In the past few years, the field of data science has grown exponentially. In today’s information-driven world, data is playing a crucial role in every industry — from cybersecurity, healthcare, online retail, banking and insurance, to digital marketing, SEO and several others. No wonder why businesses have started relying on data heavily. And this triggers a boom in diverse job openings related to data science. Among all these positions, perhaps the most overlapping two are that of a data scientist and a data analyst. There’re many who get confused between these two titles and some of them even think that data scientist is just another glammed up word for data analyst.
While the prefix of these titles may lead many to believe that professionals holding these titles carry out the same functions, it isn’t really so. The job descriptions may look somewhat similar, but there’re key differences between the careers. In this post, we’re going to highlight the individual aspects of both data scientist and data analyst and how they’re related to each other.
1- Difference by definition
A data scientist refers to a professional who analyzes massive sets of data from a business standpoint and is responsible for predicting potential trends, exploring disconnected and disparate data sources, and identifying better ways to analyze information in order to help businesses make accurate and informed decisions.
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A data analyst focuses on collecting, processing, and obtaining statistical information out of the existing datasets. They focus on developing methods to gather, process, and organize data to reveal actionable insights for present issues, and establishing the best way to demonstrate this data. Put simply, a data analyst is directed toward solving problems that can obstruct immediate improvements.
2- Difference by responsibilities
A data scientist and a data analyst may share similar job responsibilities to some extent, but some notable differences do exist. Let’s take a look at them.
- Cleansing and processing of data
- Developing machine learning models and new analytical methods
- Finding new features by exploring the value of data
- correlating disparate datasets
- Identifying new business questions which can add value
- Data visualization and storytelling
- Identifying the root issues of an outcome
From Data Analyst to Data Scientist
- Analyzing and mining business data to discover patterns and identify correlations from different data points
- Implementing new metrics for identifying not so clearly understood business parts
- Coordinating with the engineering team to collect incremental new data
- Mapping and tracing the data from different systems to find out solutions to a given business problem
- Applying statistical analysis
- Designing and creating data reports to help stakeholders make better decisions
- Identifying partialities in data acquisition and data quality issues
3- Difference by skill sets
While both data scientist and data analyst positions require solid knowledge of mathematics together with knowledge of software engineering, understanding of algorithms and good communication skills, their actual skill sets differ significantly.
Data scientist skills:
- Programming languages like R, Python, SAS, SQL, Hive, Pig, MatLab, Spark, Scala etc
- Data visualization and storytelling
- Distributed computer frameworks such as Hadoop
- Machine learning and deep statistical insights
- Business acumen
Data analyst skills:
- Data visualization tools such as Tableau
- Data storing and retrieving tools and skills
- Robust exposure to SQL and analytics
- Spreadsheet tools
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4- Difference by pay packet
Data scientists earn substantially more money than data analysts. On an average, the starting base salary of a data scientist is around $110,000 while for a data analyst, it stays around $65,000. However, the salary of the latter depends on the type of the analyst they’re — market research analyst, financial analyst, or operations analyst, among others. Learning data science is the first step for these jobs.
5- Difference by job roles
Both the groups are divided further based on their job roles.
- Data scientists are offered job roles like data developers, data researchers, data business and data creative people
- Data analysts are categorized into roles like database administrators, data architects, operations and analytics engineers
Which career is best for you — Data scientist or data analyst?
When you need to determine whether a data scientist or a data analyst career path would be the best for you, how will you proceed? We’ve already talked about the skills that are required to excel in both the positions, but there’re some other key factors that you should consider when choosing one of these two. These include your personal interests, your preferred career path, and your background. When you select the ones, you may know data science in 6 weeks.
1- Personal interests matter a lot
Do you have a keen interest in statistics and numbers? Or, is it computer science and business that keep you excited?
While a data scientist needs to have solid understanding of computer science, statistics, and mathematics, he/she also needs to have good business acumen. Apart from having robust presentation and communication skills, you need to be able to find opportunities, risks and trends in the data if your aim is to become a data scientist. In addition, communicating the findings in easy-to-understand formats should be one your key fortes.
On the other hand, work of a data analyst heavily encompasses programming, statistics, and numbers. They almost exclusively work in databases to reveal data points from complicated and sometimes, disparate sources. Also, a robust understanding of the industry they’re working in is something crucial for a data analyst.
2- Career path holds its fair share too
Where do you want to see yourself in the distant future? Apart from job responsibilities, as the level of values added by data scientists and data analysts differ significantly, so do their compensations.
Data scientists, who’re typically graduate degree holders, usually have advanced skillsets and come with more working experience. They are generally considered to be more senior that data analysts. As a result, data scientists receive healthier pay packets than data analysis professionals. And they can earn a yearly compensation between $110,000 and $163,500.
This compensation range comes down to $77,500 and $118,750 for data analysts. However, as their work encompasses databases mainly, they can increase their seniority and in turn, compensation by learning programming skills that are considered crucial in the domain. Once a data analyst gains substantial experience and acquires an advanced degree, he/she can easily move into better positions with increased compensations.
3- Don’t ignore your background
Though the positions of data scientists and data analysts may look somewhat similar, it’s the background, in terms of both educational and professional, that acts as one of the key factors when it comes to choosing one of them.
For a data scientist, a PhD or Master’s degree in mathematics, computer science, or statistics is desired. Add to it the desired professional experiences like working in statistical computer languages, working with data mining and statistical techniques, creating and working with data architectures, 5 to 7 years of experience in building statistical models and manipulating datasets, experience in using web services, and experience in working with distributed computing/data tools, among others.
At their core, most data analysts require a degree in statistics, mathematics, or business with an analytical bend of mind. Desired experiences usually include working with languages like Python, R etc, and working in agile development methodology etc.
Both the positions of data scientist and data analyst are considered highly coveted in today’s job landscape. You can certainly go for either one. Just be sure to consider the above factors to excel in your chosen trajectory.
Where the roles intersect
Though data scientists and data analysts aren’t two interchangeable roles, they hold a fundamental overlapping point — both of them draw insights from data. In the business acumen context, data scientists hold a richer skillset and have a deeper familiarity with advanced statistical modeling, Hadoop, machine learning than their counterparts in the data analysis domain. However, both professionals are capable of transforming data into insightful answers needed by business owners to take informed and better decisions. But the difference lies in their approaches and in the answers. Typically, a data scientist can help a business by formulating new questions that help it drive forward while a data analyst is able to answer critical business questions.
Today, there’re lots of ways to become a data science professional, but the ideal move should be solidifying your educational background first, in terms of obtaining a Master’s or Bachelor’s degree. And then, there’re other ways that can help you sharpen your data science skills. Ideally, before you dive into a higher-education program, you should try to figure out the industry you’ll be working in to identify the most critical software, skills, and tools.
Whether you’ll be working as a data scientist or a data analyst, some business domain expertise will be required that will vary based on the industry. For instance, if you’re working in marketing, education, or business, you’ll require a different skillset than if you work in science, healthcare, or government. Once you’ve chalked out your desired industry needs, just do some research and you’ll find an array of professional development courses, bootcamps and online classes that can help you learn and hone the requisite skills. Apart from these, there’re data science certifications available as well that can strengthen your resume and in turn, help you get a healthier pay packet. Data science bootcamp in Bay Area might provide these options.
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Difference Between a Data Scientist and a Data Analyst was originally published in Becoming Human: Artificial Intelligence Magazine on Medium, where people are continuing the conversation by highlighting and responding to this story.