The field of technology has been evolving at an exponential pace. The developments in this field have continued to make the operations of businesses, both small as well as established, extremely easy and lucrative. Plus, more importantly, it has opened up the scope for new career opportunities as well.
There are two fields of development that have fuelled all the other kinds of technology, be it Artificial Intelligence or Machine Learning – Data Science and Data Analytics. But what is the difference between the two? Do the two belong to the same domain of development?
Before we answer this question and find out the difference between data science and data analytics, it is important for us to have more in-depth knowledge about them for properly understanding their grounds.
Why Do We Need Data Science and Analytics?
In the world of Big Data, there is a dire need of having it optimally structured, organized and analyzed to make something useful out of it. If this isn’t done, the entire cloud of big data would have been of no use at all. This is where data science comes into play.
Now, before we discuss the need further, we have to know that the two concepts about how data science and data analytics are in line and complementary to each other.
- Based on the recent trend, 80% of the data by 2025 will be unstructured.
- This data will have a pool of information about all of the consumers and various civic activities, which could help greatly in shaping up strategies for private corporations and government organizations.
- The acquired data can be fuel for machine learning, artificial intelligence, and predictive analysis. Without this, even something as simple as intelligent chatbots might be a little difficult to integrate.
- The data has huge potential to forecast the future for any business in terms of revenue and operations. Once an intrinsic and thorough analysis of such big data is made, it becomes easier for corporations to form their operations and strategies based on them.
What is Data Science?
Going by the Data Science definition, it is a data field that works in provision with several scientific processes and methods, especially with the algorithms or with the intention of extracting insights or knowledge about a particular subject or organization objective. It is closely related to data mining.
To explain things in an easier sense, it is nothing but a united concept that brings together data analysis, statistics, and machine learning. This concept is adopted in order to analyze and have a deeper understanding of everything with the collective data in hand.
What is Data Analytics?
Data Analytics is an entire science behind analyzing any form of raw data with the intention of making conclusions or predictions. It can capture or fetch important information, like metrics and trends, that could be otherwise lost in the massive pool of information.
Enterprises and businesses use this technology to optimize their operational processes and bring more efficiency in the entire revenue-churning setup.
Data Science Vs Data Analytics
Now comes the trickier part. The difference between data science and data analytics is slightly difficult to catch as the two seem to be quite similar from a layman’s perspective. However, it is only when you delve deeper that you understand the two have different working processes and intent.
A lot of people might end up using the two words interchangeably, though the two belong have overall but yet different. The simplest explanation to the data science vs data analytics subject is that the former is a bigger picture and covers a large field that mines any massive pool of data.
The latter, on the other hand, is a more subject-focused concept and could be considered to be a part of the bigger picture, that is, data science. So, let’s dive in to find out the detailed difference between data science and data analytics.
|Differences||Data Science||Data Analytics|
|Scope||Macro (Superset)||Micro (Sub-set of Data Science)|
|Target||Asking the right questions, device data-driven solutions||Finding actionable insights, generate business reports|
|Uses Big Data||Yes, often||Yes, sometimes|
|Unstructured Data||Yes, often||Mostly not|
|Statistical Analysis||Advanced Knowledge||Basic to Intermediate|
|AI & Machine Learning||Yes||No|
|Programming Tools||Intermediate to Advanced||None to Basic|
Both data science and data analytics deal primarily with pools of data. However, the real difference lies in what they do with the data. The task of a data scientist is to construct and design new processes. This is done in order to produce data modeling with the use of algorithms, prototypes, and custom analysis.
Meanwhile, Data Analysts’ roles and responsibilities include examining a large set of data and finding out the trend or creating visual representations or developing charts pertaining to their findings after meticulously analyzing all the data packets on hand. This is done to help businesses chart up a better strategy.
Job Description, Skills, Salary & How to Become a Data Scientist
The best way to talk about the career opportunities of a Data Scientist vs Data Analyst is to answer a few common questions that every enthusiast and aspiring individuals have. One of the most important and essential Data Scientist Skills include working on and arranging multiple sets of undefined data with the help of multiple tools.
Moreover, these scientists also work on building their own framework and automation system. According to a definition from Drew Conway, founder of Alluvium and a data science expert, a data scientist is someone who has statistical and mathematical knowledge and also good with various programming tools.
Skills & Tools
There are a few data scientist skills that every aspiring data scientist should have. Some of the most important areas which require optimum skills to be developed by the aspirant are:
- SQL, Python & R
- Data Mining
- Statistical Analysis
- Machine Learning Modelling
- Neural Network (Deep Learning)
- Text Analytics & Natural Language Processing
As technologies evolve with time, every sector sees a huge advancement. Data science is a popular career option and a lot of people are opting for it.
The primary responsibility of a data scientist is to design any type of modeling process. They are also responsible for creating predictive models and algorithms in order to extract all of the information that any business needs, for solving complex AI-related problems.
Data science is a very lucrative career option. Though the path to success is long and requires a lot of technical knowledge and patience, the end result is rewarding. The average salary of an experienced data scientist is $123,516 per year.
However, even if you are new to the field, there won’t be a massive difference. People with less than a year’s experience can also fetch an average annual salary of over $100,000.
In India for average starting salary for the Data Science role is around 10 lakhs per annum and goes up to 20 lakhs per annum based on skills and educational background.
You may also like to read a step by step guide: How to Become a Data Scienctist?
Job Description, Skills, Salary & How to Become a Data Analyst
Now that you already know quite a lot about data science and data analysis, it could be easier for you to determine the difference that the two share. For starters, it won’t be very wrong to say that all data analysis is data science, but all data science is not data analysis.
The role of Data Analyst can differ from industry to industry. However, if you look at the core of what does a data analyst does and her/ his responsibilities, they all share a similar aspect, that is, utilizing the data to solve problems and come up with many meaningful insights about the domain they’re working in.
