What’s the Difference Between Data Science and Data Analytics

In the age of information, big data can be seen as the new frontier. Many aspiring professionals look to forge a path in data science and data analytics but are unsure of where to start. So, what’s the first step? Knowing the difference between the two. 

While data science and data analytics are treated as interchangeable, there’s a big difference in how they work with data sets to gain actionable insight. Data scientists design algorithms and models to process raw data and form their own questions. Data analysis is a subset of data science, where professionals use existing data to solve problems and identify patterns. 

Need help deciding between a data science vs data analyst job? Keep reading to find out how the two compare when it comes to responsibilities, salary, and more.

The Role of Data Scientists

Data scientists are like modern day alchemists. Instead of turning lead into gold, they turn raw, unprocessed data into forward-thinking innovations. With their extensive technical skills, they create the necessary tools and models to provide solutions to questions that businesses may not have even considered.

So, what are the day-to-day responsibilities? Here’s what a data scientist may be expected to do daily¹: 

• Clean, collect, and organize raw data
• Sift through large data sets and information
• Analyze information to identify trends and patterns, and make predictions
• Develop statistical tools, algorithms, and models
• Turn solutions and information into a digestible format

Since the work involved generally focuses on building technical models, data science can be seen as a more math-centered, scientific profession. 

Data Science Skills

If you’re thinking about pursuing a career in data science, it can be helpful to have a thorough understanding of subjects such as mathematics and statistics. A senior data scientist has a good grasp of these areas of discipline.

A few other skills you may consider developing to help prepare you for this career include:

• Strong technical writing skills
• Communication skills
• Advanced knowledge of statistics and predictive analytics
• Machine learning and data modeling
• Programming languages, such as R, Python, Java, and SQL 
• Data management 

How Long it Takes to be a Data Scientist

The educational journey to become a data scientist typically spans six to eight years in total. Different study options are accessible for those who want to pursue data science. While conventional college and university courses remain prevalent and widely utilized, online and self-paced alternatives also offer valuable opportunities for learning. Enrolling in a bootcamp presents an excellent opportunity to enhance your abilities and acquire the experience for venturing into the realm of data science. Another way is to apply for a big data analytics hybrid program so you have the flexibility to learn online and on-ground. Either way, it sharpens your technical skills in a practical, real-world setting. 

The Role of Data Analysts

Under the umbrella of data science, a professional working in a data analyst role can collect information from existing data to help clients enhance their business decisions and gain meaningful insights. Because they aren’t developing their own models for sorting data, the primary responsibilities of data analysts typically involve making a data set understandable.

Some day-to-day duties might include²: 

• Searching for data from primary and secondary sources 
• Presenting reports and findings to business clients
• Formatting and preparing information into a visual presentation (i.e diagrams, graphs)
• Translating data findings into new strategies to save money and improve business
• Maintaining and developing databases and systems

Compared to data scientists, data analysts can be thought of as creative and visual storytellers due to their work with presentations. 

Data Analysis Skills

Because data analysis is a branch of data science, there are some skills that overlap. The biggest difference between the two when it comes to skillset is that data analysts don’t necessarily have to have as great of a background in math and science. A foundational understanding will likely be sufficient.

Other skills that may benefit a data analyst are: 

• Strong verbal and communication skills
• IT and math skills
• Knowledge of Excel and SQL database
• Data visualization skills
• Observation skills

How Long it Take to be a Data Analyst

The data analyst profession requires at least four years of bachelor’s degree. Being proficient in different systems used for data analytics can be learned in a few months. Various certificate programs and bootcamps are designed to teach you about SQL, Python, or another programming language, and advanced Microsoft Excel skills.

Possible Degree Programs

For both data analysis and data science careers, earning a bachelor’s degree in any of the following majors may help employment prospects: 

• Engineering
• Mathematics
• Computer programming
• Computer science
• Statistics
• Finance

If you’re looking to pursue a career in data analysis, you’ll likely only need a bachelor’s or at least a few years of work experience. However, those looking to pursue data science may need a more advanced degree, such as a master’s degree or a doctoral degree, in a similar field.

Aspiring data scientists may also choose to start off working in data analysis. This path can potentially allow for hands-on training so that aspiring data scientists can build their experience in the industry.

Degree Costs for a Career in Data Science or Data Analysis

When you’re considering which path to pursue, keep in mind that the costs of any degree program can add up. While it’s difficult to say how much a degree in any field will cost, it can be helpful to take a few factors into consideration:

• Choice of university – Different universities charge different admissions fees. Plus, if you’re studying out-of-state, a university may charge you more in tuition and fees than in-state students. To avoid any sticker-shock later down the line, make sure to confirm tuition costs and admissions fees with your chosen university.
• Number of degrees – The number of degrees you decide to pursue can have an impact on the cost of your education. The more degrees you pursue, the more years you’ll likely need to complete them—and more years means more tuition payments. 

