Global businesses are increasingly reliant on insights gleaned from vast amounts of information. Companies now rely on data analysis to make strategic decisions, drive innovation, and stay competitive in crowded markets. Due to this ongoing demand for data expertise, a number of data-specific career paths have become particularly prominent in the past decade, with two of the most well-known of these roles being the debate between data analyst vs. data scientist.
Both play essential roles in data-driven companies today but serve slightly different purposes. Recognizing the distinct roles of data analyst and data science positions is essential for companies looking to strengthen their data teams.
It’s also an important distinction for anyone looking to start their own career in data. We’ve written this comprehensive guide to outline what each of these roles involves, and how they stand out in the evolving and complex world of data analytics.
What is a data analyst?
Overview of responsibilities
Data analytics involves collating, processing, and interpreting data. Based on this data, analysts are then tasked with providing actionable insights, which they’ll usually be expected to deliver to stakeholders and decision-makers.
This means many of their responsibilities involve data collection, cleaning, interpretation, and visualization. Their role is also closely integrated with other parts of an organization, like product development teams or senior marketing departments.
Common tools and techniques used by data analysts
Data analysts often use tools like:
- SQL
- Excel or Sheets
- Tableau
- Power BI
- Python or R
- OpenRefine
SQL, Excel, and Google Sheets are often used for managing and querying data across relational databases, which is a fundamental part of a data analyst’s job.
A data analyst relies on tools like Tableau and Power BI to help with data visualization, thanks to their dashboards and their ability to create reports. Python and R are programming languages that data professionals can then use to perform more advanced types of statistical analysis.
Key skills and competencies
Data analysts have to be skilled at both data collection and interpreting data. Often this requires high levels of proficiency in SQL and experience with data wrangling techniques. In addition to wrangling, a data analyst has to clean data regularly and often need to be skilled in data modeling.
These skills demand a strong knowledge of statistical methods and critical thinking. Further to these technical skills, data analysts also need a high level of business acumen. This can help them collaborate with other teams and allow them to better understand how data can be applied to specific departments.
In addition to knowing how to analyze data, a data analyst needs presentation skills, and an understanding of data visualization tools.
Scope and deliverables
The scope of a data analyst’s role is centered around answering specific, well-defined business questions by examining existing datasets. Rather than creating new data sources, data analysts tend to focus on extracting meaningful insights from an organization’s available data.
A data analyst’s success often hinges on the valuable insights they can offer to various departments. To support this decision-making on an ongoing basis, data analysts are also responsible for creating and maintaining dashboards and reports that visualize key metrics for statistical analyses.
What is a data scientist?
Overview of responsibilities
A data scientist is a professional who uses advanced analytical, statistical, and programming skills to uncover patterns within data and make accurate predictions that can drive business strategy.
One key difference between them and data analysts is that a data analyst primarily answers predefined questions, whereas a data scientist explores open-ended questions. While a data analyst focuses on existing data, a data scientist is more concerned with using this data to make future predictions.
Those working in data science also use data to, forecast trends, design algorithms, and create solutions that automatically adapt over time.
Common tools and techniques used by data scientists
A data scientist will usually rely on the following set of tools:
- Python, R, TensorFlow, PlyTorch or Jupyter Notebooks
- SQL
- NoSQL databases
- Machine learning (ML) frameworks and cloud services like AWS and Google Cloud
Thanks to its versatile applications, Python is one of the most popular languages for a data scientist. They also tend to use R and other tools and languages to visualize, manipulate and model data.
Someone working as a data scientist will typically work with both SQL and NoSQL databases, and later in their data career may employ advanced techniques like machine learning frameworks and working with APIs to automate data pipelines.
Key skills and competencies
To succeed as a data scientist, professionals need a mix of technical, analytical, and problem-solving skills. Expertise in programming and statistical modeling is essential for data scientists to be able to work with predictive analytics and to improve their predictive modeling capabilities.
To further support these tasks, a data scientist will also typically need expertise in a range of machine-learning algorithms. People in data science also often have to work with unstructured information, including elements like text, images, audio and videos.
Anyone working in data science should be skilled in designing experiments, such as A/B testing, to test hypotheses and measure the impact of different strategies.
Scope and deliverables
Data scientists create solutions that anticipate future trends. Where a data analyst will help a company change in the moment, a data scientist will be projecting future shifts.
Since their scope goes beyond analyzing historical data, they often need to create and deliver predictive models and algorithms that address complex business questions.
As their work is speculative, there’s more opportunity for a data scientist to experiment with predictive approaches. Further deliverables expected of a data scientist can include machine learning models, predictive analytics, and unique AI-based solutions.
