Course details
This is the final course and project for the Applied Data Analytics Certificate (ADAC) from BCIT Computing. Students must have completed all specific prerequisites in order to register. COMP 4254 is built on top of: Python Fundamentals for Data Analysis, Data Analytics Fundamentals, and Advanced Statistical Techniques for Data Analytics. Students must have experience using data visualization tools, be able to use R and apply statistical methods with algorithms in order to develop their final project. COMP 4254 is a BYOD (bring your own device) course, participants must provide their own current model PC with an i5 or higher equivalent processor, 8 GB of RAM minimum, 256 GB minimum storage and high-speed internet access. Participation is mandatory and students can expect a minimum of 3 hours per week online in real time via the BCIT Learning Hub plus using two-way audio and video. Attendance is required during scheduled class hours and students should expect an additional 10+ hours for reading and homework each week. Building on statistics for data mining, students will make use of previous tools and technologies used through out the program. A combination of R, or Python, and Data Analysis tools and visualization techniques will be integrated into an advanced data analytics project. There is an introduction to data analytics techniques with Big Data. Labs and exercises use Python and Apache Spark for data mining with a focus on Business Intelligence and Big Data. Participants will work with a variety of structured and unstructured data sources to model, analyze and visualize data. Upon successful completion of COMP 4254 students will be able to develop an advanced project from start-to-finish which includes data preparation, Business Intelligence and an in-depth analysis on a broad range of data sources. This course will be offered in the Winter (January) and Spring (April) terms.
Credits
3.0
Cost
$614.38 - $640.27 See individual course offerings below for actual costs.
Course offerings
Winter 2023
Below is one offering of COMP 4254 for the Winter 2023 term.
CRN 85466
Duration
Fri Jan 13 - Fri Mar 31 (12 weeks)
- 12 weeks
- CRN 85466
- $614.38
Class meeting times
| Dates | Days | Times | Locations |
|---|---|---|---|
| Jan 13 - Mar 31 | Fri | 18:00 - 21:00 | Online |
Instructor
Pat McGee
Course outline
Cost
$614.38
Important information
- Internet delivery format.
- Departmental approval needed
- Important course information will be sent to you prior to your course start date. Check your myBCIT email account to access this information.
-
Please email cstpts@bcit.ca for Departmental approval. Include your Student number (A0#) and COMP__ and preferred CRN __ and Program Declaration____. Course is 36 hours synchronous online classes. Late registration is not permitted.
Status
In Progress and Full
This course offering is in progress and full. Please check this page for other currently available offerings, subscribe to receive email updates or contact us with your comments or questions.
Spring/Summer 2023
Below is one offering of COMP 4254 for the Spring/Summer 2023 term.
CRN 65473
Duration
Fri Apr 14 - Fri Jun 30 (12 weeks)
- 12 weeks
- CRN 65473
- $640.27
Class meeting times
| Dates | Days | Times | Locations |
|---|---|---|---|
| Apr 14 - Jun 30 | Fri | 18:00 - 21:00 | Downtown DTC Rm. 375 |
Instructor
Pat McGee
Course outline
Course outline TBD — see Learning Outcomes in the interim.
Cost
$640.27
Important information
- Departmental approval needed
-
Please email cstpts@bcit.ca 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.
Status
Seats Available
3 seats remaining as of Wed March 29, 2023 at 2:50 pm (PDT). Availability may change at any time.
Learning Outcomes
Upon successful completion of this course, the student will be able to:
- Use current technologies to access traditional databases and Big Data.
- Apply machine learning techniques for data analytics.
- Create models for data analytics.
- Compare and contrast the effectiveness of different tools for different data analytics problems.
- Create an effective variety of visualizations for exploration, statistical validation and reporting.
- Apply data mining techniques for knowledge extraction in Python.
- Conduct predictive and prescriptive analytics with Big Data.
- Work with data lakes using unstructured data sources and Apache Spark.
- Model and analyze data from distributed sources, both structured and unstructured.
- Apply tools and methods to address challenges in obtaining, cleaning and analyzing large disparate data sources.
- Develop an advanced data analytics project.
Effective as of Spring/Summer 2021
Related Programs
Advanced Topics in Data Analytics (COMP 4254) is offered as a part of the following programs:
School of Computing and Academic Studies
- Applied Data Analytics
Certificate Part-time
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