Difference Between Data Science vs. Data Analytics: How Data Analyst vs. Data Scientist Roles Differ?

Data Analyst vs. Data Scientist , Difference Between Data Analytics and Data Science

Data Analyst vs. Data Scientist: These are two of the most in-demand jobs in information technology(IT) with impressive salaries to match. From healthcare to cybersecurity, banking, online retail, finance, digital marketing, and many other fields use Data Science in their businesses. It should, therefore, come as a no surprise that Data Scientist has been listed as the number 1 job in America for 3 years in a row and Analytics Manager comes in at number 10. However, it is becoming more apparent that many seem to carry the perception that a data scientist is just a glammed-up term for the data analyst role or they straight up do not know how to appropriately define the scope and differentiate between these roles. Here’s how Data Scientist differs from Data Analysts:

Difference Between Data Analyst vs. Data Scientist or Data Science vs. Data Analytics:

1. Data Science is the art and science of extracting actionable insight from raw data. Therefore, they should be very skilled in machine learning and in building statistical models. Such models find huge applications in spatial models, predictive modeling, supervised classification, clustering, etc. Data Analysts are not commonly responsible for building statistical models or deploying machine learning tools.

2. In order to become a Data Scientist, one must have strong business acumen and visualization skills to process insights into a business story. However, Data Analysts do not require any specialized business skills and basic visualization skills are enough in this case.

3. Data Scientists are required to excel in Predictive Analysis, which means extracting highly accurate information from existing data sets in order to determine patterns and predict future outcomes and trends. But a Data Analyst extracts valuable insights from huge data.

4. A Data Scientist estimates the unknown and predicts the future based on past patterns while data analysts find answers to a given set of questions i.e. curate meaningful insights from data. This is one of the reasons why being a Data Scientist is twice the hard work than being a Data Analyst. It also answers why Data Scientists are paid almost twice than Data Analysts.

5. A Data Scientist concerns themselves with business issues and deals with those issues which have greater business value but a Data Analyst just approaches business issues.

6. A Data Scientist needs to excel in statistics, mathematics, data mining, correlation. A Data Analyst needs to be well aware of data architecture’s tools and components.

7. Data Scientist explores and examines data from multiple disconnected sources. A Data Analyst looks at data from a single source like a CRM system or a database.

8. Data Scientists should be familiar with database systems, such as MySQL, Hive, Python, R, SAS, Scala, etc. Data Analysts should know programming languages like Python, R, SQL, HTML, and JavaScript.


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9. Data Scientists are required to do Data cleansing and Processing -Clean, Massage and organize data for analysis. Data Analysts are required to identify any data quality issues and partialities in data acquisition.

10. Data Scientists and Data analysts are further divided into four categories based on their job roles and responsibilities. Roles for Data Scientists – Data Researchers, Data Developers, Data Creative, and Data Business people.  For Data Analysts – Data Architects, Database Administrators, Analytics Engineer, and Operations.

11. According to the Bureau of Labor Statistics (BLS), the analytics job market is expected to grow 1/3rd by 2022 with approximately 131,500 jobs.  As of 2016, entry-level salary for a data analyst ranges from $50,000 to $75,000 and for experienced data analysts it is between $65,000 to %110,000. The median salary for data scientists is $113,436.

12. Data analysts review large sets of data to see trends, devise charts and create presentations visually to assist businesses to make better decisions. They usually have a bachelor’s degree in technology, a bachelor’s degree in engineering or bachelor’s degree in math, and may have experience in math, programming, science, databases, and modeling. Data scientists use many techniques to go through data, such as machine learning and artificial intelligence and data mining, which is a major difference between the two roles. Because of this higher level responsibility, most data scientists have a master’s degree or Ph.D. Data scientists are much more grounded in technical science and mathematics, and usually have a stronger background in computer science.

Job opportunities in Data Science and Data Analyst both are very good. A career in these fields is in huge demand in the USA. Also among international students and job seekers in the USA working on OPT Jobs, H1B visa jobs or F1 visa jobs –  the Data Science and Data Analyst are one of the most popular jobs and prefer to work as one.

Here’s a table to make things simpler in order to understand the difference between Data Scientist and Data Analysts:

Data Science vs. Data Analytics Comparison Table

Read some key points differences between Data Analyst vs. Data Scientist in the table below:

  Data Science Data Analytics
Fundamental Goal Asking the right business questions & finding solutions Analyzing and Mining Business Data
Quantum of Data Broad set of Data (Big Data) Limited Set of Data
Various Task Data Cleansing, preparation analysis to gain insights Data querying, aggregation to find a pattern
Definition Data Science is the art and science of extracting actionable insight from raw data Data analysts are not commonly responsible for building statistical models or deploying machine learning tools
Substantive Expertise Needed Not Necessary
Non-technical Needed Not Needed
Focus Pre-processed Data Processed Data
Bandwidth More freedom in scope and practice Less freedom in scope and practice
Purpose Finding insights from raw data Finding insights from processed data
Data Types Structured and Unstructured Data Structured Data
Benefits Data scientist explores and examines data from multiple disconnected sources Data analyst usually looks at data from a single source like the CRM
Artificial Intelligence Deals more in Artificial Intelligence Deals Less in Artificial Intelligence
Machine Learning Deals more in Machine Learning Deals Less in Machine Learning
Predictive Analysis Deals more in Predictive Analysis Deals Less in Predictive Analysis

Conclusion:

This is just the tip of the iceberg. There are various other job roles like data architect, data engineer, statistician, database administrator, business analyst, data and analytics manager, etc. They also play a vital role in getting Data Science applications up and running. The rise of big data has created many new job opportunities across both technology and traditional businesses. You can choose a career between Data Scientist and Data Analyst based on your educational and professional background, your personal interests, and your desired career trajectory. Consider all these points and choose what is best for you.