Data Scientist vs Data Analyst vs Data Engineer vs Data Architect: What’s the difference?

Data is the driving force behind most of the decision-making process lately. According to a study, 91% of companies agreed to the fact that data-driven decision-making is critical for their growth while 57% of them said that they have already started to base their decisions using data.

The ever-increasing dependence on data has led to a huge demand for analytics professionals. According to the US Bureau of labor statistics, it is predicted that the data-based jobs will see a 15% rise from 2009 to 2020, which is much larger than the national average of 4%.

Any type of decision-making has always required data. The world of today is entirely data-driven, and none of the businesses operating in it could exist without data-driven strategic planning and decision-making. Due to the data’s priceless insights and reliability, many positions in the sector today deal with it.

Having said that, it is the best time to break into this industry. However, if you are an aspiring data enthusiast, you would be wondering with few of the key questions like:

What is data science?

What do data scientists do?

How are they different from data analysts?

There are several skill-specific rolls, which should I opt for? How to decide? Which factors to consider? Where to begin? … and many more.

Well, stay tuned my friends because by the end of this article I can vouch with all of my stakes that you will not only be able to understand the critical differences and similarities between the key roles but also decide which career path to take and how to devise a roadmap for the same.

Let’s begin this Data Scientist vs Data Analyst vs Data Engineer vs Data Architect in-detailed comparison.

Data Analyst

What is Data Analytics?

Data analytics is the field wherein predictive analytics is performed over the data to generate certain informative insights.

Simply said, it is like finding what the data says about the business in the present or conducting analysis about the past. E.g. how a specific sub-brand of a product is performing between its operative countries.

What do data analysts do?

The key roles and responsibilities of a data analyst are:

1. To perform statistical analysis over data, interpret the results and communicate to the business stakeholders or clients.
2. To create monthly or quarterly reports and build visualizations on top of it.
3. To maintain and optimize statistical efficiency and quality of data.
4. To consult with clients or business stakeholders, identify and streamline the business requirements, estimate the required data and then work on the acquisition of the same.
5. To interpret any vital patterns in the data and communicate its significance and impact on the business to the relevant personas.
6. To perform data cleaning and processing so that more advanced analytics can be performed over the same.

How to become a data analyst?

The criteria to start your stint as a starter data analyst are fairly straightforward. You’ll need:

1. To earn a bachelor’s degree (preferably in tech but not necessarily)
2. To acquire a solid understanding of statistical concepts and their application in real-life.
3. To learn about data warehousing and get hands-on with any of the database tools like MYSQL, Oracle SQL, etc.
4. To learn any of the programming and scripting languages namely R, Python, or SAS to perform data cleaning and processing.
5. To get equipped with any of the data visualization tools like Tableau, Power BI, etc. to develop and produce monthly dashboards.
6. Last but not the least, get functional using Microsoft Excel, especially its advanced functionalities like VLOOKUP, HLOOKUP, etc.

In short, you’ll need to learn – Python/ R/ SAS + Tableau/ Power BI + statistics + database tool + Excel. Want a few references of where to learn from? – stay tuned and read ahead, we have got you covered.

How much can you earn as a Data Analyst?

An average data analyst can earn up to $64,808 per year as per the US market. According to Glassdoor, an average data analyst can earn up to 5 LPA in India which certainly varies w.r.t different geographies, industries, companies, etc. 

Want to know an accurate figure of how much a data analyst can earn in company X of Y city?

Day in the life of data analyst

Knowing how a day at work looks like for a data analyst can help you understand whether you would want to have the same down the line. Here we have a Data Analyst from Flipkart whose typical day at work looks like this:

• His day generally begins somewhere between 9.30 am and 10.00 am but the start time varies w.r.t the client you are working with. 
• After checking the mails, he and his team catch up for a daily standup wherein they decide the tasks they’ll be working on for the day.
• Currently, he is creating an end-to-end dashboard in Power BI wherein most of his time in the day goes in preparing data using SQL studio, creating and tweaking visualizations, and closing work around the same.
• Before the day ends for him, he generally takes two mandatory meetings. One with the client to inform the up-to-date changes and second with his internal team to inform the same.
• After logging off, he goes home and learns new tools and concepts to stay updated and at par in this competitive field.

How did I start my career as a data analyst?

I have been into analytics for close to 2 years now. I started in 2018 wherein I got enrolled in the “Postgraduate diploma program – data science and engineering” offered by Great Learning. It was a 5 months program including one month of capstone project. 

