Difference Between Data Science and Data Analytics

Driven by Information Technology (IT) and Information Technology Enabled Services (ITES), data science and data analytics are flourishing professional fields with lucrative career prospects. Machine Learning (ML) and Artificial Intelligence (AI) are quickly integrating into every aspect of our daily lives and businesses. The well-known behavioral economics expert Dan Ariely once said about big data, “Everyone talks about it, nobody really knows how to do it, everyone thinks everyone else is doing it, so everyone claims they are doing it.” Data scientists and analysts have vital roles in gaining insights into data and using them for better performance. Knowing the difference between data science and data analytics is important as it helps students choose the area they want to explore further.

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The proliferation of data has surged with technological advances such as the Internet of Things (IoT), smartphones, and social media. In a survey conducted by Techjury, the globe’s GDP was predicted to have undergone digitization by 2022, and 1.7 MB of data was created by a person every second in 2020. While this exponential growth has made organizations strategize their plans to leverage profits, individuals are looking forward to upgrading their data skills to make a career in this domain that is in demand. In order to bolster their data skills, they are often faced with two terms – Data Science and Data Analysis. Though they are often used interchangeably, they are different things. Today, we will explain the difference between data science and data analytics and how each of them can be applied for the betterment of the business. 

What is Data Science? 

It is a multidisciplinary area emphasizing the discovery of actionable findings/insights from both structured and raw data sets and giving them a shape so that they can be easily understood. It cleans, builds, and organizes datasets by leveraging algorithms and statistical models. The field focuses on finding answers to several aspects using ML, computer science, predictive analytics, and statistical techniques. The main objective of data scientists is to ask questions and find solutions or answers. They usually achieve this through the prediction of prospective trends, exploring disconnected or disparate data sources, and finding newer ways to decode information. 

Data scientists lay the foundation for an organization’s analyses by performing data wrangling, statistical modeling, and programming. This ensures that most of the business decisions are data-driven and backed by numbers. 

What is Data Analytics? 

Data Analytics focuses on processing current data sets and conducting statistical analysis of the same. Analysts emphasize identifying trends, building new methods for processing, capturing, and organizing data for finding actionable insights into current issues, and presenting the information in the best possible manner. This is done with the help of an array of tools, frameworks, and techniques through different types of analysis being conducted. The four major types of analytics include Diagnostic analysis, Descriptive analysis, Prescriptive analysis, and Predictive analysis. 

The field works to solve problems for queries such as why the sales dropped in a particular quarter, how internal attrition can affect revenue, and the reason that a particular marketing campaign performed well in a particular region. It is also result-based and aimed at bringing about instant improvements. A successful data analyst is one who can effectively communicate quantitative findings to non-technical clients or colleagues.

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What is the Difference Between Data Science and Data Analytics? 

Area	Data Science	Data Analytics
Goal-Based Differences	Data analysis focuses on answering questions for swifter and improved decision-making at businesses. It makes use of available data for unearthing actionable insights. Analytics has a specific area-wise focus with defined objectives.	Data Analytics focuses majorly on finding new questions that may not have come up till now and then finding their answers. Data science attempts to build links for shaping new questions and answering them for posterity. This is a unique aspect of this field. It has a broader field of operation.
Skill-Based Differences	The skills required for data analytics:    Intermediate statistics  Excel, SQL database BI tools like Power BI for reporting purposes  A Hadoop-based analysis is used for getting conclusions from raw data Skills in modeling, databases, statistics and predictive analytics are sure to benefit aspiring data analysts, and they do not always need to have an engineering background for the same	Skills required for data science include the following: Advanced statistics Mathematics Machine learning Predictive modeling In-depth knowledge of programming  Data Visualization with D3.js, Tableau, QlikView, and other tools  SQL, NoSQL databases like MongoDB and Cassandra Programming languages like Scala, R, and Python
Career Prospects	Data scientists should have an educational background in software engineering, data science, or computer science.   The difference in Job Roles:   Cleansing, processing, and verifying data integrity   Gathering business insights via algorithms and ML techniques   Exploratory analysis   Identification of new trends with future predictions	Data analysts ideally pursue undergraduate courses in IT (Information Technology), computer science, statistics, and mathematics.   The difference in Job Roles:    Data cleansing   Exploratory analysis   Developing KPIs and visualizations   Finding patterns and statistical tools

