This hands-on course follows on from COMP 1630, COMP 2362 (or COMP 2364) and MATH 1060. It introduces data analytics fundamentals to students who have previously acquired skills in Excel spreadsheets, database systems, Structured Query Language (SQL) programming and statistics. Starting with an overview of the business cycle for data mining, students learn how to translate a business problem into a data mining problem. Through a number of problems and case studies, students will build, assess, and deploy different models to gain insights into the data by testing probable hypotheses using primarily SQL and Excel. Discussions include the fundamentals of using the R Language for data analytics. Labs and exercises include: using advanced analytics with SQL, advanced Excel and R Studio to clean data and perform analysis. Participants will gain experience with data exploration, testing hypothesis, and predictive analytics. This is a required course in the Applied Data Analytics Certificate, ADAC from BCIT Computing. Upon successful completion, participants will be able to identify the process of data analysis, the roles of data analytics practitioners and how to create analytics models. Students will be prepared to move on to MATH 3060 Advanced Statistical Techniques for Data Analytics and COMP 4254 Advanced Topics in Data Analytics.
Below is one offering of COMP 2854 for the Fall 2023 term.
Wed Sep 13 - Wed Nov 29 (12 weeks)
- 12 weeks
- CRN 45939
Class meeting times
|Sep 13 - Nov 29||Wed||18:30 - 21:30||Burnaby SE12 Rm. 310|
- Departmental approval needed
Please email email@example.com for Departmental approval. Include your Student number (A0#) and COMP__ and preferred CRN __ and Program Declaration____. Course is 36 hours on campus. Late registration is not permitted.
Upon successful completion of this course, the student will be able to:
- Identify analytics stakeholders and roles.
- Identify the steps in data analysis process.
- Translate business problems into data analytic problems.
- Describe data refinement and data mining.
- Identify data issues.
- Troubleshoot data, prepare and clean data for data mining and analysis.
- Optimize SQL for efficient query execution.
- Recognize SQL antipatterns and apply corrective measures.
- Create data analytic models.
- Perform data exploration, testing hypothesis and predictive analytics with SQL and advanced Excel functions.
- Use R language and R Studio software for Data Analytics tasks.
- Create meaningful reporting to uncover insights and trends to support business decision making.
Effective as of Spring/Summer 2017
Data Analytics Fundamentals (COMP 2854) is offered as a part of the following programs:
School of Computing and Academic Studies
- Applied Data Analytics
If you have a question or comment about this course, please complete and submit the form below.
Interested in being notified about future offerings of Data Analytics Fundamentals (COMP 2854)? If so, fill out the information below and we'll notify you by email when courses for each new term are displayed here.
Programs and courses are subject to change without notice.