Data Science vs. Data Analytics: 4 Key Differences

Pablo Baldomá JonesPablo Baldomá Jones

Pablo Baldomá Jones

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As the world relies more on data in many aspects of business, research, and the economy, the demand for professionals with data-driven skills grows. The increasing prevalence of big data over the last decade has transformed our world in ways we are only now beginning to comprehend. Big data has evolved applications in a wide range of areas, from healthcare to ride-hailing apps and online shopping to streaming services. Behind all of this is the rise of two significant fields: data science and data analytics.

Individuals interested in a career that involves data modeling, statistics, programming, and other analytical skills have most likely come across degree programs and job postings focusing on data science or data analytics. While both fields entail working with and manipulating data, the two are not interchangeable.

In this article, we’ll look at the fundamental differences between data science and data analytics and the career paths for both disciplines.

What Is Data Science?

Data science is a multidisciplinary field that, as the name implies, is primarily concerned with data. It is a scientific branch of knowledge that can be applied in several areas such as finance, retail, and healthcare. As a multifaceted subject area, data science combines skills ranging from data analytics and computer science to machine learning and artificial intelligence.

The goal of data science is to analyze and research large amounts of raw, unstructured data to develop strategic questions that can help drive an organization forward. Data science involves highly varied, complex, and interconnected tasks, such as data modeling, algorithm development, and team management.

The specific priorities outlined in an organization’s operational plan determine the exact responsibilities of a data scientist. The most important takeaway is that a data scientist focuses less on micro, day-to-day concerns and more on macro, longer-term questions.

What Is Data Analytics?

Data analytics is both a discipline within data science and a distinct field in its own right. While data scientists ask broad, strategic questions, data analysts have a narrower and more specialized role, seeking answers to specific questions.

A data analyst generally gathers data to identify trends that can help business leaders make crucial decisions. The discipline is concerned with conducting statistical analyses to answer questions and solve problems. For instance, a data analyst may be tasked with determining which product features users prefer. They may also examine how consumer buying habits change as the weather changes.

Even though data analysts require fewer skills than data scientists, they usually have a deeper understanding of a particular area of business. Rather than having overall responsibility, they often work in an individual department such as accounting, sales, or marketing.

Data Science vs. Data Analytics: Roles, Requirements, and Return on Investment

The line between data science and data analytics may not always be clear, but we can classify their differences as follows:

1. Scope

Data science looks at the big picture, asking strategic questions and driving innovation. Data scientists take a holistic view of organizational operations. This requires a more comprehensive understanding of the business landscape, including how different departments interact, what competitors are doing, and so on.

On the other hand, data analytics focuses on the micro, identifying actionable insights by using data to answer specific questions. Data analysts typically specialize in a particular niche.

2. Roles and Responsibilities

The roles and responsibilities of a data scientist or data analyst may vary by industry and work location. It helps to carefully read job descriptions to gain a better understanding of a company's expectations. In some cases, job postings for data scientists may include tasks that are usually done by data analysts and vice versa.

Main Responsibilities of a Data Scientist

  • Identify data sources and automate the data retrieval process from those sources.
  • Prepare data for data analysts by pre-processing unstructured and structured data.
  • Create machine learning algorithms that can recognize patterns in data.
  • Build predictive models using statistical and predictive methods to project data trends.
  • Analyze data systems and find opportunities to improve efficiency, security, and optimization.

Primary Responsibilities of a Data Analyst

  • Work with management and other departments to establish short-term business goals.
  • Design and manage effective database structures.
  • Analyze datasets with statistical tools to generate meaningful insights for key business decisions.
  • Implement data collection procedures and gather information from primary and secondary sources.
  • Visualize data and present it in a way that allows non-technical team members to understand insights easily.

3. Education and Skills

A degree in data science or data analytics typically includes math, statistics, computer modeling, programming, and introductory courses in big data and data science.

While there is some overlap in analytics skills between data scientists and data analysts, the main difference is that data scientists commonly use programming languages such as Python and R. In contrast, data analysts use MS Excel or SQL to query, clean, or analyze data.

Required Education and Technical Skills in Data Science

  • A bachelor’s degree in data science or a master’s degree in a data-related field
  • Basic understanding of business models
  • Advanced knowledge of math and statistics
  • High level of expertise in programming languages
  • Machine learning and artificial intelligence
  • Data collection, cleaning, and exploration
  • Data modeling and visualization
  • Predictive analytics

Required Education and Technical Skills in Data Analytics

  • A bachelor’s degree in data analytics or any related field
  • Solid understanding of probability and statistics
  • Intermediate level of expertise in programming languages
  • Data exploration using SQL and Python
  • Data mining, warehousing, and analytics
  • Data wrangling and manipulation
  • Predictive modeling
  • Business intelligence and data visualization

Required Soft Skills

Soft or people-oriented skills are also highly recommended for a career in data science or data analytics. A data scientist must possess domain knowledge and critical thinking skills. A data analyst must have reporting, analytical, and problem-solving skills. Both roles need strong communication and management skills to interact effectively with teams.

4. Career Growth

Data scientists are generally considered more senior than data analysts. A data analyst may begin their career in an entry-level position with the primary responsibility of creating reports and dashboards. The subsequent step can be to take on a role involving strategy or advanced analytics methods. After some years of experience, a senior data analyst can advance to a managerial position, leading a team of data analysts.

Data analysts can also further their education and develop their skills to become data scientists. The decision between a career in data science vs. data analytics may depend on the trade-off.

In the United States, the average total pay of a data scientist is $120,056 per year. In contrast, the average total compensation of a data analyst is $95,492 annually. Companies usually have more job openings for data analysts than for data scientists.

The U.S. Bureau of Labor Statistics predicts over a 30% increase in employment for data scientists and related occupations between 2020 and 2030. An estimated 19,800 jobs should become available during this period.


The fields of data science and data analytics are in high demand with plenty of opportunities and growth. However, finding the ideal data-related job is not as simple as it seems. There are several factors to consider when looking for a job in either field—whether you’re a fresh graduate or a seasoned applicant. It's a multi-step process with various interview formats that can be overwhelming and intimidating.

At Celerative, we’ll make it easy for you to get the best-paying data science or data analytics jobs in the industry. Sign up with us today to land long-term remote jobs with leading startups.