8 SEP 2022 - 24 OCT 2022
6 weeks
4:30 PM PST / 7:30 PM EST

Gain your competitive edge with Python, Finance's favorite language for rapid analysis!

Learn the analytics methodologies that guide global financial institutions — from an expert doing it every day.




    Get introduced to the Python language in the Finance context you're familiar with. You'll build valuable connections with professionals on both sides of the industry during the process.



    You have some knowledge of Python but you're outside of the financial context. This is your "in". Learn how to apply Python to finance: fundamental financial concepts, calculations, analyses, and models.



    You sense there's a gap between your technical knowledge and how to apply it to fintech or finance products. This course teaches you how to conduct analysis and build financial models using Python.


Python and Finance already go hand-in-hand. Why not learn them together?

This course puts you on a fast track to learning Python, the language powering the USD 7.3 trillion FinTech industry.

Elucidate the natural bridges between Python and Finance with the best guide you could ask for: Rajah Chacko, Wells Fargo VP Quantitative Analyst.

There’s a reason ELVTR learners finish the courses they start.

Coding with Python makes financial analysis lightning-fast, but it can be daunting for the uninitiated.

This course makes it easy — you're seeing yourself get better with every assignment. You'll pool together the skills you learn after 6 weeks to build a working algorithmic trading model.


Leave behind sluggish financial analysis in tools like Excel. You'll practice using the Python language and libraries to perform financial analyses, calculations, and build models with direct support from a professional.


Discover how to unlock value from your data in the financial context. Next, move on to presentation: learn how influential Finance professionals persuade stakeholders on their analysis.


Access all the resources you need to excel in this course and beyond. You'll put theory into practice after every lesson with real-world financial tasks, Python practice problems, and resources hand-picked by Rajah.


Over the last two years, the number of job postings mentioning Python has almost tripled.* Level up your skills to compete with the best through expert training, intense practice, and honest career advice.


*Based on jobs uploaded to the eFinancialCareers resume database in the last six months.

RAJAH CHACKO LinkedIn Profile
  • Vice President, Quantitative Analyst at Wells Fargo
  • Develops and implements Python models supporting Credit and PP&R Modeling for America's fourth largest bank
  • Created the analytics library relied on by Bank of America for pricing and risk-assessment
  • Collaborates closely with traders, structurers, and technology professionals to guide Wells Fargo's key financial decisions
  • Mines cryptocurrencies in his spare time, using a self-built desktop
Rajah Chacko


THU (9/8), 4:30 PM PST/7:30 PM EST
Introduction to Python: Python in Finance

Learn about Python’s main features and how they make Python a great tool for financial specialists. Get familiarized with Anaconda and Jupyter Notebook.

  • Python: General overview
  • What makes Python a great tool for financial specialists
  • Python vs. Excel and Python 2.x for Finance
  • Anaconda and Jupyter notebook: Interface overview
  • Data types in Python

Assignment #1: After installing the Python IDE of your choice, you’ll put Python through its paces with some fairly straightforward exercises.

Go Deeper: Research a Python topic from the list provided by Rajah. Analyze what you understand and write down questions to those topics that are less understandable.

+ Class Resource Pack

MON (9/12), 4:30 PM PST/7:30 PM EST
Python Basic Syntax: Importing Libraries

Start working with libraries. Build or refresh your knowledge of basic Python syntax and commands.

  • Working with libraries: Pandas, NumPy, Matplotlib, and Seaborn
  • Python basic syntax: Assignment statements, creating variables, indentation, conditionals, and loops
  • Being Pythonic
  • Pythonic: Strings and print

Assignment #2: You’ll create a bond calculator based on its face value, coupon payment, coupon frequency, the current interest rate, and the years to maturity.

Go Deeper: The Python collections and itertools API libraries can do some interesting things. Can you use either library to calculate which combinations of two six-sided dice add up to 7?

+ Class Resource Pack

THU (9/15), 4:30 PM PST/7:30 PM EST
Working with Pandas

Learn how to import stock data from Yahoo! Finance and transform the data for analysis using Pandas library.

  • Importing stock data
  • Working with DataFrames
  • Importing from Excel or .csv files.
  • Powerful filters and indexes
  • Pythonic: Functions, class, and subclass

Assignment #3: You’ll read a .csv of a stock’s price into a dataframe and calculate some stats about it, such as its high, low, and average.