Key differences between data analyst vs. data scientist
Scope of work
A data analyst’s scope of work tends to focus on descriptive analytics, which are then visualized for decision-makers and other teams in the business. They’re usually working to answer “what happened” based on data, or “why it happened” so the company can adapt.
Meanwhile, data science professionals are asking very different questions. Their work involves data insights that are predictive and prescriptive, and instead ask “what will happen” in the future in certain scenarios. They’re then likely to ask “how do we make this happen”, so that decisions made in the present can guide the company towards a predicted future.
Tools and methods
Another key difference between these roles is that data analysts tend to rely on slightly different tools. They use business intelligence tools like PowerBI, as well as SQL queries and dashboards to process data.
Data scientists are far more reliant on programming languages like Python to create models and manipulate data infrastructure. They’re also more reliant on machine learning techniques and frameworks, and a data science professional will often work closely with more advanced statistical methods.
Some overlap does however exist, as both tend to use SQL queries and rely on dashboards for data visualization.
Data complexity
Data analysts usually work solely with structured data, and this will usually be stored in relational databases. Meanwhile, data scientists work closely with more complex structured and unstructured data, including elements from multiple sources like text, and images.
Interaction with the business
The role of a data analyst is intrinsically linked with business analytics and other teams. They have to regularly monitor KPIs, and their main deliverable is to provide actionable insights that other departments can then use to improve their own processes and results.
Data scientists work closely with fewer business-related departments but often work closely with other IT teams on data driven decision making. Data scientists will often need to work with engineers, particularly when building more complex models or working with particularly challenging data.
When looking at data analyst vs. data scientist positions, it’s important to remember that both data management roles will usually have to work with stakeholders to deliver results and, in the case of a data scientist, share and create long-term predictions.
Nature of deliverables
Deliverables are a key part of both roles. Data analysts present their analysis through reports, dashboards or visual presentations, and will often need to cater these to the needs of different departments. They’ll also typically need to be able to present these to a non-technical audience. A data scientist instead produces algorithms, data models and machine learning solutions and present these to decision-makers.
Collaboration between data analysts vs. data scientists
How they complement each other
Both data analysts and data scientists play vital roles in an organization’s data strategy. Data analysts provide the foundational insights that often inform the work of a data scientist.
One example is the way data analysts examine information like past trends, performance metrics, and customer behavior, to then reveal important patterns and correlations within the data. These findings can then guide the development of a data scientist’s predictive models.
These complimentary aspects of each role also work both ways. A data scientist’s models can be shared with a data analyst, so they can monitor current performance against future predictions. They can then suggest potential changes to current business processes so that it’s easier for a company to achieve its predicted outcomes. In an effective data department, data analysts and scientists will be working together like this regularly.
Workflow and handoffs
Workflows will depend on the particular company, but a typical process might be for an analyst to first thoroughly clean and structure data. They’d then hand this data off to data scientists, who would use it for modeling.
The data scientist would create models based on this information and pass this back to analysts. A data analyst would then interpret the results of the model, based on their current data, and then visualize and present this information to stakeholders.
As can be seen, seamless communication between both data analyst and data science roles is crucial for success in data departments.
Use cases for data analysts vs. data scientists
When to use a data analyst
A data analyst is most useful when organizations need to derive insights from existing data.
One example of this might involve annual sales. A data analyst can analyze a quarter’s sales and the trends shown within, and generate monthly reports. Another application might involve the company’s website performance. Data analysts use tools that can track and analyze website metrics through business intelligence and can present this information to digital marketing teams.
When to use a data scientist
Data scientists should be used in situations where a company wants to improve in the future.
A data scientist could create a recommendation engine for an e-commerce platform, which could help filter and direct customers to the right products. Or, they could be used to create predictive models to forecast customer churn in a SaaS, or design ML algorithms to detect fraud in a fintech company.
Combining both roles in an organization
Companies today will usually need both analysts and scientists to manage a comprehensive data strategy. To combine roles effectively, your data analysis professionals should focus on any immediate questions impacting the business. Data scientists should then be tasked with building tools that can help the company predict and adapt to questions posed by the future.
Education and career paths
Typical background of a data analyst
Those working in data analysis will have achieved a Bachelor’s degree in business, finance, mathematics, computer science, computer programming or data analytics. They may have completed an advanced degree beyond this in data science, analytics, or a related field.
They’ll also likely have completed some certification programs, or specific tools training for solutions like Google Data Analytics or Tableau. Before becoming a data analyst, they may have worked in a junior position, or roles like business intelligence analyst working with big data.
Typical background of a data scientist
Like other data professionals, data scientists typically hold a Bachelor’s degree in fields like computer science, statistics, or data science. It’s likely that they will also have completed a Master’s advanced degree in their chosen subject.
