Dates and location
Pricing
Hours
Dates and location
Pricing
Hours
Description
This certificate program is an emerging technology and outcomes driven course, offered by CPA Ontario and powered by Lighthouse Labs, Canada's Leading Digital Bootcamp Institution.
This six-week certificate program will enable you to analyze and visualize data using a popular programming language used by analysts and data scientists.
Learn how to apply high-level analytical thinking to solve data analysis problems using Python. You will work with tools like Jupyter Notebooks and Pandas and analyze real-world datasets to identify patterns and relationships. By the end of this course, you will be able to work effectively with data to drive business strategies and create value for your organization.
Why Should CPAs take a Python course?
Knowing Python and having coding skills can help CPAs future-proof their career and adapt to the profession's changing demands. It can help them work more efficiently, provide better insights to their clients, and collaborate effectively with other professionals.
This course will provide participants with the opportunity to:
- Apply high-level analytical thinking to data analysis problems;
- Gain skills to use and navigate tools, libraries, and environments for data analysis using Python;
- Develop workflows for data analysis and wrangling tasks using basic Pandas functions and SQL;
- Develop and apply basic data wrangling techniques and methodologies with various data types and sources to solve accounting and finance problems.
Program Details:
This program takes place on the Lighthouse Labs learning platform and is made up of synchronous and asynchronous components.
- 2 sessions per week
- 1 hour of live instructor led learning per session (dates & times outlined below in chart, live sessions will be recorded)
- On average you should allocated 2-3 hours of asynchronous time per session to complete program work
- Office hours available
Optional Office hours:
Monday: 2:00 - 3:00 PM EST (after the lecture)
All learners are strongly encouraged to attend the office hours after your October 16 session to ensure you have successfully set up the required software needed to complete the program.
- Tuesday: 6:00-7:00 PM EST (Bookable - 1:1 time slots)
- Wednesday: 6:00-7:00 PM EST (Bookable - 1:1 time slots)
- Thursday: 2:00 - 3:00 PM EST (after the lecture)
Note: office hours are not mandatory
Program Schedule
| Weeks | Topics | Assessment |
|---|---|---|
| Week 1
|
Data Science Environment Setup
Asynchronous Work: approximately 2-3 hours |
|
| Databases and SQL Basics
Live Instructor Led Session: Thursday, October 19, 1:00-2:00 PM EST (1 hour) Asynchronous Work: approximately 2-3 hours |
Quiz | |
| Week 2
|
Python Fundamentals
Asynchronous Work: approximately 2-3 hours |
Quiz |
| Python Programming
Asynchronous Work: approximately 2-3 hours |
Python Programming Challenge: Poker Hand | |
| Week 3
|
Python and Pandas
Asynchronous Work: approximately 2-3 hours |
Quiz
Programming Test I |
| Advanced Pandas
Asynchronous Work: approximately 2-3 hours |
Quiz | |
| Week 4
|
Python and SQL
Asynchronous Work: approximately 2-3 hours |
Quiz |
| Advanced Python
Asynchronous Work: approximately 2-3 hours |
Quiz | |
| Week 5
|
Advanced Python II
Asynchronous Work: approximately 2-3 hours |
Programming Test II |
| Python Challenge Part I
Thursday, November 16 Asynchronous Work: 4 hours NOTE: There is no instructor-led (synchronous) training for this session, and four hours of independent (asynchronous) work. |
Final Project | |
| Week 6
|
Python Challenge Part II
Asynchronous Work: 4 hours
|
Final Project |
| Demo Day
Thursday, November 23, 1:00 - 3:00 PM EST Showcase your new skills and demonstrate just how far you have come in your learning journey. This is a celebration of you! NOTE: Your participation in demo day is strongly recommended but not mandatory. It is our hope that you will take this time to connect with your peers and share your project. |
Presentation |
Assessments:
This course includes short quizzes consisting of multiple-choice questions and programming challenges throughout the course to assess knowledge and skill development. A final Python challenge (capstone project) will take place towards the end of the program.
Pass Requirements:
- To earn your CPD hours you must attend 75% of the live lectures (Mon/Thurs)
- To earn a final certificate, you must meet the attendance requirements outlined above and submit and receive a passing grade (50% or higher) on your capstone project
Who Should Attend?
Accountants and financial professionals who want to learn how to use data and data programming languages to make better business decision and solve problems.
No prior programming experience is required. Some basic understanding of arithmetic and statistics concepts and an aptitude for logic and quantitative concepts are highly desirable.
NEED APPROVAL TO ATTEND?
How to Access The Course
Course Format:
The course combines both synchronous and asynchronous components learning. Your learning will take place on the Lighthouse Labs learning platform. This is a two-step registration processs. Once you've registered and paid on the CPA Ontario portal you are considered registered for the course. You will receive an email from Lighthouse Labs at least 48 hours prior to the course start date with full program detail and information on how to access the course.
Disclaimer:
By registering for this course you consent and confirm your understanding of the following: CPA Ontario will share your first name, last name and email address with Lighthouse Labs for the purposes of providing you access to the course. It is suggested if possible, to use your business email address.
Registration, cancellation, withdrawal and all other CPA Ontario PD Seminar policies can be found here.
Speaker(s)
Jenny Hung, Bs.C (Mathematics), LL.B, CPA.CA, is a data analyst in applied finance and data science instructor. In addition to data science, Jenny is also an algorithmic trader specializing in the Python language in the Forex market, and she also writes about Algo trading as well as the issues related to the financial markets. Jenny developed a financial trading algorithm that combines both quantitative finance insights as well as AI techniques and is capable of automatically selecting trades, choosing strategies, and managing portfolio trades.