Go Deeper: Add on to Assignment 3 bypass the .csv file (by going directly to an API). Add a method that takes a ticker as an input and prints the same stats. Call that method with three different tickers.



MON (9/19), 4:30 PM PST/7:30 PM EST
Pandas Underneath the Hood: Working with NumPy

Get introduced to NumPy library and its features. Understand the difference between arrays and lists and select data with loc and iloc.

  • Selecting data with loc and iloc
  • Using NumPy for speed

  • Tradeoffs between arrays and lists
  • Array functions

  • Pythonic: how to add to empty lists and dictionaries.
  • Pythonic: applying functions with the ‘.’ operator

Assignment #4: You’ll do your first classes (and methods, if you didn’t do them in Assignment #3). You’ll adopt assignment #3 to use NumPy.

Go Deeper: Use the timeit function to compare speeds in Pandas and NumPy.

THU (9/22), 4:30 PM PST/7:30 PM EST
Data Wrangling and Visualization

Learn how to obtain macroeconomic data, clean it, and handle missing data and outliers. Learn how to use Matplotlib or Seaborn to visualize financial data.

  • Rajah's best data sources
  • Data cleansing and normalization

  • Libraries for data visualization
  • Types of charts/graphs and how to build them

  • Pythonic: List indexes, appending, inserting, removing
  • Pythonic: Iterating over lists; what happens if the list is empty?

Assignment #5: Download two macroeconomic variables that you think are related from FRED into Excel spreadsheets. Load them into Pandas dataframes and plot them in a way that tells a story.

Go Deeper: Practice manipulating and iterating lists in the problems from Rajah.

+ Class Resource Pack

MON (9/26), 4:30 PM PST/7:30 PM EST
Extracting Financial Insights from Charts and Graphs

Learn how to use charts and graphs to gain insight, and how to persuade an audience with them.

  • A picture is worth 10 ** 3 words
  • Turning charts into insights + persuading decision makers
  • Pythonic: List comprehension and nested loops

  • Case studies: Interpreting inflation and political polls
  • Breakout rooms: Interpreting charts

Assignment #6 (Group work): You'll be broken into teams of 3 or 4 people with mixed backgrounds in finance and computer science. Collaborate outside of class to tell a convincing story about your data and conclusions in a chart.

Go Deeper: Practice list comprehension, dictionary comprehension, and nested loops in problems from Rajah, using a prime number sieve.



THU (9/29), 4:30 PM PST/7:30 PM EST
Financial Calculations with Python: Part 1

In this class the students will dive into financial data analysis by learning to calculate rate of return, percentage change and getting familiar with risks.

  • Calculating rate of return (single-period, logarithm, cumulative)
  • Calculating percentage change (and why we use natural log)
  • Calculating volatility

  • Pythonic: Defining your own functions
  • Pythonic: who throws errors? How are they caught?

Assignment #7: You’ll take daily returns for a stock (or index) and calculate the (natural) logarithmic return and produce the basic statistics for the returns (mean 25th, 50th, and 75th percentile).

Go Deeper: Redo one of your previous assignments or practices with a function that throws an error. Handle that error in your main routine.

MON (10/3), 4:30 PM PST/7:30 PM EST
Financial Calculations with Python: Part 2

The students will continue to learn about important financial concepts and will calculate risk, correlation and VaR of a stock portfolio.

  • What is a Risk-free rate?
  • Pythonic: All about dictionaries (and helpful subclass defaultdict)

  • Calculating non-diversifiable risk, ꞵ, from covariance and variance
  • Calculating diversifiable risk, α
  • Pairs trading and calculating correlation
  • Calculating VaR of a stock portfolio

Assignment #8: Given a stock index and three tickers from that index, calculate the beta coefficient for those tickers.

Go Deeper: Given two stocks, calculate their correlation.

THU (10/6), 4:30 PM PST/7:30 PM EST
CAPM and Portfolio Management

Why don't we “put all of our eggs in one basket”? Learn to graph different portfolios and see how the stock makeup changes the expected return.

