Data-Driven Investing (with Python) | Financial Data Science
Become a Data Driven Investor. Rigorously Test & Statistically Validate Investments From Scratch | Quantitative Finance
What you will learn
Remove the "guesswork" from your investing forever by learning how to statistically test and validate your investment ideas rigorously on Python
Discover and master the systematic and scientific Data Driven Investing process that will transform the way you analyse investments forever
Apply everything you learn using rich, large real world data (without compromising on the mathematical and theoretical integrity of concepts)
Learn how to leverage incredibly powerful relationships and rigorous Financial Data Science techniques on Python to generate Alpha (seriously)
Understand why the math works (and why equations work the way they do) - even if your math is weak and if math freaks you out.
Explore evergreen concepts like Expected Returns, Asset Pricing Models, and Portfolio Construction in unique Financial Data Science settings, leveraging Pandas
Learn and apply powerful Quantitative Finance techniques including "sorts" to create and design portfolios, regressions to "test for alpha", and much more
Discover how to quantify risk and returns of individual stocks and investment portfolios, both manually as well as on Python working with real-world data
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.