Machine Learning Edge Professional Certification Bundle

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Machine Learning Edge Professional Certification Bundle

$199.00 USD

$347.00 USD

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You do not need programming skills to begin. The course teaches you the fundamentals of Python by building off your current excel skill set before moving to more advanced topics in machine learning. Write and walk away with working algorithms that have been used to advise Fortune 500 companies including McDonalds, Tiffanys, Gap, and more.

Course Content

Python Objects

Setting Up Your Coding Environment, Performing Calculations, Generating Outputs with Static Text and Dynamic Values

Python Objects

Python Objects & Data Structures - types of Python objects that help you to organize other objects.

Custom Functions

Creating Custom Functions, Repeating Tasks through Iterable Objects, Incorporating Conditional Logic

NumPy

Importing Open-Source Packages, NumPy Arrays, Powerful Statistical Tools

Pandas

Two New Python Objects, Filtering Data with Boolean Masks, Segmenting with Groupby

Certifications

Verify Your Learning, Boost Your Resume, Complete the Pre-Requisite to Applied Machine Learning

Data Cleaning & Exploration

Your machine learning models can only be as good as your training data. In this lesson, you will learn how to find and correct errors in your datasets using the datavisualization packages Matplotlib and Seaborn.

Regression Algorithms

Learn about regression problems and 5 machine learning algorithms used to solve them. Those models are Lasso, Ridge, and Elastic Net, which fall under the cateogry of Regularized Regression Models. The other models you will learn are Random Forest and Gradient Boosting which fall under the category of Decision Tree Ensemble Models. You will also learn about overfitting and the tools at your disposal to prevent it.

Liquidity Regressor Case Study

Build your first machine learning model from start to finish. The model you will construct is an altered version of a real machine learning algorithm used by top investment banks to advise companies such as: HP, McDonalds, Nordstrom, and many more.

Classification Algorithms

Learn about classification problems and some machine learning algorithms used to solve them. By the end of this lesson, you will be familiar with

Characteristics of Binary Classification Problems
Regularized Logistic Regression Models
Decision Tree Ensemble Classification Models

Investor Classifier Case Study Pt I

Explore the data for your second machine learning model. The model you will construct is an altered version of a real machine learning algorithm used by top investment banks to predict investor behavior when raising capital for large corporate clients.

Investor Classifier Case Study Pt II

In this lesson, you will complete your second machine learning model. The model you will construct is an altered version of a real machine learning algorithm used by top investment banks to predict investor behavior when raising capital for large corporate clients.

Professional Certification Exam

Verify Your Learning: Put your new machine learning skills to the test and see what you have learned and ensure retention. Boost Your Resume: Add an impressive certification to your resume to demonstrate your new skills to employers.

Bonus I: The Machine Learning Edge Blueprint Vol I

The full PDF version of Python Fundamentals

Bonus II: The Machine Learning Edge Blueprint Vol II

The full PDF version of Applied Machine Learning

RBC_700530b9-0f2c-4b97-8b0b-3a42d6fc1244

*These banks have used one or both of PyFi’s self-study courses to introduce their employees to Python and its application in the world of finance.

Why is Python important in Finance?

Automate
Automate the repetitive - Stop manually updating reports, reconciling data, or reformatting spreadsheets. Python does it for you.
Enhance
Enhance Excel, don't replace it - Write Python scripts that work alongside Excel to speed up calculations, automate models, and fetch real-time data.
Integrate
Integrate across platforms - Move data seamlessly between Oracle, PowerBI, Tableau, SQL databases, Excel, etc.
Accurate
More accurate forecasting & analytics - Reduce human error and leverage Python's statistical libraries to improve financial models.
Decisions
Make better decisions, faster - Turn raw data into insights with Python's data visualization and reporting capabilities

These are just a few examples of how you can use Python in finance

FP_A_Workflows_Pedro_-_visual_selection_2

Python scripts integrate SAP Analytics Cloud with Tableau by leveraging the SAP Analytics Cloud OData API. Python fetches real-time financial data, cleans it, and sends it to Tableau for visualization. This integration reduces reporting cycle time by an estimated 40% while ensuring accurate and up-to-date dashboards.

