There is a lot of confusion surrounding the job designations or titles such as “data analyst,” “data scientist,” and “data engineer“. What do these job titles mean, and what are the differences between them? Before selecting one of these career path, it will be good to get a good understanding about these job titles or designations, related roles & responsibilities and career potential. In this blog post, we will describe each title / designation and discuss the key distinctions between them. By the end of this post, you will have a better understanding of which career path and related designations are right for you!
Data analysts are those who are responsible for collecting, organizing, analyzing and extracting insights from the data. They use their findings to help businesses make better decisions. Thus, data analysts work very closely with the business analysts in order to create business impact driven by actionable insights. While business analysts focus on identifying business problems and perform root cause analysis, data analysts help with insights. Data analysts are typically found to be part of data analytics team. They are also called analytics specialists, at times.
Data analysts are those who are responsible for collecting, organizing, analyzing and extracting insights from the data.
The following represents some of the common tasks performed by data analysts:
Data analysts usually have got good expertise with Excel spreadsheet, SQL and working with relational databases such as MySQL, SQL, Oracle. They also need to have good expertise with NoSQL databases such as MongoDB, Cassandra, etc. Data analysts also need to be good with different charting and visualization tools such as Excel, Tableau, Qlikview, etc. Python and R are two other important programming languages used by data analysts as these versatile languages allow data professionals to develop custom scripts for performing complex analytics tasks including creating charts.
The following are some of the courses data analysts can take from different websites to improve their skills:
Data analysts need to be good with cloud-based tools which can help them to gather data, prepare data and create charts from the data providing insights. The following are some of cloud based tools for Amazon AWS, Google, and Azure:
There is a lot of demand of data analysts given widespread need to extract actionable insights from data and help businesses make decisions that result in great impact. Thus, you can choose to take up this career path as you would have great opportunities to create business impact. Make sure you love to work with the data.
Data scientists are those who leverage data and technology to establish truth in relation to different business processes. Data scientists require to have a good understanding of concepts of hypothesis testing apart from all the skills possessed by the data analysts. Hypotheses formulation, designing hypotheses tests, perform tests and conclude are key job responsibilities of data scientists.
Hypotheses formulation, designing hypotheses tests, perform tests and arriving at conclusions are key job responsibilities of data scientists.
In that relation, data scientists need to have strong skills with Statistics / Mathematics. The following are some of the skills data scientists require:
The following are some of the courses data scientists can take from different websites to improve their skills:
Data engineers are those who are responsible for the design, development, maintenance, and management of data systems. Data systems can be defined as the collection of processes, technologies, and people that enable an organization to turn data into insights. Data engineers require to have strong skills with different database technologies, big data processing tools, and cloud-based solutions.
Data engineers are those who are responsible for the design, development, maintenance, and management of data systems.
The following are some of the skills required for data engineering:
Data engineers work closely with data analysts and data scientists to help them get the most out of the data. The following represents some of the common tasks performed by data engineers:
The following are some of the courses data engineers can take from different websites to improve their skills:
The below represents latest Google search trends for data analyst, data scientist and data engineer which can as well be used as representation of demand for these jobs.
The following are some of the differences between data analysts, data scientists and data engineers:
The following can be taken as guidelines to decide for yourself the title which is good for you:
The above three titles are not mutually exclusive and there is a lot of overlap between them. The decision of which title to choose depends on your skills, interests, and goals.
When talking about the salaries, it is the data scientist that can get maximum salary out of these three job titles closely followed by data engineers which is then followed by data analysts. This is in line with the expertise and experience of each title. Some times, data engineers also get higher salary than the data scientists depending upon the complexity of the tasks and availability of the people with the skillset.
Data analyst, data scientist and data engineer are three job titles in the field of data that are often confused with each other. In this blog post, we tried to clear up the confusion by defining each title and sighting differences between them. We also talked about the skills required for each title and the right title for you depending upon your skills.
I hope this blog post was helpful in understanding the differences between data analyst, data scientist, and data engineer. If you have any questions, feel free to leave a comment below and I will be happy to answer them. Thank you for reading!
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