Pre-requisite: Programming in Python for Data Science
Learn the fundamentals of programming in Python, including how to clean, filter, arrange, aggregate and transform data.
What you will learn:
- The foundations of programming in Python while writing human-readable code that sets a foundation of best practices and coding style
- How to clean, filter, manipulate (wrangle) and summarize data using Python libraries for more effective data analysis
- An overview of data structures, iteration, flow control and program design relevant to data exploration and analysis
- Fundamental programming concepts such as loops, conditionals and data structures that create a solid foundation in data science programming
Elective Course: Introduction to Machine Learning
This introductory course on supervised machine learning for prediction focuses on regression and classification models. Understand how to map data to the correct model type, evaluate and select models, as well as communicate and interpret model results.
What you will learn:
- How to create predictive models and communicate results to help organizations reduce operating costs, optimize market strategies and identify trends
- How to choose a correct predictive modelling technique (g., regression or classification) given the available data for the data science problem being considered
- Fundamental machine learning concepts such as generalization errors and overfitting
- How to optimize supervised learning models with feature and model engineering that will contribute to more accurate predictions
Elective Course: Data Visualization
Understand effective data visualizations and perform exploratory data analysis using Altair in Python.
By the end of the course, students will learn how to:
- Use the Altair grammar of graphics to create data visualizations
- Select an appropriate visualization for the data
- Perform exploratory data analysis on a dataset
- Effectively communicate findings with figures
- Interpret data visualizations to answer questions and formulate follow-up questions
How am I Assessed?
You are assessed on each course according to UBC assessment guidelines to ensure successful acquisition of required skills and concepts.
To successfully complete the Key Capabilities in Data Science program, you must complete the prerequisite and two elective courses, and achieve 70% or higher in each of the courses.
Each course requires approximately 50 hours of study. Expect to spend a minimum of 8-10 hours per week on each course if you have coding experience, or 10-12 hours per week for beginners.
You should have access to a desktop or laptop computer, an email account, the internet and an up-to-date web browser. You’ll also need access to a desktop or laptop computer.
This 100% online instructor-supported program combines self-paced independent study with weekly Office Hours below. You’ll need to set aside approximately 10–12 hours a week to complete all learning activities, on top of attending Office Hours.
Course Virtual Office Hours (subject to change)
Programming in Python for Data Science
Wednesdays, 5:15-6:15pm Pacific Time
Saturdays, 9:00-10:00am Pacific Time
Introduction to Machine Learning
Wednesdays, 6:30-7:30pm Pacific Time
Saturdays, 10:15-11:15am Pacific Time
Thursdays, 6:30-7:30pm Pacific Time
Saturdays, 10-11am Pacific Time
To register, choose Available Sessions, and disregard the $0.00 cost. Under Type, you’ll see a total fee with three courses pre-selected. Click ADD TO CART to continue.