Analyzing Two Different Data Roles: Data Analyst and Data Scientist

With the increase in data, its scope increases and so do the professionals involved in the project. Each person has its own realm of working, be it a data scientist, data analyst, machine learning engineer or any other data science professional.

  • Do you know who is a Data Analyst and who is a Data Scientist?
  • Do you know what exactly they do?
  • What if I tell you that the person who works on structuring data isn’t the same as the one who works on predictive modelling?
  • What is the criteria to distinguish between these two roles?
  • Why are these two roles different?

Continue reading so that by the end of the post you will be able to clear the difference to others.

The “Who”  Question…

who is a data analyst

Who is a Data Analyst?

Data analysts are the ones who comb the data and use it to come up with replies to the queries posed in front of a business.

The results thus derived from the analysis are used for taking important business decisions.

You can also come across the title of Business Analyst being used interchangeably in this post. Don’t confuse them as a separate position.

It is just another name for data analysts. 

Data analysts are the Detectives with a magnifying glass in their hand, always searching for that right set of data which will reveal the insights.

Here is a summary of some of the tasks that a data analyst is typically expected to do.

  • Clean and organize the data
  • Use descriptive statistics for a wider analysis of the available data
  • Analyze the discovered trends in the data
  • Develop dashboards and visualizations to assist business with the interpretation of data and making decisions on its basis
  • Present the outcome of the analysis to the internal teams or clients

Who is a Data Scientist?

Data analysts move up the ladder to become data scientists.

Ok! Yes, I agree these are the most sought after people.

But do you know they are the Unicorns. Why? It is not that easy to spot a skilled one.

But with an increase in the awareness and relevance about the job, it will not be that tough to spot them now.

Data scientists are the experts in statistics and machine learning. They develop machine learning models that can predict and solve crucial business questions. Does this mean a data scientist doesn’t know what a data analyst does?

Data scientist is definitely not oblivious of a data analyst’s profile. 

Together with it, a data scientist possesses an in-depth knowledge of the skills to clean, analyze and visualize the data sets.

He is able to tutor and enhance models of machine learning.

Here are some of the tasks that a data scientist typically performs.

  • Evaluation of statistical models to judge how valid is the analysis of the models
  • Developing improved predictive algorithms using machine learning
  • Constantly testing and enhancing the efficiency of machine learning models
  • Creating data visualizations to make a summary of advanced analysis 

How is the Detective different from Unicorn?

Clearing blurred lines between the two data science professionals…

As opposed to the common assumption, not every person working in the field of data science is a data scientist.

In this section, we will discover how our detective is different from the unicorn.

  1. Data analysts are the ones who do the research and produce insights, while the data scientists are the ones who communicate these findings to the business stakeholders who have a non-technical side. Imagine how complex is everything in the data world.
    Data analysts also do the storytelling but only to the data scientists or the ones who are technically proficient. Data scientists convert these results in a graphical representation (charts, graphs, images or even easy words) to make it comprehensible.
  2. The aim of data analysts is to develop answers while data scientists develop questions, using the available data and take the decision as to which answers will prove to be beneficial for the business.
  3. When a problem arises in a business, data analysts simply address it, while data scientists make a precise prediction of the business value, after the problem is solved.
  4. Data scientists have the ability to sense the unknown facets of the business. Remember, they predict?
    Data analysts work on the evident facets of the business with a fresh approach.
  5. Data analysts analyze the data coming from one source, such as the CRM, whereas data scientists analyze the data coming from numerous disconnected sources.

The Good Old Table View of the Comparison

Features Data Analyst Data Scientist
Defining Distinction A link between IT stakeholders, they possess an in-depth business knowledge; “Why” is the favorite word. Innovation is the key; searching for new data to solve critical business problems and applying statistical knowledge to come up with an ideal solution.
Basic Requirement Should easily gauge changes, create business cases and ideate fresh functional requirements in a project Proficient technical knowledge, including an added expertise in SQL; needless to say, statistics and mathematics should be their best pals.
Historical Preview Rose to fame in 1970, when they started the documentation of each manual process. In 1980, they started aiding business objectives. Now, automate repeated tasks, recognize problems and come up with solutions These are the identifiable people since 2006 till date. This is now one of the hottest jobs of the recent times.
Responsibilities Make a requirement analysis document and analyze the requirements of the business. Convey the changes to be done to the concerned departments. After changes are implemented, undertake acceptance testing. Handle and extract huge amount of data. Expertise in machine learning is mandatory to work on data and generate actionable insights
Languages Python, R, HTML, SQL and Javascript Matlab, R, SAS, Python and SQL
Tools Tableau They do the coding using languages; not much use of tools except R and Python
Hadoop Proficiency Not mandatory Expert level knowledge to work in computing frameworks and distributed storage
Technical Knowledge Exploratory Data Analysis (EDA)

Extract Transform Load (ETL)

Exploratory Data Analysis (EDA)
Artificial Intelligence Skill Not mandatory Master in machine learning
General Difference To judge the performance, they build KPIs. Predictions are made on the basis of data trends with the use of supervised machine learning


Now that you have a fair idea as to how the two data experts are different, it’s time to get to the business- The Job Sphere; how do these two profiles differ on the basis of the various pre-requisites of getting a job.

