Data Analyst vs Data Scientist: Key Differences Explained

As big data has become increasingly important in the world of business, new jobs have emerged related to data visualization and datasets—providing critical insight to organizations of all kinds, from major corporations and healthcare organizations to government offices. Two career paths that have emerged in response to the growing emphasis on business intelligence have been data science jobs and data analytics jobs.

The main difference between a data analyst and a data scientist is that while a data analyst works with data visualization and statistical analysis to understand data and identify trends, data scientists work to create frameworks and algorithms to collect data the business can use.

Job description

Data scientists and data analysts work extensively with numbers and data to better understand them for business purposes. However, there are important differences between how each position interacts with data and their role in cleaning and qualifying it for business use. And while the job titles may look similar, there are quite a few differences in what these high-demand data professionals do. Here’s what you can expect if stepping into either role.

Role requirements

Businesses understand the critical importance of both data analysts and data scientists. Below are the roles and responsibilities of these professionals.

What are the requirements for a data analyst?

Those interested in pursuing a career in data analysis will likely encounter a few requirements, including:

• Education. Those who want to work in data analysis should have a bachelor’s degree or master’s degree in a field related to data analysis, such as mathematics or statistics.
• Programming language skills. Programming languages that have heavy usage in understanding data analysis, such as Python, SQL, CQL, and R, will also likely be highly desired, if not required, among applicants because of their use in managing data and databases.
• Soft skills. With the importance of using data to further business strategy, excellent written and verbal communication skills and outstanding analytical skills will be required. Organization and the ability to manage multiple products at once may also be required.
• Technical skills. Experience with data mining and some of the latest data technology related to data analysis, such as data frameworks and machine learning algorithms, are often desired.
• Microsoft Office skills. To effectively communicate their findings and translate them so that others can understand, data analysts must also demonstrate competency with Microsoft Office products, particularly Excel.

What are the requirements for a data scientist?

Data scientists may encounter skill requirements that demonstrate their ability to dive deeply into the data to make quantifiable and usable insights. You’ll find that the data science requirements generally focus on having a background in computer science and include more technical requirements.

• Education. The education requirements for data scientists will typically ask for an advanced degree like a master’s degree or even a Ph.D. in a related field, such as statistics, computer science, or mathematics.
• Computer programming languages. Interested professionals should expect to demonstrate expertise with programming languages related to data, including SQL, R, Java, and Python.
• Experience with data mining. Professionals should also have extensive experience with data mining and specific tasks and tools with statistics, such as creating generalized linear model regressions, statistical tests, building data architectures, and text mining.
• Experience with web services and data sources. Web services, such as S3, Spark, Hadoop, and DigitalOcean, play a significant role in the job of a data scientist, so candidates should demonstrate expertise. They should also have the training necessary for using information that comes through third-party providers, such as Google Analytics, Site Catalyst, Crimson Hexagon,  and Coremetrics.
• Experience with statistical tools and technology. Data computing tools like MySQL and Gurobi and the latest developments in technology—such as machine learning models, deep learning, artificial intelligence, artificial neural networks, and decision tree learning—will also hold an important place.

Responsibilities

With the following responsibilities, data analysts and data scientists play important roles in helping businesses understand their progress and the steps needed to improve business operations.

What are the role responsibilities of a data analyst?

A data analyst’s role will likely revolve around understanding the overall “feel” of the business. They need to understand what drives business performance and how the business can make better decisions.

A data analyst’s job description often includes the following responsibilities:

• Finding actionable insights. Data analysts will spend much of their time focusing on analyzing and interpreting data related to customers and company processes, including researching areas like customer behavior and buying statistics, to help the business find ways to improve. They can then create dashboards that convey this information to business leaders and stakeholders.
• Building algorithms that help the business create customer-centric approaches. Data analysts also work to build a data-focused understanding of customer behavior and how to predict what customers want to see and experience as well as predict business problems. For example, suppose their data predicts a certain segment of the customer population appreciates certain white papers, encounters particular problems when using a product, or otherwise needs unique help. In that case, a business analyst can use data to serve the customer better.
• Regularly run quantitative analysis. A data analyst will also regularly run a quantitative analysis to better understand business performance, such as how the business allocates resources and how well its sales processes perform. The analyst can use data they find to discover inefficiencies and bottlenecks that slow down projects or gaps where significant numbers of leads drop off. This information can help the business improve its decision-making. The analyst can use visualization tools and graphs to present the data to others.
• Reporting on KPIs. Data analysis also calls for regular reporting on business key performance indicators (KPIs), so the organization can see where changes might be needed. KPI reports allow businesses to gauge their progress week over week, month over month, and year over year. This data gives them a clear understanding of how business operations have fared.
• Working with data warehouses. Data analysts will also need to be prepared to write SQL queries so they can pull the data they need from data warehouses. This data can then be used to track business performance. For example, these queries can be used to track the average price of products sold at a particular store or the highest-spending customers, which can help improve customer service.

