Python Classes Ottawa

Create dashboards and other interactive visualizations with Python and its libraries in this hands-on course. Participants will also learn how to deploy projects online using Heroku server and GitHub.

In this FinTech Bootcamp, participants learn how to use Python for financial analysis, machine learning and automation, and SQL. The program is taught by seasoned finance and data science professionals. Students will develop the ability to analyze financial statements, build predictive models, and work with databases. By the end of this course, newly-hired financial analysts will be prepared to succeed on the job, and aspiring financial analysts will have bolstered their resumes and technical interview skills. This course offers a free retake so students can refresh the material. It also includes one-on-one mentoring sessions.

Learn the essential skills and tools to become a Python Developer. This beginner-friendly course teaches students Python for software development with Django and Django REST in addition to other developer tools such as Git and SQL. After completing this certificate, students will be able to apply for the following roles: Python Developer, Back End Developer, Software Engineer, and many more.

In this data science bootcamp, students will build programming skills and data analysis skills using Python. This course is open to beginners and is meant to get individuals up and running with Python programming and data science to generate insights from data. Topics covered include programming fundamentals, working with data frames, data analysis, data visualization, and statistical analysis. This course offers flexible scheduling options and a free retake for students to refresh the materials.

In this course, students expand their Python programming skills into machine learning and algorithms that can independently learn patterns and make decisions. The course begins with linear and logistic regressions, the most time-tested and reliable tools for approaching a machine learning problem. Students then progress to algorithms with a different theoretical basis, such as k-nearest neighbors, decision trees, and random forest. This will bring important statistical concepts to the forefront, such as bias, variance, and overfitting. Participants also learn how to measure the accuracy of your models, as well as tips for choosing effective features and algorithms.