Data Analytics vs Data Science: Which Is Better For You?

The terms data science and data analytics are commonly used interchangeably, likewise the occupations. However, they both refer to different things.  A data scientist cannot be referred to as a data analyst or vice versa. This article will highlight the key differences between data science versus data analysis. It will also guide you on how to start a career in either field.

What’s the Difference Between Data Science and Data Analytics?

There are three major differences between data science and data analytics. The differences are in the skills and tools required by each field, the roles and responsibilities of professionals in both fields, and the career paths available in each. While they both deal with data, their differences are significant and distinguish one from the other.

• Skills and Tools. Both fields require some similar but different skill sets. Data analysts must be proficient in SQL, while data scientists must be proficient in probability, statistics, multivariate calculus, and linear algebra. Data scientists must be skilled in data management, data wrangling, and machine learning, while data analysts must be skilled in data visualization.

• Job Roles/Educational Background. While a data scientist can perform the duties of a data analyst, a data analyst must obtain a higher level of education to perform the duties of a data scientist. The educational background also differs because a data analyst graduate cannot work as a data scientist until they obtain the necessary degree.
• Career Path. While data scientists can advance their careers to data architecture or data engineering, data analysts cannot enter these fields without earning the appropriate degrees.

Data Science vs. Data Analytics: Side by Side Comparison

	Data Scientist	Data Analyst
Scope	Data science is a discipline that combines the use of scientific methods, processes, and systems to obtain meaningful insights from data and make sound decisions. Data scientists use machine learning algorithms to build predictive models or artificial intelligence systems.	Data analytics is the process of analyzing raw data and statistics to extract meaningful information and patterns that can be used to make sound decisions.
Mission	The primary goal of data science is to help businesses make the right decisions, find opportunities, and make profit by drawing information from structured and unstructured data.	The goal of data analytics is to find patterns in data and use the knowledge gained to help businesses improve their performance and efficiency.
Tasks	Collect data, clean data, analyze data, discover future trends, build predictive models, model data, present results, propose solutions and strategies, work with other teams.	Gather data, interpret data, identify patterns, data querying, data analysis, data visualization.
Industries	Finance, healthcare, government, telecommunications, marketing, automotive, and oil and gas.	IT, healthcare, media, finance, insurance, business, marketing, and criminal justice.
Average Salary	$103,930	$86,200

What Is Data Science?

Data science is the science of extracting insights from raw data using analytics tools, statistical techniques, data modeling, data mining, algorithms, and machine learning principles. Data scientists gather, analyze, and interpret data to help businesses grow by identifying industry trends and making accurate predictions. Below are the main tasks of data science.

• Data wrangling: Data wrangling is a process that involves cleaning or transforming new or complex data into a format that can be accessed and used for better decision-making. This is an important process that data scientists need to perform their tasks.
• Statistical modeling: Statistical modeling is the process of generating data and making predictions about real-world situations using statistical assumptions and mathematical models.
• Programming: Programming is an important aspect of data science as programming languages are used to obtain, clean, analyze, and visualize data. They can also be used to make complex calculations and build predictive models.
• Data visualization: Data scientists present the analyzed data using data visualization. It is a graphical representation of data that is used to communicate the process and results of data science to other team members.

What Is Data Analytics?

Data analytics is the process of collecting, analyzing, interpreting, organizing, and storing data to derive actionable insights and draw conclusions that can help inform business decisions. Data analysts identify patterns in raw data and solve problems by using data and statistical analysis and techniques. The various types of data analytics are listed below.

• Descriptive analytics: Descriptive analytics is a type of analytics that is widely used to monitor patterns and trends. It is a statistical method for analyzing data to understand patterns of events or behavior.
• Diagnostic analytics: Diagnostics analytics is a technique used to find out and understand the cause of behaviors or events in order to draw meaningful conclusions.
• Predictive analytics: Predictive analytics employs statistical models to examine historical and current data to forecast future or unknown events or behavior.
• Prescriptive analytics: Prescriptive analytics is a method in which the system analyzes historical data, forecasts future events, and determines the best course of action.

Data Analytics vs. Business Analytics: What’s the Difference?

The main difference between data analytics and business analytics is how the information derived from the data is used. Big data is used in both data analytics and business analytics to identify patterns and trends that can help businesses make better decisions. However, business analysts focus on using the information gathered to make practical decisions, such as the actions that should be taken as a result of them.

Data analytics is concerned with identifying patterns in data to forecast future events and devise strategies to avoid or solve potential problems. Business analytics identifies flaws in business operations, collects data to find a solution, draws conclusions based on those insights, and implements changes to increase business efficiency and growth.

