What is the Difference between Data Analyst vs Data Scientist

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If you are interested in working with big data and number then there are two perfect career paths which are in very high demand. You can choose your career as a data analyst or as a data scientist.

Let’s know about it in detail and also What is the difference between the two?

What Does a Data Analyst Do?

A data analyst generally collects data to recognize trends that help business authorities or leaders make strategic decisions. The profession is primarily focused on performing statistical analyses to help answer questions and solve problems that the company required.

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The data analyst uses programming languages like R and SAS, visualization tools like Power BI and Tableau, and communication skills to improve and express their findings or outcomes.

In more simple words analyzing data bundles to detect trends and patterns that can help take a business-related decision, illustrating findings in an easy-to-understand way to notify data-driven decisions for the company or business.

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What Does a Data Scientist Do?

A data scientist will generally more be engaged with designing data modelling methods, creating algorithms and predictive models. Data scientists have to deal with the problems by using more advanced data techniques to make predictions strategies.

As compared to a data analyst, a data scientist is more involved in developing new tools and methods that require the company to solve complicated problems.

Data Science vs. Analytics: Educational Requirements

Most data analyst roles require a bachelor’s degree in a field like mathematics, statistics, computer science, finance or any other related field.

Data scientists as having to work with advanced tools and develop new methods typically have a master’s or doctoral degree in data science, information technology, mathematics, or statistics or any related field.

In today time, now you have the opportunity to start your career without the graduation degree also.

By earning a Professional Certificate in data analytics from Google or IBM, you can build the skills required for an entry-level role as a data analyst. Upon completion of the Google Certificate or professional certification course in data analytics, you can apply to various companies to get hired.

Data Science vs. Data Analytics

Skills Required

Both the roles have to work with data but they need different skillsets and tools. let’s know more

Data Analyst:

A data analyst must know Foundational math and statistics, Basic fluency in programming languages like R, Python, SQL, knowledge of different software and tools like SAS, Excel, Business Intelligence Software and also required skills of Analytical thinking, Data Visualization.

Data Scientist:

A data scientist must know Advanced statistics, predictive analytics and need Advanced object-oriented programming knowledge, also have a good knowledge of software and tools like Hadoop, MySQL, TensorFlow, Spark etc. Other skills needed like Machine learning and data modelling.

Data Scientist

Data Analyst vs. Data Scientist: Roles and Responsibilities

A data analyst’s day may involve figuring out how or why something happened—such as why sales dropped or creating dashboards, analysing data and prepare an understandable report.

While our data scientists are more focused on what will happen, simply put predictive strategies, using data modelling methods and big data frames such as Spark.

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Data Analysts:

  • Data querying using SQL.
  • Data analysis and forecasting using Excel.
  • Creating reports using business intelligence software.
  • Performing various types of analytics including explanatory, diagnostic, predicting or prescriptive analytics.

SQL, data analyst, data scientist

Data Scientists:

  • Data mining using APIs.
  • Data cleaning using programming languages like python or R.
  • Statistical analysis using machine learning algorithms.
  • Creating, programming and automation techniques, that simplify day-to-day processes using tools like Tensorflow to develop and train machine learning models.
  • Developing big data infrastructures.

Both roles have a common problem but they use different tools and different knowledge to solve their problems.

Each role examines data and gains actionable understandings to make business-related decisions. Data analysts use SQL, business intelligence software, and SAS, a statistical software, while data scientists use Python, JAVA, and machine learning to simplify data and interpret it.

The data scientist focuses on learning and developing frameworks for processing, analyzing, modeling, and gain conclusions from data. While a data analyst might pursue knowledge to use statistics, analytics technology, and business intelligence to answer particular questions for the company.

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These are the basic difference that you should know about if you are going into this field. Both roles look like same but they have different job roles and also they required different skills and knowledge.

By- Sahil

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