Data Wrangling: Challenges

Data wrangling is a crucial step in the data analytics process, where raw data is transformed into a more understandable and usable form for further analysis. There are a few key challenges we often face when wrangling data: 1. Scalability and Performance  Nowadays, we have access to massive amounts of data, also known as Big […]

Data Wrangling: Key Practices

To achieve accurate and reliable data analysis, documenting and tracking data, assessing data quality, using an iterative approach, collaborating with teams, and balancing automation with manual intervention are important practices. Documentation and Data Provenance   Knowing where your data comes from and how it was collected is crucial. Ensure you document the source, the methods used […]

The Importance of Data Wrangling

In simple terms, data wrangling is the process of cleaning, organizing, and transforming raw data into a format that can be easily analyzed and interpreted. Let’s dive into why this step is so crucial and how it contributes to the overall quality of your analysis. Improving data accuracy and consistency  Data often comes from multiple […]

Data Wrangling: Data Enrichment

Here are three important ways to achieve data enrichment: feature engineering, deriving new variables, and integrating external data sources.   1) Feature Engineering: Think of feature engineering as a way to get the most out of your data by creating new, more informative attributes or “features”. Let’s say you own a coffee shop, and you have a […]