The course started with learning Python, then Tableau, then MYSQL. After we were hands-on with requisite tools, we were taught statistics, machine learning, and other advanced courses.

I did not rely completely on the program and continued to do a few of the MOOCs from Udemy as well to get better equipped with basics and did lots and lots of hands-on practice by doing self-projects.

It took me close to 7 months to say that I was completely ready before I started applying for relevant jobs but the hard work was worth the effort. 

Recommended courses to become a data analyst

Below are the courses which I have personally done over the above-said tools and they have helped me to till-date. 

• Microsoft Power BI Desktop for Business Intelligence (Udemy)
• SQL – MySQL for Data Analytics and Business Intelligence (Udemy)
• Python (freeCodecamp)
• Statistics for Data Science and Business Analysis (Udemy)
• Excel (Intellipaat)

Data Scientist

What is data science?

Data science is the field wherein prescriptive analytics is performed over the data. Simply said, it is like predicting the trends in business based on the present and past data. E.g. predicting sales for a specific period.

What do data scientists do?

The key roles and responsibilities of a data scientist are:

1. To perform data mining activities and create data in the requisite format.
2. To develop models which are operational and can be put further into production.
3. To conduct in-depth optimization of models using concepts of machine learning and other advanced analytics.
4. To cater to certain Ad-hoc work requests which come their way.
5. To apply their technical knowledge and business acumen and work on real-life use cases such as anomaly detection, fraud detection, etc.
6. To maintain data integrity and work on optimizing overall performance.

How to become a data scientist?

The prerequisites to become the most sought after data professional are:

1. To acquire knowledge about statistical concepts and their applications.
2. To learn about data mining activities and various procedures conducted under it.
3. To learn the fundamentals of various algorithms under Machine learning and Deep learning with a focus on when to use which algorithm and also the mathematical structure behind each.
4. To get equipped with any of the programming languages like Python, R, or SAS.
5. To get hands-on with any of the database tools like MYSQL, Oracle SQL, etc., and data visualization tools like Tableau, Power BI, etc.

In a null shell you’ll need to learn – statistics + Machine learning &/OR Deep Learning + Python/ R/ SAS + Power BI / Tableau + knowledge of database-related tools + data mining + mathematics behind models.

How much can you earn?

According to paycale.com, the median salary of data scientists in India is Rs. 6,16,400 and the average salary of the same is Rs. 6,93,637. As per the US market, an average data scientist earns $74,239 per year.

Day in the life of a data scientist

“Data scientist” is the most looked after designation presently. But do you know how your day will look once you start your stint as a data scientist? 

Here we have a data scientist working in Walmart, India whose a typical day at work looks like this:

• Her day begins with a client call followed by a call with her internal team wherein the agendas of the respective day are outlined.
• Following that, most of the day goes behind performing EDA (exploratory data analysis), performing data cleaning, and processing it as per the model requirements.
• After a few days when the data is ready, a model is developed over the same wherein her key role is to interpret the model output and use her business acumen to communicate the mix of both to the relevant stakeholders.
• Once the end delivery is completed, she finds herself working on various ad-hoc requests submitted by the client.
• Thus, the work on a typical day depends on the stage of the project lifecycle.

Recommended courses:

Below are the courses (in addition to the above) which I have personally done over the above-said tools and they have helped me till-date.

• Machine learning
• Deep Learning
• Mathematics for Machine Learning Specialization

Data Engineers

What is data engineering?

Data engineering is the process of assimilating data from different sources into one Warehouse. Various data pipelines are built on top of the primary data warehouse which enables the data scientists and data analysts to obtain data in the most relevant and usable structure.

Further, when the dashboards and advanced models are built, it is put into production with the help of a data engineering team.

What do data engineers do?

The key roles and responsibilities of a data engineer are:

1. To develop and test various Data pipelines and maintain them as per the relevant data guidelines.
2. To enable the productionalization of machine learning and statistical models.
3. To merge predictive and prescriptive modeling in such a manner that it stays consistent with data flowing across the organization.
4. Find any hidden patterns or data inconsistency and work along with similar ad-hoc analysis.

How to become a data engineer?

The prerequisites to become a data engineer are:

1. To acquire knowledge related to data warehousing and data architecture.
2. To learn and become hands-on with any one of the ETL tools. E.g. Alteryx
3. To acquire advanced programming skills with one or more scripting languages.
4. To become familiar with databases, more specifically relational databases.
5. To be equipped with Hadoop drive analytics.

