Data Science vs Data Analytics: What’s the Difference?
Updated Oct 18, 2024
Table of Contents
Introduction
Organizations are constantly looking for ways to leverage their data to save time, reduce costs, and accelerate innovation. However, there are many different approaches to consider for generating valuable insights with data. By understanding which approach better suits specific use cases, you can maximize the impact of future data and AI initiatives.
Data science and data analytics are separate yet related fields with distinct goals, tools, technologies, and required skills. Data science uses statistical and computational methods to extract insights from data, build predictive models, and develop new algorithms, while data analytics involves analyzing data to gain insights and inform business decisions.
In this article, we’ll cover the key difference between data science and data analytics so you can understand which field better suits your use case.
What Is Data Science?
Data science is a multidisciplinary field that utilizes various tools and techniques to extract knowledge and insights from data. By combining statistics, programming, and domain expertise, data scientists can analyze and interpret complex datasets to uncover actionable insights.
Some key activities of data science include:
- Data acquisition and wrangling: Collecting, cleaning, and preparing data for analysis.
- Exploratory data analysis: Identifying patterns and trends within large datasets.
- Model building: Using algorithms to create models that make predictions on new data.
- Data visualization: Communicating insights effectively using charts, graphs, and more.
Data scientists use programming languages like Python and R to perform analysis and build AI/ML models. Python is a particularly popular language for data science, data analytics, and AI because it is versatile and has a strong open science ecosystem, with a large community of developers who create libraries and tools that make it easier to use.
What Is Data Analytics?
Data analytics is the process of analyzing data to understand past performance and make data-driven decisions. This is closely related to business intelligence, which leverages actionable insights obtained from data analytics processes to improve business performance.
Primary activities of data analytics include.
- Descriptive analytics: Summarizing and characterizing historical data.
- Diagnostic analytics: Identify trends about why something occurred.
- Data visualization: Creating charts and graphs to communicate findings.
Data analysts play a crucial role in translating insights into actionable recommendations, helping organizations improve operations and achieve strategic goals. This requires a deep understanding of an organization and the ability to view insights uncovered from datasets in a business context.
Comparing Data Science and Data Analytics
The fields of data science and data analytics overlap in some ways, and both aim to generate actionable insights from data. However, there are key differences in terms of focus, use cases, tools, and required skills.
Focus of Data Science vs Data Analytics
Data science focuses on developing algorithms and models to predict future trends and patterns based on large and complex datasets. This field is more research-oriented and leverages exploratory analysis to uncover unique insights in unstructured datasets.
Data analytics relies more on smaller, structured datasets and statistics to generate actionable insights. This field is usually more about interpreting historical data to make informed decisions rather than predicting the future.
Data Science and Data Analytics Use Cases
Data science is ideal for predictive analytics, machine learning (ML) applications, and developing algorithms to uncover future trends. For example, this could include AI applications that use predictive models to analyze user data for personalized product recommendation engines or to identify suspicious behavior for automated fraud detection.
Data analytics is best for descriptive analytics, data visualization, and identifying patterns in data sets to make better decisions. This could include transforming data into insights for business intelligence use cases like optimizing product pricing or analyzing website performance.
Common Tools and Technologies for Data Science and Data Analytics
Data scientists use programming languages like Python or R to apply statistics to data and create new AI/ML models. Many different Python libraries and frameworks, such as TensorFlow, also help data scientists build and deploy machine learning applications more efficiently.
Data analysts rely more heavily on Excel and SQL to manipulate data and extract relevant insights. They also use business intelligence and data visualization tools like Power BI and Tableau to retrieve and analyze data.
Required Skills for Data Science and Data Analytics
Data science generally requires more technical skills, such as programming and knowledge of machine learning algorithms. In addition to computer science and mathematics knowledge, data scientists need to know enough about the business to generate and communicate relevant insights.
Data analytics requires knowledge of statistics and data visualization skills to effectively interpret data and present findings to business stakeholders. Strong analytical skills and proficiency in data querying and reporting tools are also essential for data analysts.
Choosing the Right Approach: Data Science vs. Data Analytics
The right approach to your business use case depends on the availability and complexity of relevant datasets as well as the resources and skills within the organization. Both fields have a role to play in most organizations, but in certain situations, one approach can provide the most valuable insights.
Data science is more appropriate for use cases that require very large data sets or require predictive models. Although advanced techniques like machine learning can require additional resources, they’re more suitable for complex data sets and unstructured data. Example use cases include optimizing marketing campaigns and predicting customer churn by analyzing raw data from many different sources.
Data analytics is more appropriate for situations where descriptive or diagnostic analysis can be applied to structured datasets. For example, understanding customer demographics or calculating sales trends involves more straightforward data analysis and interpretation. These use cases typically generally require fewer resources than data science initiatives and are better suited for organizations that need to generate immediate insights with limited technical skills.
The Role of AI in Data Science and Data Analytics
AI is fundamentally transforming data science and data analytics by automating complex processes, enhancing predictive accuracy, and enabling more sophisticated analyses. By integrating AI into data workflows, data scientists and data analysts can handle larger datasets, uncover deeper insights, and develop more robust models with greater efficiency.
Anaconda’s research for the State of Data Science Report discovered that the majority (63%) of data science practitioners say they’re using generative AI the same amount or more this year compared to 2022. That’s because generative AI has developed rapidly over the last few years, and can now automate many repetitive tasks with greater reliability than in the past.
AI is democratizing data-related fields by lowering barriers to entry and automating many of
the complex, coding-intensive tasks. For example, AI can automate tedious data preparation work like cleaning, wrangling, and feature engineering. This is having an enormous impact on the fields of data science and data analytics, with the majority (65%) of IT workers and nearly half (45%) of data science practitioners now concerned about job security.
Despite these concerns about AI replacing data roles, Anaconda’s research also found that the majority of organizations are providing upskilling pathways to help employees learn new tools and technologies. This means AI is an opportunity for both organizations and employees to explore new possibilities in the fields of data science and data analytics.
Anacondaʼs Approach to Data Science and Data Analytics
As you can see, data science and data analytics are distinct fields that each have a role to play in most organizations. A comprehensive platform like Anaconda gives both data scientists and data analysts the tools they need to deliver measurable business value from data.
Anaconda aims to streamline entire AI and data workflows, from model development to deployment and monitoring, through its robust data ecosystem. Our platform comes with pre-installed data science, data visualization, and data processing libraries, and a robust package manager makes it easy to install other libraries for data analytics and AI/ML.
Cross-platform compatibility and the ability to work with many popular IDEs make Anaconda easy for any organization to get started. The large and active Anaconda community — much like the Python ecosystem — also has extensive documentation, tutorials, and support resources to help organizations along the way.
Anaconda is an ideal choice for both data science and data analytics projects. Request a demo to see if our tools can help you extract more value from your data.