Potential Salary

For both data scientists and data analysts, salary can depend on three major factors:

• Experience – Have you had an internship in data or previous work experience? Having additional years of experience may help you obtain a higher salary. 
• Education – Having an advanced degree, such as a master’s degree, might qualify you for a higher salary.
• Location – Where you’re working can have an effect on how much you earn, since salaries tend to reflect the cost of living in the area. Plus, if there are less professionals working in your industry in the city or state where you live, you may be able to make more money since you’ll likely be in higher demand. 

Potential Job Outlook 

When exploring data science and data analysis, you may wonder whether these careers will be around in 5, 10, or even 20 years. 

Fortunately, while the interest in data is relatively new, these careers are likely here to stay. In fact, according to studies conducted by IBM³: 

• The overall demand for data scientists is projected to grow by 39%.
• 76% of job openings for data analysts will likely ask for at least 3 years of experience in the field, while only 6% will require a master’s degree.
• 78% of job postings for data scientists and other advanced analysts will likely ask for at least 3 years of work experience, while 39% will require a master’s degree or higher.

Based on these projections from IBM, careers in data science and data analyst should be around for years to come, making them smart career paths to choose. Therefore, earning an applicable degree or obtaining years of experience in the industry may help you find employment when applying for these types of jobs.

Why Data Is A Thriving Industry

More and more companies and organizations use data to better understand their customers and enhance the customer experience. For instance, a company may use data to make more informed decisions about marketing to their customers. When they have the data to back up their actions, it’s easier for companies to advocate for certain decisions. They can stop guessing what the customer wants, and instead, use data to know for sure.

This is where the role of a data scientist or analyst can come in handy. They’re able to build narratives, find solutions, and ask forward-thinking questions that can help companies be more strategic and make better business decisions. Their job titles can be positioned as data engineer, data architect, or business analyst. When a company makes better decisions, they’re more likely to maintain and grow their success. This makes careers in data science and analysis especially valuable to businesses.

How to Choose the Right Path

Once you know the difference between data science vs data analysis and what each entails, it can be easier to decide which path is best for you. 

If you enjoy working with data but like a little more creativity, then a data analyst role might be worth considering. If you’re more interested in crunching numbers and have more of a scientific mindset, then you might lean toward data science. 

If you’re still unsure, here are a few questions to ask yourself as you decide between the two:

• What are your top priorities? (For instance, salary, tuition price, interest level)
• What are you good at? 
• Where would you like to work?
• What are the positives and negatives of each? 

Start Your Journey at Alliant International University

When you’re ready to begin your career as a data professional, Alliant International University is here to help. Our business management school, California School of Management and Leadership, can help you foster connections with industry professionals and mentors. With our tools and resources, you can build a strong, reliable network and gain real-world experience in the industry of your choice. Plus, our courses are flexible and meant to work around your needs and your schedule.

Learn more about us and our available data science degree options by exploring our website today!

Sources:

1. “Data Engineer, Data Analyst, Data Scientist – What’s the Difference?” Dataquest. April 21, 2021. https://www.dataquest.io/blog/data-analyst-data-scientist-data-engineer/. Accessed: December 22, 2021. 
2. “Data Analyst Job Description [Updated for 2022].” Data Analyst Job Description [Updated for 2022]. https://www.indeed.com/hire/job-description/data-analyst?gclid=CjwKCAjwqeWKBhBFEiwABo_XBqv8hQc7Mc3smuBsss_LVuWoAJQV-dOhaxTDUDheQ3IvutrNOfewGBoCVKUQAvD_BwE&aceid=. Accessed: December 22, 2021.
3. “Salary: Data Analyst | Glassdoor.” January 05, 2022. https://www.glassdoor.com/Salaries/data-analyst-salary-SRCH_KO0,12.htm. Accessed: December 23, 2021.
4. “Salary: Data Scientist | Glassdoor.” January 05, 2022. https://www.glassdoor.com/Salaries/data-scientist-salary-SRCH_KO0,14.htm. Accessed: December 23, 2021. 
5. “The Quant Crunch – IBM.” 2017. https://www.ibm.com/downloads/cas/3RL3VXGA. Accessed: December 23, 2021.

The content of this page is only for informational purposes and is not intended, expressly or by implication, as a guarantee of employment or salary, which vary based on many factors including but not limited to education, credentials, and experience. Alliant International University explicitly makes no representations or guarantees about the accuracy of the information provided by any prospective employer or any other website. Salary information available on the internet may not reflect the typical experience of Alliant graduates. Alliant does not guarantee that any graduate will be placed with a particular employer or in any specific employment position.