How much can you earn?

According to Glassdoor, a data engineer earns approximately Rs.8 LPA in India. As per the US market trends, an average data engineer can expect to earn $92,999 per year.

Day in the life of a data engineer

If you are someone who wishes to become a data engineer down the line but is unaware of what your typical day at work looks like, you are looking at the right place. We have a Data engineer from Nielsen whose day at work looks like this:

• His day generally begins with an internal call wherein they decide the workflow, day’s agenda, details related to project timelines, etc.
• Further, he starts with writing any relevant codes w.r.t data collection, ingestion, warehousing, and creating a pipeline.
• Later to which, in the second half he usually consults with the team of data scientists to take inputs about the written code and production process.
• Finally, he sends an accomplishment email to the concerned persons about the key tasks he has completed that day.

Recommended courses:

Below is the list of a few recommended course platforms:

• IBM Data Engineering Professional Certificate (Coursera)
• Data Engineering Essentials using SQL, Python, and PySpark (Udemy)
• Big Data Tutorial For Beginners (Simplilearn)

Also Read: Udacity Data Scientist Nanodegree Review 2021: Your Way to Learn Data Science

Data Architect

What is data architecture?

Data architecture is the building block of any organization. It answers the basic yet critical questions on “how” when the data strategy is being laid down.

It includes devising strategies related to its various components such as – data pipelines, cloud storage mechanisms, API’s and user interface, AI and ML models, data warehousing and streamlining, real-time analytics, etc.

Put simply, it is a framework which when aligned with the business processes makes the Data collection, storage, transformation, and usage more meaningful and standardized.

In a way, you can say that setting up a Data architecture precedes setting up teams of data scientists, data analysts, and data engineers in an organization.

What do data architects do?

The key roles and responsibilities of a data architect are:

1. To manage the flow of data and information across the organization.
2. To use their technical capabilities and ensure that data is secured, accurate and accessible.
3. To perform a rigorous audit of the data management system continuously and report any prevailing loopholes and also act to rectify the same.
4. To identify all the relevant data sources present both internal and external to the organization. Further, assimilate them and reduce them to a usable structure.
5. To be instrumental in creating an end-to-end data framework which includes selecting the base platform, designing the technical process over it, and deploying the same at the user level.

In short, the roles and responsibilities of a data architect are quite similar to that of an end-to-end Hadoop-based life cycle of a project.

How to become a data architect?

To become a successful data architect, you’ll need:

1. To learn about various frameworks related to the System development life cycle.
2. To get equipped with various approaches and requirements of different project management techniques.
3. To become proficient with concepts of data modeling, data administration, and data warehousing.
4. To be familiar with working on advanced analytical concepts such as NLP, Computer vision, Deep learning, Machine learning, etc.
5. To become hands-on with various relational and Non-relational databases.
6. To become hands-on with one or more of the scripting languages and data visualization tools.
7. To learn about working with various cloud-based systems.

How much do they earn?

According to a salary survey conducted by ambitionbox, a data architect in India can earn anywhere between Rs. 10 Lakh to Rs. 37 Lakh in a year. Also, according to Glassdoor, the average salary of a data architect is approximately Rs. 20 LPA in India while most of the entry-level positions can easily fetch you Rs. 16 LPA.

The Average Salary of the Data Architect in the US Market is $1,18,868 per annum.

Data Science Profile	Average Salary per annum
Data Analyst	$58,585
Data Scientist	$91,470
Data Engineer	$90,839
Data Architect	$1,18,868

Data Scientist vs Data Analyst

Although the required skill set and the key roles and responsibilities are quite similar to that of a data analyst and a data scientist, certain factors differentiate them. Let’s do the quick Data Scientist vs Data Analyst comparison with the key differences as follows –

Based on the role –

Data analysts are required to analyze the data, create visualizations using them, and then report the key relevant insights to the stakeholders.

On the other hand, data scientists are required to create predictive models and prescribe solutions based on the estimated future trends. 

Based on what they do with data –

Data analysts are required to perform predictive analytics over data while prescriptive analytics is conducted by data scientists. 

Based on the required skill set –

Data analysts are good to go even if they do not have hands-on knowledge on any of the scripting languages but being good with coding is sort of a must-have for data scientists.

