Python Fundamentals

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Python Fundamentals

$99.00 USD

$149.00 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.

Course Content

Python Basics

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

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*These banks have used one or both of PyFi’s self-study courses to introduce their financiers to Python programming 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 what you can do with Python in finance

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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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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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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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How does Python Fundamentals™ work?

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. Writing Python is very similar to building financial models 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.

By the end of the course you will...

Understand how Python and Machine Learning can be used in the world of finance to double the accuracy of your predictive models and gain efficiencies in your work not possible with Excel alone

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

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$99.00 USD

$149.00 USD

99% Satisfaction Rate
30-day Money Back Guarantee

Curriculum

Python Fundamentals is approximately 3.5 hours in length. A student can generally complete the 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