Data-Driven Investing with Python | Financial Data Science

About Data-Driven Investing with Python | Financial Data Science

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:

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2 PARTS, 8 SECTIONS TO MASTERY

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(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._

Practical Walkthroughs

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.