GET 305: Engineering Statistics and Data...
GET 305: Engineering Statistics and Data Analytics
Instructor: Oladimeji Kazeem
Last Updated: 07-10-2026 | Language: English
Who this course is for
- Students of B.Eng. Railway Engineering (Engineering and Technology)
- Lecturers and staff of institutions teaching GET 305
Related CCMAS Courses
This seminar is relevant to the following courses in the NUC Core Curriculum Minimum Academic Standards (CCMAS).
Seminar Class Schedule
14th December 2026 to 28th February 2027
Fri - 3:00 PM WAT | Sat - 12:00 PM WAT
All times are in West African Time (WAT, UTC+1).
Lecture dates (10 lectures)
-
Dec
19
Sat
Descriptive Statistics and Visualisation Next
Saturday, 19th December 2026 12:00 PM – 1:00 PM WAT Google Meet
-
Dec
19
Sat
Engineering Data and Analytical Thinking
Saturday, 19th December 2026 12:00 PM – 1:00 PM WAT Google Meet
-
Dec
22
Tue
Probability and Probability Distributions
Tuesday, 22nd December 2026 12:00 PM – 2:00 PM WAT Google Meet
-
Jan
08
Fri
Sampling, Estimation and Measurement Uncertainty
Friday, 8th January 2027 12:00 PM – 1:00 PM WAT Google Meet
-
Jan
15
Fri
Hypothesis Testing and Statistical Inference
Friday, 15th January 2027 12:00 PM – 1:00 PM WAT Google Meet
-
Jan
22
Fri
Correlation, Regression and Predictive Modelling
Friday, 22nd January 2027 12:00 PM – 1:00 PM WAT Google Meet
-
Jan
29
Fri
Design of Experiments and Engineering Quality Control
Friday, 29th January 2027 12:00 PM – 1:00 PM WAT Google Meet
-
Feb
05
Fri
Time Series Analysis, Forecasting and Predictive Maintenance
Friday, 5th February 2027 12:00 PM – 1:00 PM WAT Google Meet
-
Feb
06
Sat
Engineering Intelligence and Technical Communication
Saturday, 6th February 2027 12:00 PM – 1:00 PM WAT Google Meet
-
Feb
08
Mon
Integrated Engineering Analytics Project
Monday, 8th February 2027 12:00 PM – 5:00 PM WAT Google Meet
Course Content
Description
GET 305: Engineering Statistics and Data Analytics
A CCMAS course delivered on Diaspora BRIDGE by Oladimeji Kazeem. B.Eng. Railway Engineering · Engineering and Technology
At the end of the course, the students should be able to:
- Work with data from the point of view of knowledge convergence, machine learning, and intelligence augmentation, which significantly raises their standard for engineering analysis (the approach forces them to learn statistics in an actionable way that helps them to see the holistic importance of data analytics in modern engineering and technology);
- Anticipate the future with artificial intelligence while fulfilling the basic requirements of conventional engineering statistical programming consistent with their future careers;
- Perform, with proficiency, statistical inference tasks with language or programming toolboxes such as R, Python, Mathematica, or MATLAB, and Design Expert to summarize analysis and interpretation of industry engineering data and make appropriate conclusions based on such experimental and/or real-life industrial data;
- Construct appropriate graphical displays of data and highlight the roles of such displays in data analysis, particularly the use of statistical software packages;
- Plan and execute experimental programs to determine the performance of program-relevant industrial engineering systems and evaluate the accuracy of the measurements undertaken; and
- Demonstrate mastery of data analytics and statistical concepts by communicating the results of experimental and industry-case investigations and critically reasoned scientific and professional analysis through written and oral presentation.