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In this article, we will discuss in depth the hot topic of today’s time – Data Analytics vs. Data Science. We will focus on explaining the difference between both by considering factors like salary, skills, educational background, responsibilities, and more. Ultimately, it will become clear to choose between Data Analytics and Data Science.

Data Analytics vs. Data Science - What's the Difference?

Data Analytics vs. Data Science – What’s the Difference?

Data Analytics and Data Science are two sub-domains of Data Analysis that primarily differ in their ways and techniques of utilizing data. Data Science includes Data Analytics skills and Responsibilities, but later one doesn’t. To understand the exact difference between the two job roles – we have considered different factors – the definition, educational background, skills, tools, responsibilities, and salary packages.

What is Data Analytics & Data Science?

Data Science is a domain of Data Analysis that requires advanced knowledge of multiple disciplines of Computer Science, Statistics, Mathematics, A.I., Machine Learning, etc., with working experience in the core sector to solve business problems.

Data Analytics is a sub-domain of Data Science that requires advanced knowledge of disciplines like Mathematics, Basic programming languages of Computer Science, and Statistical Analysis.

Data Science discovers the questions after exploring meaningful relations between raw or extracted data sets, but Data Analytics finds the answers to business problems that Data Scientists uncover with their expert Analysis.

Educational & Skills requirements

To become a Data Analyst, you can follow different career paths depending on your domain. Many companies require Data Analysts to have a Bachelor’s degree in any domain – Computer Science, Statistics, Mathematics, or others- showing your expertise in the sector you’re willing to work in. 

Currently, the hiring criteria have changed for many employers due to increased demand for Data Analytics. It seems time-consuming to invest four years and money learning unnecessary skills you don’t require in the workplace environment.

Many aspiring Data Analysts are entering entry-level jobs by building in-demand skills after completing Data Analysts certification programs in less than six months or a year. Mostly, Data Science professionals get Advanced Postgraduate, Masters degree, or doctorate degrees in Information technology, Data Science, or Mathematics to become eligible as Data Scientists.

People aspiring for roles in Data Science must possess advanced skills in Mathematics, and statistics, utilizing programming languages, Machine Learning, Predictive Analytics, and Data modeling. Having expertise in coding language is a must for all Data Scientists.

Data Analytics requires proficiency in applied statistics, Data collection, Database building using SQL, Data mining, programming, Data modeling, management, Data Visualization, and Analysis. For both job roles, you must be a critical thinker and an expert problem solver who loves playing with numbers.

What do they do?

The main difference between Data Science and Data Analytics in job responsibilities. Both need data but the tools, techniques, and processes differ significantly. Data Scientists perform Analysis of primarily structured/unstructured data by developing their Data modeling processes, tools, and machine learning algorithms necessary to make future predictions for their business. 

While Data Analysts gather, clean, model, interpret, analyze, and interpret the collected/given data to solve the given business problem. 

The Role of a Data Scientist requires more technical expertise and experience with programming languages and machine learning algorithms.  We will further discuss the responsibilities for both in the following sections separately.

Salary difference

According to Glassdoor, the Average salary for a Data Analyst and Data Scientist is $70,000 and $100,000 in the U.S., which can vary with location. 

Working in Data Analytics

Data Analysts analyze existing data to discover patterns or hidden trends to fix the specific business problem. The everyday tasks can differ according to the working sector. The 21st century has seen a tremendous rise in digitalization, and the data-gathering process is crucial for every business type, whether it’s fashion, marketing, healthcare, or technology. 

Characteristics of Data Analysts

Every aspiring Data Analyst must have an analytical and mathematical mind willing to work with numbers. Having a background in mathematics, statistics, or coding knowledge can prove advantageous. 

Skills and Tools

Some standard tools which Data Analysts use during different stages of Analysis are – Google Sheets, MS Excel, PowerBI, SQL, Tableau, Python, R, SAS, and Jupyter Notebooks.  You should stay updated with the latest software tools.

Below given are some Technical and soft skills which every Data Analyst should have-

  • Basic fluency in Python and R  programming languages to manage big datasets.
  • Knowledge of Applied Statistics.
  • Data Wrangling.
  • Utilizing Querying languages like SQL.
  • Ability to visualize or present interpreted data through compelling visuals.
  • Industry knowledge of your domain.
  • Command on Soft Skills like active speaking, listening, problem-solving, critical thinking, presenting, and teamwork.

Roles and Responsibilities

Everyday typical responsibilities in Data Analytics include the following.

  • Recognizing the Data Type for Analysis.
  • Collecting data from different sources through surveys or websites.
  • Data systems maintenance.
  • Data cleaning to remove the error or organize data.
  • Analyzing patterns or trends in data sets to convert them into valuable insights.
  • Prepare reports.
  • Presenting insights to organization leaders and people with a non-technical background to assist in making data-driven intelligent decisions.

