What is the difference between a Data Analyst and Data Scientist

What is the difference between a Data Analyst and Data Scientist?

I am often caught between whether I should be a Data Analyst or Data Scientist and I’m sure I am not the only one. Some people go as far as mixing Data Analysis with Business Analysis, now to solving this dilemma, I decided to take the help of this online course I found on Heels and Tech (Microsoft Power BI). The course helped me in clarifying the difference between Data Analyst and Data Scientist and now, I am finally able to find out whether I should be a Data Analyst or Data Scientist.

I would like to break things down in a simpler way.

First of all, what is Data?

Data is the backbone and heartbeat of any business. It helps you understand your customers and improve your processes. Data scientists/Analyst use their analytical skills to transform data into profit for every organization.

Who is a Data Analyst?

Data Analysts are responsible for identifying business problems and using various tools and techniques to solve business related issues.

Who is a Data Scientist?

Data Scientists take their understanding further than Data Analysts by building models against various factors that lead to success, they harness the power of data to answer business questions and drive smart decisions. Data scientists use the best statistical and machine learning tools to convert data into actionable insights. They analyze both structured and un-structured data in order to create new value for your business.

Now, you are wondering;

What exactly is structured and unstructured data?

Structured Data is highly-organized and formatted in a way that makes it easily searchable in databases. Examples include: Names, Dates, Addresses, Credit card numbers, Stock information, Geolocation, Text file, Spreadsheet which can be extracted from a Database.

Unstructured Data has no pre-defined format or organization, making it much more difficult to collect, process, and analyze. Examples include Photos and Videos, Text, Mobile activity, Social media activity, Satellite imagery, Surveillance imagery – the list goes on and on.

Unstructured data is most often categorized as qualitative data, and it cannot be processed and analyzed using conventional tools and methods.

Structured data has the advantage of being constantly searchable, which means that all learner information can be found whenever needed. Unstructured data provides valuable insights into a person’s hobbies and interest. Talk about merging a users activity on Instagram and on Google, thereby making a decision.

Comparison between Data Scientists & Data Analysts

Data Scientist Data Analyst 
Formulates hypotheses, tests them, derives business or organizational insights.
Answers specific business questions (What is our best source of revenue? What is the age distribution of our customers?).
Works with unstructured data from multiple disconnected sources.Primarily works with structured data from a single source.
Sorts through data to make predictions.Organizes and sorts data to solve problems.
Provides predictive modeling.Provides historic analysis, data-driven decision making.
Skills: Math, programming, communications.Skills: Statistics, communications, business

Why you should upskill by taking our Data Analytics Course

  • Omnipresence of Jobs: Since industries from manufacturing to healthcare, IT to banking are leveraging data in different capacities, there is no dearth of Data Jobs for anyone who is interested and is willing to work hard. This is not just limited to industries but also across geographies. So, irrespective of someone’s geographical placement or current domain, data science and analytics is open for everyone to pursue.
  • Diversity of Roles: While Data Science/Analyst Jobs is an overarching term, within its larger meaning many other sub-roles are available. Roles such as that of a Data Scientist, Data Architect, BI Engineer, Business Analyst, Data Engineer, Database Administrator, Data and Analytics Manager are in high demand.
  • Remote working opportunities: The nature of work is constantly evolving. In present day, organizations are beginning to handle work processes virtually. Data Scientists/Analysts are able to work remotely from any part of the world they are in.
  • Great Pay: A Data Scientist/Analyst job is among the top-paying in the tech industry right now. According to GlassDoor, the national average salary for data scientist/analyst tops more than $62,000.

Data powers today’s world. From unleashing innovations to improving decision-making processes, data will continue to hold the potential to unlock the success of every industry. You too can become a part of this success by upskilling in tech with Data Analytics.

Check out Heels and Tech’s Data Analytics Course and be sure to register: https://heelsandtech.com/data-analytics-course/

Reference: Purdue University’s business analytics degree program

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