Data is crucial for modern businesses to stay ahead of their competition. Companies manage huge volumes of data and spend a significant portion of their budget on it. Skilled talents for managing, gathering, and understanding data are in great demand, Data Analysts and Data Scientists are the two hottest job profiles in the market with huge potential to grow in the future, too.
If you want to aspire to any of these profiles as your career options and want to learn what they are, what they do, and how much they earn. Then you are at the right place. In this article, you will learn how Data Analysts and Data Scientists operate. You will also read about a few key differences between Data Analysts and Data Scientists and some similarities between them. Let’s begin.
Table of Contents
Who is a Data Scientist?
Data Scientists are the one who gathers, cleans, and analyzes huge volumes of structured and unstructured data. They work with Data Engineers and Data Analysts to help them meet the data requirements, build pipelines, etc. Data Scientists process, analyze, and model data to create plans for companies. They are responsible for identifying trends and managing data using their sharp analytical skills. They usually indulge in gathering data from various data sources such as databases, websites, web apps, social media, etc.
Who is a Data Analyst?
Data Analysts are the one who turns raw data into a valuable piece of information and insights using their technical and analytical knowledge to make business decisions. Their job is to manage data systems and Databases and ensure that data is available in a readable format. Data Analysts filter data and review reports, dashboards, and KPIs to identify problems. Using statistical tools, they analyze, interpret and identify patterns hidden in the huge volumes of data that help them in diagnosis and making predictions.
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Data Analyst vs Data Scientist: Key Differences
Now that you have brief knowledge about both the profiles viz. Data Analyst and Data Scientist, In this section, let’s look at a few critical differences between Data Analysts and Data scientists to understand the two terms more clearly.
Data Analyst vs Data Scientist: Role and Responsibilities
The role and responsibilities of a Data Analyst vs Data Scientist vary based on the location and industry they are working in. Though, they still have to work on data only. Data Scientists involves in gathering data, what they can do with data, applying various Data Modeling techniques to it, and feeding it to Machine Learning Algorithms and automation. On the other hand, Data Analysts are more concerned with generating insights, extracting valuable information from raw data, asking questions such as why Sales dropped in the previous month, etc.
Sometimes Data Scientist role also involves some responsibilities of Data Analysts. Data Scientists spend around 60% of their time cleaning the data. They are responsible for building and managing ETL pipelines and gathering data through APIs. Data Scientists need to be well skilled in Python and R language for Data Cleaning and Data Processing. They use various Machine Learning Algorithms such as Radom Forest, KNN, Logistic Regression, and many more for Statistical Analysis and making predictions. Data Scientists work on developing Big Data infrastructure using Hadoop, Spark, and other tools such as Hive.
Data Analysts use the SQL language to query data and understand it better. They have to generate insights from data based on the problems of the industry and company. Data Analysts are more business-oriented as compared to Data Scientists. Moreover, Data Analysts are also required to use Excel or sheets for Data Analysis and forecasting. Data Analysts keep track of the performance of the business activities by maintaining dashboards using Business Intelligence software. They perform various techniques such as Descriptive Analytics, Diagnostic Analytics, Predictive Analytics, or Predictive Analytics.
Data Analyst vs Data Scientist: Skills
There are many similar skills between Data scientists vs Data Analysts but the main difference is that Data Scientists usually use programming languages such as Python or R and Data Analysts need to have expertise in SQL language or Excel for querying data. Data Analysts clean data to understand it better. Another difference is Data Scientists use various Machine Learning languages and both advanced Data Analysts and Data Scientists use Big Data.
Some other skill requirements of Data Scientists vs Data Analyst are listed below:
- Data Mining
- Data Warehousing
- Maths and Statistics
- Big Data/ Hadoop
- Machine Learning
- Data Visualization
- Data Mining
- Data Warehousing
- Maths and Statistics
- Business Intelligence
- Advanced Excel
- Data Visualization
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Data Analyst vs Data Scientist: Salary
Let’s talk about salary. The salary of a Data Analyst and a Data Scientist depends on the different levels of experience. Both the roles come with good salary packages because of their increasing demand in industries. Data Scientists with a graduate degree and advanced skills are more experienced and are considered more senior than Data Analysts. According to Roberthalf, the average annual package for Data Scientists lies between $105,750 and $180,250.
On the other hand, Data Analysts have an earning potential of between $83,750 and $142,500. Usually, Data Analysts workaround Databases and understand data requirements. But most Data Analysts learn other additional skills such as Python or R language and some Machine Learning languages to increase their salaries.
Data Analyst vs Data Scientist: Career Growth
If you have a business-oriented mind or want to experience real-world business data problems, you can go for an entry-level Data Analyst role that will help you to gain some experience. With this, Data Analysts can use their querying skills to play with data from the Databases, generate reports using Business Intelligence tools, and analyze data. Gradually, you can learn and apply advanced Analytical techniques, mathematics, and statistics to become a senior Data Analyst or some Data Consultant in a company.
Data Scientists are in great demand in every industry such as E-Commerce, Manufacturing, Automobiles, Logistics, Education, Healthcare, etc. Though companies still face issues of the skill gap among Data scientists and require more qualified talent in the industry. So, anyone aspiring for a Data Scientist role should work more towards learning to develop the algorithms and build predictive models.
Similarities Between Data Analyst vs Data Scientist
Data Analysts and Data Scientists, both are involved in dealing with data. These both are very closer profiles some part of it overlaps the other. Though both profiles are in great demand in industries and not one can replace the other. Data Scientists design a way how data should be stored for efficient data flow and Data Analysts use this stored data for analysis and making reports. Data scientists work on gathering new data while Data Analysts try new ways to make sense of the new data.
If you are fond of mathematics, statistics, problem-solving as well as computer programming then this field is for you. You can choose any of the two profiles that depend on whether you want to go towards a more technical side or more business side. Each role is focused on analyzing data and extracting some actionable insights from it for the welfare of the company or optimizing workflow, etc. Data Analyst vs Data Scientist uses different tools to make their job easier such as programming languages Business Intelligence tools, Statistical tools, and Machine Learning algorithms to manipulate and analyze data.
In this article, you learned who are Data Scientists and Data Analysts. You also read about some key differences between Data Analysts and Data Scientists and what are the similarities between the two. Data Analysts and Data Scientists are very close profiles who work together to solve a company’s business and data problems.
Companies need to analyze their business data stored in multiple data sources. Data needs to be loaded to the Data Warehouse to get a holistic view of the data. Hevo Data is a No-code Data Pipeline solution that helps to transfer data from 100+ data sources to desired Data Warehouse. It fully automates the process of transforming and transferring data to a destination without writing a single line of code.
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