Big Data vs. Data Analytics

Big data is a large and complex collection of data that cannot be processed and analyzed using traditional methods but can be processed and analyzed using high computing power. Data analytics is the extraction of information from raw data to make sound business decisions. Big data is collected from various sources, including social media, computers, and the Internet.

Data Science and Analytics Salary

According to the US Bureau of Labor Statistics, the average annual salary of data scientists is $103,930, with the highest 10 percent earning about $165,230 and the lowest 10 percent earning approximately $52,950. The average salary of data analysts is $86,200. The highest 10 percent earn above $144,330, and the lowest 10 percent earn less than $48,050.

Data Science vs. Data Analytics: Roles and Responsibilities

This section discusses the various roles and responsibilities of data analysts and data scientists. While they share many similarities, their tasks are quite different.

Data Scientist Roles and Responsibilities

• Data Collection: These professionals conduct extensive research to find data for their analysis.
• Data Processing and Cleaning: Their research generates unstructured and structured data, which they must process and clean to make sense of it and extract meaningful insights.
• Data Exploration: This is the first step in data analysis, during which a data scientist attempts to make sense of raw data, identify characteristics, and quickly gain insights.
• Data Modeling: Data scientists accomplish this by creating a descriptive diagram that depicts the relationships between various pieces of information that will be stored in a database.
• Data Interpretation: They must interpret the data they collect in order to make strategic decisions, which is their primary responsibility.

Data Analyst Roles and Responsibilities

• Data Analysis: Data analysts process, clean, and model data in order to make sense of it and draw conclusions.
• Data Mining: They scan through large data sets in order to extract information that will help them make decisions.

• Decision-Making: They extract information from raw data, interpret it, and draw conclusions from it in order to make well-informed decisions.
• Reports: Any decision they make must be communicated to top executives in order to be implemented.
• Teamwork: They work with members of other teams to ensure that the decisions reached are properly executed.

Data Science vs. Data Analytics: Skills and Tools

Data scientists and data analysts must have specific skills and tool knowledge in order to effectively carry out their roles and responsibilities. This section discusses the necessary skills and tools for each field.

Data Science Skills and Tools

• Statistical modeling: These professionals process their data using statistical analysis to discover relationships between variables and gain relevant information.
• Programming languages: They require programming languages to effectively write computer programs and analyze large data sets.
• SQL: Knowledge of SQL is needed for these professionals to interact with databases.
• Communication skills: Data scientists must have strong communication skills to collaborate effectively with other team members and department members.
• Business Strategy: The goal of data science is to make accurate business decisions. Data scientists must be skilled in business strategy to perform this role effectively.

Data Analytics Skills and Tools

• Microsoft Excel: Data analysts need this tool for data analysis.
• Data Visualization: These professionals use data visualization to present their findings in a way that is easily understood by everyone.
• Machine Learning: This is important for career advancement because data analytics has become an important aspect of it.
• Presentation Skills: Data analysts must be adept at presentation in order to communicate their findings to other team members and company executives.
• Critical-Thinking: To conduct effective research, data analytics experts must be able to comprehend what appears to be ambiguous and also know the appropriate questions to ask.

How to Choose Between a Data Analytics and Data Science Career

Choosing between data analytics and data science can be difficult. However, we have compiled a list of steps that you can take to assist you in making your decision. These steps are listed below.

Step 1: Identify Your Interests and Strengths

Due to the complexity of data science, it requires a higher level of education and skills than data analytics. This means that you must be certain that you are prepared before entering the field. You should identify the fields your personal and professional interests overlap and ensure that your strengths complement those fields. This will help you choose the option that you can handle.  

Step 2: Consider Your Educational Background

Your educational background should correspond to the needs and skills of either field. This is because employers of data scientists have specific requirements. Furthermore, most data scientists begin with a master’s degree, while data analysts begin with an undergraduate degree or equivalent experience.

Step 3: Consider Salaries and Career Paths

Money is a major determinant of people’s career paths. Before making a decision, research the salaries and career paths of both fields to see which best fits your preferences or expectations. This is because they both have different salary ranges and career paths.

Kickstarting Your Career as a Data Scientist or Data Analyst

There are several educational paths that can lead to a career in data science and analytics. You can attend college, a coding bootcamp, or become a self-taught professional. Contrary to popular belief, any of these paths is sufficient to begin a career in these fields. To help you get started, we’ve included some options for each path below.

Data Science and Analytics Degrees

College degrees are the best way to begin a career in data analytics or science. They are widely recognized by employers and may allow you to enter the industry more quickly than other options. However, with traditional education, you may need to earn a graduate degree to land good jobs as a data scientist or data analyst, as this is typically the minimum requirement.