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Investment_Banking_Workflows_-_visual_selection_3

Extract financial and bond data from Bloomberg and automatically calculate key financial ratios like debt-to-equity and interest coverage. Machine learning models predict default probabilities. The models are validated for accuracy and risk scores and probability metrics are exported to Excel for presentation. Save approximately 15 hours while improving the accuracy of your forecasts by 25% with this Python integration.

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Private_Equity_Workflows_-_visual_selection

Python automates the consolidation of financial data from portfolio companies, reducing manual aggregation and saving approximately 6 hours per month. IT streamlines KPI calculations, eliminating Excel formula errors and improves accuracy by 30%. Automated benchmarking and real-time alerts highlight underperformers, cutting approx. 4 hours of manual comparisons. Dynamic dashboards in Tableau replace static Excel chats, saving an additional 3 hours per month. Overall, this integration saves 13 hours monthly and improves accuracy by 30%.

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Unsure if Python, or PyFi is right for you?

Hear from PyFi students. Be Certain in your decision.

The PyFi Guarantee

We offer a 100%, no questions asked, 30 day money-back guarantee to completely de-risk your investment. Enroll now, and if for any reason you aren't completely satisfied, send us an email and you'll get a full refund for up to one month after enrollment.

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PyFi's Unique Approach

Why Top Banks In North America Use PyFi

Orientation

Get setup in your coding environment and start writing your first lines of code in under 30 minutes. Setup your environment and quickly understand how to command basic functions that you’ll build on throughout the course.

Build on your existing foundation with excel analogs

By relating lessons and techniques to concepts finance professionals are already familiar with, PyFi courses deliver higher retention rates and a more efficient learning process. You are already a logical thinker and problem solver which is demonstrated by your work in Excel. PyFi builds off this foundation.

Practical application

The Python programming language is an immense topic to cover. There are many things you can learn and even more ways you can apply Python which are not relevant to you as a finance professional. PyFi teaches you what you need to know to be effective in your work without needing to pursue a formal computer science degree.

Apply proven solutions to your work

Because Python is open source, you’re able to apply working libraries, or frameworks, to your work with simple commands. Imagine downloading sophisticated financial models you could simply plug your data into. That’s exactly what’s possible with the Python programming language and PyFi gets you started by plugging the NumPy and Pandas library directly into your financial analysis.

Prepare Your Data

If you want a great machine learning algorithm, you need great training data. Correct errors, eliminate sparse classes, and remove unwanted observations to provide your algorithm premium fuel.

Build Your Pipelines

Model pipelines tell your algorithm how to process your data. Construct pipelines that standardize your data to a common scale, define competing model classes, and specify random states.

Train & Tune Your Algorithm

Optimizing your machine learning algorithm is like tuning a race car. Train competing models and tune your hyperparameters using cross-validation to maintain the integrity of your testing data.

Select the Winning Model

Your algorithm competes against itself to produce optimal results. After training and tuning your models, you can select the winning model and use it to make superior predictions.

Objectives

Learn how to apply Real-World Algorithms in finance.

Become familiar and comfortable with coding envrionments

Use Jupyter Notebook to write and execute code
Perform calculations
Generate outputs with static text and dynamic values
Create and manipulate variables

Identify common types of Python objects

Create and manipulate important data structures
Lists, Tuples, Sets, and Dictionaries

Use python arguments

Create custom functions
Repeat tasks through iterable objects using for loops
Incorporate conditional logic using if statements

Become familiar with Python libraries starting with NumPy

Create and manipulate NumPy arrays, a new type of Python object
Perform special NumPy array math and aggregation functions
Leverage NumPy's useful randomization tools

Become familiar with the Pandas library

Create and manipulate Pandas DataFrames
Create and manipulate Pandas Series
Filter data using boolean masks
Segment and summarize data by class with groupbys

Identify overfit regression models

Master the skill of distinguishing overfit models that promise more than they deliver. By understanding the nuances of overfitting, you will be equipped to refine your models to ensure they perform reliably in real-world financial scenarios, enhancing their predictability and your confidence in their forecasts.

