Data Scientist vs. Data Analyst vs. Data Engineer

Data scientists are one of the most sought-after IT professionals today. With the relevant qualifications, a data science degree or certificate can help you get a lucrative job as a data scientist, a data analyst, or a data engineer. A recent survey by Dev Skiller, a leading tech recruitment platform, named data science the fastest growing IT field with a 295% increase in worker demand over the past year. To understand this rapid rise in demand, we need to first answer a more fundamental question: why does a business hire data science professionals?

Data is an invaluable resource for businesses worldwide. Businesses of every size, from SMBs to large multinational corporations, use data analysis to draw real-time, actionable insights that help them streamline their workflows. These insights also help businesses reallocate their resources for maximum efficiency, helping them improve customer satisfaction and plan new expansion strategies.

The data used for this is usually big data, i.e., large in volume, highly varied, and generated with velocity by the business (the three Vs. that characterize big data). Data science professionals help businesses extract, filter, and evaluate this data. A data scientist, data analyst, and data engineer all help with different stages of the process. So if you're wondering if you should become a data analyst, a data scientist, or a data engineer, let’s explore each role in further detail.

Who Is a Data Scientist?

A data scientist oversees a wide range of business intelligence-related processes in an organization. These include data collection (and integration), complex analysis, interpretation of the results, and translating these results into actionable real-world insights. They are also required to ensure that these insights are actively used to make business decisions that help the organization streamline operations, cut costs and generate more income.

Data scientists shoulder a wide array of responsibilities that require both data science skills and logical, real-world problem-solving capabilities. They are needed to discover and separate valuable chunks of information and form a set of coherent goals that they hope to achieve.

For example, if a data scientist is asked to help a business utilize its sales staff more efficiently, they would gather POS and CCTV data that shows in-store customer traffic throughout the day and identify the busiest hours so that more employees can be assigned that shift.

Data scientists are paid approximately €60,000/yr in the EU.

Who Is a Data Analyst?

A data analyst is tasked with processing and running statistical analysis on data to discover possible patterns and trends and communicates insights given via visualizations (dashboards) and reports for easy understanding. They essentially get to ask “who?”, “why?”, “how?” and “when?” from the given data to uncover specific trends that can help the organization make important business decisions.

They are also expected to capture and filter (normalize, fill in empty values, and standardize or eliminate the outliers) all the relevant data needed for these models. The data generated/collected by a business is highly unstructured. Data analysts are required to standardize all of it into a single usable format and check it for discrepancies such as missing values. As for technical analytics skills, data analysts must be proficient in database-related tools like NoSQL, SQL, Spreadsheet, R, or Python, and Visualization tools like Tableau and Power BI.

The average salary for a data analyst in Europe is between €53,939 - €95,017.

Who Is a Data Engineer?

As the term suggests, the job of a data engineer is primarily coding-based. Engineers must code and maintain different data collection pipelines (like AWS Lambda, for example) that gather big data. These pipelines are built using services like Amazon S3 and RDS and are used for efficient collection and storage of business data. The data must be stored in a format that makes it easy to interpret and easily accessible. All of these processes, collectively known as ETL or extract, transform, and load processes, are to be handled by the data engineer.

Other tasks that a data engineer is expected to handle are:

• Assisting the analyst in building and testing data models.
• Understanding the company’s objectives and goals and, through them, the kind of data they are supposed to collect.
• Ensuring complete legal and judicial compliance while collecting user data. For instance, it is illegal in many countries to capture and use data that qualify as PII or Personally Identifiable Information.

Data engineering roles strike a perfect balance between the logical, business-centered, and technical aspects of big data. Data engineers must understand the company’s data needs and how they translate to real-world insights and build appropriate pipeline architecture for steady data capture.

Data engineers are paid between €44,000 - €74,000 on average in Europe.

What’s the Right Choice for You?

Choosing a data science job comes down to two things - the area of data science you’re interested in and the kind of tools you are comfortable working with. Data scientists and analysts are usually more inclined toward business intelligence, whereas data engineers work extensively with pipeline architecture. Data scientists must also have business leadership and logical reasoning skills that help them formulate coherent data analysis goals. On the other hand, data engineers must be comfortable with data models and pipelines.

If this sounds exciting and you’re keen to shape business decisions that impact millions worldwide, consider signing up for Turing College’s distance learning programs. Our programs are curated by educators from some of the world’s top institutions like Google and Harvard. You can take these courses to upskill yourself and secure a role at your dream company.