Complete 2-in-1 Python for Business and Finance Bootcamp

Data Science, Statistics, Hypothesis Tests, Regression, Simulations for Business & Finance: Python Coding AND Theory A-Z

This is the first ever comprehensive Python Course for Business & Finance Professionals. You will learn and master Python from Zero and the full Python Data Science Stack with real Examples and Projects taken from the Business & Finance world. This isn´t just a coding course. You will understand and master all required theoretical concepts behind the projects and the code from scratch.

Best Seller Course: Python for Finance: Investment Fundamentals & Data Analytics

What you’ll learn

• Learn Python coding from Zero in a Business, Finance & Data Science context (real Examples)
• Learn Business & Finance (Time Value of Money, Capital Budgeting, Risk, Return & Correlation)
• Learn Statistics (descriptive & inferential, Probability Distributions, Confidence Intervals, Hypothesis Testing)
• Learn how to use the Bootstrapping method to perform hands-on statistical analyses and simulations
• Learn Regression (Covariance & Correlation, Linear Regression, Multiple Regression, ANOVA)
• Learn how to use all relevant and powerful Python Data Science Packages and Libraries
• Learn how to use Numpy and Scipy for numerical, financial and scientific computing
• Learn how to use Pandas to process Tabular (Financial) Data – cleaning, merging, manipulating
• Learn how to use stats (scipy) for Statistics and Hypothesis Testing
• Learn how to use statsmodels for Regression Analysis and ANOVA
• Learn how to create meaningful Visualizations and Plots with Matplotlib and Seaborn
• Learn how to create user-defined functions for Business & Finance applications
• Learn how to solve and code real Projects in Business, Finance & Statistics
• Learn how to unleash the full power of Python and Numpy with Monte Carlo Simulations
• Understand and code Sharpe Ratio, Alpha, Beta, IRR, NPV, Yield-to-Maturity (YTM)
• Learn how to code more advanced Finance concepts: Value-at-Risk, Portfolios and (Multi-) Factor Models
• Understand the difference between the Normal Distribution and Student´s t-distributions: what to use when

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