Compare different Machine Learning models

Dive deep into the world of regularized regression and decision tree ensemble algorithms. This course highlights the strengths and limitations of each, guiding you to make informed decisions on which algorithm to deploy for your financial modeling tasks, ensuring optimal balance and performance.

Explain the confusion matrix and its relation to the ROC curve

Gain clarity on evaluating model performance with an in-depth understanding of the confusion matrix and its critical role in financial predictions. Learn how it intertwines with the ROC curve to give you a comprehensive view of your model's accuracy, allowing you to enhance predictive models with precision.

Construct training data sets, testing data sets, and model pipelines

Learn to construct robust training and testing datasets and streamline your model development process with efficient pipelines. This skill set is crucial for developing models that are not only accurate but also scalable and adaptable to the fast-paced changes in the finance industry.

Perform advanced data cleaning, exploration, and visualization

Elevate your data preparation skills with advanced techniques in cleaning, exploring, and visualizing financial datasets. These foundational steps are key to uncovering insights and trends that drive successful machine learning models, setting the stage for impactful financial analysis and predictions.

Engineer features based on conditional relationships between existing features

Unlock the potential of your financial datasets by learning how to engineer features that reveal deeper insights. Understand the conditional relationships between features to create new, powerful predictors that enhance the performance of your machine learning models in predicting market movements and trends.

Build and finalize a machine learning classifier, and so much more…

Bring your financial prediction models across the finish line by mastering the art of building and finalizing machine learning classifiers. This course empowers you to not only construct but also fine-tune classifiers that stand up to the rigors of the finance world, ensuring your models are both accurate and resilient under market pressures.

Curriculum

Sample Lessons Below Each Tab

Both Python Fundamentals and Applied Machine Learning are approximately 3.5 hours in length each. A student can generally complete each course in 6-10 hours when working through all of the exercises. Your enrollment comes with lifetime access and support within the course via a comment section under each video lesson.

Introduction to Python

Setting Up Your Coding Environment:

We will walk you through setting up your coding environment step-by-step, including downloading the Anaconda distribution system and getting familiar with Jupyter Notebooks.

Performing Calculations:

You will learn how to use Python to perform basic mathematical calculations.

Generating Outputs with Static Text and Dynamic Values:

You will learn to incorporate text into your Python code and generate outputs with dynamic values.

Module Files Zip: PF 01 Jupyter Notebook
PF 01.01 Course Introduction
PF 01.02 Downloading Anaconda (Updated 11.07.23)
PF 01.03 Introducing Jupyter Notebook
PF 01.04 Calculations
PF 01.05 Exercise
PF 01.05 Solution
PF 01.06 Mathematical Operators
PF 01.07 Text Outputs
PF 01.08 Dynamic Outputs
PF 01.09 Exercises
PF 01.09 Solution
PF 01.10 Variables
PF 01.11 Exercise
PF 01.11 Solution
PF 01.12 Review

Python Objects

Python Objects:

Python is an object-oriented programming language, which means that Python code is organized around objects, or pieces of data that interact with each other based on instructions written in the code.

Data Structures:

In this section, you will learn about data structures - types of Python objects that help you to organize other objects. Examples include lists, tuples, sets, and dictionaries.

