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
4.59 (1158 reviews)
12 993
students
37.5 hours
content
May 2025
last update
$94.99
regular price
What you will 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
Course Gallery
Charts
Students
Price
Rating & Reviews
Enrollment Distribution
Comidoc Review
Our Verdict
The Complete 2-in-1 Python for Business and Finance Bootcamp offers a thorough understanding of Python programming for finance, business and data science applications. It boasts an impressive collection of financial concepts, statistics, hypothesis tests, regression, and simulations. However, the sheer depth of information might prove too overwhelming for beginners—losing some in lengthy, occasionally unfocused videos while challenging others with a lack of exercises to consolidate their learning. Nonetheless, those seeking a comprehensive dive into this niche will benefit from Alex's detailed knowledge-sharing and engagement.
What We Liked
- Comprehensive coverage of Python programming for business, finance, and data science
- In-depth explanations of financial concepts like time value of money, capital budgeting, risk, return, correlation, and various types of distributions
- Hands-on experience with statistics, hypothesis tests, regression, simulations, and various Python libraries such as NumPy, SciPy, pandas, scikit-learn, matplotlib, Seaborn, statsmodels, XLWings
- Prompt responses to questions from the instructor & interaction with students in Q&A sections help clarify doubts
- Highly detailed course updates ensure relevancy and improved learning experience
Potential Drawbacks
- Some users mentioned lengthy videos without a clear focus on main points, making it difficult to maintain attention
- The vast amount of content may overwhelm beginners who might find it challenging to keep pace with the instructor
- Concepts could be better connected to real-world use cases & how they outperform Excel alternatives
- Lack of exercises in some sections makes it difficult for learners to practice & gain confidence with new skills
Related Topics
2516506
udemy ID
19/08/2019
course created date
18/11/2019
course indexed date
Bot
course submited by