Data Analyst vs. Data Scientist: What’s the Difference?

Introduction to Data Analyst vs Data Scientist

Data Analyst vs Data Scientist: What’s the difference? It’s hot news to anyone that big data is making its presence known in the world of technology. Not just in IT, the buzzword has been heard across all domains. Whether you’re a part of the healthcare sector, work in companies that provide on-demand services (like Uber, Swiggy, Urban Company), or even the financial and banking domain. There’s very less possibility that you haven’t heard about the importance of Big Data.  And if you’re reading this, you have already jumped on the data science bandwagon.

Welcome! You have made your decision to take your career forward in the sexiest job of the century (according to Harvard at least!). 

You’re trying to take baby steps into the domain, but lo and behold, you need to learn a myriad of subjects. Machine Learning, Data Visualization, Analytics, Data Cleaning, Warehousing, Data science…and the list goes on. Two roles that catch your attention while skimming through jobs available are “Data Scientist” and “Data Analyst”.

Are they related? What skills do I need to acquire? What does a day in each role look like? Which one of them has more benefits for in a person’s career? This article is sure to offer you clarity.

Data Science

Data Science can broadly be defined as a discipline that focuses on obtaining the right solutions to problems, developing appropriate statistical models, and writing algorithms on data. This data could have been acquired through real-time streaming channels or stored in an organization’s data warehouse. Data scientists are born out of generating the needed and often unforeseen insights for a business. So basically, data scientists simply jump into uncharted waters. They try to make sense of the unknown. Are you still confused?

One of the data scientist’s primary tasks is to come up with questions (and answer them). They also determine problem areas that require detailed analysis of data. This can be done through predictive analytics, studying trends, and building models.

Why organizations need data scientists?

Data generated and stored by enterprises is largely unstructured. Simple DataViz tools are unable to deal with it. Advanced algorithms and data wrangling need to be done on such data. Only data scientists possess this ability. A skilled Data Scientist will also know how to dig out meaningful information with whatever data he comes across. In other words, they give the company direction and motive by providing data-driven solutions to problems.

Therefore, data scientists are needed by businesses to –

• Find the underlying cause of a problem by asking the right questions.
• Perform exploratory analysis on big data.
• Develop machine learning models and algorithms to learn about data.
• Improving customer acquisition and marketing by finding out key areas.

Skills required and Roles to fulfill

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Skills Required	Role Demands
➤ Statistics – Data Scientists need to have an exemplary hold on statistics and advanced mathematics. It helps in obtaining more meaningful results.	➤ Design and maintain automation tools, frameworks, etc for process pipeline.
➤ Machine Learning is the backbone of data science. Data scientists must know the mechanism behind supervised as well as unsupervised ML models.	➤ Making raw data useful by employing techniques of data cleaning, preparation, and wrangling.
➤ Programming – It is mandatory to have a good grip of programming using Python or R. One must know the libraries and data structures essential to Data Science.	➤ Develop models and algorithms for learning and interpreting data. Models are aimed to generate useful predictions.
➤ Proficiency in SQL as well as NoSQL database tools.	➤ Exploring unassociated datasets since data acquired can be from disparate sources.
➤ Handling big data using tools like Hadoop, Apache Spark, etc.	➤ Provide accurate results to enforce impactful business decisions.

Demand and Salary

The demand for data scientists is increasing by leaps and bounds not just in India, but across the world. Unlike other specific job profiles, data scientists are wanted across various sectors. Opportunities are most among BFSI (38%), followed by Energy (13%), Pharma and Healthcare (12%), and E-Commerce (11%), among others. It continues to be one of the highest-paid jobs. The average salary of a data scientist in India is ₹1,000,000/year.

Data Analysis

Data analysts usually use the models, processes, and algorithms developed by fellow data scientists. These models are used in order to generate the required insights and to solve business problems. They are wizards who work magic on data, trying to make some sense out of it. They try to understand problem domains in an organization and create productive stories out of numbers. 

Data Analysis, in simple terms, deals with processing and implementing statistical analysis on existing datasets. Above all, data analysts are needed to gather hidden insights, generate reports, perform market analysis, and improve business requirements. It is merely a subset of the roles performed by a data scientist. 

Why organizations need data analysts?

Data Analysts deliver critical insights as to whether a business is moving in the right direction. They interpret numbers, figures, and statistics into simple words and visuals for anyone to understand. This is the key to ensure effective decision-making.

Businesses utilize data analytics to better understand their client base in terms of customer trends and behavior. Results from analyzing data sets convey to an organization about where they can optimize In addition to that, they also aid in identifying areas that are unproductive and thus can have resources dedicated away. They detect such problem areas and give businesses a forecast about its potential impact. In this way, profits can be increased.

In conclusion, data analysts are crucial to drive productivity, efficiency and generate the right revenue growth for any enterprise. They strengthen businesses by helping decision-makers opt for better choices and improve overall productivity.

Skills required and Roles to fulfill

Skills Required	Role Demands
➤ Data Wrangling – This includes data tidying, preprocessing, and cleaning done in order to avoid skews in results.	➤ Solve given business problems with the datasets provided
➤ Proficiency in using MS Excel functions, pivot tables, generating charts, and programming (VB)	➤ Read and organize data to gain productive insights. Produce reports.
➤ Manipulating data using SQL. Knowledge of key statistical functions and moderate math knowledge.	➤ Ability to visualize, communicate and showcase trends and results
➤ A strong foundation in knowledge of the domain, business acumen, and moderate computer skills,	➤ Identify insights to enable executives and stakeholders to make informed decisions
➤ Generating charts and dashboards using DataViz software like Tableau, Power BI, etc.	➤ Spot patterns and relationships in data.

Demand and Salary Data Analyst Vs Data Scientist

We see a growing trend in companies’ demand for data analysts. Data analysts have become ubiquitous across all industries ranging from finance to manufacturing, entertainment to healthcare! Enterprises across the world now understand the value of analytics and its potential in changing market trends, understanding patterns and relationships to enable decision-makers to make the right decisions.

Some factors that determine a data analyst’s salary in India are – domain, location, job experience and skillset.

Data Analysts with little to no experience earn 4 Lakh per year. With more experience, the salary averages to around 6-9 LPA.

Summary

While describing both data science and analysis, it is implausible to explain one without the other. Data science focuses more on finding the right questions to generate productive insights from raw data. Business analysts work to use the data to come up with solutions to given problem statements.

 
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In this article, we discussed Data Analyst vs Data Scientist. Working in data analysis is a more refined service. Whereas data science explores a broader vista. Both streams continue to be in-demand, with appreciable career growth and excellent salary packages. If you are inquisitive, interested in solving challenges, and are savvy in programming, it’s the right time to leap into any one of the most promising jobs of the decade.