Learn data transformation with online courses
What is data transformation?
Across industries, software and hardware is continually making sense of data to keep things running: powering our smart homes, driving our cars, and helping businesses understand customer sentiment. All of that is only possible through the power of data transformation.
But what is data transformation, exactly? Simply put, it’s a catch-all term for the conversion, organization, and restructuring of one data set into a different format. This process is key to streamlining disparate data into one silo so it can be stored, migrated elsewhere, and easily analyzed.
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How does data transformation work?
Let’s look at a basic example. Say your video editing software won’t accept a video file you’re trying to import. The pixels and sound contained within can likely be salvaged by simply converting the file to a different format the software understands.Footnote 1
There are four common data transformation types:Footnote 2
Constructive, where data is added, copied, or replicated.
Destructive, where data fields or records are deleted.
Aesthetic, where data is standardized to meet standards or requirements.
Structural, where data is restructured by renaming, moving, or merging database columns.
Each data transformation type makes up the second stage of ETL. But what is ETL? Much more straightforward than you might think, it’s a three-step process for integrating data from multiple sources into a single location:Footnote 3
Data gets extracted from legacy databases and systems.
Data is transformed to increase data quality, integrity, and consistency.
Data is loaded into its intended destination.
Data transformation course curriculum
A course on data transformation may cover topics like:
Programming: People who understand software design and at least one programming language for handling large data sets — especially SQL and Python — can transform data in their home or workplace.
Big data: Around the world, countless people and organizations depend on big data. That’s why data transformation exists, in the first place.
Information technology: IT professionals use devices to interact with large volumes of data in several ways, from generation to transformation and storage.
Relational design theory: In rethinking how data gets stored, professionals may create database schemas that adhere to sound relational design theory.Footnote 4
Data analysis: Data transformation empowers people, software, and hardware to engage in more effective data analysis.
edX offers a variety of courses and programs to help new and experienced data scientists learn valuable skills and other technical information. With full degree programs, you can earn a bachelor’s degree or even a master’s degree in data science or a related field. You can also enroll in more specialized coursework with individual courses and boot camps. Explore the many options available to you.
Data transformation jobs
Now that you know what data transformation is and why hardware and software won’t function without it, you can begin exploring fascinating data transformation jobs. Careers in this field include:
Database administrator: build and organize data storage systems for their organizations. They’re often required to transform available data so that it adheres to their organization’s data governance standards.Footnote 5
Information security analyst: craft and enact security plans that may involve data transformation techniques to keep data compliant and protected from cybercriminals.Footnote 6
Data scientist: use programming skills to transform data they can analyze using statistical and mathematical knowledge, in order to to provide their organizations with valuable data insights.
Data engineer: build and manage data storage architecture.
Note that learning data skills on its own does not mean you have the skills required for the data transformation jobs listed above. Some companies may seek candidates with specific educational credentials and technical skills. Before choosing a learning path, research the roles you hope to attain.
How to learn data transformation
Instruction about data transformation can cover a number of technical topics, such as:
Data technologies: technologies like structures and file formats are the reason for data transformation. For example, to send a Word document to someone without Microsoft Word, your file needs to transform while preserving its original text and formatting.
Database query languages: languages like SQL and GraphQL are pivotal for managing, moving, and transforming large data sets.Footnote 7
Spreadsheet software: software like Microsoft Excel and Google Sheets is often the means to view data that’s been transformed to be simpler to parse.
Programming languages: Programming languages are used to write software that can transform data into formats preferred by other software, users, or the programmer themself.
If you’re ready to start learning about data transformation, you can explore a wide variety of degree programs, like an online bachelor of data science, or a master of data science program, which can help provide you with a well rounded understanding of the field. You can also build relevant skills with a data analytics boot camp or other boot camps offered through edX. Start your learning journey today.