Making data-driven decisions is a critical aspect of any successful business. Organizations use data in different ways, from identifying patterns in consumer behavior to making budget projections.
As a hiring manager, understanding the difference between these two roles can help you determine which type of information professional you need on your team. Read on to learn how companies can make better strategic decisions by hiring professionals like data scientists and data analysts to correctly interpret and leverage their information.
Data Scientists vs. Data Analysts
Data analysts collect, sort, and interpret data to create reports and generate valuable business insights. They focus on finding patterns in information and identifying factors that can affect business outcomes. Meanwhile, data scientists design algorithms and statistical models that power data analysis. This allows them to develop automated tools that can accurately process raw data, uncover hidden relationships, and visualize large sets of information.
Though the roles differ, collaboration is frequent between these professionals. Data scientists develop the tools that data analysts use to clean and review raw data.
What Do Data Analysts Do?
A data analysts’ primary focus is to solve problems or identify meaningful relationships between a business’ choices and outcomes. This involves collecting and preparing data for analysis by removing outliers, duplicates, formatting issues, and any other unwanted data. Then, they can use various tools to create and analyze reports to present their findings.
Key duties involved with the data analysis process:
- Collecting data: Data analysts can gather data from other sources or conduct research to compile key information.
- Organizing databases: This involves creating and maintaining systems that make it easier to find and access relevant data.
- Identifying relevant statistics: Data analysts use techniques like regression analysis and factor analysis to uncover relationships between variables and find statistics that could answer questions or solve business problems.
- Visualizing data: By creating dashboards, diagrams, maps, and other visual models, data analysts can share their findings with stakeholders and company leaders.
- Conducting presentations: Data analysts are responsible for presenting their analyses and explaining their insights in a way that others can understand.
Data analysts can review any type of business data, from financial information to market research. They collaborate with business leaders to determine what they want to learn, then design data analysis projects based on their needs.
Key Qualifications
The techniques and tools that data analysts use can vary depending on the company’s goals and the type of data they’re assessing. Here are some common skills to look for:
- Calculus
- SQL
- Knowledge of statistics
- Critical thinking
- Python
- Data visualization
- Attention to detail
Education requirements vary, but many data analysts have a bachelor’s degree in statistics, computer science, or another related field. Some also choose to obtain data analysis certifications.
What Do Data Scientists Do?
Data scientists create machine learning algorithms that can sort, classify, and analyze data. They typically work with raw data, which is unstructured information that they directly collect from the source. They also use their knowledge of math and computer science to code models that can help solve highly complex problems. Key duties include:
- Automating data mining: Data scientists write programs to reduce manual tasks and speed up the data collection process.
- Creating software tools: Writing code is a core aspect of data science. Data scientists use Python and other programming languages to design custom software tools to address specific data sets.
- Conducting research: By thoroughly researching data sources and data analysis techniques, data scientists can build faster, more accurate models and learn how to leverage information more efficiently.
- Consulting on business processes: Data scientists identify big-picture findings from their projects and use them to make strategic recommendations.
Data scientists may also work closely with programmers and computer scientists to build more complex models and tools.
Related: Data Scientist vs. Data Engineer: What’s the Difference?
Key Qualifications
Data scientists use many hard data analysis skills on a regular basis. This can include high-level skills like:
- Statistical modeling
- Data processing frameworks
- Predictive analysis
- Object-oriented programming
Many data scientists hold advanced degrees like masters or PhDs, often specializing in data science, computer science, statistics, or related fields. Some also have professional certificates in different software or methodologies.
Finding the Right Data Experts for Your Team
To get the most out of your company’s data, you may need to work with data scientists, data analysts (or both). With qualified data professionals on your team, you can help your company improve its processes, become more efficient, and implement more effective strategies.
Being able to properly define each role’s job responsibilities in the context of your organization can help you find and onboard candidates with the right technical skills and qualifications. Need a data scientist or analyst? Fill out the form below and one of our experts will contact you and help you source a star employee for your staff with ease.
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