Module Files Zip: Jupyter Notebook
PF 02.01 Python Object Types
PF 02.02 Exercise
PF 02.02 Solution
PF 02.03 Lists
PF 02.04 Accessing List Objects
PF 02.05 Exercise
PF 02.05 Solution
PF 02.06 Changing List Objects
PF 02.07 More List Functions
PF 02.08 Exercise
PF 02.08 Solution
PF 02.09 Tuples
PF 02.10 Sets
PF 02.11 Using Sets to Remove Duplicates
PF 02.12 Set Operations
PF 02.13 Exercise
PF 02.13 Solution
PF 02.14 Dictionaries
PF 02.15 Accessing Dictionary Items
PF 02.16 Exercise
PF 02.16 Solution
PF 02.17 Dictionary Functions
PF 02.18 Exercise
PF 02.18 Solution
PF 02.19 Review

Custom Functions

Creating Custom Functions:

In addition to Python's built-in functions (similar to Excel's built-in functions), you can create custom functions that allow you to package and reuse your own custom code.

Repeating Tasks through Iterable Objects:

Learn how to automate tedious tasks by repeating actions through iterable objects using variables and For Loops.

Incorporating Conditional Logic:

Write more complex and useful applications that evaluate and respond to conditions that you define.

PF.03 Module Files: Jupyter Notebook
PF 03.01 Creating Custom Functions
PF 03.02 Exercise
PF 03.02 Solution
PF 03.03 Adding Arguments
PF 03.04 For Loops
PF 03.05 Exercise
PF 03.05 Solution
PF 03.06 Filing a List For Loop
PF 03.07 Exercise
PF 03.07 Solution
PF 03.08 Conditional Logic
PF 03.09 Exercise
PF 03.09 Solution
PF 03.10 Review

Numpy

Importing Open-Source Packages:

Instead of building everything on your own, learn to leverage the work of others by importing third-party packages.

NumPy Arrays:

Get familiar with creating and manipulating a powerful new type of Python object: The NumPy Array.

Powerful Statistical Tools:

Use NumPy's powerful statistical functions to quickly and easily analyze large quantities of data.

PF 04. Module Files: Juptyer Notebook
PF 04.01 Importing Libraries
PF 04.02 NumPy
PF 04.03 NumPy Arrays
PF 04.04 Multidimensional Arrays
PF 04.05 Exercise
PF 04.05 Solution
PF 04.06 Reshape and Transpose
PF 04.07 Selecting Objects
PF 04.08 Exercise
PF 04.08 Solution
PF 04.09 Array Calculations
PF 04.10 Array Functions
PF 04.11 Exercise
PF 04.11 Solution
PF 04.12 The Random Module
PF 04.13 Setting a Random Seed
PF 04.14 Random Sampling with .choice()
PF 04.15 Generating Sequences
PF 04.16 Exercise PF 04.16 Solution
PF 04.17 Review

Pandas

Two New Python Objects:

Pandas provides you with two new Python objects, the DataFrame and the Series, which you can use to import and manipulate data from your Excel files.

Filtering Data with Boolean Masks:

Use the Boolean object type to create indicator variables, test conditions, and filter your data.

Segmenting with Groupby:

Segment and summarize your data across categories using the useful .groupby() function.

Module Zip Files: Juptyer Notebook, Stock Data
PF 05.01 Pandas
PF 05.02 Importing Data
PF 05.03 Import From .csv
PF 05.04 Get to Know Your Dataframe
PF 05.05 Exercise
PF 05.05 Solution
PF 05.06 Summary Statistics
PF 05.07 Exercise
PF 05.07 Solution
PF 05.08 Series
PF 05.09 Series Functions
PF 05.10 Feature Engineering
PF 05.11 Exercise
PF 05.11 Solution
PF 05.12 Boolean Masks
PF 05.13 Indicator Variables
PF 05.14 Exercise
PF 05.14 Solution
PF 05.15 Segmenting with .groupby()
PF 05.16 Exercise
PF 05.16 Solution
PF 05.17 Review

Certification Exam

Verify Your Learning:

Put your new Python skills to the test to see what you have learned and ensure retention.

