By Austin Chia April 13, 2023
There is a lot of confusion surrounding the terms "data science" and "data analytics." Some people use them interchangeably, others see them as completely different concepts.
In this blog post, we'll demystify the differences between these two terms. We will start by defining each term, followed by the key differences between them, similarities, roles each of them support, as well as uses cases.
Read on for a detailed comparison!
What is Data Science?
Data science is an interdisciplinary field that combines mathematics, statistics, computer science, and domain-specific knowledge to extract insights from large sets of structured and unstructured data.
The data science field involves collecting, organizing, cleaning, analyzing, and interpreting data to discover patterns and correlations that can be used to make predictions or decisions through artificial intelligence or machine learning.
What is Data Analytics?
Data analytics is the process of analyzing raw data to discover trends and insights. It involves cleaning, organizing, visualizing, summarizing, predicting, and forecasting.
The goal of data analytics is to use the data to generate actionable insights for decision-making or for crafting a strategy.
(Learn about the related practices of ETL & data normalization.)
Differences between Data Science & Data Analytics
When I first entered the world of data, these two terms seemed to be used interchangeably by my non-technical co-workers as well as in many of the online courses I took on. However, I always wondered what their differences are. After spending some time researching and being in the field, I discovered some key factors that differentiate the two.
Here are some of the differences between data science and data analytics:
Goal
The goal of data science is to extract insights from large sets of structured and unstructured data. The goal of data analytics is to use the data to generate actionable insights for decision-making.
Processes
Data science uses various techniques such as machine learning, deep learning, predictive modeling, and natural language processing (NLP) to uncover patterns and trends in data. Some examples of tools used in data science include:
- Python
- R
- Apache Spark
- Tensorflow
- Git
Data analytics focuses on using statistical methods, visualization and a variety of tools for analyzing and interpreting data. Some examples of tools used in data analytics include:
- Microsoft Excel
- SQL
- Python
- R
- Tableau
- Microsoft Power BI
Output
The output of data science is often predictive models that can be used to make decisions or predictions. On the other hand, the output of data analytics is generally insights or reports.
Remember, though, that outputs are not the same at outcomes.
Skillsets needed
Data science requires expertise in mathematics, statistics, coding and domain-specific knowledge. Conversely, data analytics is focused on understanding the data and deriving insights from it — you don’t need to understand the maths, stats and codes behind it.
Scope
Data science has a much broader scope than data analytics and includes activities such as data wrangling, feature engineering and building machine learning models.
Data analytics is more narrowly focused on analyzing data to generate insights or develop strategies.
(Read about the 4 types of data analytics your business needs.)
Similarities between Data Science & Data Analytics
Despite their differences, there are some similarities between data science and data analytics. Here are some similarities I found between these two fields in my experience speaking and working with both data scientists and data analysts:
- Both data science and analytics use data to draw insights and make decisions.
- Both processes involve collecting, cleaning, organizing and analyzing data.
- Both processes involve using statistical methods and techniques to discover patterns in the data.
- Both roles require knowledge of programming languages such as Python or R.
- Both processes involve creating visualizations for presenting data.
Roles supported by Data Science and Data Analytics
Data science and analytics are two distinct fields with their own roles that you can specialize in. You can also earn a data science or analytics certification.
Here is a quick overview of the different roles that each one supports:
Data science roles
With a greater focus on predictive analysis, data science jobs tend to involve building machine learning models and algorithms. Within data science, these are common roles:
- Data Scientists work on building predictive models using large amounts of data.
- Machine Learning Engineers research machine learning models to automate AI/ML models.
- Artificial Intelligence (AI) Engineers build and maintain AI systems.
- Data Engineers work on the architecture and design of data pipelines.
However, roles in the data science field are not limited to these, as the data science field is a larger umbrella that includes all roles related to data.
Data analytics roles
Roles in data analytics are slightly different: they're focused on analyzing data and deriving insights from it. Roles involved in data analytics may include:
- Business Analysts analyze business processes through customer data by creating reports to drive business decisions.
- Data Analysts analyze raw data to uncover trends, patterns and correlations.
- Data Visualization Developers create visual representations of data.
- Statistical Analysts use statistical methods to draw insights from data.
- Business Intelligence Analysts extracts and cleans data from business data warehouses for deeper analysis in business intelligence (BI).
- Marketing Analysts use data to optimize marketing campaigns and understand customer behavior.
- Financial Analysts analyze financial data such as income statements and balance sheets to make decisions.
Use cases
Now that you understand the roles each field supports, you might be curious about their specific use cases for different industries. Here are some actual common use cases I've seen and heard about!
Data science use cases
Predicting customer churn
A common use for data science is:
- Predicting customer churn.
- Ensuring that the churn rate stays low.
By understanding customer behavior and creating predictive models, data scientists help companies create strategies to retain customers and minimize churn.
Creating personalized product recommendations for e-commerce
Personalized product recommendations are another great use for data science.
By analyzing customer data, data scientists can build machine-learning models that generate personalized product recommendations for each user. This will drive more relevant sales on websites.
(See how much you can do with analyzing log data.)
Developing automated fraud detection systems
The banking and finance industry can also harness the power of data science through the use of fraud detection systems. By creating predictive models that analyze patterns in customer data, data scientists can help companies identify suspicious behavior and alert them of potential fraud cases.
(Create a fraud risk scoring model with Splunk.)
Use cases for data analytics
Data analytics also has its own set of use cases. In fact, many of these uses are becoming more commonplace across many businesses that want to leverage insight from the large amount of data they produce.
Analyzing user engagement for mobile apps
One example of data analytics being used is measuring user engagement on apps. Also known as mobile analytics, this allows companies to measure user engagement and understand how users are interacting with their apps.
This helps them tweak their product and user experience to optimize results.
In my line of work in data, I had opportunities to dive into mobile data to test the usage of mobile features on an app. This drove more business decisions in selecting which areas to work on and prioritize for mobile development.
(Learn about the closely related behavior analytics.)
Analyzing customer data to generate better marketing campaigns
Data analytics can also be used in the marketing world by analyzing customer data and creating segmented audiences that serve a personalized ad message. By understanding customer behavior, data analytics can help marketers to:
- Develop better campaigns.
- Target the right people with the right message at the right time.
Optimizing pricing and inventory management for retailers
Retailers can also benefit from data analytics by understanding customer behavior and creating strategies to optimize their inventory management and pricing.
Data analytics can help retailers find the best price points for their products, as well as understand when it's time to restock or discount items.
Analyzing website performance and user journey
Finally, data analytics can also be used to track and analyze website performance. This allows businesses to understand:
- How users interact with their websites.
- What improvements need to be made in order to provide a better user experience.
By understanding the user journey, businesses can create strategies to optimize the customer experience. This may also be useful for businesses that rely heavily on website traffic to bring in revenue, such as blogs and e-commerce sites.
Final thoughts
In conclusion, data science is the practice of creating predictive models using data, while data analytics is the practice of extracting, cleaning, and processing data to bring about insight. Though both practices involve working with data, they each have their own set of roles and use cases that can provide businesses with valuable insights.
I hope this article has helped shed some light on the differences between data science and data analytics and their respective use cases.
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This posting does not necessarily represent Splunk's position, strategies or opinion.