The roles above have one thing in common: all are about working with data. Before we answer the main question from the title, let's think if, for example, all cars are the same? Sure, they all have a lot in common - they drive, transport people and goods etc. What is then the element that makes them different? The answer is simple: they have different sets of features which make them possible to perform various tasks.
Data Analyst
The job that evolved first out of the three is Data Analyst. Data analysis is, in short, the process of specific data extraction out of more general data collections or sets. One may analyze, among others, market trends, customer preferences, historical financial results, check sales channels or which marketing products get to customers most successfully. Thanks to data analysis, we are able to make safer strategic decisions in a company.
Data analysis is focused on the use of data for understanding and decision-making processes related to a specific business, or an issue. It may embrace using statistical data analysis techniques, data visualization to identify trends and patterns, or using machine learning to make estimations. Data analysts usually work with structural data e.g. those composed in spreadsheets or databases.
Data Analyst should have some fair knowledge of statistics. Basic knowledge of Python or request-making in SQL (necessary while working with Big Data) can also be an asset.
Data Analyst should also be well-oriented in visualization tools. It is essential that the analyst has good presentation skills to analyze and discuss their results with the team in a clear way and to draw right conclusions. There exists a risk of losing less obvious analysis results without good presentation skills.
Data Analyst - most typical toolkit:
- Microsoft Excel:
One of the Microsoft basics we do not need to introduce. Its flexibility, simplicity of use and more complex interface for advanced users make it present in almost every company since its first appearance in 1987.
- Power BI:
Business Intelligence toolkit from Microsoft - a cloud solution that is very useful in data visualization.
- SQL:
Request language for database work.
Data Engineer
Data enginery deals with building infrastructure and systems which are necessary in storing, processing and analyzing large pieces of data. It may be related to designing and implementing data pipelines, creating and maintaining data warehouses and preparing non-standard tools for data extraction, transformation and loading. Data engineers very often work with structured and unstructured data. To build up scalable transfer processing systems, they use technologies like Hadoop, Spark and AWS.
Data engineer develops the foundations for various operations on data. Their role is to take care of the environment where the scientists (Data Scientist) and analysts (Data Analyst) work. Data engineers are supposed to work both with structured (SQL) and unstructured (NoSQL) data. They also make it possible that the other two can perform more advanced data analyses.
An inseparable part of analysts` work is working with Big Data. Their task here is to clean up, manage, transform and reduce duplicated data collections (so-called deduplication).
Data Engineer should understand simple algorithms and know the basics of Java or Python as they are the two simplest languages in Big Data. The role of the Data Engineer is also closely related to the role of the software engineer as the Data Engineer is assigned to the development of platforms and architecture.
It is a Data Engineer`s role to maintain the whole architecture: monitor and manage errors, test, build the data flow logic that is resistant to errors, administer databases and make sure that the data flow is stable (whereby data is transferred continuously, without interruptions and information loss. It makes it possible to keep the integrity and quality of the data as well as to facilitate effective communication between systems. An unstable data flow leads to errors, inconsistencies or information loss).
Data Engineer - most typical toolkit:
- Kubernetes:
A Google-developed program to clusterize and automatize application implementation in an adaptive and dynamic way, depending on the traffic. It is the newest technology which revolutionized the world of the computing cloud.
- Apache Spark:
A platform for fast processing, analyzing and managing Big Data, created by Apache. It provides support both for input and stream data.
- Docker:
It is a platform that helps engineers build compatible applications, which work in different environments.
Data Scientist
“Data Scientist: The Sexiest Job of the 21st Century” ~ Harvard Business School
In the first place, a good Data Scientist is a good Data Analyst with a broader skill set (mainly technical). Each company searches for data scientists, in order to increase its effectiveness and to optimize production. In recent years, Data Science has been largely exploited, like fields such as Machine Learning or Artificial Intelligence.
