An Introduction To The Importance Of Data Wrangling
Data science is an intriguing field that brings to mind genius brains creating algorithms to predict popular videos, revolutionise healthcare and more. But the truth is a little less glamorous. Before you get to the fancy algorithms and dazzling visualisations, there’s the often overlooked but important step of data wrangling.
It is the unglorified but essential groundwork that ensures the smooth running of any data science project. For those looking to study a Graduate Certificate in Data Science, mastering this step is pivotal, as it forms the basis of all subsequent analysis and insights. Here’s why data wrangling deserves more recognition and why it can be the hidden gem that propels you towards a successful career in data science.
Unveiling The Power of Clean Data
Imagine having a massive amount of data at your fingertips, such as customer records, social media feeds, financial transactions and more, which is a goldmine of potential insights. However, this data still needs to be ready for analysis. It’s riddled with inconsistencies, missing values and duplicate entries. Names can be spelt differently, dates can be formatted haphazardly and some helpful information can simply be absent. This is the messy reality of most raw data.
Data wrangling tackles this head-on. It involves a series of techniques to transform this unorganised data into a clean, consistent and usable format. This can include:
- Identifying and Removing Errors: This could involve fixing typos, correcting inconsistent date formats and removing duplicate entries.
- Data Imputation: For missing values, data wrangling techniques can estimate the most likely value to fill the gaps, ensuring a complete dataset.
- Data Transformation: This could involve converting data types (e.g. changing text to numbers), standardising units (e.g. converting all currencies to a single unit) and creating new variables derived from existing data.
Wrangling data builds a solid foundation for your analysis, as clean data results in accurate results, which lead to more actionable insights.
Makes Your Workflows More Efficient
Data wrangling isn’t just about cleaning data; it’s about efficiency. Here’s how:
- Reduces Analysis Time: Clean data is easier for analytical tools to process, saving you valuable time when running analyses and generating reports.
- Improved Collaboration: When everyone on your team is working with the same wrangled data set, there’s less confusion and wasted effort.
- Fewer Errors: Clean data minimises the risk of errors creeping into your analysis, leading to more reliable and trustworthy results.
Data wrangling skills can give you a significant advantage in a world where time is money. By becoming adept at data wrangling, you can streamline your workflow, increase productivity and, ultimately, become a more valuable asset to any data science team.
Why Do Data Wrangling Skills Matter?
So, why should you care about data wrangling? Here are a few reasons:
Demand Is High
Studies suggest that data wrangling can take up anywhere from 50% to 80% of a data scientist’s time. This means that strong data-wrangling skills are in high demand.
A Stepping Stone To Success
Mastering data wrangling gives you a solid foundation in data manipulation and management. This is an important skill set for any aspiring data scientist.
A Gateway To Deeper Learning
A strong understanding of data wrangling allows you to delve deeper into more advanced topics like data modelling and machine learning.
Developing Your Data Wrangling Prowess
Fortunately, data wrangling is a skill that can be nurtured in the following ways:
Learn Programming Languages
Languages like Python and R offer powerful libraries specifically designed for data manipulation and cleaning. Mastering these languages will help you tackle even the most complex data challenges without breaking a sweat.
Practice Makes Perfect
There are many publicly available datasets online that can serve as your data-wrangling playground. Experiment with various techniques and watch your skills grow.
Seek Out The Community
The data science community thrives on collaboration. Join online forums or attend workshops to learn from experienced data wranglers and share your own experiences.
Conclusion
Data wrangling might not be the flashiest aspect of data science, but it is undeniably important. By mastering this skill, you’ll be well on your way to becoming a pro data scientist who can deliver clean, reliable and actionable insights. So, the next time you hear about data science, remember that some data wranglers play a significant part in refining the data so that it actually works.