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Career Prospects in Data Science and Data Analytics 

While there are differences between the two fields, they are two of the in-demand profiles in the business world at the moment. The career progression is similar for data analysts and data scientists. While data scientists should have an educational background in software engineering, data science, or computer science, data analysts ideally pursue undergraduate courses in IT (Information Technology), computer science, statistics, and mathematics. Here is a comparison of job roles in both domains.

Difference in Job Roles for Data Analytics and Data Science

Data Science	Data Analytics
Cleansing, processing, and verifying data integrity	Data cleansing
Gathering business insights via algorithms and ML techniques	Exploratory analysis
Exploratory analysis	Developing KPIs and visualizations
Identification of new trends with future predictions	Finding patterns and statistical tools

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Data Science or Data Analytics: Which one to Choose? 

While it is tough to choose between data analytics and data science, there are some things to keep in mind. Data analytics is better for professionals with 2-5 years of work experience, an interest in building data models, and expertise in data warehousing. They should ideally have capabilities for using this expertise with tools like Python, Excel, SQL, Tableau, and Power BI for performing analytical tasks and creating dashboards. Professionals in areas like data warehousing, database administration, sales/marketing/finance, Ops, QA engineering, and SCM should consider data analytics. 

Data science is a better pathway for professionals with 1-10 years of work experience and a desire to learn Python programming. It is a good option for those working as business analysts, BI engineers, IT application engineers, data analysts, and architects. Aspirants in this space with a desire to boost analytical skills may expect a rewarding career in data science.

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Before deciding which one to choose, students should also reflect on their interests. If they are excited by numbers, statistics, and programming, and want to work exclusively in uncovering data points from complex and disparate sources, then data analytics is a perfect fit for them. However, if their area of interest lies in possessing a knowledge of the business world along with a blend of maths, statistics, and computer science, then a career in data science should be pursued.

Difference Between Data Science and Data Analytics with an Example

Data Science

Data Analytics

To be a data analyst, it is not mandatory to have an engineering background, but strong skills in database, statistics, modeling, and predictive analytics are needed. Here are some of the examples by which data analytics can be used. Increasing the quality of medical care through a digitized healthcare system and electronic health record systems Stopping hackers in their tracks by creating improved detection of threats Developing reasonable warranties for products

Data science focuses on mathematics, advanced statistics, machine learning, predictive modeling, and programming. Here are some examples of channeling data to expand a business. In the manufacturing industry by creating forecasting of product demand For optimizing supply chain in the logistics industry Credit scoring for financial institutions by analyzing a customer’s banking history

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Similarities Between Data Science and Data Analytics

There are several similarities between data science and data analytics. Both make use of data to better comprehend an organization’s operations, which aids decision-making. Both are heavily STEM-focused fields that are in high demand in a variety of sectors. A few more similarities between the two are listed below:

Massive quantities of data

Data scientists and analytics experts work with massive data sets that contain millions of data points. It’s possible that these enormous datasets contain low-quality data that needs to be wrangled (cleaned), maintained, and organized in order for any analysis to be accurate.

• Technical skills

Both fields demand expertise in statistics, Excel, data visualization, modeling, and programming (such as R, Python, Tableau, and SQL). Highly analytical individuals that approach problem-solving and project management methodically are required for success in both disciplines.

• Communication skills

Data scientists and analysts work with colleagues from other departments, many of whom might not have technical backgrounds. It is the responsibility of experts in both domains to communicate their findings in an understandable and compelling way.

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