Become a Data Driven Investor. Take the guesswork out of your investing forever. Leverage the power of Financial Data Science, Financial Analysis, Python, and Quantitative Finance to make robust investment decisions (and generate Alpha).
Discover how to use rigorous statistical techniques on Python to guide your investment decisions (even if you don’t know statistics or your math is weak).
Say hello to the most comprehensive Data Driven Investing course on the internet. Featuring:
# 2 PARTS, 8 SECTIONS TO MASTERY
(plus, all future updates included!)
Structured learning path, Designed for Distinction™ including:
- 12.5 hours of engaging, practical, on-demand HD video lessons
- Real-world applications throughout the course
- 200+ quiz questions with impeccably detailed solutions to help you stay on track and retain your knowledge
- Assignments that take you outside your comfort zone and empower you to apply everything you learn
- A Practice Test to hone in and gain confidence in the core evergreen fundamentals
- Python code (built from scratch) to help you build a replicable system for investing
- Mathematical proofs for the mathematically curious
- An instructor who’s insanely passionate about Finance, Investing, Python, and Financial Data Science
PART I: INVESTMENT ANALYSIS FUNDAMENTALS
Start by gaining a solid command of the core fundamentals that drive the entire investment analysis / financial analysis process.
Explore Investment Security Relationships & Estimate Returns
- Discover powerful relationships between Price, Risk, and Returns
- Intuitively explore the baseline fundamental law of Financial Analysis – The Law of One Price.
- Learn what “Shorting” a stock actually means and how it works
- Learn how to calculate stock returns and portfolio returns from scratch
- Work with real-world data on Python and know exactly what your code does and why it works
Estimate Expected Returns of Financial Securities
- Explore what “expected returns” are and how to estimate them starting with the simple mean
- Dive deeper with “state contingent” expected returns that synthesize your opinions with the data
- Learn how to calculate expected returns using Asset Pricing Models like the CAPM (Capital Asset Pricing Model)
- Discover Multi-Factor Asset Pricing Models including the “Fama French 3 Factor Model”, Carhart 4 (“Momentum”), and more
- Master the theoretical foundation and apply what you learn using real-world data on Python your own!
Quantify Stock Risk and Estimate Portfolio Risk
- Examine the risk of a stock and learn how to quantify total risk from scratch
- Apply your knowledge to any stock you want to explore and work with
- Discover the 3 factors that influence portfolio risk (1 of which is more important than the other two combined)
- Explore how to estimate portfolio risk for ‘simple’ 2-asset portfolios
- Learn how to measure portfolio risk of multiple stocks (including working with real-world data on Python!)
Check your Mastery
- So. Much. Knowledge, Skills, and Experience. Are you up for the challenge? – Take the “Test Towards Mastery”
- Identify areas you need to improve on and get better at in the context of Financial Analysis / Investment Analysis
- Set yourself up for success in Financial Data Science / Quantitative Finance by ensuring you have a rigorous foundation in place
PART II: DATA DRIVEN INVESTING | FINANCIAL DATA SCIENCE / QUANTITATIVE FINANCE
Skyrocket your financial analysis / investment analysis skills to a whole new level by learning how to leverage Financial Data Science, Quantitative Finance and Python for your investing.
Discover Data Driven Investing and Hypothesis Design
- Discover what “data driven investing” actually is, and what it entails
- Explore the 5 Step Data Driven Investing process that’s designed to help you take the guesswork out of your investment decision making
- Learn how to develop investment ideas (including how/where to source them from)
- Explore the intricacies of “research questions” in the context of Financial Data Science / Data Driven Investing
- Transform your investment ideas into testable hypotheses (even if you don’t know what a “testable hypothesis” is)
Source, Clean, and Explore Real-World Data
- Explore how and where you can source data to test and validate your own hypotheses
- Master the backbone of financial data science – data cleaning – and avoid the “GIGO” trap (even if you don’t know what “GIGO” is)
- Work with large datasets (arguably “Big Data”) with over 1 million observations using Python!
