Data Science vs Data Analytics: Explained

Though people talk about data science and data analytics widely, not everyone knows the exact difference between them. Therefore, before you commit a lifetime to data science, it is imperative to understand the difference between data science and data analytics. While data science is the technique of turning raw data into something that adds value to the business, data analytics is examining data, drawing interpretations, and presenting it to the stakeholders to aid business decisions, among other things.
Here, we focus on important distinctions that make data science and data analytics different. We’ll also walk you through how distinguished the role and responsibilities of data scientists and data analytics are.

Data Science Vs Data Analytics: Key Differences Explained

Large, medium, or small companies generate massive amounts of data that often goes obsolete. However, with the integration of data science and its intermediary processes into business enterprises, the data collected by enterprises is turned into action-oriented conclusions that are used to solve business problems, formulate business strategies, and understand employees and other parties involved in business, among others. Data science has a few subsets like artificial intelligence, data analytics, machine learning, and deep learning.
Sometimes newcomers or experienced professionals use data science and data analytics interchangeably. For those individuals, here we look at the key difference between data science and data analysis.

1. Meaning

Data science is a comprehensive process of extracting information from large data sets. It is a continuous process that offers respite to companies in times of predicament. They are also used while launching a product, formulating a marketing strategy, making important business decisions, and tackling business risks and challenging situations.

Meanwhile, many have tried to define data analysis. However, the simplest meaning of the business concept is that it summarizes it as the process of examining large data sets to provide answers to complex business problem, guide businesses in making important decisions, analyse the product market, etc.

Companies combine data science and analytics to develop a lethal weapon that utilizes data to offer a competitive advantage.

2. Application

Data science can be used to conduct activities like data wrangling, statistical modelling, and programming. On the other hand, data analytics can be used for risk management, budgeting, forecasting, marketing and sales, product development, and project management.

3. Techniques

Data mining, data cleansing, data engineering, and data sorting are some of the popular types of data science techniques that data scientists undertake to ease data collection.
Meanwhile, data analysts use descriptive, diagnostic, predictive, and prescriptive analysis techniques to interpret data.

These are the key differences between data science from data analytics. Being well-versed in the difference between these two processes helps aspiring data scientists and data analysts perform their duties and tasks with precision and efficiency.

If you are planning a career in data science, and are confused about what to choose, here are two job profiles that are in huge demand in the current business milieu. The demand for trained and skilled data scientists and data analysts is undoubtedly increasing. Here’s a guide on the distinct role, responsibilities, and duties of data scientists and data analysts.

Data Analyst vs. Data Scientist

A data scientist is a skilled professional who designs new processes for prototyping, data modelling, and algorithms. They are also responsible for monitoring data extraction, cleaning, and mining processes.

Meanwhile, data analysts are skilled in examining large data sets and converting them into actionable and understandable conclusions to solve business problems. They identify trends, make charts, and create and design a visual representation of interpreted data.

The duties, roles, responsibilities, and skills of data analysts and data scientists highlight their difference. Here’s the difference between data analysts and data scientists.

1. Roles and responsibilities

Here are the roles and responsibilities performed by data scientists:

  • Identify new trends in data collection and interpretation
  • Draw business insights with the help of artificial intelligence and machine learning programmes
  • Perform data mining, data extraction, and data cleaning, among other processes

On the other hand, data analysts perform the below-mentioned roles and responsibilities:

  • Interpret data to find a pattern in them
  • Perform data querying and other similar processes using SQL and other data analytics applications
  • Use data visualization tools to explain patterns and trends to stakeholders

2. Skills

An aspiring data scientist must possess these skills to perform their duties efficiently:

  • Well-versed with math and statistical analysis
  • Basic understanding of different coding languages like R, Java, C, C++
  • Proficient in different data wrangling, data cleaning, and other data science processes

Meanwhile, a data analyst must have the below-mentioned skills.

  • Well-versed with excel and SQL processes
  • Prolific visualization and communication skills
  • Proficient in programming languages like Python, R, others

3. Educational qualifications

A data scientist must have a keen interest in computer science, software engineering, or data science to grow in the field. In addition, a strong foundation in math and statistics will help aspiring data scientists adapt and learn the data science process easily.

Meanwhile, data analysts must have a strong educational foundation in information technology, mathematics, or statistics to pursue a data analytics career. Therefore, many aspiring data analysts pursue different certification courses to gain deep knowledge about the field and learn new skills.

Emeritus India offers some of the best data analytics courses for aspiring data analysts to learn new things and skyrocket their careers. Enrol in our data analytics certification courses to get a prestigious job and competitive pay package.

Data Science vs. Data Analytics: How to Choose the Right Career Path?

Once you understand the difference between data science and data analytics, it is easy to forecast which career path to embark on. You can evaluate a career in data science and data analysis on these three parameters.

  • Personal background
  • Your interests
  • Desired salary and future growth

Analyzing data science and career paths based on these factors will help you answer- which career is right for you?


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