Also, if you are starting your stint as a data analyst then you may not be required to learn the mathematical concepts. But if you gradually progress towards being a data scientist then knowing the maths behind the key algorithms is imperative.

Now that we have talked about how these roles are different, let’s articulate a few of the key similarities which these two roles share.

Based on day-to-day work –

Both data analysts and data scientists have a similar daily schedule which includes client meetings, drafting business emails, performing data cleaning and studying the data to identify any hidden trends or patterns, and lastly to maintain consistent communication with their team.

Based on the application of business acumen –

Regardless of the designations, both roles require you to apply a sense of business to the problem statement and integrate data with business at all points within a project.

Based on the required soft skills –

Both of them are required to have the ability to see the bigger picture, have good verbal and written communication skills, and also have a problem-solving-oriented mindset. If you want to be a data analyst, check out Udacity’s data analyst nanodegree.

Data Engineer vs. Data Architect

The two roles also have many similarities between them yet they are different at the fundamental level. The key differences between these two roles are:

Based on the roles –

Data architects are required to set the vision towards a data framework while data engineers are required to build and maintain them.

Based on who they impact in an organization –

Data architects directly work and guide the data scientists by ensuring the basic guidelines of building a predictive model are met while the data engineers are required to build and maintain the data frameworks which will, in turn, impact the overall data management system in an organization.

Having talked about the key differences, let’s see what these roles have in common.

Based on skills –

Both data engineers and data architects have a more or less similar skill set to bring to the table.

Based on their day-to-day work –

Both of them spend most of their day writing codes and queries to streamline the overall structure of the framework and ensure that the data flowing across the organization is in its best state w.r.t authenticity, accuracy, and sustainability.

Must see -> Udacity Programming for Data Science with Python Nanodegree Review 2021

So this was in a detailed comparison of Data Scientist vs Data Analyst vs Data Engineer vs Data Architect. Now, let’s move on to the FAQs or the More relevant Questions that you might want to know.

More relevant Questions

I have been working as a data scientist for close to 2 years now and have mentored many data enthusiasts to embark on their data journey. However, there are some common questions which most or all of the aspirants have asked which are:

I do not have any technical degree in my graduation. Can I still become a data professional?

The good news is – YES! 

However, the way the hiring process is done for data-based roles requires you to have a technical degree preferably in the space of engineering, computer science, statistics, or mathematics.

The only way to bypass this harsh but prevalent hiring condition is to become very much fundamentally clear with what is there on your resume.

Learn the required skills, do an ample amount of practice, do relevant projects, create a job-oriented resume and you should be good to go.

It might take you more time if you are an absolute fresher to land on your first job but just hang in there.

Most of my students have hung in there for 6 to 8 months before they got their first data jobs. Perseverance outwits anything, my friend.

How to transition from a different domain into analytics?

When you transition from a different domain you bring along all the needed soft skills such as team handling, client handling, business communication, etc.

As far as learning the requisite skills is concerned, there are a million good options out there which you can explore and choose the one for you.

Even if you want to do self-study and take it at your own pace, that’s brilliant too. My best advice would be to start somewhere and then figure it out along the way.

Where to get projects from?

You can get projects under all domains such as dashboarding, machine learning, NLP, etc. over the internet. You can find it on Youtube, Kaggle, Github, etc.

However, I would advise you to not just follow along with the guided project. Add some pinch of your creativeness and knowledge.

Also, anywhere between 5-6 projects should be good to go before you start applying for roles.

How to develop the business acumen required for this field?

Developing the right sense of business acumen will take its share of time. My best advice would be to read as many case studies as you can.

Pick up an application – e.g. – marketing mix modeling. It is an algorithm using which you can identify how much contribution is coming from different media tactics towards sales.

Type that on the internet as it is or tag along with the keywords “research paper” or “case study”. And lastly, combine that knowledge with its real-time application in your job.

This is the approach I have followed so far but I am sure there are other best practices around too.

Also Read: Top 10 Best Data Science Scholarships 2022: You Should Not Be Missing

The Finish Line – Data Science Profile Comparison

In this blog, we have discussed the various data-based roles in depth. If you have stayed along till the end, I am sure you would have decided whether you want to make a career in the field of data and if yes, which sub-field to go for.

Hope you find this Data Scientist vs Data Analyst vs Data Engineer vs Data Architect comparison informative and useful.

Whatever you decide, be ready to put in the requisite amount of hard work and I am sure you will become a successful Data professional down the line.

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