If you’re new to the data field, and just looking to get started in your data career, you’re probably wondering Data Analyst vs Data Scientist, What’s even the difference?

Well we’re here to help you protect your precious time – time that otherwise may be wasted time barking up the wrong tree. Although the roles overlap in competencies and expectations there are some key differences. If we had boil those down to one or two statements, you could say:

“A data scientist is someone who can predict the future based on past patterns whereas a data analyst is someone who merely curates meaningful insights from data.”

or perhaps….

“A data scientist is expected to generate their own questions while a data analyst finds answers to a given set of questions from data.”

And while the above statements are TRUE, you’ll want to scratch deeper before spending time prepping CVs and pumping out cover letters. Read on.

What are Data Analysts and Data Scientists?

In this article, you will learn:

1. Data Scientist vs. Data Analyst
2. Requirements and Skills
3. Responsibilities
4. Choosing Between a Data Analytics and Data Science Career
5. Consider your Personal Background
6. Consider your Interests
7. Consider your Desired Salary and Career Path

Data Scientist vs Data Analyst

Requirements and Skills

Data Analyst

Data analyst requirements may vary, but an undergraduate degree in STEM (science, technology, engineering, or math) is typically a must. An advanced degree can help as an advantage, but it is often not a must-have qualification. Applicants do need to prove strong skills in programming, math, databases, science, predictive analytics, and modeling.

Here is a list of typical data analyst requirements and skills:

• A degree—in statistics, business with a focus of analytics, or mathematics.
• Languages—should have experience with R, Python, and SQL/CQL languages.
• Skills—should possess strong analytical skills,reporting acumen, and intellectual curiosity.
• Technologies—should be well versed in data mining techniques and technologies like Spark, large-scale data frameworks, MapReduce, machine learning, and neural networks.
• A proactive approach—that enables the applicant to simultaneously manage multiple priorities.
• Workflows—knows how to work with agile development methodologies and various related pipelines.
• Office proficiency—advanced knowledge of Excel and Office.
• Communication—must be in possession of strong written and verbal communication skills.

We documented the data analyst’s career path HERE – if you’d like to explore that further.

Data Scientist

Data scientists are expected to be well-educated. According to KDnuggets, 46% of data scientists have an advanced Ph.D degree and 88% hold a master’s degree. KDnuggets found that 32% of data scientists hold degrees in mathematics and statistics, while 19% hold computer science degrees, and 16% hold engineering degrees.

Here is a list of typical data scientist requirements and skills:

• A degree— most data scientists hold a master’s or Ph.D. in computer science, mathematics, or statistics.
• Languages—should have experience with R, Python, and SQL/CQL languages.
• Techniques—should know how to perform various data mining and statistics tasks, including random forest, generalized linear model/regression, trees, boosting, social network analysis, and text mining.
• Data expertise—should have proven experience performing data architectures tasks.
• Machine learning—a solid foundation with machine learning operations, working with techniques like artificial neural networks, decision tree learning, and clustering.
• Statistics—a solid understanding of advanced statistical concepts and techniques, such as statistical tests, regression, and properties of distributions.
• Seniority—proven experience of five to seven years, performing tasks such as building statistical models and manipulating data sets.
• Web services—experience using services like S3, Redshift, DigitalOcean, and Spark.
• Analysis tools—prior experience of analyzing data using third-party providers, such as Google Analytics, Crimson Hexagon, Coremetrics, Site Catalyst, Facebook Insights, and AdWords.
• Distributed data/computing tools—prior experience using tools like Hadoop, Gurobi, Hive, Spark, MySQL, and Map/Reduce.
• Visualization—proven experience in presenting visualized data to stakeholders using tools like D3, Periscope, ggplot, and Business Objects.

Data scientists are expected to not only understand data, but also to present their insights to stakeholders. This skillset combination of mathematics and coding with the ability to present and explain insights in layman’s isn’t easy to achieve or find. This is why “data scientist” is considered a sought after position with lucrative offers.

