The world of data science is expanding as we speak. Big data has penetrated almost every industry creating numerous job positions. Among these positions, Data Scientists, Data Analysts, and Data Engineers are the ones that are the most sought after at the moment.
As you’ve probably guessed, these occupations share one thing in common – they are all somehow implicated with data. However, they can’t be used interchangeably. Their responsibilities, the tools they use, and the required knowledge are simply not the same.
If you are thinking about pursuing a career path in data science, knowing the difference between scientist, analyst, and engineer will help you make a better decision. Scroll down below to learn what makes data scientists, analysts, and engineers different.
Who is a Data Analyst?
Data analyst, as the name implies, is a data-related job. It is the most basic job, and it’s not strictly tied to the data industry. As a result, you can see data analysts working across verticals, including medical, financial, automotive, IT, and more.
A data analyst can be anyone with outstanding statistical knowledge and a bachelor’s degree. In addition, data analysts can benefit from technical skills. There is a lot of competition out there, and being proficient in more tools and having more experience than other job candidates is always welcome.
A data analyst has to have a deep understanding of the business he/she works for. Only then, their data reporting, modeling, and handling skills can help companies get ahead.
Who is a Data Engineer?
Many data engineers have started as data analysts. However, working with data and obtaining new technical skills through the years is not enough to become a data engineer. This job position requires that a person understands performance optimization and data pipelining.
Data engineers both create and integrate APIs. It’s all something people can learn while working and gaining experience as data analysts. However, they have an alternative path that encompasses obtaining a master’s degree in a data-related field.
Who is a Data Scientist?
Finally, a data scientist is a person responsible for interpreting and analyzing complex sets of data. Data scientists have to understand some of the most challenging processes, technologies, and methodologies in the field, such as data conditioning and machine learning, to do it. In addition, data scientists have to be well-versed in various tools to perform advanced statistical analysis.
At this point, you are starting to get the difference between these occupations. However, to understand it on a more meaningful level, you should dive into skill-set requirements.
Data Analyst vs. Data Engineer vs. Data Scientist: Skill-requirement comparison
Data analyst doesn’t have to be deeply involved in programming to take care of the tasks. However, the occupation does require some technical knowledge in the domains of SQL, Spread-sheets, and R or Python. In addition, a basic understanding of building machine learning models can be of help as the field is rapidly advancing, and data analysts might soon end up having to use ML.
SQL has been a standard database language for quite some time now. Therefore, programming in SQL is the essential skill every data analyst should have. Data analysts often work on managing and storing the data and relating to multiple databases. They also build database structures from scratch or change the forms of the current ones. Given that SQL is a commonly used database language across industries, mastering it is a must-do.
Extensive knowledge in using Microsoft Excel’s VBA lookups and Macros is also essential. Excel is often used for quick analytics, and many startups and small-business depend on it.
Since both R and Python can process the data faster than Excel, they are usually in the Data Analyst’s toolkit. Basic knowledge in Python enables analysts to prepare data, perform analysis, predict trends, and create visualizations.
A data engineer often knows everything a data analyst does because it is the knowledge foundation to master other crafts. For instance, they need more in-depth knowledge in Phyton for deep learning and machine learning. They need to build complex algorithms. It doesn’t only require programming knowledge but also mastery of math and statistics.
Extensive knowledge in Python also enables them to use advanced software library frameworks to distribute substantial data sets. Moreover, they can do it across clusters of computers. Some basic knowledge in machine learning is also required as it helps them build more accurate data pipelines and understand the needs of data scientists better.
At last, we have a data scientist – the occupation that combines the skill-sets of both data analyst and engineer. Most data scientists have a master’s degree in mathematics and statistics, computer science, or engineering. It’s simply because the job and implicated technologies require top-notch skills in mathematics.
Data scientists have to be able to understand and manipulate both structured and unstructured large data sets data. This is why they have to be proficient in distributed SQL programming, relational and non-relational data queries, Python coding, and machine learning techniques (logistic regression, decision trees, supervised machine learning).
Data scientists also work on planning, executing, and managing ML projects. It often implies using real-time data streams combined with previously built data pipelines. Therefore, it demands from a data scientist to use cloud-native platforms and advanced ML solutions such as PandioML.
Data scientist occupation includes more programming and math than analyst and engineer combined. Therefore, transitioning to this field demands commitment, consistent learning, and training, and using the emerging tech solutions.
While they all work with data, Data Analysts, Data Engineers, and Data scientists are all unique occupations. As you can see, they involve a different amount of programming and math. Hopefully, now you understand the difference between these occupations and choose to pursue a career path that reflects your personal needs and expectations.