media data mining.