After all it is the “sexiest job of the 21st century”.

Following are the differential basis:

  • Roles and Responsibilities
  • Qualifications
  • Skills
  • Application Areas
  • Salary
  • Hiring Companies
Roles and Responsibilities
Data Analyst Data Scientist
  • Manage reporting so as to fulfill the business motive
  • Perform a dedicated data analysis that answers the stakeholders’ queries; should do it out of curiosity to achieve maximum business performance
  • Able to get hold of the business operations and turn queries into a base for a task oriented analysis
  • Communicate research results and put them into business specific actions
  • Recognize the business requirements and devise measurement plans like instrumentation, data collection etc.
  • Apply queries in the databases to gather complex data
  • Apply improvements in internal data processing; automate manual procedures; improvise delivery and presentation of the data
  • Proficient in working with the analytic tools and languages (like Python and R) to put forth actionable insights into important business metrics like revenue, customer acquisition, product enhancement etc.
  • Coordinate and work with the business stakeholders that include Data, Design, Product and Executive teams; provide assistance to them in technical issues relating to data

* The roles and responsibilities detailed above are a general view. Have a look at the REAL job description of the data analyst and data scientist, given below.

The Job Description of the Data Analyst at SOCIETE GENERALE


The Job Description of the Data Scientist at Google

Data Scientist at Google

Data Analyst Data Scientist
  • Knows business intelligence and data warehousing
  • Proficient in analytics based on Hadoop (For example: MapReduce, HBase, Casscading, Hive etc.)
  • In-depth knowledge of analytics and SQL
  • Tools and skills related to data storing
  • Master of data architecture tools and components
  • Expert decision maker
  • Fair knowledge of database systems, such as Hive, MySQL etc.
  • Expert in a variety of analytical functions like rank, mode, median etc and their applicability on the data sets
  • Better off having a sound knowledge of MapReduce, Javascript and Python etc.
  • Expert in predictive analysis, mathematics, data mining and correlation
  • Knowledge of ‘R’ is an icing on the cake
  • Master of machine learning and deep statistical insights
Data Analyst Data Scientist
  • Statistics
  • Maths
  • Programming languages, such as HTML, Python, R etc.
  • Spreadsheet tools like Excel
  • Data visualization tools, such as Tableau
  • Statistics
  • Maths
  • Programming languages, such as SAS, MATLAB, Python, SQL, R
  • Hadoop, the distribution computing framework
  • Data visualization and storytelling
  • Expertise in machine learning
  • Business insight
Areas of Application
Data Analyst Data Scientist
  • Healthcare
  • Travel
  • Energy management
  • Gaming
  • Recommender system
  • Digital advertisement
  • Image/Speech recognition
  • Internet research
Data Analyst Data Scientist
Average Base Pay (India): ₹ 462,096 p.a

Source: Glassdoor (Updated 25th May 2019)

Average Base Pay (India): ₹  950,000 p.a

Source: Glassdoor (Updated 25th May 2019)

Hiring Companies
Data Analyst Data Scientist
SOCIETE GENERALE logoSociety Generale
Adobe logoAdobe
Philips Logo Philips
Boeing LogoBoeing
Procter & Gamble logoProcter & Gamble
Cotiviti logoCotiviti (Hyderabad)
Amazon logoAmazon
Source: Glassdoor (Updated 25th May 2019) Source: Glassdoor (Updated 25th May 2019)

*These are just a few of the big names that hire data analysts and data scientists. You will come across many such big firms in your job search.

The Practical Picture

Aren’t you curious to know the data analysis and data science workflow?

Have a look….

Data Analysis Workflow Data Science Workflow

*The tools shown in the above processes are not the only ones used; there are many other ways/tools to carry out the above procedures. There might be 1 or even 2 tools to carry out these processes. It depends on the businesses and the organizations.

Data analysts and data scientists are imperative for the success of a business and with both working in unison, there is nothing that can obstruct the organization’s aim to achieve the target objectives.

I hope that by now the confusion, regarding who is a unicorn (data scientist) and who is the detective (data analyst), must have vanished into thin air.

Having a clear vision always helps you in moving ahead in the right direction with a focused approach.


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