A Comparative Note on Data Analyst vs Data Scientist
If you are interested in pursuing a career in data science, the skills and knowledge may astonish and interest you. There are various positions like data analysts, data scientists, data architects, database administrators, and so forth. Let us understand more about the data analysts and data scientists here.
Both data analysts and data scientists are data science professionals dealing with data and making data work for business benefit. Let us understand the professional similarities and differences here.
A data analyst can be called a junior data scientist. They translate data sourced from market research, sales reports, and other resources into business language for better decision making.
As a data analyst, you will analyze trends and provide data visualization for the findings and actionable insights. They are in their early stage of data science career or you can say, data science career starts from being an analyst.
Generally, data analysts are graduates in statistics, computer science, or mathematics. These days some universities provide specific data science courses in their graduate programs. Apart from technical skills they possess a working knowledge of SQL, Python and R programming; analytical skills, verbal and communication skills, intellectual curiosity, and strong business acumen.
As you advance in your career, you can become the data scientist, where you use algorithms to extract knowledge from structured and unstructured data. You will be asked to find patterns, build algorithms, design experiments, and share the results in a much easier way for business insights. As a data scientist, you will understand the business problem from the stakeholders, pull out data, conduct exploratory data analysis, compile code or build a model for production.
As you level up from being a data analyst, you will possess all the skills and knowledge of a data analyst. Also, most of them are subject matter experts by acquiring a masterâ€™s or Ph.D. in their subject. Further, they have gained experience in statistical and data mining techniques, creating data architectures, building statistical models, using web services, analyzing data from third-party providers, distributed data tools, and presenting data using Periscope, business objects, and other tools. During their career life, they also gain knowledge in machine learning and advanced statistical techniques.
Data Analyst vs Data Scientist: A comparison table
|Skills||Data Analyst||Data Scientist|
|Qualification||Mathematics and Statistics||Mathematics, Computer Science, and Statistics|
|Business skills||Business intelligence||Business acumen|
|Data visualization||Uses tools like Tableau||Story-telling and advanced data visualization tools|
|Computing frameworks||SAS||Hadoop, Big data|
|Other skills||Advanced Excel skills||Machine learning|
|Data science certifications||Data analyst certification||2 or more certifications including data scientist certification|
Moving further, let us understand the differences between these two in-demand professions.
Data Analyst vs Data Scientist: Strong Differences
The employers expect a strong business acumen and advanced data visualization skills who can convert insights into the story for business success. They interact more with non-tech people, business leaders, and clients. Whereas, a data analyst may not have excellent business knowledge and they generally interact within the team.
Data scientists examine data from multiple sources whereas data analysts may work on a single source of data.
Data scientists pose business questions as they understand their industry, and try to solve those for business benefits. Whereas a data analyst makes efforts to solve the given business problem.
Data scientists build statistical models and are well versed in machine learning. Whereas a data analyst might not have these skills as they are in their early stage of the data science career.
Data Analyst vs Data Scientists: Compensation
The average salary of a data analyst depends on their work profile like financial analyst, operations analyst, market research analyst, and so forth.
As per salary.com, the average data analyst salary in the United States id USD 75862. Typically, the salary falls between USD 66389 and USD 85198. Likewise, the average salary for Data scientist II in the United States is USD 82311, with a range that falls between USD 73233 and USD 91680.
A data analyst can continue their education and skills to become a data scientist. A data scientist after gaining years of experience may become the director of data science in their industry. The difference between these two data science professionals lies in the way how they deal with data.
Learn data science to make data work for you.