A Depleting Focus from Relational Algebra to Linear Algebra
As the popularity of machine learning (ML) and artificial intelligence (AI) continues to rise, many aspiring data professionals are focusing heavily on subjects like linear algebra, which is foundational for these advanced technologies. However, there’s a growing concern that another important discipline — relational algebra, the basis for managing relational databases (RDBMS) — is being neglected. Despite the excitement around ML, most businesses still rely on relational databases for storing and managing critical data. This shift in focus may create skill gaps that could affect data accessibility and operational efficiency.
The buzz around ML and AI has attracted many people to these fields, and with good reason. They’re exciting, powerful, and have the potential to revolutionize industries. Linear algebra, which plays a crucial role in machine learning algorithms, has therefore become a must-learn subject. However, in the rush to master ML, many learners are overlooking the foundational skills needed to work with real-world business data, particularly relational algebra and SQL. These skills are vital for handling the vast amounts of data stored in relational databases.
Even though new technologies like NoSQL databases and cloud-native systems are emerging, most businesses still rely on relational databases to store their transactional and operational data. This is especially true in industries like finance, retail, and healthcare. Relational algebra provides the foundation for SQL, the most widely used query language in the business world. Whether it’s for an enterprise application, an ERP system, or a financial database, relational databases are often the backbone of daily business operations.
Aspiring data professionals often dive straight into the technical side of machine learning, but they sometimes miss the crucial step of learning how to handle and manage data. Before you can apply any ML model, the data has to be extracted, cleaned, and transformed. Most of this data will come from relational databases, and understanding relational algebra concepts — like joins, unions, and selections — helps in writing efficient queries. It also allows for a deeper understanding of how data is structured, making the whole process of data preparation more efficient.
Even though ML and AI are gaining popularity, data management remains a critical part of running a successful business. Without properly managing and accessing data, even the best machine learning models can’t deliver accurate or meaningful insights. Proficiency in relational algebra and SQL ensures that data can be queried, organized, and made available for analysis. If this foundation is missing, businesses may struggle with data quality and accessibility issues, which can hinder their ability to fully leverage advanced technologies like AI.
Understanding relational algebra doesn’t just help with writing queries; it’s also essential for query optimization. In relational databases, optimizing a query can mean faster, more cost-effective data retrieval, especially when dealing with large datasets. This becomes crucial as businesses scale and need to process more and more data. Efficient queries can save both time and resources, directly impacting the bottom line of a company.
For a data professional to be truly effective in today’s data-driven world, it’s important to be skilled in both relational and linear algebra. Relational algebra ensures that data is accessible and processed efficiently, while linear algebra powers the advanced models that can extract meaningful patterns from that data. Data preparation is often 80% of the work in machine learning projects, and a solid understanding of relational databases is key to doing that work well.
The solution lies in balancing both skill sets. While it’s essential to dive into linear algebra and machine learning techniques, data professionals should not overlook the importance of relational algebra and RDBMS. Training programs, courses, and self-study efforts should place equal emphasis on these foundational skills. A strong focus on SQL and understanding the principles of relational databases will enable data professionals to bridge the gap between advanced analytics and real-world data management.
In the end, relational algebra and linear algebra are not mutually exclusive; they are complementary. Businesses need both efficient data management and advanced machine learning models to thrive in today’s data-driven economy. By mastering both disciplines, data professionals can position themselves as well-rounded experts capable of handling the entire data lifecycle — from extraction to prediction.
If you loved this story, please feel free to check my other articles on this topic here: https://ankit-rathi.github.io/data-ai-concepts/
Ankit Rathi is a data techie and weekend tradevestor. His interest lies primarily in building end-to-end data applications/products and making money in stock market using Tradevesting methodology.
