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They say that data is the new oil. And with good reason: it’s a valuable resource that fuels the modern economy and powers companies’ decision-making processes.

And the more high-octane and well-treated this fuel is, the faster the company gets the right insights and turns them into profit. The “chemists” who process this new fuel are data analysts and data scientists. And both roles are in high-demand in today’s rapidly evolving economy. So, how do the two differ?

Both analysts and scientists use data to benefit companies. Still, these roles possess their own strengths and involve different specializations. If you’re thinking about a career in data, it’s important to understand the difference between data science vs. data analytics in terms of skillsets, responsibilities, and salaries.


Data analytics specialists draw conclusions from data and communicate their findings to businesses and stakeholders.

Case: An analyst in the hospital sifts through datasets and retrieves info that can help improve patient treatment procedures. Hospital managers may not be familiar with statistical methods, so the data analyst translates the results in a way that everyone can understand. For example, they can create dashboards that visualize the most up-to-date data on the current status of each patient.

Data science specialists, for their part, help organizations to automate their decision-making processes.

Case: A company wants to improve its customer retention rates. They bring in a data scientist to help. The data scientist analyzes customer data and builds predictive models to identify factors that are strongly correlated with customer churn. The models are then used to make recommendations to the company, such as improving customer service interactions or offering special promotions to at-risk customers.

Similarly, data science specialists can make a model that uses browsing and purchasing history to identify which ads are most likely to be clicked by a particular customer. This model can then make informed recommendations to the marketing team.

So how do the roles differ?

When we talk about data analytics, we're really talking about the process of taking data and using statistical and quantitative methods to extract insights from it. It's like a detective story where you're looking for clues in the data that can help you solve a problem or make a better decision.

On the other hand, data science is a more all-encompassing term that includes data analytics, but also includes other techniques like machine learning, data cleansing, and data preparation. It's like a toolbox that you can use to extract insights from data where you have a range of tools and techniques at your disposal.

So while data analytics is focused on analyzing historical data and identifying patterns or trends, data science is a more comprehensive approach that involves a broader set of tools and techniques to extract insights from data. It's like the difference between a hammer and a whole set of tools: both can be useful, but one gives you more options and capabilities.

Hard skills

When it comes to hard skills in data analytics and data science, the main difference is in the depth of technical knowledge you’re expected to have. The deeper the knowledge, the better it pays — more on that below.

For data analytics, you'll need to have a strong understanding of statistics and quantitative methods, as well as experience with data analysis tools like Excel, SQL, and Tableau.

On the other hand, data science requires a broader set of skills, including statistics and quantitative methods, programming skills (Python or R are common languages used in data science), machine learning, and data visualization. You'll need to be comfortable with coding and have experience using libraries like TensorFlow, scikit-learn, and PyTorch. Additionally, you'll need to be able to work with big data and distributed computing systems like Hadoop and Spark.

So while both data analytics and data science require a solid foundation in statistics and quantitative methods, data science demands a more extensive set of hard skills that includes programming, machine learning, and big data technologies. It's a more technical field that requires a deeper understanding of computer science and software engineering, whereas data analytics is more focused on the analysis and interpretation of data.

Soft skills

People on the data analytics side often work within business teams and need to be able to make their findings clear to non-specialists. So, communication skills are crucial.

That’s not to imply that you don’t need people skills at all in data science. The scientists’ work has real value and implications for businesses. So, it’s helpful for them to understand how businesses operate in general, as well as how data and machine learning models fit into a specific company’s processes. Still, for data scientists deep technical expertise is the primary requirement.

Organizationally, data science specialists tend to be a bit further from business teams. They usually form a separate group that works with different areas of a company.


Both disciplines are in demand. Data science came in second on Insider’s list of the top 10 most in-demand industries in 2022. And the number of job openings for both data analytics and data science positions is expected to increase over the next several years.

When it comes to salaries, however, there’s a big difference between the two specializations.

According to Payscale, the average base salary for data analytics specialists in the U.S. is around $65,000 per year. That is more than the $58,260 annual mean wage nationwide. However, the average base salary for a data science specialist in the U.S. is significantly higher — currently about $98,000 per year. For senior data science specialists, it’s over $130,000. Meanwhile, senior data analysts can only count on $86,400 per year.

Also, note that for many the word “data” is associated with the tech industry only. Still, it's just one of the many domains where data analytics and data science specialists can find jobs. Banking, insurance, finance, healthcare, etc. — choose what works best for you.

The best way into the profession

Now that you know the specifics of each path, let’s talk about how to get you into the profession.

The good thing is that you don’t need a profile education here. Nowadays, there are plenty of courses that will equip you with the skills you need to get a job in data analytics or data science fast. For example, TripleTen — the best coding bootcamp three years in a row.

Our Data Science and Data Analytics bootcamps begin with a free 20-hour intro course. It teaches you some Python and gives you a sense of what it’s like to work with data. If you like the introduction, you can opt-in for the full program.

Both programs are designed to help you find work in the industry. So, they emphasize the use of important professional tools like the Jupyter development environment. Both programs are practice-intensive, and you’ll graduate with a portfolio of projects to show to potential employers.

Each program takes 20 hours of work per week to complete.

Whether you choose the data analytics or data science track, you can take a career preparation course at no extra charge. Here you will polish your resume, put your portfolio in order, and complete mock interviews. Then, after you graduate from the program, TripleTen will provide ongoing support as you search for a job.

Hopefully, you now have a better idea of what sets these two similar-sounding specializations apart — and which suits you better, personally and professionally. Becoming a data analytics or data science specialist is a challenging but doable venture. TripleTen is on hand to prepare any student for an entry-level job in either profession.

Find out more about TripleTen’s Data Science and Data Analytics bootcamps today.

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