Data science vs data analytics: Unpacking the differences

Though you may encounter the terms “data science” and “data analytics” being used interchangeably in conversations or online, they refer to two distinctly different concepts. Data science is an area of expertise that combines many disciplines such as mathematics, computer science, software engineering and statistics. It focuses on data collection and management of large-scale structured and unstructured data for various academic and business applications. Meanwhile, data analytics is the act of examining datasets to extract value and find answers to specific questions. Let’s explore data science vs data analytics in more detail.

Overview: Data science vs data analytics

Think of data science as the overarching umbrella that covers a wide range of tasks performed to find patterns in large datasets, structure data for use, train machine learning models and develop artificial intelligence (AI) applications. Data analytics is a task that resides under the data science umbrella and is done to query, interpret and visualize datasets. Data scientists will often perform data analysis tasks to understand a dataset or evaluate outcomes.

Business users will also perform data analytics within business intelligence (BI) platforms for insight into current market conditions or probable decision-making outcomes. Many functions of data analytics—such as making predictions—are built on machine learning algorithms and models that are developed by data scientists. In other words, while the two concepts are not the same, they are heavily intertwined.

Data science: An area of expertise

As an area of expertise, data science is much larger in scope than the task of conducting data analytics and is considered its own career path. Those who work in the field of data science are known as data scientists. These professionals build statistical models, develop algorithms, train machine learning models and create frameworks to:

• Forecast short- and long-term outcomes
• Solve business problems
• Identify opportunities
• Support business strategy
• Automate tasks and processes
• Power BI platforms

In the world of information technology, data science jobs are currently in demand for many organizations and industries. To pursue a data science career, you need a deep understanding and expansive knowledge of machine learning and AI. Your skill set should include the ability to write in the programming languages Python, SAS, R and Scala. And you should have experience working with big data platforms such as Hadoop or Apache Spark. Additionally, data science requires experience in SQL database coding and an ability to work with unstructured data of various types, such as video, audio, pictures and text.

Data scientists will typically perform data analytics when collecting, cleaning and evaluating data. By analyzing datasets, data scientists can better understand their potential use in an algorithm or machine learning model. Data scientists also work closely with data engineers, who are responsible for building the data pipelines that provide the scientists with the data their models need, as well as the pipelines that models rely on for use in large-scale production.

The data science lifecycle

Data science is iterative, meaning data scientists form hypotheses and experiment to see if a desired outcome can be achieved using available data. This iterative process is known as the data science lifecycle, which usually follows seven phases:

1. Identifying an opportunity or problem
2. Data mining (extracting relevant data from large datasets)
3. Data cleaning (removing duplicates, correcting errors, etc.)
4. Data exploration (analyzing and understanding the data)
5. Feature engineering (using domain knowledge to extract details from the data)
6. Predictive modeling (using the data to predict future outcomes and behaviors)
7. Data visualizing (representing data points with graphical tools such as charts or animations)

Data analytics: Tasks to contextualize data

The task of data analytics is done to contextualize a dataset as it currently exists so that more informed decisions can be made. How effectively and efficiently an organization can conduct data analytics is determined by its data strategy and data architecture, which allows an organization, its users and its applications to access different types of data regardless of where that data resides. Having the right data strategy and data architecture is especially important for an organization that plans to use automation and AI for its data analytics.

The types of data analytics

Predictive analytics: Predictive analytics helps to identify trends, correlations and causation within one or more datasets. For example, retailers can predict which stores are most likely to sell out of a particular kind of product. Healthcare systems can also forecast which regions will experience a rise in flu cases or other infections.

Prescriptive analytics: Prescriptive analytics predicts likely outcomes and makes decision recommendations. An electrical engineer can use prescriptive analytics to digitally design and test out various electrical systems to see expected energy output and predict the eventual lifespan of the system’s components.

Diagnostic analytics: Diagnostic analytics helps pinpoint the reason an event occurred. Manufacturers can analyze a failed component on an assembly line and determine the reason behind its failure.

Descriptive analytics: Descriptive analytics evaluates the quantities and qualities of a dataset. A content streaming provider will often use descriptive analytics to understand how many subscribers it has lost or gained over a given period and what content is being watched.

The benefits of data analytics

Business decision-makers can perform data analytics to gain actionable insights regarding sales, marketing, product development and other business factors. Data scientists also rely on data analytics to understand datasets and develop algorithms and machine learning models that benefit research or improve business performance.

The dedicated data analyst

Virtually any stakeholder of any discipline can analyze data. For example, business analysts can use BI dashboards to conduct in-depth business analytics and visualize key performance metrics compiled from relevant datasets. They may also use tools such as Excel to sort, calculate and visualize data. However, many organizations employ professional data analysts dedicated to data wrangling and interpreting findings to answer specific questions that demand a lot of time and attention. Some general use cases for a full-time data analyst include:

◉ Working to find out why a company-wide marketing campaign failed to meet its goals

◉ Investigating why a healthcare organization is experiencing a high rate of employee turnover

◉ Assisting forensic auditors in understanding a company’s financial behaviors

Data analysts rely on range of analytical and programming skills, along with specialized solutions that include:

◉ Statistical analysis software

◉ Database management systems (DBMS)

◉ BI platforms

◉ Data visualization tools and data modeling aids such as QlikView, D3.js and Tableau

Data science, data analytics and IBM

Practicing data science isn’t without its challenges. There can be fragmented data, a short supply of data science skills and rigid IT standards for training and deployment. It can also be challenging to operationalize data analytics models.

IBM’s data science and AI lifecycle product portfolio is built upon our longstanding commitment to open source technologies. It includes a range of capabilities that enable enterprises to unlock the value of their data in new ways. One example is watsonx, a next generation data and AI platform built to help organizations multiply the power of AI for business.

Watsonx comprises of three powerful components: the watsonx.ai studio for new foundation models, generative AI and machine learning; the watsonx.data fit-for-purpose store for the flexibility of a data lake and the performance of a data warehouse; plus, the watsonx.governance toolkit, to enable AI workflows that are built with responsibility, transparency and explainability.

Source: ibm.com