Skills & Tools Needed
Data analysis also requires some technical knowledge. Some of the most common skills and tools that one would need for this job are:
- Excel, SQL & Tableau/ QlikView/ PowerBI
- R & Python
- Data Manipulation
- Data Reporting & Visualization
- Basic Arithmetic & Statistics
- Statistical Analysis
From the outside, you might feel that there is a lot of similarity between the responsibility of a data scientist and a data analyst. However, the definition of the job description makes a difference.
A data analyst is responsible for maintaining databases and systems. Further, they are also responsible for interpreting data sets with the use of statistical tools and preparing reports of the trends that can be captured from the massive amount of available data.
With all the data set put together, it is a lot easier to find out patterns among the users and predicting their actions.
Though the salaries in data analysis are not as rewarding as data science, the difference is not that massive. Data analysis is always slightly inferior to data science and an individual in the field, with no experience, can fetch a salary of around $74,210 annually. An experienced and professional data analyst can easily earn over $80,000 annually.
In India average starting salary for the Data Analyst role is around 5 lakhs per annum and goes up over 10 lakhs per annum based on skills and education background.
Prerequisites & Things to Consider Before Choosing a Career in Data Science and Data Analytics
The mindset, preparation, and determination of a data scientist and a data analyst can be similar in a lot of ways. But despite that, there is a difference in the educational background that each of the two requires.
Here are the basic things to consider before choosing a career either in data science or data analysis:
- Educational background
- Career path
- Desired salary
Education is one of the primary differentiating factors between choosing a career in data science or data analysis. In fact, this is one of the defining factors, according to Martin Schedlbauer, a person who holds multiple positions at the North Eastern University’s Khoury College of Computer Sciences.
To put things in perspective, a typical data scientist would be more focused on pursuing an advanced degree, in STEM related fields like IT, computer science, mathematics, statistics, etc. Though this is not mandatory, they require more technical knowledge than data analysts.
On the other hand, a typical data analyst can make do with an undergraduate degree in any of the following fiends – engineering, business, commerce, or science. However, a supporting relevant degree in database management and data modeling is preferred.
The interest of the enthusiast also marks a difference between Data Analyst and Data Scientist. Basically, if you are wondering how to become a Data Scientist, you have to nurture your interests in that field. Since data science is a more technically tolling career option, zealous individuals are required to have a strong interest in statistics, math, and computer science.
Moreover, besides just these technicalities, a wannabe data scientist should also have good interest and knowledge of the business world as this field is highly relevant. If you have your interests shaped up in such a direction, without a doubt, data science is for you.
On the other hand, if you are wondering how to become a Data Analyst or how to judge whether it is for you, you should love numbers. Statistics and programming should interest you greatly. As a wannabe data analyst, you should always have a very comprehensive understanding of the specific knowledge you work in.
Your Career Path or Trajectory
The career path you are in should also have a defining role behind shaping up the data scientist or analyst in you. If you have already dropped out of studies after your undergrads, and don’t have any interest in joining a full-time university, you might want to take up any vocational or diploma course that hones your with the technicalities, like database management, hacking, etc.
However, if you have come across Data Science and are already pursuing your undergrads in a technical field, you have all the time in the world to find out more about the requisites and enroll for a professional Data Science certification course to be on track with the career option.
And needless to say, your interests also play a huge role. Your interest in hacking and programming languages is probably one of the major indications of whether you will get into the field of data science or data analysis.
Last but not least, the desired salary is another major indication to which one of the careers you choose. Though it is a fact that you should always do what you love doing, money is a massive motivation.
A lot of people, who had absolutely no idea about data science until high school, get into the technical field and programming just because they want to land a high salary job and have a secure future.
No one would recommend you to take this step as fitting in the role of Data Analyst or scientist while having no interest whatsoever might not last long in terms of future prospects. You might end up getting bored with your job.
So, even if you are looking to make a career in Big Bata Analytics, you have to ensure that the monetary motivation is not temporary for you.
But before you take any step, don’t forget to consider that a job in either of the fields can pay you at least $5000 to $10,000 a month. This figure would go up with experience and level of skills attained.
How Do Both Career Options Stack Up?
Now, having discussed everything, from skills required for Data Analyst and Data Scientist to how much they can potentially earn, the only bottom line that is left here is your decision.
Data Scientist vs Data Analyst career perspectives has been discussed and debated over for a couple of years now. So, this breakdown of the entire interest, career, and educational background required for the professional aspect was highly needed.
Now, if you take a look at how the two stack up against each other, you won’t find a lot of difference. In fact, if any of your acquaintances come over to ask you what you do for a living, and you say “I am a data scientist/I am a data analyst”, they won’t be able to differentiate until they work in the analytics domain.
The difference is felt only by the people who are working in the field or are closely related to the career by some means or the other. You, as a Data Scientist, will have to have extra proven knowledge, qualification, and experience. This means harder work and a little longer wait before you can land a dream job.
On the other hand, the wait to be a Data Analyst is much shorter in comparison and your efforts will be rewarded earlier. Though there cannot be any comparison in terms of the efforts as both the streams aren’t easy in their own respective field.
It is best to know inside out of the entire career opportunity before you choose one. Also, it would be great if you could consider following certain personalities who have already made a massive impression for themselves in this field. Martin Schedlbauer perhaps would be an ideal person to follow and look up to in this regard.
In fact, he even mentioned that most of the people who are skilled in data-related fields inevitably go ahead with either the Data Science or Data Analytics field. However, they make big career calls with their personal interests, skills, background, and future prospects, though the last one falls completely irrelevant in the field.
Whatever you decide, you have to consider all of the pros and cons of each of the career fields. So which one have you chosen?