Data Science vs Data Analytics: A Complete Breakdown
Having difficulty in choosing the right career path between data science vs data analytics? You are not the only one who is confused about this! Most people are struggling hard to understand the difference between data science and data analytics, the two rapidly growing fields. You might have noticed that both of them are related to data, yet they serve different purposes and demand different types of skills and knowledge.
It is very challenging to pick the most suitable career path to pursue or hire the right candidate for the different fields. What if there could be something that helps you to understand their different purpose, job outlook, skills, different tools usage and more?
In this blog, I will discuss the differences between data science and data analytics in simple and easy terms. At the end of this article, you will be able to learn about each field and how they help businesses to make data-driven decisions.
Let’s read this article carefully.
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Table of Contents
Data Science vs Data Analytics: At a Glance
Data Science and Data Analytics are different branches of the same tree and are frequently used interchangeably. They both serve different purposes and their core principles are also distinct from each other. In this section, I am going to elaborate on the concept of both disciplines before discussing the difference between data science and data analytics.
While data analytics focuses on analyzing preset data to make data-driven decisions, data science focuses on developing predictive analysis and detecting hidden data patterns.
Having a clear idea of their differences will assist you in finding out which data field fits you best and aligns with your business objectives.
What is Data Science?
The technique of collecting vital insights from large and complex data sets is referred to as data science. Machine learning, statistical models, and artificial intelligence (AI) are some of the advanced tools it employs. To deal with complex challenges, data scientists make predictive data models. Math, domain expertise, and coding are all integrated into this field of programming. Offering solutions to ‘what might happen’ questions is its primary purpose. Data science shows wide use in areas like healthcare, custom suggestions, and fraud alerts.
What is Data Analytics?
The study and interpretation of data to come up with significant findings is referred to as data analytics. Assessing data trends, the latest data patterns, and valuable insights is its primary purpose. In order to offer solutions to “Why things happened” or “what happened” questions, data analysts take the help of tools like MySQL, SQL, Excel, and visualization tools. Data analytics assists businesses in planning strategies, motor performances, and optimizing processes. It is employed to make real-time data-driven decisions in finance, supply chain management, and marketing domains.
Data Science vs Data Analytics: What are the Differences?
Both these fields are two different subjects of the same discipline, usually reciprocal to each other. I have tried to mention every single factor that distinguishes data science from data analytics and help you to understand them better. This leads you to choose the most suitable one according to your interests and passion for one specific data field.
Let’s get going.
1. Purpose
The major purpose of data science is to detect hidden data patterns and predict unforeseen events. It utilizes advanced ways or technologies to offer solutions to questions such as ‘What might happen?’ ‘How will it happen?’.
However, data analytics is the discipline of using the latest and real-time data in order to detect unknown data patterns and provide vital insights. It offers solutions to questions such as ‘What happened?’ and ‘Why did it happen?’ It also implies that while data analytics is solution-focused and decision-driven, data science is highly research-driven.
2. Approach
This approach to data science is predictive and exploring. It develops data models to predict future trends and events. Data analytics uses an evaluation and descriptive approach to analysis. In order to detect data patterns and fix specific challenges, it examines previous data. For practical usage, data analytics relies on analyzing data patterns, but data science entails studies and simulations.
3. Role and Responsibilities
A data scientist designs machine learning models, creates algorithms, and assesses data to detect data patterns. They perform a more research-specific task. A data analyst assists businesses in making data-driven decisions by cleaning, organizing, and decoding data. Analysts focus on developing reports and dashboards to show their findings. Due to this, the roles and responsibilities of a data scientist are more complex and focused than the duties of a data analyst.
4. Coding Language
Data scientists often work with advanced coding languages such as Python, Julia, and R. Both developing data models and running simulations need these. The main programs data analysts utilize are Exel, SQL, and often Python for simpler basic data analysis jobs. Data Scientists should be more focused on coding, while Data Analysts prefer software or techniques that are easy to learn.
5. Scope
The scope of data science is macro, while the scope of data analytics is micro. As data science addresses big data, artificial intelligence, and data predictive models, its reach is wider. This remains true for fields such as finance, technology, and healthcare. The scope of data analytics is highly focused on business data, operational productivity, and fast decision-making. It better meets specific business needs.
6. Qualifications
Higher degrees or certifications in data science, statistics, or computer science like doctoral or master’s degrees, are usually essential for data scientists. A bachelor’s degree in related fields, such as statistics or computer science, is necessary for joining a data science course and becoming a certified one.
