Specialization in data could mean a whole load of possibilities for you in today’s market. All it takes is an analytical mindset that can evaluate trends and figures to unfold the story beneath all the information.

A career as a Data Scientist or a Data Analyst is the ideal choice for someone who has a keen mind for juggling numbers. The tech industry has a great demand for able data scientists with job opportunities simply skyrocketing. Harvard Business Review even lent the title of ‘Sexiest job of the 20th century’ to Data Science.

But choosing between a career as a Data Scientist and a Data Analyst can be a bit confusing considering the job profile of both these occupations run parallel to each other. They also require extremely similar skills and can play equally well.

Understanding the differences between the two could make it a whole lot easier for someone who is trying to compare the job requirements of these two fields. While the two jobs might look rather similar on the surface there is a certain degree of contrasting traits that mark a rather significant difference between the two jobs.

Here we’ll take you through what it means to be a data scientist as opposed to a data analyst and give you a few pointers about how you can aim for either of these occupations.

Who is a data scientist?

Data Scientists use past patterns and data trends to predict the future. They toggle the numbers to map out the future of certain industry concepts.

They are tasked with asking the right questions and also approximating solutions at a macro level. The predictive nature of the work involved will typically require the usage of automation systems along with predictive models. A large part of what a data scientist does involves tool design, algorithm building, and various data frameworks.

A data scientist should be able to approach problems in an innovative manner that goes beyond the knowledge of simple statistics. A certain aspect of hacker-like thinking is a great asset for anyone in this business.

Who is a data analyst?

Data analysts assess the given data to generate meaningful inputs. They are tasked with generating unique perspectives on the existing data and interpreting them in the most useful manner.

A large part of a data analyst’s job revolves around statistical tools along with database management software. These tools are used to generate trend charts which can then be used to quantify and assess the data. It involves data curation using tools like SQL.

Data analyst teams are meant to be interdisciplinary in order to effectively get the best out of the available information. Making queries, categorizing relevant figures and charts, and finding proxies for missing data make up the preliminary part of an analyst’s job. They are then required to work in these teams to spot any detail that might be profitable for a company.

Useful tools to learn for a data analyst are:-

  • Vizualisation Softwares – Tableau or PowerBI
  • Programming Languages – R or SAS
  • Soft Skills – Presentation and Communication

Comparing between Data Analysts and Data Scientists

  • Job Description

Data Analysts are deeply involved in the functioning of the company where they are employed. They are required to manage all the data generated and then sort through this data to analyze the trends

Here’s what you will find yourself doing as a Data Analyst

  • Setting up data infrastructure

Collection and consolidation of the available data are one of the most fundamental tasks done by an analyst. The technical part of this job requires analysts to automate routines that have the most utility in a range of different scenarios.

  • Interdepartmental Collaboration

Data analysts are directly involved with other stakeholders in the company such as the employees in the marketing and sales department. An analyst’s team will also have members who work on other aspects of data science such as architects and developers.

  • Submitting reports

Regular drafts are required regarding the general company figures.

  • Extrapolating patterns

The data has to be channeled into meaningful insights that benefit the organization.

Data Scientists deal with a more global level of data assessment. They need to have an in-depth understanding of the industry as well. Their skills are put to use in analyzing and solving relevant problems in unique ways. A data scientist’s job schedule would be made of the following things

  • Model Generation

Data scientists will have to design predictive models which can channel probabilistic values on long term trends

  • Data merging

It can be quite crucial to find all the data channels and bring them together to get the most accurate insight possible

  • Data Visualisation

The analyses done on the data have to be captured in graphic representations like charts. This can help data scientist showcase their work.

  • Extrapolating patterns

Both data analysts and scientists are involved in searching for relevant patterns in the available data. The difference is simply that data scientists operate on a larger level beyond company data.

  • Prerequisites

A data analysis job can be landed by someone with a basic Bachelor’s degree in STEM. Being naturally skilled with numbers and analytics is also a major plus point.

Data Scientists on the other hand are required to be experts at coding as well as modeling techniques. This job requires a minimum of a Master’s degree which shows proof of knowledge in programming and analysis tools such as Tableau, Python, Hive, PySpark, and Impala.

The difference between the two is implicated by the level of tools that are used. It is rather common for a budding data analyst to later shift tracks and become a data scientist. Data science is a field that requires quite a lot of experience as opposed to analytics.

Conclusion:

Both data analysts and data scientists have a lot to look forward to in today’s rapidly evolving industrial environment.

The difference between data analysts and data scientists emerges when you take a look at the kind of output that is generated with the data.

Analysts are trend interpreters while scientists are trend predictors. This is also reflected in the software tools that are being utilized.