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.
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.
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.
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
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.
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
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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Frequently Asked Questions
Is data science a part of data analytics?
No, data analytics isn’t a part of data science, and there is a difference between data science and data analytics. Data science is a wider term, emphasizing the right connections between the datasets. At the same time, one of its mechanisms is data analytics, which looks for more specific findings and answers to queries raised from the data that has been gathered.
Who gets paid more, data scientist or data analyst?
Usually, the salary of a data scientist is more than that of a data analyst. However, the salary depends on the years of experience and the skills an individual possesses. The average annual salary of a data scientist in the USA is $1,17,212. In comparison, the average annual salary may be around $69,517 for data analysts.
Which is better, data science or data analytics?
Both data science and data analytics are good from different perspectives. However, data science is considered better as students can start working with a graduate degree and learn advanced skills while working. Data scientists are in high demand currently due to the excessive use of data in almost every sector.
What should you study before pursuing data science?
You can have a graduate degree in statistics or machine learning before choosing to learn data science. It is not mandatory to have a formal degree in data science to become a data scientist.
What are data science and analysis?
Data science is a multidisciplinary field that is fixated on unearthing answers from large sets of raw and structured data and finding actionable insights to problems that haven’t been thought of yet. On the other hand, the primary focus of data analytics is to assess the processing and performance of statistics of existing datasets.
upGrad Abroad Editorial Team |4611 articles published
We are a dedicated team of study-abroad experts, ensuring intensive research and comprehensive information in each of our blogs. With every piece written, we aim at simplifying the overseas education ...
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