Data Science in Mining
Queen’s Mining’s professional development courses are made for the modern mining workforce.
Learn more about Queen’s Mining’s online professional development courses, including:
What You’ll Learn
This course presents a comprehensive overview of the key elements of data science for engineers, with application to mining problems. Topics include data cleaning, organization and manipulation, data collection, visualization and noise filtering. Regression as a Data analysis technique is covered. The focus is on the practical application of data science in the engineering context to make predictions and decisions based on the statistical inference of data.
Who is the Course For?
This course is intended to be offered to anyone who is interested in learning more about the application of data science in the mining industry. The flexible nature of this online course will attract geographically diverse professionals. The course will be designed so that anyone from any background can take the course, however, we expect this course will also appeal specifically to professionals working in the mining industry. Knowledge in any programming language is preferred for this course, although not required.
Why Enroll in Data Science in Mining?
This course lays the foundation for an understanding of the concepts and tools that are used in data science, through examples that are applicable to mining. Through an overview of key Python programming constructs, important libraries for data science, data management tools, basic data analysis techniques and hands-on interactive labs, the learners will be able to automate procedures, make use of data and statistics to help make important decisions and develop and evaluate hypotheses.
About Queen’s Mining
The Robert M. Buchan Department of Mining is one of the oldest and largest Mining schools in the world. Consistently ranked in the top 10 worldwide, Queen’s Mining has a reputation for training outstanding graduates and has strong and long-standing connections with the industry. Queen’s Mining now offers a range of online programming.
TOP
10
consistently ranked mining engineering schools in the world
125
years of mining education experience
25
%
of mining engineers in Canada are Queen’s Grads
Course Learning Outcome (CLO)
This course focuses on 4 learning outcomes. By the end of this course students should be able to:
Course Learning Outcome (CLO) List
No.
Description
1
Describe data management and visualization in a programming environment
2
Obtain data from various resources
3
Analyze data using data analysis techniques to be able to make decisions and predictions
4
Detect outliers and filter data
Course Evaluation
The design of this course will include two pathway options; assessed versus non-assessed. Learner-to-Instructor interaction will be at a moderate level in the assessed course, providing students with the opportunity to interact with the instructor with questions, and receive assessment feedback. The non-assessed version of the course will include slightly less in-depth instruction of concepts, resulting in a lesser time commitment for students. Self-check quizzes will be offered to enhance student learning and understanding of concepts.
1. Overview of Data Analytics [CLO 1]
2. Basic statistics and visualization with Python [CLO 1]
3. Obtaining data [CLO 2]
4. Regression [CLO 3]
5. Data transformation and string manipulation [CLOs 3 & 4]
Program Requirements
This course is designed for learners with little to no knowledge or experience in data science and is ideal for professionals who want to expand their set of tools and understanding to handle and manipulate data and help make decisions based on data in their workplace.
Time Commitment
This course represents a study period of approximately 20 hours in total. The course material is divided into 5 modules and will be available for learners to complete at their own pace over a total of 12 weeks. Note that learners who adhere to a predetermined study schedule are more likely to successfully complete the course on time.
Course Fees
Key Dates
Course Offering Schedule
This offering will commences April 1 and runs until midnight, June 24 – registration is open until May 13.
Get Started
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