In today’s highly connected and technology-fuelled world, data is becoming increasingly important. It’s a useful tool to obtain information that can help support business strategy and decision-making.

The Value of Data

Data is knowledge, and knowledge is power. Data can provide evidence for business assumptions, observations, and hypotheses. Without data, businesses would waste a lot of their resources on incorrect observations.

Moreover, data helps with the continuous monitoring of a business’s health. By utilising data, organisations can become more adept at tackling challenges. This enables businesses to function on a proactive rather than reactive basis. As such, data is a proven resource for businesses across industries. This means that most businesses require people in charge of such data.

More particularly, data analysts and data scientists are two highly demanded jobs, according to the 2020 World Economic Forum Future of Jobs Report. However, while the interest in data professionals is evident, it’s not always clear what these data jobs entail. More specifically, the difference between data analysts and data scientists goes unnoticed. While they do seem similar, this article will focus on key distinctions between these jobs. This includes highlighting differences in job tasks, qualifications, and skills required.

Job Tasks

Data analysts, given their different levels of expertise, entail different job tasks. While data analysts focus solely on numbers, data scientists go beyond that. They incorporate business knowledge as well.

What Does a Data Analyst Do?

Data analysts often work with structured data, using it to find solutions to business problems. They do so using data querying, scripting languages, data visualisation software, and statistical analysis. Additionally, daily tasks include collaborating with leaders, acquiring information, restructuring data, pinpointing actionable data patterns, and presenting findings in a digestible manner.

What Does a Data Scientist Do?

On the other hand, data scientists often make use of more advanced techniques on structured and unstructured data. This helps them with garnering insights and making novel future predictions. As such, a data scientist can be seen as a more advanced version of a data analyst. This is because they explore larger quantities of raw data and are more specialised than data analysts. Some of their day-to-day tasks include gathering data, processing data, constructing predictive machine learning algorithms using big data sets, monitoring data accuracy, creating reports, and automating data collection.

Qualifications

Both data analysts and data scientists require degrees or data courses. However, while data analysts don’t require an advanced qualification, an advanced degree is a necessity for data scientists.

What Degree Does a Data Analyst Need?

Data analyst roles usually require a bachelor’s degree in subjects such as mathematics, computer science, finance or statistics. Yet, most data analysts have a STEM degree or have graduated from a data course. While some data analyst positions may require an advanced degree in analytics or a similar field, this is rarely the case.

What Degrees Does a Data Scientist Need?

As data scientists create processes for data modelling and production, they need more advanced degrees. These include master’s degrees in data science, math, statistics, or computer science. Such advanced degrees can be seen as essential to a data scientist’s career advancement and tech skills.

Skills

While the skills data analysts and data scientists require overlap in certain areas, there is a significant difference in expertise. Both jobs require an understanding of maths, good communication skills, and the ability to transform data. Yet, they differ in the extent of expertise and tech skills required.

What Skills Does a Data Analyst Need?

Some key skills a successful data analysts need include:

• Coding skills, as well as experience with data management programs, analytics, and data visualisation software.
• A skillset comprising of advanced mathematics and tech skills. This is necessary for programming and honing their predictive skills.
• Enhanced analytical skills and a keen eye for detail. This is because data analysts work with large amounts of data, facts, and figures. As such, they should be able to obtain, organise, analyse, and break down large amounts of information while maintaining attention to detail. This helps them ensure accuracy throughout the data analysis process.
• The ability to deal with uncertainty in a fast-paced environment.
• A wide breadth of knowledge on common concepts and work practices that require them to follow a standard set of rules, procedures, or instructions.
• The ability to communicate and cooperate within teams.

What Skills Does a Data Scientist Need?

On the other hand, data scientists should have:

• Extensive quantitative knowledge, including an in-depth understanding of problem-solving, quantitative modelling, and statistical skills.
• An understanding of (and experience with) R, SQL, Python, and mathematical software such as Hadoop or Weka.
• The ability to write code to build models and algorithms to help them gain insight from data.
• The ability to communicate findings using language and visuals that cross-departmental stakeholders can understand.
• The ability to process, filter, and present large quantities of data.
• The ability to correlate data with business problems. This requires intense curiosity that scratches the surface of a problem. Through this inquisitiveness, answers to business problems can be found using hypothesis testing.

As such, there are certain key differences between data analysts and data scientists. These differences are important to account for if you’re interested in gaining experience as either a data analyst or data scientist. It’s important to acknowledge that data scientists require advanced STEM degrees, while data analyst positions don’t –– with a relevant bachelor’s degree sufficing. Additionally, data scientists are more involved in business processes compared to data analysts, which deal only with data.

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