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).The first half of the course picks up where Introduction to Accounting Data Analytics and Visualization left off: using in an integrated development environment to automate data analytic tasks. We discuss how to manage code and share results within Jupyter Notebook, a popular development environment for data analytic software like Python and R. We then review some fundamental programming skills, such as mathematical operators, functions, conditional statements and loops using Python software.
The second half of the course focuses on assembling data for machine learning purposes. We introduce students to Pandas dataframes and Numpy for structuring and manipulating data. We then analyze the data using visualizations and linear regression. Finally, we explain how to use Python for interacting with SQL data.
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.
Syllabus
Syllabus - What you will learn from this course
Week 1
INTRODUCTION TO THE COURSE
MODULE 1: FOUNDATIONS
Week 2
MODULE 2: INTRODUCTION TO PYTHON
Week 3
MODULE 3: INTRODUCTION TO PYTHON PROGRAMMING
Week 4
MODULE 4: PYTHON PROGRAMMING
Week 5
MODULE 5: DATA ANALYSIS WITH PYTHON
Week 6
MODULE 6: INTRODUCTION TO VISUALIZATION IN PYTHON
Week 7
MODULE 7: PRODUCTION DATA ANALYTICS
Week 8
MODULE 8: INTRODUCTION TO DATABASES IN PYTHON
FAQ
When will I have access to the lectures and assignments?
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.
The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
What will I get if I subscribe to this Specialization?
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
Is financial aid available?
Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.
Reviews
Perfect to start, they focus on teach the basics true it is up to you if you want to practice and learn more
It is very easy to learn and also very interesting because you can modify and try other things.
Really Nice course but you will not explain all the module very indepth. All the module are basic and easy to learn. I am very happy to completed this course.
very useful and important to computer science engineering students
