The appearance of artificial intelligence, cloud native data warehousing, and business intelligence powered by automation places data at the epicenter of the U.S. economy. There is no discussion over whether or not businesses should make use of their data; they are competing with each other for talent. Data Analytics and Data Science are the two leading occupations of the current era.
Although having many similar features, there is a major difference between the two occupations regarding what skills are required, depth of expertise needed, everyday duties, and career prospects. There is no universal solution to the question which occupation is “better.” Much depends on the skillset you have, your attitude to programming, and where do you envision yourself in 5 years. In this article, we will analyze the structural differences that should help you choose between them.
1. Defining the Core Mission: Rearview Mirror vs. Crystal Ball
One thing that distinguishes a data analyst from a data scientist is the difference between the missions behind their work. It can be explained using time perspectives.
Core mission of a Data Analyst
First of all, the task of a data analyst is to look into the past and present events. The major mission of a data analyst is to find the answers in past records to the questions presented by the company’s leadership. Why did we have a decrease in Q3 retail sales in Midwest? Who reacted to our marketing campaign on the web in the strongest way?
To put it simply, data analysts play translators and use their skills and knowledge to convert messy, sometimes even inconsistent data kept in relational databases into easily readable business visualization dashboards and reports. The core of a data analyst’s daily tasks includes interaction with everyday processes within the company.
Core mission of a Data Scientist
On the contrary, the mission of a data scientist revolves around the future and things which are not known yet. Data scientists take raw unstructured data and apply predictive analytics to generate new insights.
Data scientists use advanced mathematics, statistics, and software engineering to create algorithms. Instead of simply identifying certain trends in data, data scientists develop machine learning pipelines to make profit out of them. All of that is done to create innovative data-driven products.
2. Skill Sets & Tooling Comparison
Since both professions are completely different from each other with regard to the mission statement, there is a great difference in the daily tooling.
Data Analyst Tooling
It is easy to say that data analytics is a simpler field than data science. This is the reason why there is a lower barrier to enter it. SQL, or Structured Query Language, is the major programming language for data analysts. The vast majority of the data analysts work with SQL querying data warehouses and producing visual analytics using business intelligence software such as Tableau and PowerBI. Although they use python or R to automate some tasks, SQL and visual sheets prevail.
Data Scientist Tooling
The daily tooling used by data scientists is much more complicated. It is a more technically challenging profession that requires high skills in programming languages Python and R. Writing highly sophisticated code from scratch is an inevitable part of a data scientist’s workday.
There are numerous specialized libraries such as Scikit-Learn, TensorFlow, PyTorch. Besides, due to the appearance of artificial intelligence in corporate life, the skills to interact with vector databases, RAG pipelines, and cloud computing platforms (AWS and Google Vertex AI) are now demanded among data scientists.
3. Salary Differences & Upward Mobility
Of course, when discussing relative superiority of any two professions, financial issues play an important role. According to the data from the hiring market, data scientists have a definite edge over data analysts in terms of salaries.
The median base salary range for a mid-level Data Analyst in the United States is somewhere between $86K and $95K per year. Meanwhile, the salary for a mid-level data scientist position is somewhere between $112K-$127K annually. Thus, getting promoted to a data scientist raises one’s salary by 25% or even higher even with the same level of qualifications in SQL or Python.
4. Education Requirement and On-ramp
Educational requirements to enter the workforce as data analyst or data scientist are yet another distinguishing feature. Whereas data analytics is considered a more democratic profession, the on-ramp to data science is steep.
Requirements in education to become a Data Analyst
Data analyst can be regarded as one of the easiest-to-enter professions in America today. More than 49% of data analyst openings ask for a bachelor’s degree from an applicant only. Moreover, 18% of companies are ready to substitute the university degree with certification courses such as Google Data Analytics Professional Certificate.
Education Requirements for a Data Scientist
As compared with democratic data analysts, data science is a very academically closed profession. More than 54% of available openings require or suggest having a Master’s or even Ph.D. degree in mathematics, statistics, computer science, or other quantitative disciplines. The reason for such requirement is that data scientists need to have profound knowledge of algorithms which are used in their machine learning pipelines. Moreover, self-education in data science takes much more time.
5. The Verdict: Which Career Should You Choose?
As it was already stated, no career path is superior to another one. Choosing between data analyst or data scientist, you should pay attention to whether your skill set, habits, and personal preferences match the profession.
Frequently Asked Questions (FAQ)
Is there a chance for a Data Analyst to becIs there a chance that data scientists will become redundant because of artificial intelligence?
Certainly not. Although artificial intelligence has made much progress and is able to perform routine tasks such as data cleaning and coding of the simplest functions, there is a gap between AI and human intellect that is far from closing anytime soon. Businesses rely on data scientists who can turn real-life business problems into data problems and design safe algorithms and models.
What is the job of Analytics Engineers?
Analytics Engineer is a rather new occupation which combines characteristics of data scientists and data engineers. Unlike data scientists who create algorithms and data analysts who create visual analytics, analytics engineers deal with data from databases and are responsible for its transformation. Such transformations are made with the help of tools such as SQL and dbt.
Which profession allows working remotely more effectively?
Regarding remote work, data scientist and data analyst have very similar conditions. According to the recent data from the USA, 21% of open data scientist positions provide full-time telecommute options, while 22% of data analysts are able to work from home. So, there is a similarity in geographic freedom of the two professions.
Which industries in the US pay the most money to data talent?
Finance and technology industries pay the most handsome wages among all the industries in the United States to data scientists and data analysts. However, there are other industries which provide good compensation packages to data scientists such as healthcare services and production facilities due to needs in predictive medicine and logistics optimization.
