Data Wrangling

Course goals

After completing this course, students should be able to:

1. Extract parts of the data that are relevant for the data analytics task.
2. Validate the data against integrity constraints.
3. Prepare the data and test its suitability for the analytics process using different data preparation techniques including cleaning, normalization, discretization and reduction.
4. Handle and process large volumes of data, for example from data streams, and integrate data from multiple sources.
5. Discover the bias in the data and apply bias mitigation algorithms.

Content

Content:

With the current advances in the data collection process, we collect vast amount of data that comes from different sources and follows different structures. The volume, variety and velocity of collecting the data pose extra challenges on maintaining the quality of the data, which influences any data analytics and decision-making task. In order to prepare the data for the different tasks, data wrangling steps ensure the transformation of raw unstructured data into clean, organized and suitable formats. 

The course is designed to balance conceptual understanding with practical implementations (applied & real-world data), using both SQL and Python for data extraction, integration, preparation and validation.

Teaching methods:

• Two lectures per week

• One hands-on tutorial per week

• Two take-home group assignments

• Two peer-graded individual assignments

Assessment:

Your final grade in the course consists of the following grading components:

1. Group assignments (20% of the final grade). There are two group assignments. Each assignment is graded and worth 10% of the final grade.

2. Individual assignments (10% of the final grade). There will two individual assignments where each student should submit their own report for the assignment and grade 3 submissions from other students. Each assignment is 5% of the final grade. 

3. Midterm exam (35% of the final grade): In -approximately- the 5-th week of the course, there is a midterm exam with multiple choice and open questions that covers the first part of the course.

4. Final exam (35% of the final grade): At the end of the course, there is a final exam with multiple choice and open questions that covers (mainly) the material after the midterm.

    

To pass the course, the weighted final grade across all components must be at least 5.5. 

In order to qualify for the resit exam: 

1. the final grade must be greater than or equal to 4.0 and strictly less than 5.5 (AANV); and 

2. a minimum of two assignments have been submitted; and

3. at least one of the exams has been attended; missing both exams will result in an NVD/ND grade.

More information, material, announcements, and grades can be found either on the course website or in BrightSpace.