Become a Data Scientist
Working as a data scientist requires an analyst and problem-solver mindset. Learn those hard and soft skills with us and get experience using real-world data sets.
Learn how to clean, analyze, pre-process, use machine learning algorithms, and interpret the generated data models to advance your career in Data Science.
Data Science Expertise in 10 weeks
The CREWES Data Science is a 10-week course consisting of theoretical and hands-on data analysis and machine learning modeling provided by the CREWES Data Science Initiative. It is intended for anyone who wants to understand Data Science and apply it to real-world scenarios. You will use examples from the Oil and Gas industry, marketing, and economics, and work with classmates as a data science team on your final project.
During the course, you will go through a complete data science workflow including data-loading, building machine learning models and predictions, and interpreting results using your experience and insight. You will get familiar with the fast-paced data science work environment, learn commonly-used algorithms and packages, and explore the path you want to follow.
In this course, you will learn how to use Python for data analysis, data cleaning, visualization, data processing, machine learning, model interpretation, teamwork, and how to present your developments and finds.
A hands-on final project with real-world data
The final project will enable students to apply their new skills and professional experience to a practical, real-world situation using the full data science workflow: computer programming, data processing, modeling, and business practice. Students will work in groups on a final project using well-log data (or any data of your desire). You will learn all the steps of a project pitch and results demonstrations.
By the completion of this course, successful students will be able to:
- Use Python for data analysis and modeling, taking advantage of the most used libraries
- Practice teamwork, communication, and collaboration across the whole data science workflow
- Interpret and handle challenging real-life data sets
- Create (or refine) your portfolio to support your career path
Topics of Instruction
- Python basics
- Data types
- Python libraries
- Work with tabular data (dataframes)
- Load data: tabular data to dataframes
- Visualization: types of plots; Matplotlib and Seaborn libraries
- Data analyses: check the data; look for missing data; data visualization
- Data cleaning: remove bad data
- Data preparation: recover missing values; data augmentation; prepare for modeling
- Scikit-Learn and XGBoost libraries
- Regression models
- Classification models
- Clustering models
- Hybrid models (combining different machine learning models)
- Deep learning with Tensorflow
- Model interpretation
- Project management
- Team communication
- Data and code sharing with GitHub
A 20% discount on registration is available to:
- Current students at the University of Calgary
- Former University of Calgary students who have completed their final term in Fall 2021 or Winter 2022
To receive this discount, you must email Dr. Marcelo Guarido (email@example.com) with either your Enrolment Verification letter or your unofficial transcript from the “My UCalgary” student portal before registering for the course. You will receive a discount code to use when registering. Use of the discount code will be verified after enrolment, students who did not receive prior approval for the discount will need to pay the balance or will be withdrawn from the course.
About Consortium for Research in Elastic Wave Exploration Seismology (CREWES): is an applied geophysical research group concentrating on the acquisition, analysis, inversion, and interpretation of multicomponent seismic data. Its Data Science division (CREWES Data Science Initiative, or CREWES DSI) focuses on a broader set of projects related to Environmental, Energy, Geoscience, and Engineering.
What do I need to apply?
- A strong interest in learning data science and Python programming
- Prior experience in programming is recommended but not required, as you will go through from the basics to the modeling expertise during the course
- Understanding of linear algebra is a plus but not mandatory