What is the difference between a Data Analyst and a Data Scientist?
By Kat Campise, Data Scientist, Ph.D.
Given that both data analysts and data scientists “analyze” data, the confusion between the two is understandable. The relative newness of data science also compounds the issue. Indeed, if you review data science job postings, there are variations as to how a business defines their data scientist role. Often what they are seeking is a data analyst or data engineer rather than a data scientist. Thus, there exists a perceived soft partition between data scientist and data analysts that requires a firmer delineation. With this in mind, our objective is to help answer the primary question regarding the difference between a data scientist and a data analyst.
This is not to state that there aren’t shared functions between the two; however, a fundamental differentiator is the level of complexity and in-depth expertise required of data scientists at each stage of the data science cycle. The emphasis here is the “science” of data which requires a measure of scientific thinking and methodological research that is above and beyond merely deriving “actionable” insight as based on a neatly presented data set. As such, there are at least three key areas that separate a data analyst from a data scientist: the driving questions or problems, model building, and analyzing past vs. future performance.
Analysis Starts with a Question
In general, data analysts already have a specifically defined question as aligned with business objectives. Therefore, their analysis is pre-defined from the standpoint that they already have a set of well-established parameters for their analysis.
In contrast, data scientists are responsible for defining and refining the essential problems or questions that the data may or may not answer. This is a more nebulous vantage point as data scientists must navigate the available data to determine whether the essential question or problem can be answered or solved based on the data collected. If the data and problem/question are not aligned, then another round of exploratory data analysis is usually conducted, or the problem/question is redefined.
Once the essential problem/question is established, the data scientist may task a data analyst with identifying trends, producing intermediary data visualization outputs, and creating departmental dependent reports (i.e., sales, customer service, accounting, etc.) for review.
As we can see, both roles perform an analysis – data scientists do so at a higher level. Data scientists are hypothesis testing throughout the entire analytical process and deploy sophisticated statistical methods to arrive at the next step in the data science iteration.
Data Science and Model Building
Data analysts and data scientists work with statistical models. The primary separation appears with an increased level of complexity required for actually building the statistical models. More specifically, data scientists build statistical models and use their advanced expertise in statistics to deploy machine learning algorithms for greater predictive and inferential precision.
For example, a data analyst may be responsible for cleaning the targeted dataset as a preprocessing step – though a data scientist can perform this task as well. The purpose here is to transform any raw data into a clean and structured format. A data scientist will then identify the best possible machine learning model – which can be a labor-intensive and highly iterative process that isn’t consistently straightforward but does require knowledge of the correct tools for fine-tuning the various modular aspects – and then deploy it.
But, the process doesn’t end there. Every model will require adjustment over time, particularly in light of the fast-paced and competitive globalization of all industries where scalability becomes a pain point. A data analyst may be assigned to analyze the model’s performance and report their findings to the data scientist who will then decide whether to re-optimize the model or construct another more robust model. As such, data analysts perform a narrower function within the overall cycle of insight extraction.
Analyzing the Past vs. Determining Future Improvements
While it’s true that data analysts and data scientists identify trends to better inform strategic decision making, the prevailing difference is the analysis of past performance vs. future improvements. There is an added dimension, as described above, that includes the automation of the predictive metrics via machine learning. While a well-educated and highly trained data analyst can interpret the metrics accrued from a predictive model, it is the job of the data scientist to determine the model’s precision and discern when and where it needs to be adjusted.
As such, the primary goal of a data analyst is to assess what has already occurred and communicate these facts to the other stakeholders (including data scientists). Meanwhile, a data scientist will evaluate past performance, compare the current trend with the results from the predictive model(s) currently in place, then recalibrate if needed and as current performance trends shift. Summarily, a data analysts job is centered on descriptive and retrospective techniques regarding the facts revealed by the data. Data scientists are focused on predictive and prescriptive forecasts as to what the data says about the future and how stakeholders can specifically improve their performance to meet KPI metrics or other business objectives.
The Data Science Toolkit
Some may say that the tools used by data scientists are another differentiator; however, this is not consistently true. Data analysts and data scientists often use the same or highly similar software or programming languages during the course of their workday: Excel, Tableau, R, Python, SAS, SPSS, MATLAB, SQL, MySQL, Cognos, etc. Also, depending on the industry, both will also need a certain level of familiarity with the software, reporting, and regulations that are unique to their employer. The difference here would be of degree, i.e., what those tools are used explicitly for, rather than being separate types of tools.
Leveraging the Talents of Both
To summarize, the essential differences between data analysts and data scientists can be accurately stated as a matter of granularity, meaning there is overlap in a few responsibilities, but there is a hard partition when we dig deeper into what each actually does on a daily basis. Notably, data scientists can easily perform a data analysts job (at least, they should be able to). Certainly, as far as a data scientist is concerned, data analytics ought to be second nature.
Both use data visualization and must communicate the results of their analysis, but the objectives of their analyses also have the aforementioned “degree of difference” within the context of descriptive vs. predictive or prescriptive results. It’s also true that both must solve a particular problem or answer a specific question (or set of problems and questions). However, data scientists assist with developing then testing the veracity of the question in relation to the available data (hypothesis testing). Data analysts tend to work with a fixed set questions. At the same time, data scientists have trickier navigation. They must understand how to accurately frame the question, then establishing a research cycle which includes knowing when to continue with the original question vs. when and how to adjust either the question or the targeted data set without incorporating bias (in machine learning, there are mathematical controls for this aspect).
In terms of statistical models, data scientists are the architects of these tools. Data analysts definitely use those tools as well, but they aren’t crafting them together – they’re using them for descriptive analysis and comparing their output to key performance indicators (KPIs). To be clear, both data scientists and data analysts are important functions. But, it is important for enterprises and job seekers to understand the key differentiators between the two so that their talents and abilities can be fully leveraged and provide mutual benefit for the employee and employer.
