Part 2 : DATA ANALYSIS TECHNIQUES

Learning Some Jargons:

As we delve into the intricacies of data analysis techniques, it’s crucial to familiarize ourselves with some key technical jargon.

This knowledge lays a solid foundation for understanding upcoming explanations. Here are some concepts to get started:

Rows: The horizontal alignment of your data, representing individual observations or entries.

Columns: The vertical alignment of your data, showcasing different attributes or variables.

Datasets: Collections of data presented in tabular form, comprising rows and columns. You’ll often hear data and datasets used interchangeably.

Variables: Characteristics that can be measured and vary between entities, typically represented by column headings (e.g., sex, age).

Codebook: A document or file providing detailed insights into the variables within a dataset. Also known as Metadata or a data dictionary.

Sample: A subset of elements from a larger group selected for study or analysis.

Data Wrangling: The pivotal process of collecting, cleaning, and transforming raw data into a structured format suitable for analysis. It’s where the majority of a data analyst’s time is invested.

Correlation: A statistical measure examining the relationship between two or more variables.

Hypothesis: An educated guess about the expected outcome of data analysis.

This list isn’t exhaustive, so feel free to share additional key concepts in the comments. Let’s enhance our data knowledge together! 📊💡