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?
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.
- 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. - 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.
- 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.
- 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. - 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 |
THE JOB DIMENSION
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 |
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* 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.
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The Job Description of the Data Analyst at SOCIETE GENERALE
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The Job Description of the Data Scientist at Google
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| Qualifications | |
| Data Analyst | Data Scientist |
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| Skills | |
| Data Analyst | Data Scientist |
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| Areas of Application | |
| Data Analyst | Data Scientist |
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| Salary | |
| 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 |
 Society Generale |
 Adobe |
 Philips |
 IBM |
 Boeing |
 Procter & Gamble |
 Cotiviti (Hyderabad) |
 Amazon |
| 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….
*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.
https://www.glassdoor.co.in/Salaries/india-data-analyst-salary-SRCH_IL.0,5_IN115_KO6,18.htm
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