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Learn how data science can unlock business insights and accelerate digital transformation and enable data-driven decision making.

Data science combines math and statistics, specialized programming, advanced analytics, artificial intelligence (AI), and machine learning with specific subject matter expertise to uncover actionable insights hidden in an organizations data. These insights can be used to guide decision making and strategic planning.

The accelerating volume of data sources, and subsequently data, has made data science is one of the fastest growing field across every industry. As a result, it is no surprise that the role of the data scientist was dubbed the sexiest job of the 21st century by Harvard Business Review (link resides outside of IBM). Organizations are increasingly reliant on them to interpret data and provide actionable recommendations to improve business outcomes.

The data science lifecycle involves various roles, tools, and processes, which enables analysts to glean actionable insights. Typically, a data science project undergoes the following stages:

Data science is considered a discipline, while data scientists are the practitioners within that field. Data scientists are not necessarily directly responsible for all the processes involved in the data science lifecycle. For example, data pipelines are typically handled by data engineersbut the data scientist may make recommendations about what sort of data is useful or required. While data scientists can build machine learning models, scaling these efforts at a larger level requires more software engineering skills to optimize a program to run more quickly. As a result, its common for a data scientist to partner with machine learning engineers to scale machine learning models.

Data scientist responsibilities can commonly overlap with a data analyst, particularly with exploratory data analysis and data visualization. However, a data scientists skillset is typically broader than the average data analyst. Comparatively speaking, data scientist leverage common programming languages, such as R and Python, to conduct more statistical inference and data visualization.

To perform these tasks, data scientists require computer science and pure science skills beyond those of a typical business analyst or data analyst. The data scientist must also understand the specifics of the business, such as automobile manufacturing, eCommerce, or healthcare.

In short, a data scientist must be able to:

These skills are in high demand, and as a result, many individuals that are breaking into a data science career, explore a variety of data science programs, such as certification programs, data science courses, and degree programs offered by educational institutions.

It may be easy to confuse the terms data science and business intelligence (BI) because they both relate to an organizations data and analysis of that data, but they do differ in focus.

Business intelligence (BI) is typically an umbrella term for the technology that enables data preparation, data mining, data management, and data visualization. Business intelligence tools and processes allow end users to identify actionable information from raw data, facilitating data-driven decision-making within organizations across various industries. While data science tools overlap in much of this regard, business intelligence focuses more on data from the past, and the insights from BI tools are more descriptive in nature. It uses data to understand what happened before to inform a course of action. BI is geared toward static (unchanging) data that is usually structured. While data science uses descriptive data, it typically utilizes it to determine predictive variables, which are then used to categorize data or to make forecasts

Data science and BI are not mutually exclusivedigitally savvy organizations use both to fully understand and extract value from their data.

Data scientists rely on popular programming languages to conduct exploratory data analysis and statistical regression. These open source tools support pre-built statistical modeling, machine learning, and graphics capabilities. These languages include the following (read more at "Python vs. R: What's the Difference?"):

To facilitate sharing code and other information, data scientists may use GitHub and Jupyter notebooks.

Some data scientists may prefer a user interface, and two common enterprise tools for statistical analysis include:

Data scientists also gain proficiency in using big data processing platforms, such as Apache Spark, the open source framework Apache Hadoop, and NoSQL databases. They are also skilled with a wide range of data visualization tools, including simple graphics tools included with business presentation and spreadsheet applications (like Microsoft Excel), built-for-purpose commercial visualization tools like Tableau and IBM Cognos, and open source tools like D3.js (a JavaScript library for creating interactive data visualizations) and RAW Graphs. For building machine learning models, data scientists frequently turn to several frameworks like PyTorch, TensorFlow, MXNet, and Spark MLib.

Given the steep learning curve in data science, many companies are seeking to accelerate their return on investment for AI projects; they often struggle to hire the talent needed to realize data science projects full potential. To address this gap, they are turning to multipersona data science and machine learning (DSML) platforms, giving rise to the role of citizen data scientist.

Multipersona DSML platforms use automation, self-service portals, and low-code/no-code user interfaces so that people with little or no background in digital technology or expert data science can create business value using data science and machine learning. These platforms also support expert data scientists by also offering a more technical interface. Using a multipersona DSML platform encourages collaboration across the enterprise.

Cloud computing scales data science by providing access to additional processing power, storage, and other tools required for data science projects.

Since data science frequently leverages large data sets, tools that can scale with the size of the data is incredibly important, particularly for time-sensitive projects. Cloud storage solutions, such as data lakes, provide access to storage infrastructure, which are capable of ingesting and processing large volumes of data with ease. These storage systems provide flexibility to end users, allowing them to spin up large clusters as needed. They can also add incremental compute nodes to expedite data processing jobs, allowing the business to make short-term tradeoffs for a larger long-term outcome. Cloud platforms typically have different pricing models, such a per-use or subscriptions, to meet the needs of their end userwhether they are a large enterprise or a small startup.

Open source technologies are widely used in data science tool sets. When theyre hosted in the cloud, teams dont need to install, configure, maintain, or update them locally. Several cloud providers, including IBM Cloud, also offer prepackaged tool kits that enable data scientists to build models without coding, further democratizing access to technology innovations and data insights.

Enterprises can unlock numerous benefits from data science. Common use cases include process optimization through intelligent automation and enhanced targeting and personalization to improve the customer experience (CX). However, more specific examples include:

Here are a few representative use cases for data science and artificial intelligence:

IBM Cloud offers a highly secure public cloud infrastructure with a full-stack platform that includes more than 170 products and services, many of which were designed to support data science and AI.

IBMs data science and AI lifecycle product portfolio is built upon our longstanding commitment to open source technologies and includes a range of capabilities that enable enterprises to unlock the value of their data in new ways.

AutoAI, a powerful new automated development capability in IBM Watson Studio, speeds the data preparation, model development, and feature engineering stages of the data science lifecycle. This allows data scientists to be more efficient and helps them make better-informed decisions about which models will perform best for real-world use cases. AutoAI simplifies enterprise data science across any cloud environment.

The IBM Cloud Pak for Data platform provides a fully integrated and extensible data and information architecture built on the Red Hat OpenShift Container Platform that runs on any cloud. With IBM Cloud Pak for Data, enterprises can more easily collect, organize and analyze data, making it possible to infuse insights from AI throughout the entire organization.

Want to learn more about building and running data science models on IBM Cloud? Get started for no-charge by signing up for an IBM Cloud account today.

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What is Data Science | IBM