What are the role responsibilities of a data scientist?

The responsibilities of a data scientist will look to many to be a combination of tasks related to computer programming skills, statistical analysis, and software engineering. Their qualifications should make it so they can take a data science project from start to finish. Here are some typical responsibilities that come with being a data scientist:

• Using predictive modeling. The data scientist will use various forms of predictive analytics to better understand customer behavior and preferences, ad performance, and other customer-facing metrics so the business can generate more revenue. When the business has better forecasting regarding when and where to show ads, for example, they can streamline their strategy.
• Develop algorithms and data models. Data scientists find ways to use unstructured data the business collects. Data scientists can create algorithms and data models customized to their unique industry based on where the business would like to improve efficiency, service, and brand reach. For example, UPS used a customized algorithm to track their deliveries and routes and create more efficient routes that save 85 million miles a year.
• Create their own A/B testing frameworks. A/B tests play a critical role, allowing businesses to compare the performance of different features, such as logo designs. However, they can be subject to different types of bias or statistical errors. Data scientists create frameworks to help minimize these problems and maximize the value of the test outcome.
• Create customized tools. The tool created by data scientists to monitor business performance, such as taking into account the unique buying behavior of customers, can help organizations track their progress and outcomes. They can then use this information to modify business practices and bring in automation for a more streamlined path to revenue.
• Analyze new data sources. Understanding how the new data source fits into the preexisting customer models and statistical models and how it impacts the information business analytics offers plays an important role in data science. For example, if a car dealer shifts from selling cars exclusively in-person to offering some through online sales, understanding how this fits with the current business model can help drive performance.

Salary

Professionals interested in pursuing work as data scientists and data analysts may also benefit from knowing how much they can expect to make, including the earning potential of freelance work.

How much does a data analyst make?

Companies highly value data analyst roles, which lends itself to a higher average salary than other roles. According to the Bureau of Labor Statistics (BLS), these professionals typically command a median salary of $86,200, with the highest 10% earning more than $144,330. Professionals interested in freelance work, though, can usually expect to make between $20 and $50 an hour, according to Upwork data.

How much does a data scientist make?

The data science career path can offer professionals a strong salary. According to BLS data, the mean annual wage of data scientists is $98,230. The upper 10% of those in the field, however, can see salaries over $165,230, which accounts for the years of education and qualifications needed to excel in this field. Based on Upwork data, those interested in working independently can typically earn between $25 and $50 per hour as data scientists.

Freelancing for data jobs

Freelancers working with data have a number of interesting projects to work on. Common projects for data scientists might include:

• Data cleaning. This involves helping businesses sort through their data and organize it so they can use it in their own algorithms and models.
• Consulting. Sometimes, businesses need help with particular data science tasks, such as building an algorithm or statistical model. They might turn to a data scientist to help them learn the best path forward to problem-solving and how to create the algorithm or model.
• Building data models or running statistical analysis. If a business needs to run its data through particular models or gain insight from a certain statistical analysis, it might ask for a data scientist’s help.

Ready to put your skill set to the test? Browse freelance data scientist jobs on Upwork. Whether you’re interested in developing a Python script or extracting drilling point data into an Excel sheet, there’s a project for you.

Some freelancing opportunities data analysts might encounter include:

• Data visualization. A client might ask for help in taking various types of data, sorting through it, and creating a digestible visual representation.
• Creating dashboards. You might use various programs, such as Tableau or Dash, to create dashboards that clients can continue to use to understand their data.
• Research. You might also help clients with data collection, such as tracking traffic on their websites and identifying insights they can use for their business.

Browse Upwork for freelance data analysis jobs now to see how you can start building your career. From analyzing Amazon and Shopify data to creating customized dashboards, there’s someone who needs your expertise.

Which is right for you?

If you want to pursue a career related to data, it’s a good idea to determine if you’ll fit better as a data analyst or data scientist. The factors that will likely impact your decision include your educational background, work preferences, career goals, etc.

Some professionals who start out as data analysts may even choose to continue their education and eventually move on to a data science role further on in their career path. The years of experience gained as an analyst can lead to a data scientist role.

If you’re ready to start looking for work as a freelance data scientist or data analyst, start with Upwork. We make the freelancing process easier, allowing you to easily browse posted jobs, bid on the ones you like, build your portfolio, and manage your relationships with clients. Get started today.