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course

Intro to Python

Check Upcoming Dates
Course Code
CEBD 1100
Duration
30 hours
When companies, researchers, engineers, and analysts develop solutions, create products, or make policy recommendations, they rely on Data Analysis to make informed decisions. In this course, you will learn about the basics of programming in Python and applying these skills to analyzing data. You'll start with the basics of designing an algorithm, basic Python programming, an introduction to the Scipy scientific computing ecosystem, and learn the fundamentals of machine learning.

Note that you will be required to do 5-10 hours of work per week outside of class time. Those with little to no prior knowledge will require more time to gain familiarity with the concepts.

Upcoming date(s)

Term
Section
mode
Fees

January 15 – March 19, 2025
We 18:00 – 21:00
Winter
1
Online
$800.00
Term
Winter
Section
1
mode
Online
Fees
$800.00

April 3 – June 5, 2025
Th 18:00 – 21:00
Spring
1
Online
$800.00
Term
Spring
Section
1
mode
Online
Fees
$800.00

Your takeaways

This course is a great way to help you:
• Gain a foundation in numerical programming and algorithm development;
• Learn Python as a language for writing scripts, packages, and performing data analysis;
• Develop and refine skills in git for version control and bash for operating within Linux environments;
• Learn basic principles and pitfalls of machine learning applied to real-world problems.

Our approach

This course employs project-based learning that's focused on the acquisition of practical, real-world skills and not just theory. You'll be taught in our computer labs by an industry pro using state-of-the-art technologies, software and equipment.

Who benefits the most?

Individuals who want to improve the performance of their organization by harnessing Big Data.
• Professionals who want to leverage data for better decision-making.
• Entrepreneurs with projects that could benefit from data analytics.
• Students in fields like geography, biology, psychology, humanities or any other field with big data.
• IT professionals who want to transition to Big Data from more traditional sectors.
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