The popularity of data science across multiple fields and industries has created more opportunities for specialization in specific areas and skills. For data scientists who are not just interested in the collection and analysis of data, but also what happens to the data once a project has ended, database design is a skillset that reflects these interests. Through specialization in database management and/or design, data scientists can learn more about how to explore and manipulate a dataset. Every data scientist should know database design in order to expand their skills in managing the organization and security of information and data, as well as the long-term storage and sustainability of a dataset.

What is Database Design?

Database design is the method(s) or process of organizing data within the context of a database system. While data can be stored and organized using files or some other online or offline device, large stores of data require a more advanced system of data storage and cleaning. Databases act as such a system by offering a structure that can be used to sort and classify information and data in a way that is neat and organized. By storing data within a database instead of another type of system, it is also easier to manage and analyze the data that you have. It is more common to store data within a database that has the capacity and features to not only store a large volume of data but also to search through a dataset and return the information that you need when you need it. 

Database design works in concert with database management to create a method for organizing and searching through a dataset. In addition, most databases are designed to manage information and data in a way that addresses concerns around privacy and security. When a large amount of data is stored in one central location, this data can be more vulnerable to attacks from outside sources. Most databases require a design that includes specific permissions or security protocols that ensure that the data is both protected and accessible. There are three primary areas of database design that every data scientist should know.

1. SQL, NoSQL, and Database Management Systems

While database design is a very general skillset, there are specific databases that correspond to database management systems that every data scientist should know. While there are several databases that you can learn, some of the most commonly used data science databases include relational databases, NoSQL databases, and network databases. Each of these databases corresponds to a database management system (DBMS) which can be used to structure your dataset in a way that simplifies the process of organizing, analyzing, and visualizing a dataset.

Of all of the databases and management systems available to data scientists that want to learn more about database design, working with SQL and relational database management systems is the most popular. SQL, or Structured Query Language, is a programming language that is essential for writing queries and searching through DBMS like MySQL or SQLServer. Working with SQL and database management systems is especially useful for data scientists because SQL is one of the most popular data science skills within multiple fields and industries. At the same time, NoSQL databases are also useful when working with unstructured data.

2. Data Cleaning and Organization

The second reason that data scientists should learn more about database design is the importance of data cleaning and organization. After identifying the type of data that you have, and the database which corresponds to that type of data, data scientists can import their data into one of these databases in order to clean it. Data cleaning is the process of exploring or searching through a dataset in order to identify things like missing values, outliers, and other inconsistencies or inaccuracies that need to be corrected or removed. 

Data scientists can use their knowledge of programming languages in order to automate the process of data cleaning within a database management system. For example, relational databases can be used to organize a dataset into rows and columns in order to set the stage for exploratory and comparative data analysis. If you are using a SQL database, working in a system such as PostgreSQL or SQLServer is also useful for querying and creating metadata and other organizational criteria for a dataset. Through learning how to write queries, data scientists are also able to streamline the exploratory phase of a data analysis project.

3. Data Storage and Security

While data collection and cleaning is generally prioritized as the lead-up or foundation to the data analysis process, it is also important for data scientists to understand what to do with a dataset after the data has been cleaned and analyzed. Through the knowledge of database design and management, data scientists can learn how to store a collection of data over time. Creating a database allows you to store your dataset into tables within a program, as well as on a hard drive and/or in the cloud. 

Proper data storage makes it significantly easier for data scientists to return to a dataset at a later date, as well as to work on projects that require the collection of data over time. Once this data is stored, data scientists that are working with users or other stakeholders can also use knowledge of database design to assist in the process of securing the safety of the information and data that has been stored. When working with databases, this can be as simple as learning how to set permissions around the accessibility of a dataset, storing data in a way that respects and maintains the integrity of the collection, as well as performing regular audits of engagement with the database. Overall, database design should be informed by the tenets of database security and data scientists must acknowledge the importance of including safety protocols in both the storage and sharing of datasets and analyses.

Pursuing a Career in Database Design

For data scientists, in particular, training in database design allows you to pursue careers in an industry that focuses on the storage and organization of data. These skills are especially useful in companies or institutions that work with large stores of data, archives, and institutional repositories, such as the government, museums, colleges, and universities, as well as all finance, social media platforms, and other technology companies. In addition to pursuing a career in data science, increasing your knowledge and background in database design can also lead to a career in cybersecurity, database administration, and other positions within the world of information science and technology that privilege the protection of sensitive data. 

Especially when it comes to pursuing employment within smaller companies or teams, having knowledge of database design makes you a more well-rounded candidate for positions as a researcher or analyst. Pairing database design with training in data science can lead to multiple opportunities which reflect increased interest in the skills of data stewardship and sustainability. Through taking courses or acquiring specialization in these areas, data science students and professionals are able to market themselves as not only specializing in the analysis of data but in some of the most important steps of the data collection and storage process. 

Want to Learn More About Database Design?

As a subset of the field of information and data science, database design offers skills and training in the management of database systems, the organization and cleaning of data, as well as its storage and security. Complementing any of Noble Desktop’s data science classes, database design is a useful skill for data science students and professionals that are interested in learning more about databases and querying. 

Noble Desktop’s SQL courses include training in relational databases, storage systems, and programming. The SQL Bootcamp includes more advanced knowledge of working with different tables and data types. Whether you take part in a bootcamp or certificate program, and of the courses available will offer valuable instruction in database design!