These days, job portals have been flooded with lots of openings related to data science. There are different requirements for different job titles like data analysts, data scientists, and for the personnels of Data Engineering. These job roles seem similar and all of these are based on data. But they have certain differences you need to consider. Have you ever wondered about the differences between them from one another?
A well-known economics professor, Dan Ariely once said, “Everyone discusses big data, everyone feels that everyone else is doing the same, everyone claims to do it, but no one knows how it works.”
This concept is applicable to a lot of data terms. A lot of people discuss the terms like ‘data analysis’, ‘data science’, ‘data mining’ and ‘big data’ but no one knows the exact differences between them. A lot of experts cannot define them clearly. We are going to focus on the major differences between job positions.
Data Science vs. Data Analytics
While both data scientists and data analysts are responsible to work with data. The key difference is in what they are going to do with data.
In simple words, data analysts analyze a huge amount of data to develop charts, know the trends, and create visual presentations to help businesses in making better and more informed decisions.
On the other side, data scientists build and design new data products and modeling processes with algorithms, prototypes, custom analysis, and predictive models.
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Working as Data Analysts
Data analysts are known to perform different roles and duties as per the companies and industries they work for. Basically, they use data for problem-solving and getting sensible insights. They are known to analyze well-defined data sets with varied tools to deal with different business needs. For example, they can answer why a campaign worked better in some areas, why sales dropped in a specific period, and how revenue is affected by internal attrition.
Data analysts have a lot of titles and fields, including market research analyst, database analyst, financial analyst, sales analyst, advertising analyst, marketing analyst, operations analyst, customer success analyst, and global strategy analyst. The data analysts have the ability to convey some amount of research to non-technical clients or colleagues and they also have technical expertise.
Tools and Skills
Some of the major skills of data analysts are data warehouse/data mining, R or SAS, data modeling, statistical analysis, SQL, data analysis, and data management and reporting.
Key Responsibilities
Data analysts are engaged in maintaining and designing databases and data systems with statistical tools for the interpretation of data sets and to prepare reports to convey patterns, trends, and predictions on the basis of key findings. Here are some of the key roles in detail –
- Mining and analyzing business data to know the patterns and correlations from several points.
- Writing collective SQL queries to know the answers to complex queries.
- Implementing certain metrics to decode some unknown aspects of the business.
- Identifying important data quality partialities and issues in the acquisition of data.
- Coordinating with the team of engineers to collect incremental data.
- Tracing and mapping the system-wide data to solve specific business issues.
- To apply statistical data.
- Create and design data reports with several reporting tools to help make better business decisions.
Background
Data analysts have a background in statistics and mathematics. They can also combine non-quantitative backgrounds and learn certain tools that are vital to use numbers to make key decisions.
Working as a Data Scientist
On the other side, data scientists ask questions, build statistical models, and write algorithms to estimate the unknown. Heavy coding is the major difference between data scientists and data analysts. Data scientists use different tools to arrange undefined data sets at the same time and build their own frameworks and automation systems.
Tools and Skills
They use tools like software development, machine learning, Java, Hadoop, data warehouse/data mining, python, data analysis, and Object-Oriented language.
Background
Founder of Alluvium and data science expert, Drew Conway made a diagram that explains the data scientist as a professional with statistical and mathematical knowledge, substantive knowledge, and hacking skills.
Key Responsibilities
Usually, data scientists are responsible to design data modeling processes and create predictive models and algorithms to extract the details required to fix complex issues with an organization.
- Data Processing and Cleaning – They are responsible to process, clean and organize data.
- To become a keen leader on data value by unlocking it and finding new products or features.
- Correlate disparate datasets.
- Develop machine learning models and new analytical approaches.
- Know new queries to add values
- Data visualization and storytelling
- Performing causality tests with an epidemiological approach or A/B experiments to know the root causes of observed outcomes.
Data Analysts vs. Data Scientist – Career
After having proper knowledge of key differences between the data scientist and data analyst and their career, you can choose the right path. Consider the following factors to know the right path for your professional and personal goals –
- Your own interests
- Professional and academic background
- Your preferred career approach
Your Own Interests
Are you passionate about data science and business analysis and you getting inclined by statistics and numbers? Data analysts are more into statistics, numbers, and programming. They work exclusively in databases to protect data of an organization to reveal data points from disparate and complex sources.
Data analysts must have complete knowhow of industry to work in. The role of data analysts may be the best fit for interests.
Data scientists should have a blend of statistics, math, computer science along with knowledge and interest in the corporate world. If this description aligns well with the experience and background that you have, you may definitely want to become a data scientist.
This way, you should know the career which matches your own interests to know better insight into the work you excel at and you enjoy. Take your time and think of it as an equation. You can align your work with what you like to have a satisfying and rewarding career for several years to come.
1. Professional and Academic Background
In different ways, data scientists and data analysts play the same roles but they have different academic and professional backgrounds. As discussed above, data analysts play a vital role in analyzing huge amounts of data to develop charts, know key trends, and create presentations to make more informed business decisions. To associate their academic background with such tasks, they pursue an undergraduate degree in STEM (science, technology, engineering, and math) and even advanced level degrees. They also gain experience in science, math, databases, programming, predictive analytics, and modeling.
On the other side, data scientists are more inclined to building and designing new processes when it comes to data production and modeling. Along with it, they use different techniques like machine learning and data mining. For career advancement, PhD or master’s degree is vital in data science.
As compared to data analysts, data scientists are a lot more on the mathematical and technical side. So, they need to have a more professional background in computer science.
When choosing the right career path, you need to consider academic requirements. If you have decided to invest with an advanced degree in your career, you might have the experiential and academic background to choose any path. No matter where you go, you need to consider the desired and existing level of education and you can narrow down the choices with your experience.
2. Your preferred career approach
For data analysts and data scientists, different experience levels are required. Hence, they get different salaries on the basis of their roles. According to the 2019 Salary Guide by Robert Half Technology (RHT), data analysts can earn from $81,750 to $138,000 per annum. These professionals can learn further programming languages like Python and R to improve their earning potential as they work mostly in databases.
As per the reports of PayScale, data analysts often improve their income and switch to other jobs with over 10 years of experience. After acquiring an advanced degree, two common career options are moving into the position of a data scientist or the role of a developer, according to the director of tech services at, LaSalle Network, Blake Angove.
Usually, data scientists earn graduate degrees. So, they have an advanced set of skills and they are very experienced. So, they are known to be appointed in more senior positions as compared to data analysts. So, their earning potential is often higher for their jobs. Data scientists have got the highest boost on their salaries for IT jobs at 6.4 percent from 2016 to 2017 and they have landed to an annual range of $116,000 to $163,500.
Hence, the career approach for data scientists is considered positive. They have more opportunities to advance in senior positions like data engineering and data architect.
The Takeaway – Which Job is Right for You?
The job roles of data scientists and data analysts are quite different because of a lot of differences in key roles, career approaches, and academic backgrounds. Both fields are promising and growing. So, one can’t go wrong by choosing any of them.
Qualified professionals have great demand in data-based careers in the modern job market as businesses have a strong need to utilize and protect their data. You need to consider certain factors like your own interests, background, and expected salary to choose the right career and get started on your success path.
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