Nowadays (January 2023), we then have a lot to do with information overflow that is difficult to manage for humans (and for Excel too). This data growth results in developing computing technologies like High-Performance Computing. Now, almost every business can benefit from their own data (if their engineers and analysts take care of the quality and proper data extraction) to maximize their income. Life today is not only about innovation - it is about optimization too. We want to work as optimally as possible, with no resource waste and with 100 percent (or sometimes even 120 percent) effectiveness. We can blindly look for it and guess but at the same time we can employ a Data Scientists team or create our own.
Companies extract data to analyze and have insight into various trends and practices. To do so, companies employ qualified and experienced scientists who know the specialized tools and have proper programming skills. Also, a good Data Scientist should have knowledge about machine learning algorithms.
Such algorithms are responsible for estimating future events. Thus, Data Science is a broad field, which includes both data analytics, data engineering and other sub-fields, such as machine learning and statistics. Scientists who work with data are responsible for collecting, cleaning up and preparing data for analysis as well as for building and implementing models in order to solve complex problems. They very often work with various tools and technologies and can be in charge of much more: from collecting and storing data to implementing machine learning models in production.
Data Scientist - a typical toolkit:
- Tools used by Data Analyst and Data Engineer
- Computing Clouds (AWS, Google Cloud, Azure)
- Advanced Python frameworks such as PyTorch, NumPy, SciPy
Comparing Data Analyst with Data Engineer and Data Scientist
Data Analyst |
Data Engineer |
Data Scientist |
|---|---|---|
| Cleaning up and data collection: Data analysts very often work with big and complex data sets and it is important to make sure that the data is accurate, consistent and in a proper format before analyzing. | Designing and building data pipelines: Data engineers are responsible for creating and maintaining infrastructure for transferring and processing data. It can be related to ETL process (extract, transform, load) or building non-standard tools for transferring data from various sources to a central repository. | Cleaning up and data collection: Scientists are often responsible for collecting and preparing data for analysis which can be related to extracting information from various resources, cleaning up, formatting and providing the best quality of the data. |
| Exploring and data visualization: Data Analysts use tools such as Excel, SQL or Tableau for data exploring and understanding. They also create visualizations, which help them identify trends, patterns and dependencies. | Maintaining and setting up systems for data storage: Data Engineers are responsible for choosing and implementing proper systems for storing various types of data, such as relational databases, NoSQL databases or data warehouses. | Models building and implementation: Data scientists who deal with data build and implement models with the use of machine learning and other techniques in order to solve complex problems and make estimations. |
| Data analysis: Data Analysts use statistical techniques to analyze data and to draw conscious conclusions. It can be related to e.g. performing statistical tests. | Providing best data quality: Data Engineers are responsible for making sure that data is clean, accurate and consistent and any issues occurring are identified and resolved. | Advanced data analysis: Data analysts use various techniques e.g. statistical analysis, machine learning and data visualization to analyze data and to draw conclusions. |
| Communicating results: Data Analysts are responsible for communicating their analyses to stakeholders, very often in the form of dashboards, reports and presentations. | Building and maintaining data processing systems: Data Engineers very often work with technologies like Hadoop, Spark and AWS to build scalable data processing systems, which can operate on large pieces of data. | Communicating results: Data Scientists are responsible for communicating their analyses to stakeholders, very often in the form of dashboards, reports and presentations. |
| Cooperation with other teams: Data Analysts very often cooperate with teams from various departments such as marketing, sales, finances etc. in order to help them solve business problems and make conscious decisions. | Cooperation with Data Scientists and Data Analysts: Data Engineers cooperate closely with Data Scientists and Data Analysts to understand their needs related to data and help them access and use the data efficiently. | Cooperation with other teams: Data Scientists very often cooperate with teams from various departments such as marketing, sales, finances etc. in order to help them solve business problems and make conscious decisions. |
| Following up the business development: the common feature is a dynamic and expansive environment; regardless of the role, all should be up to date with the newest tools and techniques and be open to learn new things to “stay in the loop” in the business. | ||
Check out Data Science consulting workshops and talk to an expert to learn more.
