With the advent of the internet, data around us has been ubiquitous both in creation and storage. And with the enormous amounts of data being generated, stored, and processed every second, two new terms have been introduced to the industrial vocabulary that has reshaped the entire operating mechanism of the constituent organizations. The two terms are Data Science and Data Analytics that have collectively revolutionized the way data is being used across various sectors and functions.
Did you know that 90% of all the data that exists today was created in the past 2 years?
Well, you most probably would not have known that. Now, with data around us growing at such a rapid rate, it should not come as a surprise to you that excel sheets are not enough to process all of this data. We need something that is much more powerful and at least 100 times more efficient to handle all of this data. The tools that fill this void and answer our cry for help are Data Analytics and Data Science.
These tools have helped us tame this ocean of disparate information and somehow make sense out of it. It has allowed us to draw hidden inputs from the data and use them to enhance our decision-making capabilities. It has exposed us to so many awesome opportunities and enhanced our capabilities as a civilization.
But even though we have upgraded our data processing capabilities to such a great extent with Data analytics and Data Science, we are still nowhere near 100% utilization of this data. In fact, as of now, we are only able to analyze 0.5% of all the data that we produce and store.
Some Key Points to Consider
The above fact underscores Two Key Points for us-
- The sheer magnitude of the data that we have at our disposal.
- The scope of growth for Data Analytics and Data Science.
Both Data analytics and data science work on Big data. So first let’s get acquainted with big data. Every bit of data that is produced from any source and is stored owing to its intrinsic value, when grouped together comes under Big data. And Big data is getting even bigger as more and more data is being accumulated by organizations. So the potential of growth for both Data Science and Data analytics is immense.
Organizations around the world have recognized the power of data and are actively investing in infrastructure to process and analyze data. The world around us is transitioning into a more data-driven and performance-centric body. And this transition is happening by leveraging the powerful tools called Data analytics and Data science to harness the power of data. So rest assured because these data processing tools will only increase in popularity and are not going anywhere soon.
One thing that you should know about Data analytics and Data science is that even though they work on the same underlying asset (data), they differ from each other at a very fundamental level. People in and around the domain tend to use these terms interchangeably that alters the accuracy of their articulation.
These statements do not convey the right message. So to avoid this let’s look at Data science and Data analytics through a fresh set of lenses and understand why they cannot be used interchangeably.
Data Science vs Data Analytics
The difference between the two is not substantial but there’s a clear distinction between the two when it comes to their skillset and their day-to-day tasks. So let’s understand them by comparing the two on some similar parameters. So let’s cut to the chase.
Data Scientists are the group of people that are always on the lookout for new methods to collect data that is currently not considered valuable enough to be stored. They identify the hidden value of these data sets and devise theories and hypotheses to test the dataset. The Data scientists then formulate data models and algorithms to make the processing of the new datasets easier.
They essentially work on data that is not well-understood and figure out ways in which this data can be useful to their parent organization. The responsibilities of a Data scientist include acquiring the data, filtering the data of unwanted fat, retrieving valuable inputs from the data, formulating mathematical models and algorithms, and communicating these insights to the concerned.
The major difference between a Data Analyst and a Data scientist is the type of data they work on. Data scientists tend to work on relatively new data that is unfamiliar on the other hand Data analysts work on data that has already been collected and the underlying value of these data sets is already recognized across the sector. Data analysts use various data processing and data visualization tools to identify the trends hidden inside the data.
The primary objective of a Data analyst is to answer a question sourced from across the various functions of an organization. The question may come from the technical team, the HR team, the finance team, or the higher-ups of the organization. And the Data analyst should be able to communicate actionable insights so that the problem may be solved.
The responsibilities of a data analyst include collecting and interpreting the data, identifying trends from the data, queuing the data using SQL, using different analytics tools like predictive analytics, prescriptive analytics, descriptive analytics, and diagnostic analytics, and using data visualization tools like Tableau, IBM Cognos Analytics, etc.
To read more about the four tools used in Data analytics in detail you can check out our article on Data analytics and Why is it of interest to companies?
Skills Required in Data Science vs Data analytics
The skills required to be a Data Scientist and a Data Analyst are very different from each other. A close look at the requisite skillsets demarcates the boundaries of Data Science and Data analytics even more clearly.
|Data Scientist||Data Analyst|
As can be seen from the table, Data scientists are required to have a mastery of a wide set of skills when compared to data analysts. They assume more elite roles in an organization and handle much more responsibilities than Data analysts.
Therefore, data scientists are expected to have 8 to 9 years of experience before they can take up roles in any organization. As against Data analysts that can even assume roles in any organization as a fresher. Data scientists are paid higher than Data analysts as they perform more complex tasks and have greater work experience.
Data Science Vs Data Analytics- Which one is better?
The question stands void at this point. If you have reached this point in the article then the answer to this question should be crystal clear. Obviously, Data Scientist is a better career option than a Data analyst. They are paid better, have a more advanced skillset, have more experience, and tend to assume better roles in organizations.
So it should not come as a surprise that being a Data scientist is a better choice.
Then why even bother discussing a role like Data analyst that is no match for Data scientists?
The answer is simple. You cannot become a Data Scientist right out the gate. A Data scientist must have a mastery of multiple domains that is difficult to achieve early in your career. And even if you have the requisite skills, the role requires at least 8 to 9 years of experience.
To become a Data scientist at some stage in your career, you need to start somewhere right?
Data analyst is where you start. Every Data scientist has to gain experience as a Data analyst while polishing existing skills and learning new skills. A Data scientist is nothing but a seasoned Data analyst with some extra skills to leverage in his work.
So we hope that the debate is now over. Of course, Data scientist is a better career path. But you cannot directly jump to the third step before taking the first and the second step. To be a data scientist, you need to assume the role of a Data analyst and evolve into a Data scientist through years of hard work and constant learning.
That’s the gist of the matter.
We hope that this article on Data analytics vs Data science settles the debate once and for all.