Data Analytics vs. Data Science: What’s the Difference?
With similar terminology such as big data, data science, data analysis, and data mining, knowing the difference between each one is crucial when choosing which one you want to become an expert of. In this blog, you will learn about the often confusing differences between data analytics and data science, and their significant distinctions in relation to your future career.
What’s the difference between Data Analytics and Data Science?
You've come to the right place if you're wondering whether to pursue a career in Data Analytics or Data Science, but you don't know the difference between the two. So, let’s break it down.
Data Analytics vs. Data Science
While both data scientists and analysts use data, the key distinction between them is in how and what ways they use it.
To help firms make more strategic decisions, data analysts analyze enormous data sets to find trends in their customers, ways in which the data can be used to solve complex problems, build charts, and make visual presentations for company leadership and stakeholders.
While data scientists also find trends in sets of data, they differ in that they create algorithms and data models, and use machine learning techniques to forecast the outcome and improve the quality of data sets.
What does a data analyst do?
Although the responsibilities of data analysts may differ among businesses and industries, they always use data to make informed decisions and address issues. Data analysts use a variety of methods to evaluate sets of data in order to provide practical business solutions, such as helping to promote financial health and predicting losses.
Businesses produce pieces of data like log files, customer data, transaction data, and other types of data; and data analysts are responsible for turning them into useful insights. To help businesses and organizations make better decisions, data analysts use techniques for manipulating data to analyze and interpret complex data sets.
Data analysts work in many different industries and hold a variety of positions, such as database analysts, business analysts, market research analysts, sales analysts, financial analysts, marketing analysts, advertising analysts, customer success analysts, operations analysts, pricing analysts, and international strategy analysts.
The best data analysts are technical experts who can explain quantitative results to non-technical co workers or clients.
What does a data scientist do?
In contrast to data analytics, data scientists forecast trends through the development of statistical models, algorithms, and questions. The primary distinction between a data analyst and a data scientist is heavy coding.
Data scientists are knowledgeable experts that identify business opportunities and challenges, and create the best solution using cutting-edge tools and methodologies. To create predictive models and solve challenging issues, they take a more statistical approach and use data visualization tools and machine learning algorithms.
Data scientists are able to extract valuable information from a jumble of unstructured and unorganized data. In the end, they present the data and insights from their findings to corporate leaders and stakeholders.
Data scientists work in many different industries and hold many different positions, such as data mining engineer, machine learning engineer, data architect, hadoop engineer, data warehouse architect, commercial intelligence manager, and competitive intelligence analyst.
Data Analytics vs. Data Science career: which one is right for you?
Now that you understand the differences between data analytics and data science, the next step is to choose which career path is right for you. You should consider these three important factors to help you decide which path aligns with your personal and professional goals.
1. Educational and personal background
The first thing you must consider is your educational and personal background. What is your degree in and what qualifications do companies look for when hiring data analysts and data scientists.
In order to help businesses make better strategic decisions, data analysts analyze huge data sets to find trends, build charts, and make visual presentations. Because of this, businesses often tend to look for candidates with backgrounds in science, math, programming, databases, modeling, and predictive analytics.
Additionally, data analysts generally pursue undergraduate STEM degrees, and even graduate degrees or certificates in analytics or a similar discipline, to ensure that their education is in line with the objectives and goals of the company.
Data scientists are more concerned with developing methods for producing and modeling data — because hard skills like data mining and machine learning are oftentimes needed to complete these processes, a more advanced specialized degree is needed for professional development. In this case, a master’s in data science would be sufficient. NJIT has one of the best data science programs in the state, as data scientists continue to rank for best jobs in America.
It's important to look into the educational prerequisites when deciding which job route is best for you. You will most likely have the educational and professional credentials to pursue either option if you have previously decided to invest in your career with an advanced degree.
However, since companies are more likely to consider people with a master's degree for data science positions, you could be more motivated to continue with a data analytics role if you're still debating whether returning to school is the correct choice for you.
2. Skills and interests
Are statistics and numbers your thing, or do you also have a thing for business and computer science?
Data analysts need to be well-versed in the industry in which they work, and they love programming, statistics, and numbers. Data analysts are responsible for protecting their organization's data, hence they spend almost all of their time examining databases for data from complex and frequently scattered sources. A career in analytical data may be the ideal fit for your interests if this applies to you.
Data scientists are required to be knowledgeable in a combination of arithmetic, statistics, and computer science, in addition to having an interest in the working understanding of the business sector. If this job description more closely fits your background and experience, becoming a data scientist might be the best choice for you.
You'll have a better idea of the type of work that you'll love and probably thrive at if you know which career suits your particular interests. Take your time and give this aspect of the equation some thought because doing so can help to ensure that you’re happy in your career for many years to come.
3. Professional career goals
Data scientists and data analysts are paid differently for their tasks since they need varying levels of experience and education.
Earning potential for data analysts is from $83,750 to $142,500. However, because these professionals mostly work with databases, they can raise their wages by learning new programming languages like R and Python.
Data analysts with more than ten years of experience frequently take advantage of their earning potential and move on to other positions, according to PayScale. Following the completion of an advanced degree, two popular job changes include moving into a developer or data scientist position.
Data scientists are regarded as being more senior than data analysts because they frequently possess advanced skills, a doctorate degree, and more experience. As a result, they frequently receive more pay for their services. The estimated average yearly income for data scientists is between $105,750 and $180,250.
Professionals in data science have a promising career path with lots of room to develop to advanced positions like data architects or data engineers.
Which data career is right for you?
Given the considerable differences in function responsibilities, educational requirements, and career paths, data analysts and data scientists have job titles that are misleadingly identical.
Regardless of your perspective, qualified candidates for data-focused occupations are in high demand on the job market right now because of how strongly firms need to make sense of, and profit from, their data.
You can choose the occupation that is the best fit for you and get started on your route to success once you have taken into account aspects like your background, interests, and desired salary.
After reading this blog, we hope that you have been able to come to a decision of which path is right for you. NJIT offers two different data science tracks for you to choose from — data science - computing option and data science - statistics option . Each track provides both data analytics and data science courses, allowing you to see first hand which career choice is the best fit for you. Get started on your data career and apply today.
