Defining the Field: Comparing Data Science vs. Data Analytics

In today's data-driven world, making informed decisions is crucial. Businesses generate vast amounts of data daily, and the ability to analyze and draw insights from this data has become a key differentiator. As the need for data-driven decision-making rises, so does the importance of understanding the differences between data science and data analytics. In this blog, we'll define and compare these two fields, exploring their roles, required skills, career paths, and more.

What Is Data Science?

Data science is a multidisciplinary field that uses scientific methods, algorithms, and systems to extract knowledge and insights from structured and unstructured data. A data scientist's role involves handling vast amounts of data, developing algorithms, and creating predictive models.

Core concepts in data science include:

• Machine Learning: Machine learning involves training algorithms to learn from and make predictions on data. This is essential for tasks such as fraud detection, recommendations, and more.¹
  
• Data Mining: This refers to discovering patterns and knowledge from large amounts of data. It involves methods at the intersection of machine learning, statistics, and database systems
  
• Big Data: Data scientists work with large data sets, often referred to as Big Data, which require advanced tools and methods to analyze.²
  

What Is Data Analytics?

Data analytics is a more focused discipline that involves examining data sets to draw conclusions about the information they contain. By applying various algorithms and procedures, data analysts aim to uncover actionable insights that guide business decisions and strategies.

Core concepts and processes in data analytics include:

• Data Collection: The initial step involves gathering data from various sources such as databases, APIs, and even manual entry.
  
• Data Cleaning: Before analyzing, data must be cleaned to fix or remove incorrect and inconsistent records. Data cleaning improves the quality of the data, leading to more accurate results.
  
• Data Analysis: This involves applying statistical and computational techniques to analyze data sets. Analysts use tools like SQL, Excel, and BI software to execute these tasks.
  
• Data Visualization: Presenting data in visual formats like graphs and charts helps stakeholders understand the insights derived. Tools like Tableau and Power BI are commonly used.
  

Key Difference Between Data Science and Data Analytics

Understanding the nuances between these two fields is crucial, but so is grasping the unique skill sets that each demands.

Skill Sets Needed

Data science and data analytics require distinct yet overlapping skills.

• Data Science: Data scientists need strong programming skills in languages like Python or R, proficiency in machine learning, a deep understanding of statistics, and data mining capabilities. They must also be well-versed in Big Data technologies such as Hadoop and Spark.
  
• Data Analytics: Data analysts must be proficient in data querying languages like SQL, data visualization tools like Tableau, statistical analysis tools like Excel and SPSS, and strong business intelligence skills. They also need critical thinking skills to interpret data correctly.

Roles and Responsibilities

• Data Scientist: Data scientists are innovators. Their daily tasks include creating complex models, developing algorithms, and conducting extensive data research to predict future trends. They work on unstructured data and often build new data processes.
  
• Data Analyst: Data analysts focus on interpreting existing data to provide actionable insights. Their responsibilities include collecting and cleaning data, performing statistical analysis, and presenting insights through dashboards and reports. Their work is more structured and often revolves around improving current processes.
  

Industries and Applications

• Data Science: Data scientists can be found in diverse industries such as technology, healthcare, finance, e-commerce, and even government sectors. Their work often involves developing new products or improving existing systems through predictive modeling.
  
• Data Analytics: Data analysts are prevalent in business environments, including retail, marketing, finance, and operations. They focus on optimizing business processes, understanding customer behavior, and enhancing decision-making with data insights.
  

Focus and Objectives

• Data Science: The main focus of data science is innovation and developing advanced predictive models. This field aims to uncover patterns and trends in data that can lead to new solutions or products.
  
• Data Analytics: The primary objective of data analytics is to generate insights that can drive business decisions. It is aimed at understanding the current state, optimizing existing processes, and supporting decision-making through insights and trends.
  

Data Science vs. Data Analytics Salary Expectations

When considering career paths, salary expectations can be a significant factor.

• Data Science Salary: Data scientists generally command higher salaries due to the complexity and specialized nature of their work. According to Glassdoor, the average salary for a data scientist in the United States is around $113,000 annually. This figure can rise substantially with experience and specialization, particularly in high-demand industries like tech and finance.³
  
• Data Analytics Salary: Data analysts can expect competitive salaries as well. The average salary for a data analyst in the United States is approximately $62,000 annually. While this is lower compared to data scientists, opportunities for advancement and specialization can lead to higher earnings, especially for senior data analysts and those in managerial roles.⁴ 

Factors Influencing Salary:

• Industry: Data professionals in industries such as finance, healthcare, and technology tend to earn higher salaries due to the complexity and critical nature of the work.
  
• Location: Salaries can vary significantly based on geographical location. For instance, data scientists and analysts in major tech hubs like San Francisco or New York typically earn more than those in smaller cities.
  
• Experience Level: As with many professions, the more experience you have, the higher your earning potential. Advanced positions and specialized skills can lead to significant salary increases.
  

Educational Requirements for Data-Specific Roles

To enter and succeed in either data science or data analytics, specific educational backgrounds are typically required.

Data Science Educational Requirements:

• Degrees: Most data scientists hold at least a master's degree in fields like computer science, statistics, mathematics, or engineering. Many also pursue Ph.D. degrees to deepen their expertise in specialized areas.
  
• Certifications: Certifications can be a quicker path to showcasing your skills. Relevant certifications can be obtained through online programs, courses, or universities. However, if you are looking to obtain a higher leadership position, a degree is often required.
  
• Skills Development: Continuous learning is crucial due to the rapidly evolving nature of technology and methodologies in data science. Participation in boot camps, seminars, and workshops can also be beneficial.
  

Data Analytics Educational Requirements:

• Degrees: Data analysts typically need a bachelor's degree in fields such as business, economics, statistics, or information technology. Some roles may also require a master's degree, particularly for more advanced positions.
  
• Certifications: Professional certifications can also be highly regarded. Programs like Google Data Analytics, Microsoft Certified: Data Analyst Associate, and SAS Certified Data Scientist can provide a competitive edge if an individual does not have a bachelor’s degree in the field. These certifications often focus on one sector, a degree can provide more of a well-rounded education in all aspects of data analysis. 
  
• Skills Development: Proficiency in data analysis tools (like SQL, Excel, Tableau) and ongoing learning through online courses can significantly enhance one’s expertise and career prospects.
  

Transform Your Career Path with Data Science

Understanding the difference between data science and data analytics is the first step in deciding which path suits your career goals. While both fields offer exciting opportunities and lucrative salaries, your choice will depend on your skills, interests, and professional aspirations.

For those aiming to advance their career in data science, the New York Institute of Technology offers an Online Data Science, M.S. program. Our comprehensive curriculum covers essential technical skills, such as Big Data analytics, programming, machine learning, and statistical analysis. Under the guidance of our expert faculty, you'll engage in practical projects and network with industry professionals, enhancing both your knowledge and career prospects. The program's focus on real-world application ensures that you not only learn but also apply the skills in impactful ways.

Don’t wait to take your career to the next level. Contact an admissions outreach advisor today to learn more about this cutting-edge program. Transform your passion for data into a rewarding and dynamic career.