Data Analytics vs Data Science: Which is Better?
People often use data analytics and data science interchangeably, but these two data fields aren’t entirely the same. If you take a look at data analytics vs data science job descriptions, you can get a clear understanding of the major differences between each discipline. This piece contains the similarities, differences, and career prospects you’ll enjoy from each field.
What is Data Analytics?
Data analytics involves the curation and analysis of data to derive meaningful insights. When a data analyst uses statistical models to examine raw and undefined data, they can uncover valuable insights to help companies make actionable business decisions. They have robust technical and analytics tools and use exploratory data analysis techniques to meet business goals.
There are four primary types of data analytics, including descriptive, diagnostics, perspective, and predictive analytics. Each kind of data analytics is unique, but the overall goal of all of them is to help companies make data-driven decisions.
What is Data Science?
Data science is a broad discipline that involves a combination of statistical tools and analysis, domain expertise, mathematics, and heavy coding and programming skills to extract actionable insights from data. Data can range from raw and unstructured to structured and defined. As a result, there are different options available if you want to become a data scientist.
Data scientists often run correlational and predictive analyses to parse out significance within datasets, like regression analysis, ANOVA, machine learning models, and more. Data science has different approaches, including data analytics, data mining, machine learning, predictive modeling, and business analysis. Each type of data science has a range of applications across industries.
Data Analytics vs Data Science: What’s the Difference?
One of the key differences between data science and data analytics is that data analytics is a more specific field than data science. Data analytics covers every step from when the data is collected, cleaned, and modeled to when the information is presented. Data science includes all of this as well as additional tools like programming languages or artificial intelligence.
While data science includes several data-related fields, data analytics is strictly limited to extracting actionable insights from data to aid business performance. So, while a data scientist can fill the role of a data analyst without any additional education, a data analyst might have to expand their knowledge base to fill the role of a data scientist.
According to ZipRecruiter, data analysts earn an average annual salary of $67,294 in the United States. While the average is $67,294, salaries can range from as low as $47,500 to as high as $113,000 yearly. On the other hand, data scientists earn an average salary of $119,413 annually, almost twice as much as a data analyst. Salaries ranged between $36,500 and $190,500.
Both data analytics and data science pave the way for similar career paths, however, data scientists tend to have more opportunities. Data scientists and data analysts can work in the banking and financial sector, healthcare, telecommunications, media, entertainment, automotive industry, and any other industry that relies significantly on processing large volumes of data.
Data Analytics Careers
The most obvious career path is data analytics itself. However, learning data analytics doesn’t restrict you to working as a data analyst alone. You can find gainful employment as a systems analyst, business analyst, marketing analyst, big data analyst, or financial analyst.
Data Science Careers
You can work as a data scientist across industries, but this isn’t the only career path to explore. Data science knowledge can also get you employed as a machine learning engineer, big data engineer, enterprise architect, and machine learning scientist.
You may also work as a data analyst, data architect, application architect, business intelligence developer, statistician, data engineer, information and research scientist. These are just a few of some of the best data science careers.
You will need a similar set of technical skills to work in either data science or data analytics. It’s always safer to gather as many skills as possible if you want to excel in either field. The following lists contain some of the most vital skills for each discipline.
Data Analytics Skills
- Data visualization
- Statistical programming
- Structured query programming languages (SQL)
- Microsoft excel
- Machine learning
- Predictive modeling
Data Science Skills
- Data experimentation
- Mathematics and statistics
- Data transformation
- Deep learning
- Data storytelling and visualization
- Database management
How to Choose Between Data Science vs Data Analytics
Deciding between data science and data analytics depends on your interests, career goals, and occupational and educational background. The biggest difference between the two fields is one has a much larger scope than the other. Weighing these factors and comparing them to the two paths set before you will help you decide which career option is ideal.
Do you enjoy finding patterns or investigating large volumes of data to find valuable insights like predictive models? A career in data analytics might be an ideal option for you. It will also be a good fit if you’re a problem solver with critical thinking skills. You’ll want to make sure you can interpret data, identify problems and future trends, and provide valuable solutions to improve productivity.
For data science, you need to have all these interests and more. You must be open-minded and adaptable. You must enjoy working with people at different levels, so communication and collaboration skills are a must. People who love to research would also find fulfillment in a data science career, as the field is constantly evolving.
What is your career goal? Do you want to streamline your career to a limited number of data analytic disciplines, or do you want to leave the door open for more opportunities? If you want to gather enough knowledge to explore several options throughout your career trajectory, you should consider data science.
It’s also important to note that the average salary of data scientists is typically higher than that of data analysts. If one of your primary career goals is generating as much money as possible, you should dive into data science as it offers higher earning potential. However, you may have to start small with data analytics and work your way up with time and experience.
Traditionally, a data scientist is a highly educated person with an advanced degree. For data analytics, the bar might be a little lower. Most data analysts proceed with their lifetime careers with a bachelor’s degree, though some seek out graduate degrees to pursue management roles.
While university education is popular among data analysts and data scientists, bootcamp and self-learning have started gaining ground in the tech industry. Bootcamps prepare students for junior data science and data analytics job roles. The best data science bootcamps are fast-paced so that you can kick start your lucrative career in less than a year.
If you already work in the information technology or computer science industries, diving into data science and data analytics will be easy. Those with work experience in tech don’t need to start with entry-level data science jobs. They can get mid-level or even senior roles depending on their years of experience.
Data Analytics vs Data Science: Choosing What’s Right for You
Data analytics is a branch of data science that deals with the analytical aspects of data management. On the other hand, data science is a broader field of study that goes beyond simply collecting and analyzing data. The path you choose should depend on your interests, background, and career goals.
The good news is that both data scientists and data analysts are in high demand. According to the US Bureau of Labor Statistics, there is a projected job growth rate of 25 percent in data science, data analytics, and related fields over the next decade. So, irrespective of the career path you choose, you can rest assured that you’re not going to run out of data analytics or data science roles.