Informatics vs. Data Science: What Are The Differences?
Cherish HudgensTable of contents
Unpacking the Varied Paths of Data Expertise
Photo by Shahadat Rahman on Unsplash
Hello! This article clarifies the difference between data science and informatics based on my level of understanding. This post is intended for anyone interested in learning more about either field.
Also, in this post, I will reference the Nielsen Norman Group, a group that sets standards for UX/UI interfaces.
3.That’s Great! Now, what Is informatics?
4.What is the difference between Informatics and Data Science, and where do they overlap?
5.The collaboration between AI and informatics has created a new world
Telling them apart
I am an informatics major. When I began learning about this field, most of its topics had been very new to me.
There is one question I would always ask myself while studying Informatics in university: Is data science informatics, or is informatics data science? Well, yes. They’re fraternal to one another. They share qualities that join them at a fundamental level, but they also have qualities that are unique to each of them as separate fields. They overlap in a few areas of discipline. I’m sure the same can be said for other interrelated fields.
I remember wondering if there were aspects to each one that joined them together or set them apart, and as it turns out, that is the case for either instance.
It can take time for a learner to wrap their mind around the most abstract concepts regarding this field, and I was no different. However, once that information is absorbed, it will stick to memory once truly understood.
What is data science?
Data science is massive and very broad. This field is home to such topics as machine learning, statistical analysis, data mining, data engineering, deep learning, and data visualization — which, in stating these areas, only name a select few topics under the discipline.
This field largely determines data behavior, analysis, and improvement methods.
Data scientists work out the preliminary steps of collecting, cleaning, sorting, and analyzing the behavior of the data.
Here are a few examples that help define data science:
- Data Acquisition and Cleaning:
Data science is all about the study of data. Based on my level of understanding, observing how data behave through these analyses is crucial in determining data’s value as a potential tool. Data collected in raw forms can be too cumbersome to maintain efficient usage and may lack regulation, analysis, and cleansing. For that reason, there is a process data scientists undergo to wrangle the raw data into something usable and manageable. Many repositories get sourced by data professionals, and the data is preprocessed and cleaned to ensure quality and integrity.
Exploratory Data Analysis (EDA): Before developing models are developed, the Exploratory Data Analysis (EDA) then is conducted by a data scientist to understand the underlying patterns, correlations, and anomalies within that bit of data.
Model Building and Evaluation: This technique utilizes statistical and machine learning algorithms for optimizing performance. Data Scientists come up with predictive models tailored to specific objectives to streamline business or healthcare services. These models then undergo rigorous evaluation to assess their efficacy and universality.
Deployment and Maintenance: However, it does not always end with just the configuration of models; Data Scientists oversee the deployment of models into production environments and continuously monitor their performance, iterating and refining as necessary.
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That’s Great! Now, what Is informatics?
Informatics is on par with computer science for being a top-level domain to numerous subjects, with either one acting as an amalgam of interdisciplinary topics. The informatics field forms a universe encompassing the study of information that results from data insights. This field studies information pulled from various data input points and showcases the impact of data across many domains.
Making a distinction
If data science is the study of behavioral properties of data processes, then informatics is the study of the behavioral properties of computational systems that work with said data.
Informatics tailors the data collected, analyzed, and sorted to specific user or service needs using → cleaned data.
Health Informatics
Informatics, namely health informatics, enlists such components as decision theory, human-computer interaction (HCI), and statistical analysis (similarly to data science) that give way to helpful insights for human-computer interactions, business insights, and medical efficiency.
The data collected begins to morph into information that converts to real-world solutions to common problems or gaps in application.
Human-Computer Interaction
So much of what comprises informatics requires designing functionality around a human’s physical and cognitive capabilities. It is the culmination of a broad range of computer science subjects surrounding data-driven tasks carried out by computer systems.
Heuristics is an important area that deals with computational design to meet the usability needs of users. Heuristics is one aspect that plays a crucial role in understanding human-computer interaction (HCI). The goal of heuristics involves organizing and presenting information in ways that achieve specific objectives for various fields, such as business, medicine, or science.
What is the difference between Informatics and Data Science, and where do they overlap?
Informatics and data science are two related fields that support problem-solving and decision-making tasks concerning computer systems. Both fields utilize data management measures such as collecting, analyzing, and interpreting data to make informed decisions.
Data science and informatics share a lot of similarities. Data science plays a substantial role in informatics due to its empirical outputs from data. Also, data science helps to gather data points, analyze them, and extract meaningful insights to guide the decision-making process for businesses.
These helpful insights provide relevant information for informaticists to model new systems or improve existing ones operating for a company or business. The ultimate goal of this process is to improve healthcare processes, increase efficiency, and streamline tasks.
Informatics utilizes and catches the residual effects of data science processes to maintain the scalability, storage, usability, and accentuation of the data collected.
Now, with the help of informatics, data is harnessed effectively to aid in practical situations.
Information Systems Development: This is the part where the Systems Development Life Cycle is defined. Here lies the origin and overall scope for system problems to be solved or to become more resourceful in a new way. This has been created based on initial responses to a problem statement. Also, the problem statement sets the tone for the entire duration of a project’s life cycle, and it is the one factor that really drives research or user case studies.
Data Management and Integration: Informatics deals with the storage, retrieval, and integration of vast amounts of data, ensuring its accessibility and usability across disparate platforms and applications.
Human-Computer Interaction (HCI): During my initial journey through my informatics major, I quickly noticed how most of the coursework in college centered around modeling computer systems around specific user needs. A notable example signifying potential errors where there is a lack of proper usability is the Norman Door problem. This example speaks of the possible issues that happen due to counterintuitive designs that are implemented. Any virtual or concrete interface that is designed poorly can lead to confusion or frustration experienced by users. I’m sure many of us have been there, and as many of us may know, this can result in a displeasing experience.
Ethical and Legal Considerations: Data safety is of utmost importance, specifically because data is highly- sensitive. Informatics experts navigate ethical and legal frameworks to ensure compliance with regulations such as HIPAA and GDPR. Health informatics especially comes to mind when considering how important it is to conceal health records.
The collaboration between AI and informatics has created a new world
The current uprising in AI technology has taken so many of us by storm through the creation of smart technology, virtual assistants, and automation.
Moreover, the potential of informatics and data science has taken off and reached new heights within recent years, with the employment of such technologies as Midjourney, ChatGPT, Kittl, and other services for AI-generated content. As compelling as the field of data science is, it is still worth visiting a different, all-encompassing angle to problem-solving.
Now, this all speaks volumes about the undeniable hold that data-centered informatics has over many aspects of our current technological climate. It’s proven to have wide-reaching effects on current times and impacts many areas of our lives.
References
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