Strong technical skills and a thorough knowledge of machine learning are highly recommended. An Information Technology (IT) bachelor’s degree in mathematics or business is usually essential for a data analyst. Data analysts frequently only require certification programs such as Power BI or Tableau.
7. Programming Skills
Data science needs a detailed understanding of programming, while you only need basic programming or coding skills to join a data analytics course and become a certified data analyst.
To develop algorithms and data models, data scientists need advanced programming skills. They employ advanced and modern data frameworks and extensive libraries. Fundamental programming skills are essential for data analysts to conduct activities such as report automation and database searching. Compared to data scientists, data analytics do less amount of coding work.
Some other significant skills for data scientists are machine learning, AI, predictive modelling, big data and computer programming. On the other hand, the necessary skills to become a data analyst are Microsoft Excel, SQL, data visualization, SAS and communication skills.
8. Data Type
Structured data is the specific data type used in data analysis, while unstructured data is addressed by data science. Structured, unstructured, and semi-structured are all kinds of data types popularly used by data scientists. They manage large datasets from various sources. Structured data, such as spreadsheets and well-organized databases, is the main source of data for data analysts. Collecting and assessing well-optimized data sets are part of their job responsibility.
9. Use of Machine Learning
Machine learning is a major element of data science for artificial intelligence and predictive data modelling. One of the key roles and responsibilities of a data scientist is related to machine learning. Machine learning is rarely employed in data analytics. In order to get an idea of the real-time data, it prioritizes more on statistical procedures and visualization software.
10. Types
The various types of data science are AI or artificial intelligence, deep learning and machine learning. Image detection and natural language processing are two other sub-types of data science.
There are four different types of data analytics and they are diagnostic, predictive, prescriptive and descriptive analytics. These types of data analytics help you in solving critical business issues and manage real-time data.
11. Tools Used
While we are talking about tools, both data analytics and data science operate differently, requiring different types of tools. PyTorch, Apache Spark, SAS, Tableau, TensorFlow, BigML and Weka are some of the popular tools used by data scientists.
On the other hand, PowerBI, Qlik, Looker, Exel, Splunk, Sisense, and SQL are some of the widely used tools by data analysts.
While the tools used by data scientists help in building data models and big data, data analysis tools assist in data reporting and data visualization. The tools used by data scientists are complex compared to the ones used by data analysts.
12. Major Fields
Robotics, automated cars, and medical research are some of the few sectors that employ data science to boost entire data findings. It has a significant effect on creativity and innovation. Marketing, supply chain, sales, and data management all make up a considerable amount of application data analytics. It helps in boosting the efficiency and client happiness.
13. Salaries
As a data scientist fresher, you can expect salaries ranging from 7L to 8L per annum. Once becoming a professional data scientist after gaining expertise in this field, top companies will provide you with opportunities for high salary packages ranging from 13 LPA to 15 LPA.
As a data analyst fresher, you can expect salaries ranging from 4L to 7L per annum. Once gaining expertise in this data analytics field, top employers will offer you a good salary package ranging from 10 LPA to 12 LPA.
Data Science vs Data Analytics: Which Data Career Fits You?
Depending on your goals and areas of preference, you may choose between data science and data analytics. Data science might be the perfect career field for you if you have a passion for working with AI, making algorithms, and solving complex data challenges.
For individuals who love to solve mathematical problems, coding, and creativity, this field is appropriate. Excellent technical proficiency is essential for the roles of data science, which often involves developing data predictive models.
However, data analytics is also another great choice if you choose to employ data analysis to make data-driven decisions. It is perfect for candidates who have an interest in e employing tools such as visualization tools, SQL, and Excel. Businesses can boost performance and functionality with the help of data analytics that emphasizes practical solutions.
Huge opportunities are arrived in these two professions. Pick data analytics for real-time insights that are highly effective and company-focused or data science for duties that are tech-driven and highly challenging.
Wrapping Up,
While data science and data analytics are closely related, they serve different purposes and suit the various demands of data management and data handling. If you have complete knowledge and characteristics of these two distinct disciplines, this can help you in making data-driven decisions and valuable insights.
Hoping, I have discussed the comparison between data science vs data analytics in this above article clearly and in an effortless manner. If you have any other contrasting factors in mind that I have missed above, please feel free to state them in the below comments section.
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Happy reading.