Boost Your Resume:

Add an impressive certification to your resume to demonstrate your new skills to employers.

Complete the Prerequisite to Applied Machine Learning:

After completing Python Fundamentals, you will have the necessary prerequisite knowledge to dive into more advanced topics like Applied Machine Learning.

Jupyter Notebook (Exam Exercises)

Data Cleaning & Exploration

Your machine learning models can only be as good as your training data. In this lesson, you will learn how to find and correct errors in your datasets using the data visualization packages Matplotlib and Seaborn.

Module Zip Files : Jupyter Notebook, Stock Data v2
ML 01.01 The ML Process
ML 01.02 Matplotlib and Seaborn
ML 01.03 A Few Quick Notes
ML 01.04 Exercise
ML 01.04 Solution
ML 01.05 .countplot()
ML 01.06 Exercise
ML 01.06 Solution
ML 01.07 Replace & Sparse Classes
ML 01.08 Exercise
ML 01.08 Solution
ML 01.09 Exercise
ML 01.09 Solution
ML 01.10 Spotting Outlier
ML 01.11 Exercise
ML 01.11 Solution
ML 01.12 Exercise
ML 01.12 Solution
ML 01.13 Exercise
ML 01.13 Solution
ML 01.14 Exercise
ML 01.14 Solution
ML 01.15 Exercise
ML 01.15 Solution
ML 01.16 NaN Object
ML 01.17 Exercise
ML 01.17 Solution
ML 01.18 Dropping Null Values
ML 01.19 Exercise
ML 01.19 Solution
ML 01.20 Box Plots
ML 01.21 Exercise
ML 01.21 Solution
ML 01.22 Saving Your Dataframe
ML 01.23 Review

Regression Algorithms

Learn about regression problems and 5 machine learning algorithms used to solve them. Those models are Lasso, Ridge, and Elastic Net, which fall under the category of Regularized Regression Models. The other models you will learn are Random Forest and Gradient Boosting which fall under the category of Decision Tree Ensemble Models. You will also learn about overfitting and the tools at your disposal to prevent it.

ML 01.02 What are Regression Algorithms
ML 02.02 Real Relationships and Overfitting
ML 02.03 Regularizations
ML 02.04 Decision Tree Ensemble Methods

Liquidity Regressor Case Study

Module ZipFiles: Liquidity_client.csv, Liqudiity_data.csv, ML 03 Jupyter Notebook
ML 03.01 Case Study Overview
ML 03.02 Exercise
ML 03.02 Solution
ML 03.03 Metadata
ML 03.04 Exercise
ML 03.04 Solution
ML 03.05 Splitting Your Data
ML 03.06 Exercise
ML 03.06 Solution
ML 03.07 train_test_split()
ML 03.08 Unpacking Lists
ML 03.09 Exercise
ML 03.09 Solution
ML 03.10 Progress Checkpoint
ML 03.11 Model Pipelines
ML 03.12 Exercise
ML 03.12 Solution
ML 03.13 Progress Checkpoint
ML 03.14 Hyperparameter Tuning
ML 03.15 Exercise
ML 03.15 Solution
ML 03.16 Exercise
ML 03.16 Solution
ML 03.17 Aggregating Hyperparameter Grids
ML 03.18 Progress Checkpoints
ML 03.19 Cross Validation
ML 03.20 Creating Untrained Models
ML 03.21 Exercise
ML 03.21 Solution
ML 03.22 Training and Tuning Models
ML 03.23 Exercise
ML 03.23 Solution
ML 03.24 Model Evaluation
ML 03.25 Exercise
ML 03.25 Solution
ML 03.26 Progress Checkpoint
ML 03.27 Visualizing Model Predictions
ML 03.28 Exercise
ML 03.28 Solution
ML 03.29 Using Your Model
ML 03.30 Review

