When you consider career paths in data, two names often rise to the top of that list: data analyst and data scientist. Many professionals in the industry conflate the two roles, and it’s easy to understand why.
There is ample overlap between the day-to-day functions of a data analyst and a data scientist—the required skills heavily complement each other. So, it’s common for data analysts to transition into a data scientist career path as they gain more experience. To clarify the differences between these two roles, we’ll take an in-depth look at:
Key functions and skills of a data analyst
Key functions and skills of a data scientist
6 key differences between the two roles
What does a data analyst do?
Data analysts are responsible for performing data analysis, which involves collecting, cleaning, transforming, and manipulating data sets to uncover meaningful trends and insights. They must be able to:
Work with large datasets from multiple sources
Identify data patterns
Create reports and visualizations for business stakeholders
Create data analytics strategies
Present the results in a way that can be easily understood
Use data visualization tools to create tell data stories
Data analysts are essential members of organizations looking to make the most out of their data. Utilizing the interactive layer of the modern data stack, data analysts uncover insights that empower organizations to make better decisions and stay competitive in a rapidly evolving world.
How do you become a data analyst?
To become a data analyst of the future, one must have the right combination of technical, analytical, and interpersonal skills:
Technical: This means having knowledge of database query languages like SQL, and possibly some knowledge of a programming language like Python. In addition, data analysts should also be familiar with databases and working with large data sets. Basic knowledge in mathematics, statistics, and machine learning principles is also important.
Analytics: It’s not enough to know how to work with data sets—data analysts need to possess business savvy within their specific area of focus to spot anomalies and understand business impact.
Interpersonal: Moreover, data analysts must possess soft skills such as communication, problem solving, and collaboration to effectively communicate their insights to stakeholders and decision makers.
A career in data analysis requires dedication and a willingness to stay up to date on the latest technologies and trends in data analysis. Certifications related to the field of interest can be helpful in gaining an advantage when applying for jobs.
With the right skills, knowledge, and experience, becoming a data analyst can be an incredibly rewarding career path. By leveraging their skillset to uncover valuable insights, data analysts are essential members of any data-driven organization.
What does a data scientist do?
A data scientist specializes in analyzing large sets of data to uncover patterns, trends, and actionable insights.
Data scientists use a combination of mathematics, computer science, and business acumen to interpret the often complex and overwhelming amount of digital data that organizations generate each day. They are responsible for finding meaningful connections between different pieces of information. Data scientists use tools such as predictive analytics, machine learning and artificial intelligence to create reports, charts, and visualizations—creating data stories that empower decision-makers.
Data scientists also work with data engineers, who are responsible for building the infrastructure necessary to collect, store, and process large amounts of data. By combining their expertise in both areas, data scientists are able to unlock the potential of data and create meaningful insights to help businesses grow.
Data scientists have become increasingly important as companies struggle to keep up with the rapid pace of technological change, requiring more in-depth insights into their rapidly-growing data. Data scientists are also sought after by organizations due to their ability to bridge the gap between business knowledge and technical prowess. They are an increasingly valuable resource for businesses looking to gain a competitive edge.
How do you become a data scientist?
Data scientists can be found in almost any industry. From healthcare, to finance, to marketing—their skills are in demand in a wide range of businesses. A data scientist’s responsibilities and insights vary depending on the type of organization they work for, but all data scientists share the common goal of turning data into valuable information that can be used to inform decisions.
Like a data analyst, becoming a data scientist also requires a mix of technical, analytical and interpersonal skills:
Technical: In order to become a data scientist, you need to have a strong foundation in mathematics and computer science. This includes knowledge of statistical analysis, machine learning algorithms, and programming languages such as Python and R. Having an understanding of data engineering principles is also beneficial. Data scientists are experts at data management—meaning they are able to collect, store, process, and analyze large amounts of data.
Interpersonal: In addition to technical skills, data scientists must also have strong communication and problem-solving skills. They need to be able to explain the results of their analysis in a way that is understandable and actionable for decision makers.
Analytical: Data scientists should also have an ability to identify problems, develop solutions, and collaborate with different teams.
With the right mix skills, data scientists can become an invaluable asset to any organization looking to unlock the potential of their data. Becoming a data scientist requires dedication and hard work, but with the right knowledge and training, it can be a rewarding and fulfilling career path.
6 differences between data analysts and data scientists
1. Main areas of focus
Data analysts typically focus on specific areas of data analysis, such as data modeling and visualization. Data scientists perform more holistic analyses that require knowledge of both structured and unstructured data.
2. Datasets used
Data analysts tend to work with existing datasets, while data scientists often design and build new datasets and different types of data models.
3. Methods for interpreting data
While data analysts typically use established methods to interpret data, data scientists must identify and develop new methods for interpreting complex datasets.
4. How insights are generated
Data analysts are often responsible for generating reports with visualizations of their findings. Data scientists must interpret the meaning behind the numbers in order to generate actionable insights.
5. Tools used
Data analysts use existing tools, such as spreadsheets and statistical analysis software. Data scientists need to build AI models and write advanced algorithms for data analysis.
6. Who they work with
Data analysts tend to work within a single department or organization while data scientists may be called upon to work across multiple departments or organizations. As a result, data scientists require more advanced problem-solving and communication skills.
Transform your data into actionable insights
Data analysts and data scientists have different backgrounds, responsibilities, and skills. However, they are both essential to turning data into insights that businesses can use to make decisions. The six differences between data analysts and data scientists show that each position has its own strengths that contribute to a company’s success.
Learn how self-service analytics can empower your data teams to focus on the areas of your business that have the most impact—sign up for a ThoughtSpot free trial today.