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Data Science vs Data Analytics: Why Data Makes the World Go Round
Data science and data analytics are two concepts that normally converge with one another and are often used interchangeably. However, this is not a potato-potahto kind of situation. Even though it might seem that the two terms are similar, there are important differences between the two. Making a data science vs data analytics distinction can possibly be strenuous only because both are connected to one another in varied ways.
A popular adage in the industry goes –
“If data science is a home for all methods and tools, data analytics is a small room in that house”.
In simpler words, data science is considered an umbrella term and data analytics are one of the many fields that exist within this umbrella. If you are one of the many who is unclear about the differences between both these concepts, then you have come to the right place. Through the course of this blog, we will take you through all things data science vs data analytics.
Data Science vs Data Analytics: Definitions
Before delving into the dissimilarities between the two, let us look at what each of these terms mean:
What is Data Science?
As mentioned earlier, data science is an umbrella term and comprises a multitude of disciplines. It includes data analytics, business analytics, data engineering, data architecture, and more. What data science does is that it digs out extremely valuable and useful insights from a pit of raw data. Data is used to comprehend the future.
In addition to this, it unearths new questions that may not have been previously addressed. Why is this done? Among other things, it helps direct all kinds of innovation. Programming, mathematics, statistics, and more are used to obtain significant and valuable insights from all the structured and unstructured data that is available.
Also Read: What is Data Science? Why is This Career Path in Demand?
What is Data Analytics?
In data analytics, existing information is processed followed by statistical analysis of the same. Professionals in the field get a hold of the information, process it, and then organize it to divulge actionable insights for various issues. The insights and conclusions gathered by data analysts permit organizations to make decisions. These insights help develop instant and immediate results.
A data analyst converts large amounts of figures that exist in the form of data into a language that is easily understandable for the purpose of making decisions.
Data Science vs Data Analytics: Key Differences
Now that we have established what the two terms mean, let us look at the differences between data science and data analytics.
Data science as a field has a lot of scopes. As a data scientist, you must have a sense and understanding of business and must possess skills like data visualization because both these factors help convert insights into a business story. They use numerous disconnected sources to unearth and examine data. Data scientists use machine learning algorithms to gather insights that further help businesses and aid in achieving their goals. Having extensive knowledge of programming is extremely important in this field. Amongst all the programming languages, Python is the most frequently used. This is followed by others like C++, Java, and many more.
Unlike data science, the scope of data analytics is smaller in comparison. Also, these professionals are not required to have a sense and understanding of business or even advanced visualization skills. Instead of multiple sources, they use one source i.e. the CRM system to explore data. In data analytics, the analysis of data provides key answers. Unlike data science, this field does not use machine learning in the process of analyzing and gathering insights. But having said that, data analysts must have basic programming skills along with knowledge of languages like R and Python.
Data Science vs Data Analytics: Skills Required
Below are some of the other important skills that every professional in these fields should possess:
– Experience in data warehousing and data mining, and statistical tools and technology.
– Knowledge of web services like Hadoop, Spark, S3, machine learning, data visualization, and story-telling.
– For the purpose of querying, cleaning, or even understanding the data, knowledge of SQL or Excel is a must.
– For data modeling, they should know how to use Excel as a tool and technique.
– Experience with basic data visualization tools like Tableau is required.
If you do not possess either of these skills and would like to, there are numerous online learning platforms like Emeritus that offer such courses.
Data Science vs Data Analytics: Job Opportunities
Whichever field you decide to build your career in, it is always crucial to know the salary range in the respective field. The same applies to data science and data analytics. When it comes to professionals in these fields, data scientists earn a lot more compared to data analysts. To help you get a better idea of the expected salary, given below is the salary range in both these fields.
Data Scientist Salary
The salary of data scientists depends on their work experience and education. Based on data collected by the Bureau of Labour Statistics (BLS), $98,230 is the mean annual salary of people in this profession. The salary range of entry-level professionals is between $52,950 and $71,790 and that of experienced professionals is $103,930.
Data Analyst Salary
When it comes to data analysts, they are extremely valued by organizations. This demand and importance of data analysts are directly reflects in the salary they earn too. Like most professions, the salary range of data analysts is dependent on their education, skills, and work experience, according to the BLS. Generally, they earn a median salary of $86,200 based on the Bureau of Labour Statistics (BLS). While the entry-level salary for these professionals ranges between $50,000 and $75,000, experienced professionals in this field earn anywhere between $65,000 to $110,000.
While we have provided a list of differences between science and data analytics, the bottom line is no matter the number of differentiating factors between the two fields, one will always remain incomplete without the other if either ceases to exist.
We hope that this blog helped you understand data science and data analytics individually, as well as the differences between them. Also, if this blog helped you decide which one of the two fields you would like to pursue a career in but lack the required skills, do check out the courses that we offer on Emeritus to learn the necessary skills and expand your knowledge in these fields!
By Annabel George
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