Classification Algorithms

Learn about classification problems and some machine learning algorithms used to solve them. By the end of this lesson, you will be familiar with ▪ Characteristics of Binary Classification Problems ▪ Regularized Logistic Regression Models ▪ Decision Tree Ensemble Classification Models

ML 04.01 Binary Classification
ML 04.02 Logistic Regression
ML 04.03 Decision Tree Classifier

Investor Classifier Case Study Pt I

ML 05 Module Zip File: Investor_data.csv, Jupyter Notebook
ML 05.01 Case Study Overview
ML 05.02 Exercise
ML 05.02 Solution
ML 05.03 Metadata
ML 05.04 Exercise
ML 05.04 Solution
ML 05.05 One Error
ML 05.06 Exercise
ML 05.06 Solution
ML 05.07 Countplot of Investors
ML 05.08 Exercise
ML 05.08 Solution
ML 05.09 Exploring Relationships
ML 05.10 Exercise
ML 05.10 Solution
ML 05.11 Reviewing Your Results
ML 05.12 Feature Engineering
ML 05.13 Exercise
ML 05.14 Solution
ML 05.14 Reviewing Tier Change
ML 05.15 Controlling for Demotions
ML 05.16 Exercise
ML 05.16 Solution
ML 05.17 Analyzing Goldman Sachs
ML 05.18 Exercise
ML 05.18 Solution
ML 05.19 Implot()
ML 05.20 Exercise
ML 05.20 Solution
ML 05.21 Review

Investor Classifier Cast Study Pt II

ML 06 Module Zip File: Investor_data2.csv, Jupyter Notebook
ML 06.01 Import Packages and Data
ML 06.02 Exercise
ML 06.02 Solution
ML 06.03 Dummy Variables ML 06.04 Exercise
ML 06.04 Solution
ML 06.05 Remove Redundant Target
ML 06.06 Splitting Data
ML 06.07 Exercise
ML 06.07 Solution
ML 06.08 Model Pipeline
ML 06.09 Exercise
ML 06.09 Solution
ML 06.10 Validating Pipelines
ML 06.11 Hyperparameters
ML 06.12 Exercise ML 06.12 Solution
ML 06.13 Validating Hyperparameter Grids
ML 06.14 Cross Validation
ML 06.15 Exercise
ML 06.15 Solution
ML 06.16 Fitting Untrainer Models
ML 06.17 Exercise
ML 06.17 Solution
ML 06.18 AUROC
ML 06.19 Confusion Matrix
ML 06.20 Perfect AUROC
ML 06.21 Calculating AUROC
ML 06.22 Exercise
ML 06.22 Solution
ML 06.23 Review

Professional Certification Exam

Verify Your Learning: Put your new Machine Learning skills to the test to see what you have learned and ensure retention.

Boost Your Resume: Add an impressive certification to your resume to demonstrate your new skills to employers.

Jupyter Notebook (Exam Exercises)

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$199.00 USD

$347.00 USD

Lifetime access
Instructor Support
30-day Money Back Guarantee

Learn the same algorithms that were used to advise the following companies:

*Slight changes to the code made to protect IP

linkedin_certificate_applied_machine_learning

Completion Certificates

After completing each course and passing the certification exam, students will be granted a PyFi Certification for that specific course. Use these certificates as a signal to employers that you have the technical skills to immediately add value to your team.

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Meet Zach Washam,
PyFi Founder & Head of Instruction

While learning Python as an investment banker, Zach made an interesting observation: Python programming had a lot in common with the Excel models he made at work. By thinking of Python like Excel, Zach quickly learned the coding language and invented Wells Fargo Securities' first machine learning algorithm for investment banking and capital markets. ‍

After submitting two algorithms for patent protection and winning Wells Fargo's 2018 "Local Sphere Innovation Award," Zach left investment banking to launch PyFi. ‍

Now, Zach’s courses have been delivered to thousands of finance students and professionals around the world.

