Data science vs data analytics: what's the difference?
By Indeed Editorial Team
Updated 14 January 2023
Published 28 April 2022
The Indeed Editorial Team comprises a diverse and talented team of writers, researchers and subject matter experts equipped with Indeed's data and insights to deliver useful tips to help guide your career journey.
Big data can have a significant influence on the choices and success of a business. Data science and data analytics are two major fields in big data that can help a company collect, analyse and generate data reports. Understanding the differences between them may help you make a career choice that inspires you. In this article, we define data science vs data analytics, discuss the similarities and differences between the two and review considerations to make when choosing a career between data analytics and data science.
Data science vs data analytics
To distinguish between data science vs data analytics, it's important to start with their definitions. Here are the definitions of data science and data analytics:
Data science is a practice that seeks to gain insight from all relevant data, such as industry-wide statistics of the population, customer habits and data collected from social media platforms. The overall aim is to find correlations between events to reach a positive solution. Data science uses statistical software to extract useful information for businesses to make informed decisions.
Businesses can use data science as a tool to achieve various goals. One of the most prominent uses for business intelligence and data science is creating predictive models for supply chain management, customer service and sales forecasts. These models can assist businesses to optimise their operations, decrease costs and focus on what is essential.
Data analytics is a field of study that uses multiple data sources to create valuable information. Experimentation and statistical analysis are used to conduct research and discover trends or patterns within the data. Its primary aim is to influence critical business decisions based on the data. Another purpose of data analytics might be to identify critical trends and variables for analysis, allowing businesses to make the best decisions.
Data analytics can be helpful to many industries to monitor and collect data on employee performance and resource allocation. The data helps a company ensure it allocates its resources efficiently and improves decision-making to increase revenue. For example, website data analytic tools can generate reports for an e-commerce company on the best-selling products. It can use such information to create strategies to help increase leads to other products.
Related: How to become a data analyst
What are the similarities between data science and data analytics?
Data science and data analytics are two of the most significant research-based fields. Here are the similarities they have:
Reveal data trends
Businesses can use data science and analytics to reveal data trends. Data science uses multiple analysis methods to create predictive models for companies. They can use these models to prepare for various scenarios. For example, if a trend anticipates an increase in customer demand, a business can purchase more stock to fulfil the demand. Data analytics uses statistical methods to analyse a company's data to determine various trends. For example, monitoring sales trends can help a business calculate demand forecasts and prepare for them.
Create new models
Data science and data analytics can allow businesses to make models that utilise their data from multiple sources to create new experimental models. If the model performs well, they continue to run the same experiment, analysing the results further until achieving a satisfying outcome. Businesses can also create a model based on multiple stock markets, historical data or other sources of information.
Use statistical applications
Data science and data analytics may use statistical applications to extract useful information from multiple data sources. It includes the statistical analysis of time-series data using time-series analysis and machine learning systems. These statistics or relationships within the data can help a company make informed decisions and understand the various risks they face.
Data science can use statistical software to evaluate the outcome of experiments on a specific input variable. This evaluation allows businesses to understand how their real-world experiment results relate to the original hypothesis. Data analysts use similar processes to identify trends, associations and outliers because of circumstances outside their control, depending on their assumptions during experimentation.
Allow for data sharing
Data science and data analytics allow data sharing between two or more people to find the right problem solutions. Sharing data can enable businesses to harness the power of multiple people, allowing them to share better solutions with their competitors. The power of both these fields rests on how well they share data between a multitude of sources to create meaningful information. Data science uses predictive modelling for high-level business decisions and data analytics uses statistical methods for analysis.
Harness the power of computing
Data science and data analytics, high-level computer sciences, rely on computers to create conclusions based on data. Both of these fields use the processing power of computers to conduct an infinite number of experiments and analysis. Data science uses data collected via computers to analyse social media trends. Both data science and data analytics harness the power of computing to make more accurate business decisions.
What are the differences between data science and data analytics?
The following are the differences between data science and data analytics:
Role within companies
The role of data science is to find patterns in the data to allow businesses to make high-level decisions. It includes using algorithms, statistics, previous results and experimentation designed to see trends and make predictions about the behaviour of the business. Data analytics focuses on statistical analysis to discover trends, associations and factors from other sources of information. Data analytics focuses more on gathering information from historical data or other sources from outside the company.
Data science finds the predictive behaviour of a company through multiple statistical analysis methods like regression and classification. Analytics uses statistical techniques to focus on the importance of data collection and analysis. It can help you make better business decisions by correctly assigning the weight of the costs and benefits of a particular process.
The most significant difference between data science vs. data analytics is that data science uses real-world experiments using statistical methods to gain new empirical knowledge. Data analytics uses historical data from a company or other sources and applies statistical analysis to make critical business decisions. Data science is more about discovery, whereas data analytics focuses more on increasing revenue for businesses.
Data analytics essentially deals with lower risk based on past trends and other factors outside the company's control. Data science focuses on higher risk, as it can find patterns and implement them accurately for future use. This is mainly because data science may find new trends using the experimental data collected.
Choosing between data science or data analytics
Consider the following points when choosing between data science or data analytics fields:
Data scientists and data analysts usually have very similar careers as both implement computer science. Data science can be more popular than data analytics, depending on your career goals. If you enjoy working in a specific industry or business that uses statistical analysis or quantifying and measuring correlations, data analytics could be a good choice because it can also help with estimation, preparation and production. Businesses that rely on predictive modelling also rely on data science.
Data science is more prevalent in industries that use predictive modelling methods, which require past data analysis. For example, it's common for social media analytics to use data collection to identify the most influential people on the internet to generate leads. It's also helpful in sports analytics as it studies the relationship between specific events and other factors. Therefore, it may help you find a job in an industry that uses data science to enable you to continue working towards your career goals.
Companies can use data science for their software engineering and development jobs by applying data analytics to statistical programmes. A data analytics approach focuses on analysing data from all relevant sources. It can be useful for knowing if a specific process involving a massive number of factors works. Data science focuses on ways to gather the data.
Data science and data analytics both require skills in statistics. The former requires more complex programming skills to help create programmes and systems to collect data. Data analytics requires more analytical skills to help assess the data a company collects. You might also require computer skills to execute analysis on spreadsheet programmes.
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