Data — Analyst/ Engineer/ Scientist

No! Data analysts, data engineers, data scientists are not the same.

Think it like this, a person who is not into coffee would barely be able to differentiate between Espresso, Cappuccino, Latte, etc. For him, all of these are just coffee, nothing more, nothing less.

However, an avid coffee drinker can easily tell what makes one different from others, what are the ingredients used and what each one looks like.

A similar thing happens in Data Science too. Due to the similarity in name, people often consider data analysts, data engineers and data scientists to be the same profiles and this is where they go wrong.

It was only recently when I started developing an interest in data and just like you, I too was irked with these similar and related terms. I had only one thought when I read these terms- how does it make the difference whether you call it “task” or “assignment” when the meaning is the same.

However, later I realized these are all different terms with unique job roles. While that took me an in-depth digging of the internet, let me make it simple for you to understand.

The reason why these terms confuse you is that all those technical terms might not be processed easily by you and that is why we will try to understand them using examples.

Consider the following example:

You are planning to take admission in graduation and there are multiple universities available. But you are not sure which one you should be choosing for a better career. So, what would you do?

Well, a simple way to sort the best university amongst all is by checking all the data available, comparing pros and cons, looking at the previous year’s success rates etc. After all these processes, you would reach a conclusion as to which is the best university.

However, the data thus available is raw and there could be chances of error in them. Relying solely on these data can end you up making the wrong decision. And therefore, you need a more advanced method to filter the useful data which would finally be analyzed by the industry experts to check for any complexities that lie in the future.

So, what did you get from this example?

The initial step of collecting the right data from different sources and creating a pipeline out of it is done by the Data engineers. The data thus obtained is analyzed by the Database analysts to obtain meaningful insights that could be aligned with the business agenda. On the other hand, data scientists work almost everywhere, from investigating, extracting and reporting meaningful insights to building data products that could be later used to frame decisions.

Roles and Responsibilities:

Data Analyst vs Data Engineer vs Data Scientist roles

What skills do you must have to be a Data Analyst?

· Statistics: Since data analysts play with the numbers, stats knowledge is of utmost importance.

· SQL: SQL is a friendly tool for data analysts as they have to deal with databases to extract information.

· Microsoft Excel: A deep understanding of excel is required for doing quick analytics and working with light databases.

· Data Visualization tools: Data Analysts are responsible for creating visual representations of complex data, so learning how to use tools like Tableau, Infogram, Power BI, etc are important.

Skills needed to be a Data Engineer

· SQL: Data engineers have to deal with data management. So, they need to work with SQL databases on a regular basis.

· ETL solutions: ETL tools are required to build processes for moving data between systems.

· Data warehouse software: A data engineer is expected to know about data warehouse solutions including Amazon, Redshift, Panoply, BigQuery and Snowflake.

· Coding ability: As a data engineer, you need to know coding languages like Python and Java since these are used in ETL tasks.

· Big Data tools: Knowledge of Hadoop-based technologies is a frequent requirement for this position.

Skills needed to be a Data Scientist

· R/ Python: To be a successful data scientist, you need to be familiar with languages like R and Python.

· Hadoop: Since Hadoop is known to store all forms of data and data scientists have to work on these data, so you are expected to have the knowledge of Hadoop.

· SQL: SQL is needed to handle the unstructured data and to query the databases and therefore, good command over SQL is a must.

· Algebra/ Statistics/ ML: Machine learning, Statistics and Algebra are the must-have core skills for data scientists.

· Communication skills: Communication skills are particularly crucial in technical roles, especially when it comes to data scientists.

· Data visualization: To be honest, this is one of the most fun parts of Machine Learning. To fit in the role of data scientist, you need to be able to build stories out of Visualizations.

· Business acumen: Basically, you are supposed to have general knowledge of the business, the ability to interpret and communicate the language of business with others.

If you are thinking to build a career in Data Science, these courses might help:

Data Analyst: Coursera

Data Engineering: Coursera

Best Data Scientist Program: Coursera

And with that, let me tell you about a new kind of Data Science role that is the hot topic these days- Citizen Data Scientist

CDSs are responsible for gaining deep knowledge about the new data, enhancing the existing models and creating new models to gain added insights.

A CDS gathers data useful enough to create useful products for an organization and also looks for possible complexities to deal with.

To Conclude

Data science and analytics has become an extremely important part of businesses today. Understanding the difference between Data Analysts, Data Engineers, and Data Scientists might look a little confusing but I hope this article would have solved your doubts.

If you find this article informative, share it with others to help them understand these terms too.