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*These banks have used one or both of PyFi’s self-study courses to introduce their employees to Python and its application in the world of finance.

How Can We Help You?

What makes PyFi courses unique?

PyFi's Python training is custom-tailored for finance professionals with minimal to no coding experience.

We teach Python by building off your existing Excel skill set and focus on concepts relevant to you. We're not building rocketships, we're teaching you how to work through finance workflows.

With instructor support, and the most relevant concepts, PyFi is the market's best choice for someone in finance, who has decided to learn Python.

How will learning Python help my finance career?

Python is customizable and works with your current tech stack, and your next positions tech stack.

Because its free to use, and the most popular coding language in finance, the long tail effects of learning Python are only going to compound the return on your investment.

In the immediate term, you can plug into your current workflows involving Excel, BI tools, Data providers, etc., to eliminate redundant steps while also improving the accuracy of your financial models.

Imagine cutting down your hours by 30, or 50%, and bringing your stakeholders models that are twice as accurate as what your contemporaries can produce in Excel.

So what happens to the trajectory of your finance career when you're that much more productive than your colleagues?

Whats included in the Professional Certification Bundle?

Both PyFi self study courses, Python Fundamentals, and Applied Machine Learning are included. You will also receive access to The Machine Learning Edge Blueprint, Vol I and Vol II which are the complimentary PDF versions of each course.

As you progress through both courses you can receive instructor support via the comment section under each video lesson.

At the end of each course, you'll be invited to take a rigorous certification exam to test your knowledge and ensure retention.

How long does it take to complete a course?

What if I don't like the course?

PyFi offers a no questions asked, 30 day money-back guarantee. If you aren't completely satisfied with the course, send us an email and we will refund your money up to one month after you enroll.

How can we afford to do this? Because fewer than 1% of our customers ask for refunds. PyFi provides the best Python training on the market, and we know you are going to love it.

Do I need to work from a specific OS?

No. You can work through PyFi content from any OS.

Do I need to understand investing and portfolios to take the courses?

The finance specific topics that are discussed in the courses can be picked up as you work through each topic.

Have any questions? We’re ready to help!

Highly Recommend!

What an excellent course. The Python portion gets you quickly up to speed on Python data structures, common libraries, and functions.

The ML portion gives two end to end examples of structuring, training, testing, and selecting ML models.

The pace of the course and level of detail given were great. The videos also had the right level of enthusiasm and inflection to keep you engaged and interested. There are opportunities to practice what you learn after almost every video.

I really want to thank the course creators for a job well done and I hope they create more courses. I highly recommend this training.

PyFi - Intro to Python

Great foundational course for Python. I had no previous experience in Python and this course equips beginners with practical and easy to learn fundamentals of the programming. I felt the instruction was clear and provided great applications for finance.

Intro to Python

This course provides an excellent foundation for Python, focusing on essentials before applications. Zach Washam clearly defines the course purpose, aligning perfectly with my goals. The structured, multi-session format worked great with my busy schedule, and the pre-class preparation was well-communicated with a helpful video guide.

Zach’s articulate teaching and consistent use of precise terminology made learning seamless. He not only covered the basics thoroughly but also provided guidance for tailoring and advancing your Python experience. His detailed, example-driven responses to questions were particularly valuable.

Introduction to Python Course

The Introduction to Python course was a great start on the basics of Python. Zach was great at answering questions and taking the class along the journey to build a strong foundation of Python knowledge. This has me excited to continue learning more Python and applying it to my everyday work.

I'm looking forward to seeing how the course evolves in the future, and I'll be recommending this to my team members in FP&A. I'd love to see more examples added, especially depending on the format of the course (self-study vs. live).

I found this Python course incredibly useful and easy to follow. It covers the basics of Python programming and provides a solid foundation. The examples are clear, and while I haven't yet applied Python in my work, I’m excited to explore how it can be used in finance. I’m definitely looking forward to learning more and seeing how Python can enhance my work in the future.

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