Data Analyst, Data Scientist or Data Engineer: What to Become?

data analysts vs data scientists vs data engineers

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!

Table of Contents

Shall I become a data analyst?

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:

Conducting research and interviews
Collect / gather & prepare data: This involves collecting data from various sources such as databases, surveys, interviews, and web applications. Data can be collected manually or through automated means with software programs or scripts. The data is then prepared for analysis by cleaning errors, standardizing formats and eliminating duplicates.
Identifying patterns and trends: Once the data has been acquired and preprocessed, a data analyst will then apply statistical methods to explore trends in the data. They will use techniques such as descriptive statistics to summarize the characteristics of a dataset
Interpret data and extract insights: They interpret their results by identifying patterns or insights within the data that can assist in making important business decisions. They visualize the insights using charts, diagrams or other graphical displays which require an understanding of visual design principles that help communicate findings clearly and effectively.
Creating reports and presentations: They are also responsible for creating reports or presenting their findings directly to stakeholders within an organization who may not have any technical knowledge of analytics but need accurate insights to make informed decisions quickly.

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:

Introduction to Data Analysis
SQL for Data Analysis
Data Visualization with Excel, Tableau or Qlikview
Excel for Data Analysis: One of the most common and widely used tools for data analysis is Microsoft Excel spreadsheet. It is recommended that data analysts get a good expertise in working with Microsoft Excel. This tool allows data analysts to easily manipulate, analyze, and visualize data. It also has an extensive library of formulas that can be applied to data sets for statistical analysis. Excel can be used to create charts, graphs and other visuals from the data which can help in analysis and communication of results.
Data catalog fundamentals

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:

Amazon AWS Athena, Amazon AWS Glue, Amazon Redshift
Google BigQuery, Google Data Studio
Azure Synapse Analytics, Azure Databricks, Azure HDInsight

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.

Shall I become a data scientist?

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:

Strong knowledge of Statistics
Strong knowledge of machine learning / deep learning algorithms
R or Python programming
Knowledge of cloud-based tools that can be used for building machine learning models; Some of these tools include Amazon Sagemaker, Azure ML Studio, Google Cloud AI Platform, etc.

The following are some of the courses data scientists can take from different websites to improve their skills:

Introduction to Data Science with R or Python
Statistics for Data Science using R or Python
Introduction to Machine learning; Advanced concepts
Cloud-based tools expertise for tools such as Amazon Sagemaker, Azure ML Studio, Google Cloud AI Platform, etc.

Shall I become a data engineer?

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:

Strong knowledge of relational databases such as MySQL, SQL Server, Oracle, etc
They work with databases and big data systems to help businesses make better decisions.
Data engineers usually have got good expertise with Hadoop, MapReduce, and Spark.

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:

Designing and building new data systems
Optimizing existing data systems
Working with large data sets
Designing and implementing data security and privacy controls
Overall governance of data systems

The following are some of the courses data engineers can take from different websites to improve their skills:

Introduction to Data Engineering
Data Warehousing for Business Intelligence
Big Data Processing with Hadoop and Spark
Big data & machine learning fundamentals
Cloud-based solutions for data engineering such as Amazon EMR, Google Cloud Dataproc, Azure HDInsight, etc.

Differences between data analysts, data scientists, and data engineers

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:

Data analyst vs data scientist

Data analysts use tools such as Excel, Tableau, and SQL to work with data. Data scientists require to have a good understanding of statistics and mathematics in addition to all the skills possessed by data analysts.
Data scientists need to be able to form hypotheses, design tests, perform tests, and draw conclusions.

Data analyst vs data engineer

Data engineers are responsible for the design, development, maintenance, and management of data systems. Data engineers require to have strong skills with different database technologies, big data processing tools, and cloud-based solutions. Data analysts use tools such as Excel, Tableau, and SQL to work with data.

Data scientist vs data engineer

Data scientists require to have a good understanding of statistics and mathematics and machine learning algorithms in addition to all the skills possessed by data analysts. Data engineers are responsible for the design, development, maintenance, and management of data systems.

Which title or job or career path is right for you?

The following can be taken as guidelines to decide for yourself the title which is good for you:

If you are good with statistics, mathematics, and machine learning algorithms then data scientist is the right title for you. Remember that you should have a scientific mindset of seeking truth while working on any problem.
If you are good with different database technologies, big data processing tools, and cloud-based solutions then data engineer is the right title for you. You would be involved in building data systems.
If you are good with Excel, Tableau, and SQL and can work with data then data analyst is the right title for you. You would be involved in extracting insights from data sets.

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.

What’s the difference in salaries of data analysts, data scientists and data engineers?

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.

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Conclusion

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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Ajitesh Kumar

I have been recently working in the area of Data analytics including Data Science and Machine Learning / Deep Learning. I am also passionate about different technologies including programming languages such as Java/JEE, Javascript, Python, R, Julia, etc, and technologies such as Blockchain, mobile computing, cloud-native technologies, application security, cloud computing platforms, big data, etc. For latest updates and blogs, follow us on Twitter. I would love to connect with you on Linkedin.
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Data Analyst, Data Scientist or Data Engineer: What to Become?

Shall I become a data analyst?

Shall I become a data scientist?

Shall I become a data engineer?