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| 1.
| understand the data science workflow, including identifying business problems, understanding data sources, data cleaning and preparation, exploratory data analysis, modeling, and communication of results. |
| 2.
| apply the principles of data science to real-world scenarios by designing and executing data-driven experiments, and iteratively refining their approach based on the results, and effectively communicating their findings |
| 3.
| identify issues in the data science workflow, such as data quality, ethical considerations, and limitations of the models, and develop strategies to address them |
Mapping from Course Learning Outcomes to Programme Learning Outcomes
| | PLO a | PLO b | PLO c | PLO d | PLO e | PLO f | PLO g | PLO h | PLO i | PLO j |
| CLO 1 | | | | | | | | | | |
| CLO 2 | | | | | | | | | | |
| CLO 3 | | | | | | | | | | |
T - Teach, P - Practice
For BEng(CompSc) Programme Learning Outcomes, please refer to
here.
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Calendar Entry:
In this course, students will learn data science step by step through real analytics example: data mining, modelling, tableau visualization and more. Unlike many classes where everything works just the way it should and the training is smooth sailing, this course will give students a data science odyssey through experiencing the pains a data scientist goes through on a daily basis. Corrupt data, anomalies, irregularities, etc. Upon completing this course, the students will enhance their data wrangling skills and learn how to 1) model their data, 2) curve-fit their data, and 3) how to communicate their findings. The students will develop a good understanding of Tableau, SQL, SSIS, and Gretl that give them a safe ride in data lakes.
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