Data Preparation
About This Course
This course equips learners with essential skills for data preparation, a critical step in data science workflows. Learners will explore techniques for data discovery, validation, and cleaning to ensure accuracy and reliability. The course also covers methods for structuring, enriching, and filtering data, enabling learners to transform raw datasets into actionable insights. Through practical exercises, learners will apply these techniques to real world data science problems, gaining hands on experience in preparing data for analysis and modelling. By the end of the course, learners will be able to design and implement robust data preparation strategies that enhance the quality and effectiveness of data driven solutions.
What You'll Learn
1. Explain the importance of data preparation in data science.
2. Describe various techniques for data discovery.
3. Identify and apply various data validation and cleaning techniques.
4. Describe various data structuring, enrichment, and filtering techniques.
5. Apply data preparation techniques to data science problems.
Entry Requirements
No pre-requisites are needed to enroll in the individual courses; however, learners without qualifications or work experience in relevant engineering or the related fields may find the course contents challenging. Prior knowledge in STEM field will be an advantage.