Data Science for Non-Computing Faculty
Why colleges run this
Commerce, science and management departments hold data they cannot analyse, and send it to computing colleagues who do not know the domain. This closes that gap.
Course outcomes
CO1
Prepare a raw dataset for analysis and record every change made to it
K3CO2
Summarise a dataset using measures appropriate to its distribution
K3CO3
Test a stated hypothesis and interpret the result without overclaiming
K4CO4
Select a chart that represents the data honestly and explain why
K5CO5
Produce an end-to-end analysis of departmental data for a non-specialist reader
K6Modules tap a module for its theory & lab
Hours shown are the recommended 5-day format — module time scales to the duration you pick.
01Data and its shape6 h
Theory
Types, cleaning, missing values, the cost of bad data
Lab
Clean a supplied messy dataset
Output
Cleaned dataset with a change log
02Describing data6 h
Theory
Distributions, central tendency, spread, the summary that misleads
Lab
Summarise your own departmental data
Output
Descriptive summary
03Relationships and inference6 h
Theory
Correlation, significance, confidence, the errors that get published
Lab
Test a stated hypothesis
Output
Test result with interpretation
04Visualising honestly6 h
Theory
Chart choice, axes, scale, the chart that lies
Lab
Rebuild a misleading chart
Output
Before-and-after chart pair
05A departmental analysis6 h
Theory
End-to-end analysis on your own data, written for a non-specialist
Lab
Produce and present the analysis
Output
Analysis report
Every participant receives
Certificate of completion Course material LMS access Interview question bank Mock interview & viva practice