What will AIXplorers learn in this course?
- Understand how to define a strong data science problem before starting analysis.
- Learn how to collect relevant, reliable, and ethical data from different sources.
- Explore essential data preprocessing steps, including cleaning, duplicate removal, and handling missing values.
- Identify and manage outliers using practical techniques such as the Z-score method.
- Understand how to address inconsistencies in dates, units, and formatting.
- Learn the purpose of normalization, standardization, and transformations.
- Explore feature engineering techniques that improve model performance.
- Use data exploration methods to identify patterns, relationships, trends, and outliers.
What are the requirements or prerequisites for taking this course?
- Basic understanding of data science concepts.
- Familiarity with Part 1 of the course or introductory knowledge of data, AI, and machine learning.
- Basic comfort with Excel sheets or structured datasets.
- Interest in learning how raw data becomes useful for analysis and modeling.
- No advanced coding expertise required, but basic awareness of Python or Pandas is helpful.
Who is the course for?
- Beginners who want to understand the practical data science workflow.
- Learners who completed Part 1 and want to move into deeper data preparation.
- Students and professionals interested in data analysis, AI, and machine learning.
- Aspiring data scientists who want to learn how to clean and explore datasets.
- Business professionals who want to understand how data-driven insights are created.