They may also have completed other advanced courses in areas like machine learning algorithms. Data scientists often come from a background in software engineering, mathematics, or from a career in academic research or data engineering.
Data analyst vs. data scientist: transitioning between roles
Data scientist is generally considered to be a higher, more challenging position than data analyst, and it is possible to transition with the right training.
If you’re considering transitioning into data science, you should improve your programming skills in Python while building your knowledge of machine learning techniques and algorithms. It’s also worth gaining experience data mining and working with large data sets, particularly on how to retrieve data, and advanced analytics, though you’ll likely be doing this already as a data analyst.
Challenges and considerations for each role
Challenges faced by data analysts
In the comparison between data analyst vs. data scientist, both roles face very different challenges. As they track, collate and maintain existing data sets, data analysts have to make sure data is always accurate and of a high quality through regular data cleaning.
This can become an additional challenge when people working in data analysis have to manage data silos across several different departments. Those in data analytics also face the additional challenge of having to always keep up with the changing goals and needs of the business, as it’s these needs they need to support through their insights.
Challenges faced by data scientists
Handling unstructured data sources, or incomplete datasets, can be a constant challenge for a data scientist when it comes to data manipulation. Other data scientist responsibilities include making sure that any models they build can generalize well to real-world scenarios, otherwise they aren’t easily applicable.
Due to the complexity of their models and roles, data scientists require the ability to communicate their results to non-technical team members or decision-makers, which can be an additional challenge without the right skillset or job outlook.
Considerations for organizations building data teams
If you’re building a new big data team, don’t look at things as a rivalry between data analyst vs. data scientist positions. Instead, align the responsibilities of your data analysis team and your scientists together with your core business needs.
This makes their job easier, and means you’ll get more accurate insights from them. You should also foster a close working relationship between analysts and scientists so they’re able to easily collaborate. Finally, make sure you’re investing in all the required tools and tech so that both roles have the solutions they need to accurately manipulate and visualize data.
FAQ
What is the difference between a data analyst vs. data scientist?
Though both in computer science there are many differences between data analyst vs. data scientist even though both are statistical analysis roles, but the main one is that people in data analytics work with existing data to provide insights; as their name suggests, they focus on analyzing data. Meanwhile a data scientist use data to build models to predict the future. Their jobs also differ from other data professionals like data engineers.
Does a data analyst vs. data scientist use the same tools?
There is some overlap, as both roles use Python and SQL queries to manage databases. However, data analysts typically also use other analysis tools as well as both visualization tools and statistical software to handle data visualization for business analytics. Data scientists tend to use more advanced tools in their work. This usually includes tools like machine learning frameworks, or solutions that help them handle other data sources or conduct predictive modeling.
Can a data analyst become a data scientist?
Yes, it’s common for a data analyst to later transition to data science. In fact, working first with analysts and data scientists later is a natural career path as both jobs involve working with data visualization, data collection and statistical analysis. To make the transition they need to learn programming, machine learning, and advanced analytics.
Do data analysts need to know programming?
A basic knowledge of computer science and SQL is essential since analysts will use SQL to collect, manage and extract data. A familiarity with Python or R can be advantageous for analysts and data scientists when data mining. Meanwhile, data scientists require more programming skills so they can work with machine learning and more advanced approaches to predictive modeling.
Which computer science role is more strategic: data analyst vs. data scientist?
Both computer science roles work with big data technologies to leverage data but both are also strategic in their own key ways. Data scientists typically focus more on long-term, predictive projects, and strategies that will benefit the future of the company. Meanwhile, a data analyst typically handles strategies and suggestions that can impact immediate business needs. This is why an efficient data department should employ both analysts and scientists. You might also employ for other roles for further data driven decision making, like a data engineer.
Data Analysts and Scientists: A Winning Combination
Remember that these computer science data roles have some key differences, particularly that data analysts collect pre-existing data to uncover actionable insights for companies. Data scientist responsibilities then include using this data to predict future outcomes. Data analysts work in the present, whereas a data scientist works in the future, though the best departments make sure that these roles collaborate together for data-driven success.
If you’re considering a career in data or computer science, then the right role for you will depend on your skillset, job outlook and aspirations. If you have more of a business or analytical background, you should consider first becoming a data analyst. For those with software development experience and more advanced qualifications, it might be worth working to become a data scientist. Remember that it’s common for analysts to transition to a career as a data scientist once they’ve gained enough experience.
If you’re building a data department, make sure you’re hiring data scientists as well as data analysts. Employing many data analysts and data scientists means you can obtain both short-term insights into your company’s performance and long-term predictions that can transform your capabilities in the future.