Working in Data Science

Data Scientists have more technical expertise in programming than Data Analysts.  Data Science incorporates multiple disciplines like Data Analytics, machine learning, data engineering, and predictive analytics. The responsibilities in Data Science require the analysts to perform heavy coding to create their own automated systems for data collection. 

Characteristics of Data Scientists

Many Data Scientists complete a Postgraduate in Data Science to gain the required skills for this role. Data Scientists are proficient in handling big Data. Data Scientists view data from a business perspective to make predictions necessary to make crucial business strategies for the future. 

Skills and Tools

Some standard tools which Data Analysts use during different stages of Analysis are – Hadoop, Tensorflow, MySQL, Python, R, SAS, Excel, Tableau, Java, Matlab, Scala, Apache Spark, and PowerBI. Given below are skills that every Data Scientist should possess.

  • Proficiency in advanced object-oriented programming languages to manage, sort, and analyze big data sets.
  • Expertise in integrating Machine Learning with Deep learning to provide collected data quality.
  • Data visualization.
  • Familiarity with big data.
  • Advanced Predictive analytics and statistics.

Roles and Responsibilities

Everyday typical responsibilities in Data Science include the following.

  • Gathering, mining, and processing structured/unstructured data.
  • Designing Machine learning algorithms and predictive statistical models.
  • Writing coding programs for automating Big Data processing & collection.
  • Developing tools to check data accuracy.
  • Creating Data visualization reports and dashboards.
  • Proficiency in working with Big Data platforms.

Choosing Between a Data Analytics and Data Science Career

After understanding the above points, you must have understood the job skills, roles, and responsibilities for Data Analytics and Data Science. Further, the below three factors can assist in determining a specific career path.

1. Consider your personal background.

We have noticed that educational requirements differ for Data Analytics and Data Science. To become eligible for Data Analyst entry-level jobs, the educational background may only matter for some companies. 

You can also enroll in a Data Analytics Certification program to become job-ready as a Data Analyst. But, Data Analysts pursue undergraduate/postgraduate programs in Data Analytics, I.T., science, engineering, or related domains to gain the necessary skills. 

Data Scientists require mainly Master’s Degrees or 5 plus years of experience to become eligible for this role. Investing in educational degrees makes you eligible for both job roles. Your present & desired education program with sufficient experience can help you determine the right option.

2. Consider your interests.

If you are interested in Data Analytics, you must feel excited about working with math, statistics, coding, and solving business problems. Anyone can become proficient in tools and communication with the practice. Researching more on tools and skills mentioned above can assist you in figuring out your interest areas. 

Data Scientists develop their programs via Machine Learning algorithms and models and have skills in programming, domain knowledge, and analytics. Take time and study in depth about tools, skills, and responsibilities involved with the roles of Data Scientists. The salary factor may seem compelling initially, but your interest in both domains would take you further in your career.

3. Consider your desired salary and career path.

The job outlook is positive for both domains due to increasing demand. Data Scientists earn more than Data Analysts due to the complexity of their responsibilities. Data Analysts advance in their job roles by learning more programming skills and machine learning to increase their salary increment. Data Analysts obtain Advanced Degrees in Data Science to shift to Data Scientist and other job roles. 

According to RHT reports, Data Science professionals receive an average salary of $105,750 to $180,250 yearly. With experience, both Data Analysts and Data Scientists get salary increments and job positions. 

Which data career is right for you?

Consider all the above-described factors in deciding on a specific career path. Data Science and Data Analytics job roles are one of the most demanding jobs of the current time, with an increase in job openings every year. In both roles, you must work with varying data sets. The selection of two depends strongly on your career objectives and interest.

Frequently asked questions (FAQ)

Can a data analyst become a data scientist?‎

The game is all about learning the required skills of a Data Scientist. Many Data analysts become eligible to become Data Scientists after pursuing an advanced degree and upskilling tools and programming languages knowledge. Clear your mathematical and programming concepts required as a Data Scientist.

Should I study data analytics or data science?‎

Whether you study Data Analytics or Data Science, your career trajectory will always be positive due to the high demand for both roles. The decision depends on the skills you want to learn. If you’re new to the world of Data Analysis without any degree or experience, go for the entry-level role of Data Analyst.

Is data analytics a good career?‎

Yes, Data Analytics is an intelligent career choice due to factors like stability, security, salary package, and demand. According to the U.S. Bureau of Labor Statistics, demand for Data Analysts will continue to increase till 2030 by 22%.

Do data analysts code?

Coding is optional for different job titles of Data Analyst. A basic understanding of Python, R, and SQL concepts is helpful for database building, cleaning, mining, or organizing. Check the eligibility criteria and skills requirements for the Data Analyst role for the industry you’re willing to work in.

Mark Roberts is a freelance writer and tech enthusiast based in San Diego, specializing in internet security and Ai tools.

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