Data Analyst vs Data Scientist vs Data Engineer – what’s the difference?

With data influencing every moment in our lives – from social media to major business sectors – it is important to distinguish the differences in some of the key roles that make it possible for us to have the technology capabilities we have today.

Businesses use data analysts, data scientists, and data engineers for various purposes. Some broad reasons include extrapolating customer data to conclude important recommendations to drive imperative business decisions, using data to best understand performance shortcomings and identify key points to solve in order further business strategy, or to simply understand the data itself and withdraw important conclusions. Whatever the reason may be, the 3 aforementioned roles hold significance and have unique attributes in supporting business goals.

Many believe these roles are interchangeable when in fact there are distinguishing attributes to each position, which we have outlined below.

Data Analyst

Data Analysts play an important role in requesting information from a database, or querying. They can process and leverage data sets to provide summarized reports and visuals. The primary function of a data analyst is inferencing raw data to scout patterns and make conclusions. They use methodological processes and apply algorithms to arrive at these conclusions. Although a data analyst may use algorithms to support their function, they are not expected to create it nor are they expected to have a strong mathematical or research background, but their role requires a foundational baseline comprehension of core skills in statistics, data munging, data visualization, and exploratory data analysis.

Data analysts help to simplify complex data to ad-hoc reports and charts, allowing for organizations to put the data to use. Various companies across numerous industries hire analysts to help them make better business decisions, in addition to help dispel existing models or methods. The starting salary for an entry-level data analyst ranges from $55,000-$65,000.

Skills: SQL, R, Python, Javascript, C/C++, HTML, Microsoft Excel, SPSS, SAS, SAS Miner, SSAS.

Education: Bachelor's degree in math, statistics, computer science, information management, finance or economics.

Data Scientist

Similar to data analysts, data scientists use advanced level of data analysis to derive conclusions. The difference is that data scientists amalgamate a wide range of skillsets, including the application of statistics, machine learning, mathematics, programming, and problem-solving, in order to provide valuable insight. They capture data in a unique way by creating algorithms, mining, cleaning, and aligning data, in addition to filtering through unstructured data.

The job of a data scientist doesn’t stop there. Not only are they responsible for designing new algorithms and handling high volumes of data, they are also expected to infer and interpret it and deliver conclusions of their findings. Their conclusions are expected to have a direct business impact, and they’re able to do so by building complex correlation/statistical models, structuring the data based on assumptions, and writing queries. The job of a data scientist requires both strong business acumen and advanced data visualization competencies. Their conclusions must narrate a clear and compelling story to serve business needs.

Although data scientists solve business problems, they may be assigned a project without a problem in mind, and should one arise, it is up to the data scientist to ask the right questions, find patterns in multiple datasets, and provide recommendations. Their substantial knowledge of various skills and techniques combined with their demanding responsibilities result in a high starting salary, ranging from $115,000-$125,000.

Skills: Python, R, SQL, SAS, Pig, Spark Scala, Apache Spark, Hadoop, Java, Perl, C/C++, machine learning, deep learning, and statistics.

Education: Bachelor’s degree in computer science, software/computer engineering, applied math, physics, or statistics is preferred, but undergraduate majors are flexible. Master’s degree is a requirement, and half of all data scientists hold PhD’s.

Data Engineer

Data engineers and data scientists work closely together, and as a result, many interchange these two roles. Data engineers report to data scientists with “big data” that they prepare in order to be analyzed by the scientist. The engineer’s job is more closely tied to developing, constructing, and maintaining architectures. They are software engineers who mine through raw data that contains errors – whether it is human, machine, or instrument – and format it to make the data usable. In addition, they write queries on their designed and integrated data that they build from several resources.

Data engineers’ primary function revolves around design and architecture, including ensuring that the data is accessible and capable of optimizing the company’s performance. Their role supports the data scientist by warranting that the architecture in place supports the needs of the primary stakeholder, the company. Additionally, engineers also create large data warehouses by running some ETL (Extract, Transform and Load) that is used for analysis by the scientists. Engineers develop data processes for construction, mining, and modeling that are delivered to the data science team. The starting salary for an entry-level data engineer ranges from $95,000-$110,000.

Skills: Hadoop, MapReduce, Hive, Pig, Data streaming, NoSQL, SQL, programming.

Education: Bachelor’s degree in computer science, software/computer engineering, applied math, physics, or statistics. Master’s degree is not required, but serves as an advantage.

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