Top Data Science Degrees

• Master of Science in Statistics: Data Science, Stanford University
• Master of Science in Data Science, Columbia University
• Master of Science in Data Science, DePaul University
• Master of Science in Data Science, University of Rochester 
• Master of Science in Data Science, Indiana University

Top Data Analytics Degrees

• Master of Science in Analytics, The University of Chicago
• Master of Science in Analytics, Northwestern University
• Master of Science in Computer Science: Concentration in Data Analytics, Boston University
• Master of Science in Data Analytics, Drew University 
• Master of Science in Data Analytics, The George Washington University 

Data Science and Analytics Courses

As an alternative to a college degree, you can learn through online platforms, which offer a variety of courses in data science and analytics and are also recognized by industry employers. Some of the courses are provided by highly regarded educational institutions in the country. Some of the best online data science and analytics courses are listed below to help you get started.

Top Data Science Courses

• Master of Science in Computer Science (Data Science Track), Coursera
• Data Science Specialization, Coursera
• The Data Science Course 2021: Complete Data Science Bootcamp, Udemy
• Professional Certificate in Data Science, edX
• Data Scientist Nanodegree Program, Udacity

Top Data Analytics Courses

• Google Data Analytics Professional Certificate, Coursera
• Beginner Statistics for Data Analytics – Learn the Easy Way!, Udemy
• Data Analyst Nanodegree Program, Udacity
• Professional Certificate in Data Analysis for Life Sciences, edX
• Data Analytics: Hands-On, Pluralsight

Data Science and Analytics Bootcamp Programs

Coding bootcamps are another excellent way to launch a career as a data scientist or analyst. They offer relevant, in-demand training in a specific field to help you start a career in tech. They are also more fast-paced, flexible, and affordable than most alternatives. Below are some of the best data analytics and data science bootcamps.

Top Data Science Bootcamps

• Flatiron School:  The data science bootcamp at Flatiron School is a 15-week, full-time program and a 20-60-week, part-time program. It includes data analysis and engineering courses, machine learning, scientific computing, quantitative methods, and a capstone project. The program costs $16,900 and includes career services to assist you in finding a good job.
• BrainStation: This program lasts three months and includes classes in data analysis and visualization, machine learning, big data, and a capstone project. The program is available in both part-time and full-time formats, and it includes career services to help students prepare for the job market.
• Thinkful: Thinkful charges $9,500 for its six-month part-time data science program. The curriculum covers courses in analytics and experimentation, machine learning, data and analysis, statistics, and a capstone project. The school also offers career services to its students.

• General Assembly: This program is entirely online and covers topics such as data science fundamentals, data analysis, statistical modeling, machine learning, and a capstone project. The school also provides career guidance to ensure that students are job-ready as soon as they graduate.
• Le Wagon: This is a nine-week full-time program that covers data analysis, artificial intelligence, machine learning, data engineering, decision science, deep learning, and a capstone project. The course also includes career services.

Top Data Analytics Bootcamps

• CareerFoundry: This is a five to eight-month online program with a job guarantee for its students. The tuition is $6,900, and the curriculum includes data analysis, excel, data cleaning, data visualization, analytical methods, descriptive analytics, and a capstone project. 
• Springboard: Springboard charges $10,140 for a six-month fully online, career-focused program. The curriculum includes structured thinking, analyzing business problems, SQL, and data visualization. The course also includes one-on-one mentoring and career support.
• Ironhack: This course covers Python, data analysis, and machine learning. The course is entirely online, and financing options are available to make tuition more affordable for students.

• Clarusway: This is a 13-week program that covers statistics, data analysis, database design, Python, SQL, data visualization, tableau, and a variety of projects. Students will also receive career services such as coaching hours, resume development, and interview preparation.
• Lighthouse Labs: This is a six-week course with a curriculum that includes data analysis, data collection, data cleaning, data visualization, and a capstone project. The program tuition is $2,000.

Can a Data Analyst Become a Data Scientist?

A data analyst can become a data scientist if they earn a degree or get additional education in data science. Both fields necessitate a bachelor’s degree, but data scientists typically hold a master’s degree. Furthermore, data science is more complex because it requires more mathematical and programming skills to excel, that is why it requires an advanced degree.

Data Analytics vs. Data Science: Which Is Better?

There is no wrong choice between data science and data analytics because they are both good fields to work in. It’s all a matter of personal preference. If your skills, interests, and career goals align with data science, you can pursue it, and vice versa. They both involve working with big data and making decisions for organizations. You can also begin a career in any of them by following the same educational paths.

Data Analytics vs. Data Science FAQ