Data Science vs. Data Analytics: Understanding the Distinctions

2:23 pm
September 19, 2023

Summary:

Data science and data analytics are two distinct concepts that are often used interchangeably but have different focuses and tasks. Data science is a multidisciplinary field that combines mathematics, computer science, and statistics to collect and manage large-scale structured and unstructured data. It involves tasks such as developing machine learning models and artificial intelligence applications. On the other hand, data analytics involves examining datasets to extract value and find answers to specific questions. It is a subset of data science and is used to query, interpret, and visualize data. While data science and data analytics are closely related, it is important to understand the differences between the two.

Overview: Data Science vs. Data Analytics

Data science is an umbrella term that encompasses various tasks related to finding patterns in large datasets, training machine learning models, and developing AI applications. Data analytics, on the other hand, is a subset of data science and focuses on querying, interpreting, and visualizing datasets. Data analytics is often used within business intelligence platforms to gain insight into market conditions and support decision-making. While the two concepts are intertwined, data science is a broader area of expertise compared to data analytics.

Data Science: An Area of Expertise

Data science is considered its own career path and involves building statistical models, developing algorithms, and training machine learning models. Data scientists forecast outcomes, solve business problems, identify opportunities, support business strategy, automate tasks and processes, and develop AI applications. To pursue a career in data science, individuals need a deep understanding of machine learning and AI, programming languages such as Python, SAS, R, and Scala, and experience working with big data platforms. Data scientists also perform data analytics tasks, working closely with data engineers to collect, clean, and evaluate data.

The Data Science Lifecycle

Data science follows an iterative process known as the data science lifecycle. This process includes identifying opportunities or problems, data mining, data cleaning, data exploration, feature engineering, predictive modeling, and data visualization. Data scientists form hypotheses and experiment with data to achieve desired outcomes.

Data Analytics: Tasks to Contextualize Data

Data analytics is focused on contextualizing datasets to make more informed decisions. It relies on data strategies and architectures to access different types of data, regardless of where it resides. There are different types of data analytics, including predictive analytics, prescriptive analytics, diagnostic analytics, and descriptive analytics. Each type serves a specific purpose in analyzing and understanding data to gain insights and answer questions.

The Benefits of Data Analytics

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

The Dedicated Data Analyst

While anyone can analyze data, organizations often employ dedicated data analysts who specialize in data wrangling and interpreting findings. These analysts use analytical and programming skills and specialized tools such as statistical analysis software, database management systems, BI platforms, and data visualization tools. They are responsible for answering specific questions that require extensive time and attention.

Data Science, Data Analytics, and IBM

IBM offers a range of data science and AI lifecycle products to help organizations leverage the value of their data. Watsonx is an AI platform that includes components for foundation models, data storage, and governance. It enables organizations to train, deploy, and scale AI across their businesses while ensuring responsible and transparent workflows.

FAQ

What are the differences between data science and data analytics?

Data science is a multidisciplinary field that focuses on collecting and managing large-scale structured and unstructured data. It involves tasks such as developing machine learning models and AI applications. Data analytics, on the other hand, involves querying, interpreting, and visualizing datasets to extract value and answer specific questions. Data analytics is a subset of data science.

What is the data science lifecycle?

The data science lifecycle is an iterative process that data scientists follow in their work. It includes identifying opportunities, data mining, data cleaning, data exploration, feature engineering, predictive modeling, and data visualization. Data scientists form hypotheses and experiment with data to achieve desired outcomes.

What are the types of data analytics?

There are four types of data analytics: predictive analytics, prescriptive analytics, diagnostic analytics, and descriptive analytics. Predictive analytics identifies trends and correlations within datasets to make predictions. Prescriptive analytics recommends decisions and predicts likely outcomes. Diagnostic analytics helps pinpoint the reasons behind events. Descriptive analytics evaluates quantities and qualities of datasets.

What is the role of a data analyst?

Data analysts are dedicated professionals who specialize in wrangling data and interpreting findings. They use analytical and programming skills, as well as specialized tools, to answer specific questions that require a significant amount of time and attention. Data analysts help organizations understand data, make informed decisions, and find solutions to complex problems.


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