Many times in finance, companies are tasked with analyzing historical data to better predict future outcomes, whether its to value a company, invest in a stock, or to better predict risk. Using Python and Machine learning techniques students can learn to better predict in a faster and more accurate way.

This course will begin with advanced Python and statistic topics such as object-orientated programming and regression models. After learning this first module, students will learn how to apply these concepts using real-world financial data by building a predictive returns model using regression.

The next section of the course will introduce students to important financial statements and ratios. After students learn these financial concepts, they will be introduced on how to pull data from these statements and compute these important financial ratios using Python.

After an initial analysis, students will cover more advanced financial modeling topics that measure present value, cost of capital, and the rate of return of an investment. Students will then apply those concepts using Python and Machine learning to build predictive financial models. After building these models, students will learn how to build out a budgeting model using only Python. Lastly, students will learn how to visualize their findings and export them into a PDF report, Excel file, or SQL database.

At the end of the course, students will feel comfortable making decisions driven by data analysis in Python. This course is perfect for anyone working in all areas of finance, risk management, reporting, general financial management, financial investments.

This course has a prerequisite

This course requires students to be comfortable with Python and its data science libraries (NumPy and Pandas). If a student has not worked in Python before, we require a student to enroll in our Python for Data Science Bootcamp or Python for Finance Bootcamp before taking this course.