- Discover quick “hacks” to easily clean data on Python (and become aware of issues that are easy to miss)
- Learn while exploring meaningful questions on the impact of ESG in financial markets
Conduct Exploratory Data Analysis
- Discover how to conduct one of the most common financial data science techniques – “exploratory data analysis” using Python
- Evaluate intriguing relationships between returns and ESG (or another factor of your choice)
- Learn how to statistically test and validate hypotheses using ‘simple’ t-tests
- Never compromise on the mathematical integrity of the concepts – understand why equations work the way they do
- Explore how to “update” beliefs and avoid losing money by leveraging the power of financial data science, quantitative finance, and Python
Design and Construct Investment Portfolios
- Explore exactly what it takes to design and construct investment portfolios that are based on individual investment ideas
- Learn how to sort firms into “buckets” to help identify monotonic relationships (a vital analysis technique of financial data science)
- Leverage the power of Pandas in Python to conduct investment analysis like the Pros (Hedge Funds, Financial Data Scientists, Applied Researchers)
- Strengthen your financial data science skills by becoming aware of Python’s surprising default settings (and what you can do to overcome them)
- Plot charts that drive meaningful insights for Quantitative Finance, including exploring portfolio performance over time using Matplotlib and Seaborn
Statistically Test and Validate Hypotheses
- Say goodbye to guesswork, hope, and luck when it comes to making investment decisions
- Rigorously test and statistically validate your investment ideas by applying robust financial data science techniques on Python
- Add the use of sophisticated tools including simple t-stats and more ‘complex’ regressions to your suite of financial data science analytics
- Explore what it really takes to search for and generate Alpha (to “beat the market“)
- Learn and apply tried and tested financial data science and quantitative finance techniques used by hedge funds, financial data scientists, and researchers on Python
DESIGNED FOR DISTINCTION™
We’ve used the same tried and tested, proven to work teaching techniques that have helped our clients ace their professional exams (e.g., ACA, ACCA, CFA®, CIMA), get hired by the most renowned investment banks in the world, manage their own portfolios, take control of their finances, get past their fear of math and equations, and so much more.
You’re in good hands.
Here’s how we’ll help you master incredibly powerful Financial Data Science & Financial Analysis techniques to become a robust data driven investor who leverages the power of Python…
A Solid Foundation
You’ll gain a solid foundation of the core fundamentals that drive the entire financial analysis / investment analysis process. These fundamentals are the essence of financial analysis done right.
And they’ll hold you in mighty good stead both when you start applying financial data science techniques in Part II of this course, but also long after you’ve completed this course. Top skills in quantitative finance – for the rest of your life.
Forget about watching videos where all the Python code is pre-written. We’ll start from blank Python scripts on Jupyter Notebooks (like the real world).
And we’ll build all the Python code from scratch, one line at a time. That way you’ll literally see how we conduct rigorous financial analysis / financial data science using data-driven investing as the core basis, one step at a time.
Hundreds of Quiz Questions, Dozen Assignments, and Much More
Apply what you learn immediately with 200+ quiz questions, all with impeccably detailed solutions. Plus, over a dozen assignments that take you outside your comfort zone. There’s also a Practice Test to help you truly hone your knowledge and skills. And boatloads of practical, hands-on walkthroughs where we apply financial data science / quantitative finance techniques in data driven investing environments on Python.
Proofs & Resources
Mathematical proofs for the mathematically curious. And also because, what’s a quantitative finance course without proofs?!
Step-by-step mathematical proofs, workable and reusable Python code (in .ipynb Jupyter notebook and .py versions), variable cheat sheets – all included. Seriously.
This is the only course you need to genuinely master Data Driven Investing, and apply Financial Data Science & Quantitative Finance techniques on Python without compromising on the theoretical integrity of concepts.
Who this course is for:
- Individual / retail investors looking to “up” their Financial Analysis game by leveraging the power of Financial Data Science and Python
- Hedge Funds Analysts and Associates looking to master Long-Short / Asset Pricing style Quantitative Finance techniques on Python
- Portfolio Managers wanting to move away from subjective decision making to robust data-driven investment analysis techniques on Python, grounded in the academic and practitioner research
- CFA®, ACA, ACCA students looking to apply familiar concepts in practical, data-driven / financial data science environments
- Current and prospective Analysts and Investment Bankers wanting to gain a solid foundation in data driven investment analysis
- Anyone who’s curious about learning and applying Financial Data Science using Python
- No prior Finance or Investing knowledge required. We start from the very basics. And transform you into a robust Data Driven Investor.
- Basic coding knowledge is REQUIRED. You don’t need to be an ‘expert’ in Python, but you do need to know the basics (e.g., difference between strings, floats, lists, dictionaries)
- It’s okay if math freaks you out. Seriously. Every single equation is explained one variable at a time. We rip it apart to its core, and show you how simple it really is.
- Knowledge of basic statistical analysis is useful but NOT essential. Every relevant statistical test is taught from scratch.
- You’ll need a calculator, pen and paper (seriously), and your development environment (e.g. Jupyter Notebooks, Text Editors)
- We work with Jupyter Notebooks in the course, but .py versions of all Python code is available for download.
Last Updated 10/2021