Responsibilities

Data Analyst

Data analyst responsibilities often vary between industries and companies, but some tasks remain the same. For example, a core responsibility of data analysts is analyzing and interpreting data.

Here are the key responsibilities:

• Research and analyze consumer data.
• Work and modify customer-centric algorithm models to align with customer requirements.
• Analyze large databases and present actionable insights.
• Support daily decision making with insights gathered from ad hoc and recurring quantitative analysis.
• Perform tasks related to analytics, determining KPIs, generating financial reports, as well as designing and optimizing dashboards.
• Visualize data, goals, and metrics.
• Extract data from warehouses, often via writing SQL queries.

Many start as a data analyst and then, after gaining experience and learning additional skills, they are able to work as data scientists.

Data Scientist

Data scientists are commonly required to know more statistics than software engineers and more programming than statisticians. A data scientist knows how to run a data science project from beginning to end, performing tasks that involve storing and cleaning big data, building predictive models, generating insights, and turning findings into stories.

Here are the key responsibilities:

• Mine and analyze company data, providing insights for optimizing and improving product development, business strategies, and marketing techniques.
• Leverage predictive modeling, providing suggestions that help optimize and increase revenue generation, customer engagement, and ad targeting.
• Develop custom algorithms and data models.
• Develop processes and tools that help analyze and monitor the performance of models and the accuracy of data.
• Assess data-gathering techniques and new data sources for accuracy and effectiveness.
• Test the quality of models and develop A/B testing frameworks for the company.
• Implement models and monitor results while collaborating with different teams in the organization.

Data Analyst vs Data Scientist: Choosing The Best Career Path For You

To determine which career path is best suited with your professional and personal goals, you should first understand the responsibilities and requirements each job entails and then consider the following aspects.

Personal Background: Undergraduate vs Advanced Degrees

When determining which career path to take, you should consider your current background. This can help you assess the steps required to get accepted into each role. If you have advanced STEM degrees (or perhaps another kind of formal, advanced STEM credential), you can start applying for data science roles. However, if you don’t currently have an advanced degree, and you are not sure if you want to go back to school, you might want to apply for a data analyst position.

Interests: Statistics and Numbers vs Business and Computer Science

There are many similarities between data science and data analysis roles, but there are key differences that can help you determine which role is best aligned with your interests. Typically, data analysis involves numbers and statistics, while data science requires business knowledge and computer science skills. While a data analyst needs to understand their organization, they are not required to be well-versed in business processes and computer science.

Desired Salary and Career Path

Salaries are often determined based on certain criteria. Experience, skills, and education levels often factor into the total sum offered to each role and individual. Since data scientists are required to hold advanced degrees, and they need a vast level of experience in diverse areas, their salaries are often higher than those offered to data analysts.

According to Robert Half Technology (RHT)’s 2020 Salary Guide, the earning potential of data analysts ranges from $83,750 to $142,500, depending on applicant qualifications and experience. Data analysts with additional programming skills, like Python and R, can get a higher salary.

Data analysts with more than ten years of experience who acquire an advanced degree can transition to other, higher-paying, positions, such as data scientist and developer roles. According to RHT, the average annual salary of data scientists ranges from $105,750 to $180,250. There are also other career paths data professionals can take, advancing to senior positions like data engineer or data architect.

Conclusion

If you’re still here, weighing out your options between Data Analyst vs Data Scientist  – It might seem like data analyst and data scientist responsibilities are the same, but the two roles are vastly different. Data scientist roles are often occupied by professionals with more seniority and experience, in possession of advanced degrees and a diverse set of skills.

Data analysts, on the other hand, can start working after acquiring a STEM undergraduate degree and a love for numbers and statistics. Both roles have their own key responsibilities and requirements. Before applying, consider whether the role aligns with your interests and goals and then proceed with the path that suits you best.