Data analytics is the practice of examining data sets to answer business questions, while data science is the broader field that includes analytics along with statistics, machine learning, predictive modeling and visualization. Analytics is one stage of the data science lifecycle.
Both fields work with big data, the huge volumes of raw and unstructured data generated every day, but they differ in scope, skills and goals.
Key takeaways
- Data science is the wider discipline, and data analytics is one of its phases.
- Analytics focuses on analyzing existing data to solve business problems.
- Data science adds statistics, domain knowledge and machine learning to predict and model outcomes.
- Analytics is often seen as a practical entry point, and moving from analytics to data science is generally easier than the reverse.
What is big data and why does it matter?
As business models become more interactive, traditional ways of gathering data are no longer enough. The large quantity of data generated daily from many sources is called big data. Organizations then need systems that collate it, filter it for the relevant audience, apply statistical and machine learning models, and predict future outcomes from current data. It works like a feedback loop.
A familiar example is a social media feed. If a user watches a certain kind of video or reads a certain type of post, similar content and similar ads appear later. Platforms such as Facebook collect these interactions to track what each user likes and ignores. In this case, a machine uses the data: the system picks the next suggestion based on earlier choices, and online shopping recommendations work the same way.
Personalization at that level depends on both data analytics and data science.
What is data analytics?
Data analytics uses tools and techniques to examine large amounts of data and extract business benefits, rather than relying on manual organization alone. It involves a few straightforward steps.
- Define requirements: decide what data is needed and how to group it, based on the target audience or business problem. Data may be sorted by age, location, gender, interests or lifestyle.
- Collect data: gather it from online and offline sources, such as computers, surveys and social media.
- Organize data: prepare it for analysis. Spreadsheets are the most common tool, although frameworks such as Apache Hadoop and Spark are increasingly used instead.
- Clean data: remove incomplete, inconsistent and duplicate records and correct errors so the data is ready to analyze.
What is data science?
Data science has a broader scope than analytics. Analytics sits inside it as one phase of the lifecycle. A data scientist uses programming languages such as Python and SQL, like an analyst, but combines them with statistical knowledge and domain expertise to produce insights that can substantially improve a business. Machine learning algorithms are used to interpret text, images, video and audio.
A data scientist identifies and defines potential business problems from many unrelated sources and gets data from them. After the data is analyzed, a model is built and tested for accuracy, repeating the process to improve it.
Main components of data science
- Statistics: collecting, analyzing, interpreting and presenting data using mathematical methods.
- Data visualization: presenting results as diagrams, charts and graphs that are easy to read, which speeds up decisions by highlighting key takeaways.
- Machine learning: intelligent algorithms that learn on their own and predict human behavior as accurately as possible. See also the machine learning category.
How do data analytics and data science differ?
| Aspect | Data analytics | Data science |
|---|---|---|
| Scope | One phase of the lifecycle | The complete process, including analytics |
| Main goal | Analyze data sets to solve business problems | Produce insights, build models and predict outcomes |
| Typical skills | Programming such as Python and SQL, data preparation | Analytics skills plus statistics, domain knowledge and machine learning |
| Typical output | Graphs, charts and diagrams of insights | Tested models and the full business process around them |
Data science covers the entire business process: working with stakeholders, storytelling, data analysis, preparation, model building, testing and deployment. Analytics is one of those stages, and a large one, in which big data is analyzed and the insights are presented as graphs, charts and diagrams. For more on data science topics, see the data science category.
Which one should you start with?
Data science offers a broad future, but anyone who wants to stay closer to programming and practical analysis may find data analytics the better starting point. It is easier to move up from analytics to data science than the other way around. Both fields are hungry for data, and working across them builds the full picture.
Frequently asked questions
Is data analytics part of data science?
Yes. Analytics is one phase of the data science lifecycle, focused on analyzing data and presenting insights.
What is big data?
Big data is the large volume of raw or unstructured data generated every day from many sources, which requires dedicated systems to collate and analyze.
Do both roles use Python and SQL?
Yes. Python and SQL are common to both, but data scientists add statistics, domain knowledge and machine learning.
Which is easier to start with?
Data analytics is generally the more approachable entry point, and it provides a path toward data science.
Conclusion
Data analytics and data science are closely connected but not identical. Analytics examines data to answer business questions, while data science covers the whole process from problem definition to tested models. Beginning with analytics is a practical way to grow into the wider field.
