February 28th, 2024
Data Analysis vs. Data Science: What’s the Difference?
By Alex Kuo · 10 min read
Information and data have become the core of many businesses and processes. It’s so prevalent that statistics suggest each person, on average, creates 1.7 MB of data per second. As a result, companies face a huge ordeal in crawling through vast data silos to find useful information that can be used to create meaningful and actionable insights.
With so much emphasis on using data, many people conflate the terms data science and data analytics (or data analysis). Let’s dig a bit deeper to unravel how these seemingly similar terms can be worlds apart.
What Is Data Analysis?
Data analysis is a subset of data analytics, a field in statistics and information science that focuses on existing datasets and extracting useful information. Data analysis typically has a clear goal in mind and strictly uses cleaned-up datasets that contain only information relevant to reaching that goal.
Data analysts focus on answering a specific question posed by a decision-maker, usually in regard to how to optimize a process or determine a particular statistic or outcome based on historical data. This means that data analysis has a clearly defined scope that aims to produce actionable results.
What Is Data Science?
Data science is a broad, multidisciplinary field that includes data analytics but also deals with more abstract concepts related to data, such as machine learning and forecasting.
Unlike data analytics, data science doesn’t have a particular question to answer but focuses on determining new questions that are worth solving and how to get there. With that in mind, data science frequently uses huge swaths of unstructured data to glean insights into patterns and relationships that can emerge from seemingly independent points.
Data Analysis vs. Data Science
Both data analysts and data scientists essentially work with datasets and extract useful information from raw data.
However, data science is a much more overarching field, helping businesses and other industries determine new ways to conduct data analytics and leverage modern tools to create relationships that didn’t exist before. Therefore, the scope of data science is much larger.
A data scientist performing data analytics for a single company is usually focused on delivering a single, tangible result that allows the business to decide on its next steps or analyze its key performance indicators. On the other hand, a data scientist conducting research is in charge of creating and maintaining algorithms that can crawl through and interpret various datasets.
Alongside the scope differences come industrial applications. Since data analytics focuses on creating immediate results, it’s much more prevalent in industries that urgently need new data points to use. These include healthcare, marketing, travel, gaming, manufacturing, development, and more. Data science is more prevalent in research-oriented industries that benefit from creating new ways to utilize information, such as machine learning, AI development, search engine development, and corporate analytics.
While they are different, the fields typically go hand in hand. Data science lays out the groundwork, creating algorithms and ways to sort through unfiltered data. Data analytics then takes those tools to determine exactly how they correlate to the task at hand and develop solutions to more tangible problems.
Key Differences to Explore
Even though data science and data analytics use data in different ways, they’re still heavily data-reliant fields. The differences are mainly exhibited in how they structure processes, develop tools, and require human input to progress.
Process
The data science process is iterative, meaning that every insight and development is used to fuel future research. Therefore, the process loops some of these steps:
- Determining the problem at hand or an opportunity for further research based on existing information.
- Creating data collection tools so that the process can use relevant information if one does not exist yet.
- Developing and managing algorithms that can sort and filter through unstructured data.
- Integrating new data into existing systems.
- Exploratory data analysis to determine which model and practice would be most suitable to adopt.
- Creating and training algorithms and predictive models that turn data into an engine that can use it.
- The trained model can be used to leverage insight into current data sets or be put to work broadly, becoming a staple for future data analysis methodologies.
By contrast, data analytics typically has a much more defined process that has a clear start and end:
- Identifying key issues or performance indicators to track.
- Sourcing, compiling, and cleaning data from existing databases to remove errors or duplicates and apply a uniform structure.
- Using data analytics tools to transform data into useful information.
- Visualizing the information to be more human-readable and actionable.
Essentially, data analysis begins from scratch every time but leverages existing information or historical context to provide more timely and useful results.
Tools
Data science is a more programming-oriented field. Since it’s used to make new discoveries that advance the data analytics industry, it creates new tools from scratch, typically leveraging machine learning models to develop new algorithms or data mining techniques.
Data analytics relies on data analysis and data visualization tools to use existing information and turn it into insights.
Roles & Skills Needed
A data analyst typically has a background in statistics, mathematics, or computer science, using existing data analysis tools to supplement their work. Since data analytics often uses previously compiled solutions, they often only need to know how to manage databases and extract information from them via code.
This means that coding knowledge might be a secondary requirement rather than a staple. Instead, most experts are focused on performing a statistical analysis or descriptive analysis of existing datasets. In senior roles, predictive analytics become more vital, not only extracting insights from existing information but also creating forecasts that could indicate success at a certain task.
Data analyst roles often “cap out” at a certain point, limiting a person’s progression within the field. Since the process is ultimately based on leveraging existing tools, many people start working towards broader data science roles after a decade or so in the industry.
A data scientist usually needs a master’s degree in a relevant field such as data science, mathematics, or computer science, with a heavier focus on statistics and development. As such, data scientists typically need to know contemporary coding practices, particularly in algorithms and object-oriented programming.
Upgrade Your Data Science and Analysis with Julius AI
Data science and data analysis are both industries that involve learning about and interacting with extensive datasets in order to extract useful insights from them. However, this task is typically insurmountable for a human, considering the sheer volume of input and information available.
Luckily, that’s where AI tools such as Julius AI come in. Being able to process both extensive datasets and natural language, Julius AI can make short work of traditional data analytics processes. You can use it to perform initial exploratory analytics or even try to solve the entire problem with minimal input.
Start using Julius AI to identify trends in your data and further your business or personal growth.
