| University | Qualifi Ltd |
|---|---|
| Subject | DAI403 Data Science Using Python (H/651/0601) |
DAI403 Assignment Brief
| Qualification | Level 4 Diploma in Artificial Intelligence (610/3934/2) |
|---|---|
| Unit Code | DAI403 |
| Unit Title | Data Science Using Python |
| Unit Reference | H/651/0601 |
| Credits | 20 |
| TQT | 200 |
| GLH | 120 |
Assignment Aim
This unit introduces students to data science through the Python programming language. It covers fundamental Python programming concepts, data analysis techniques, and the use of Python libraries such as Pandas, NumPy, and Matplotlib for data manipulation and visualization. Students will learn how to apply Python to real-world data science problems, exploring topics such as machine learning algorithms, data wrangling, and exploratory data analysis. Students will have the opportunity to carry out projects to apply learned concepts in practical scenarios. Throughout the unit, the focus is on understanding and applying statistical methods for data analysis, ensuring a strong foundational skill set for aspiring data scientists.
Learning Outcomes and Assignment Criteria
| Learning Outcomes
When awarded credit for this unit, a learner will: |
Assessment Criteria
Assessment of this learning outcome will require a learner to demonstrate that they can: |
| 1. Understand Python for data analysis, | 1.1 Describe Python fundamentals and data science concepts |
| 1.2 Write a small Python script on basic operations like variable assignments, loops, and conditionals | |
| 2. Be able to convert a given dataset to derive insights using data visualization
|
2.1 Organize a given dataset using Pandas before performing basic manipulations. |
| 2.2 Convert cleaned dataset into a series of visualizations to showcase different types of plots. | |
| 2.3 Interpret the results from the visualizations. | |
| 3. Be able to apply basic machine learning concepts using Python
|
3.1 Apply statistical methods to a dataset with emphasis on concepts like mean, median, mode, variance |
| 3.2 Explain the results of the statistical summary and visual exploration on a chosen dataset. | |
| 4. Be able to analyze data to solve a given real-world data science problem.
|
4.1 Compare provided statistical methods and choose one to interpret its dataset |
| 4.2 Present insights on the given real-world data science problem |
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