Data Science, Statistics, Hypothesis Tests, Regression, Simulations for Business & Finance: Python Coding AND Theory A-Z
InstructorAlexander HagmannThe curriculum covers essential financial concepts such as Time Value of Money, Net Present Value, and Internal Rate of Return alongside Python implementation.
Instruction includes descriptive and inferential statistics, utilizing libraries like Numpy, Pandas, Matplotlib, and Seabos for data manipulation and visualization.
Editorial course preview
These 3 complementary views highlight concrete, legible examples from the course presentation.
This image displays a Jupyter Notebook output showing a Pandas DataFrame containing stock market data, including columns for ticker symbols, exchange names, company names, last prices, and market capitalizations.
A dual-box visual metaphor representing the course structure: one volume for business finance theory featuring charts, and another for practical coding with Python libraries.
This dense line chart visualizes multiple overlapping colorful trajectories trending upwards, illustrating the output of a Monte Carlo simulation or random walk analysis.
One sampled review suggests that early modules include frequent coding exercises, but some learners noted a reduction in hands-on opportunities as the course progresses into more complex topics. This signal comes from a review created before the displayed update date; while the update label suggests recent changes, it does not prove these specific sections were revised.
One signal suggests the instructor may not be consistently responsive to student questions within the Q&A forum. This observation was recorded prior to the displayed update, and the current status of support remains uncertain.
The curriculum aligns well with the stated goals of business and finance professionals starting from zero.
