After completing the course, you will…
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Learn Python coding from Zero in a Business, Finance & Data Science context (real Examples)
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Learn Business & Finance (Time Value of Money, Capital Budgeting, Risk, Return & Correlation)
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Learn Statistics (descriptive & inferential, Probability Distributions, Confidence Intervals, Hypothesis Testing)
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Learn how to use the Bootstrapping method to perform hands-on statistical analyses and simulations
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Learn Regression (Covariance & Correlation, Linear Regression, Multiple Regression, ANOVA)
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Learn how to use all relevant and powerful Python Data Science Packages and Libraries
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Learn how to use Numpy and Scipy for numerical, financial and scientific computing
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Learn how to use Pandas to process Tabular (Financial) Data – cleaning, merging, manipulating
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Learn how to use stats (scipy) for Statistics and Hypothesis Testing
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Learn how to use statsmodels for Regression Analysis and ANOVA
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Learn how to create meaningful Visualizations and Plots with Matplotlib and Seaborn
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Learn how to create user-defined functions for Business & Finance applications
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Learn how to solve and code real Projects in Business, Finance & Statistics
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Learn how to unleash the full power of Python and Numpy with Monte Carlo Simulations
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Understand and code Sharpe Ratio, Alpha, Beta, IRR, NPV, Yield-to-Maturity (YTM)
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Learn how to code more advanced Finance concepts: Value-at-Risk, Portfolios and (Multi-) Factor Models
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Understand the difference between the Normal Distribution and Student´s t-distributions: what to use when
Who this course is for:
- All Business and Finance Professionals (Python is the future)
- Python Developers / Computer Scientists who want to step into Business, Finance & Data Science Roles
- Researchers who need to analyze large data sets and perform statistical & regression analysis
- Everyone who want to complement/replace Excel at work to increase productivity
- Everyone who want to get the full picture: Coding and underlying Theory (Statistics, Regression, Finance)
Deal Score+1
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