  • Understanding CAPM concepts
  • The brass ring of increasing returns and lowering risk
  • What is the efficient frontier?
  • Calculating and graphing the CAPM (Capital Asset Pricing Model) of your portfolio
  • Pythonic: Tuples and subclass namedtuples

Assignment #9: Analyzing five stocks, you’ll come up with their expected returns and volatilities. Construct three portfolios with high return (and high risk), medium return (and medium risk), and low return (and low risk). Challenge: Can you greatly reduce risk and only slightly reduce returns?

Go Deeper: Complete a small problem set to get familiar with 2 subclasses: defaultdict and namedtuples.

THU (10/13), 4:30 PM PST/7:30 PM EST
Linear Regression

Learn the ins and outs of the simplest predictive model, statsmodels, and how to build a linear regression.

  • The Ordinary Least Squares (OLS) model
  • Use of linear regression for financial analysis
  • Building linear regression with statsmodels
  • Pythonic: Writing results to a file

Assignment #10: For the first part, you’ll regress (and plot) an OLS of the US inflation rate. For the second part, you’ll build on assignment #8 and use statsmodels to calculate the beta coefficients of your three stocks.

Go Deeper: Write a .csv file and Excel spreadsheet. Write and append to a text file.



MON (10/17), 4:30 PM PST/7:30 PM EST
Time Series Analysis

Learn about time series using stocks and how to add moving averages to determine trends.

  • Differences between OLS and time series
  • OLS vs. time series
  • How Pandas can support time series
  • Graphing monthly prices with daily data
  • Calculating SMA and EMA and defining trends
  • Pythonic: Financial APIs, PyPi, and Rajah's preferred Financial libraries

Assignment #11: Download several stock prices, and plot the prices and a simple moving average.

Go Deeper: Load a financial library of your choice and test out some features that interest you.

+ Class Resource Pack

THU (10/20), 4:30 PM PST/7:30 PM EST
Algorithmic Trading

Learn how to build a working trading strategy.

  • Benefits of algorithmic trading
  • Types of algorithmic trading strategies
  • Risks and challenges of algorithmic trading
  • Building an algorithmic trading strategy

Course Project: Algorithmic Trading Model
For this final project, you'll pull together all the Python skills you've developed in the last 6 weeks to build a basic algorithmic trading model. Find an existing algorithmic trading strategy, either online or in the libraries you've worked with in class. What will you optimize? Without overfitting, refine it to increase its return in an algorithmic model.

MON (10/24), 4:30 PM PST/7:30 PM EST
BONUS Class: Cryptocurrency Beyond the Basics with a Mystery Fintech Guest Speaker

Learn the reality of crypto's role in Finance from a FinTech leader. Rajah demos his go-to algorithm for mining a popular coin.

  • Crypto: Commodity or currency?
  • Crypto vs. stock exchanges
  • The big boys: Bitcoin and Ether
  • Impact of current events and regulations

  • Analyzing crypto data
  • How to mine Ether: Rajah's preferred algorithm

What our students say

Jake LoarUniversity of Akron

"Rajah's willingness to explain the material in depth and the examples he provides. The assignments have also been an active learning process and very beneficial overall."

Erik Kent Sully

"The ability to learn compact coding methods to advance the metaphorical axe and learning about financial trends and the market behavior is finding the metaphorical right trees. This course has helped me in learning hours of python in minutes while also gaining an edge in my way to observe financial markets"

Vaibhav VarkhedkarIQVIA

"Instructor is extremely patient and answers every question"

Galen Hughes

"I am a big fan of Rajah's communication style and the way he runs class. He is an excellent instructor and always makes time for questions."

Zeeshan Rehmani

"I enjoy the organization of the course syllabus and how it directly gets into the use of the material in real life situations."

Gina Nelson

"The teacher stands out more than anything because he's very responsive and on top of discord discussions and he's encouraging."

Yoann TagroMaXentric Technologies, LLC

"1. Great finance website for non-financial background people like me.
2. Great resources available to practice on our own later one.
3. We learn in a collaborative and friendly environment "no competition"."

Terrence CortRed Hat

"Lots of industry relevance. Not just about the code."

Jose Angel Martinez

"I enjoy the step by step approach and examples that demonstrate how to make solutions to problems."

Ben GoldsmithDeloitte

"The content is broad enough to cover the necessary topics/fundamentals, but also focused enough on specific topics required for financial analysis. Also, the classes themselves are a little more challenging at times (which is good), while the assignments are still reasonable (not too difficult) which is great for beginners like me."

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