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

这是有史以来第一个面向商业和金融专业人士的综合性 Python 课程。您将从零开始学习并掌握 Python 和完整的 Python 数据科学堆栈，其中包含来自商业和金融领域的真实示例和项目。

这不仅仅是一门编码课程。您将从头开始理解并掌握项目和代码背后所有必需的理论概念。您不仅将成为 Python 编码方面的专家，还将成为

商业与金融（货币时间价值、资本预算、风险、回报和相关性、蒙特卡罗模拟、生产和金融中的质量和风险管理、抵押贷款、年金和退休计划、投资组合理论、资产定价和因子模型、价值-有风险）

统计（描述性和推理性统计、置信区间、假设检验、正态分布和学生 t 分布、p 值、Bootstrapping 方法、蒙特卡罗模拟、回报正态性）

回归（协方差和相关性、线性回归、多元回归及其陷阱、回归系数的假设检验、逻辑回归、方差分析、虚拟变量、机器学习链接、Fama-French 因子模型）

本课程遵循一个相辅相成的概念：同时学习 Python 和理论：

拥有正确的上下文和正确的示例（避免玩具示例！）时，学习 Python 会更有效。

使用 Python 可以更轻松、更有效地学习和掌握商业、金融、统计和回归中的基本理论和概念，因为您可以模拟、可视化和动态解释理论、数学和公式背后的直觉。

本课程深入介绍了所有相关和常用的 Python 数据科学包：

Python 的基础知识（标准库）

用于数值、科学、金融、统计编码和模拟的 Numpy 和 Scipy

Pandas 用于处理、处理、清理、聚合和操作表格（财务）数据。您应得的不仅仅是 Excel！

用于执行回归分析、假设检验和方差分析的 statsmodels

Matplotlib 和 Seaborn 用于科学数据可视化

本课程不仅仅是视频：

具有数千行代码的可下载 Jupyter Notebook

可下载的 PDF 文件包含数百张幻灯片，解释和重复最重要的概念

可下载的 Jupyter Notebook 包含数百个编码练习，包括。提示和解决方案

我在编码课程中严格遵循一个简单的规则：不解释为什么不编写代码。您不会听到诸如“…这就是 Python 代码，请随意谷歌搜索更多背景信息并自己弄清楚”之类的评论。你的老板、你的客户、你的商业伙伴和你的大学都不接受这一点。你为什么要在一门建立你职业生涯的课程中接受这一点？如果无法解释并出售给其他人，即使是最好的（编码）结果也没有什么价值。

我是 Alexander Hagmann，金融专业人士，也是（金融）数据科学和金融领域的畅销讲师，使用 Python。完成我课程的学生在全球最大和最受欢迎的科技和金融公司工作。根据我自己的经验，以及在网上和面对面指导过数千名专业人士和公司，有一个关键发现：专业人士通常从 Python 生态系统的错误部分开始，在错误的上下文中，以错误的语气和错误的职业开始小路。

Bestseller | h264, yuv420p, 1280×270 |ENGLISH, aac, 44100 Hz, 2 channels | 35h 44 mn | 10.99 GB
Created by: Alexander Hagmann

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

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

Requirements

No (Python) Coding required. This Course starts from complete zero und teaches you everything from scratch.
No specific Business/Finance, Statistics & Data Science knowledge needed! The course intuitively explains basic and advanced concepts.
A desktop computer (Windows, Mac, or Linux) capable of storing and running Anaconda. The course will walk you through installing the necessary free software.
An internet connection capable of streaming videos.
Some high school level math skills would be great (not mandatory, but it helps)

Description

Hi and welcome to this Course!

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. You will become an expert not only in Python Coding but also in

Business & Finance (Time Value of Money, Capital Budgeting, Risk, Return & Correlation, Monte Carlo Simulations, Quality and Risk Management in Production and Finance, Mortgage Loans, Annuities and Retirement Planning, Portfolio Theory, Asset Pricing & Factor Models, Value-at-Risk)

Statistics (descriptive & inferential statistics, Confidence Intervals, Hypothesis Testing, Normal Distribution & Student´s t-Distribution, p-value, Bootstrapping Method, Monte Carlo Simulations, Normality of Returns)

Regression (Covariance & Correlation, Linear Regression, Multiple Regression and its pitfalls, Hypothesis Testing of Regression Coefficients, Logistic Regression, ANOVA, Dummy Variables, Links to Machine Learning, Fama-French Factor Models)

This course follows a mutually reinforcing concept: Learning Python and Theory simultaneously:

Learning Python is more effective when having the right context and the right examples (avoid toy examples!).

Learning and mastering essential theories and concepts in Business, Finance, Statistics and Regression is way easier and more effective with Python as you can simulate, visualize and dynamically explain the intuition behind theories, math and formulas.

This course covers in-depth all relevant and commonly used Python Data Science Packages:

Python from the very Basics (Standard Library)

Numpy and Scipy for Numeric, Scientific, Financial, Statistical Coding and Simulations

Pandas to handle, process, clean, aggregate and manipulate Tabular (Financial) Data. You deserve more than just Excel!

statsmodels to perform Regression Analysis, Hypothesis Testing and ANOVA

Matplotlib and Seaborn for scientific Data Visualization

This course isn´t just videos:

Downloadable Jupyter Notebooks with thousands of lines of code

Downloadable PDF Files containing hundreds of slides explaining and repeating the most important concepts

Downloadable Jupyter Notebook with hundreds of coding exercises incl. hints and solutions

I strictly follow one simple rule in my coding courses: No code without explaining the WHY. You won´t hear comments like “…that´s the Python code, feel free to google for more background information and figure it out yourself”. Your boss, your clients, your business partners and your colleges don´t accept that. Why should you ever accept this in a course that builds your career? Even the best (coding) results have only little value, if they can´t be explained and sold to others.

I am Alexander Hagmann, Finance Professional and best-selling Instructor for (Financial) Data Science and Finance with Python. Students who completed my courses work in the largest and most popular tech and finance companies all over the world. From my own experience and having coached thousands of professionals and companies online and in-person, there is one key finding: Professionals typically start with the wrong parts of the Python Ecosystem, in the wrong context, with the wrong tone and for the wrong career path.

Do it right the first time and save time and nerves! What are you waiting for? There is no risk for you as you have a 30 Days Money Back Guarantee.

Thanks and looking forward to seeing you in the Course!
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)