What is Data Science? The Complete Guide

Data science applies advanced analytics techniques and scientific principles to extract valuable information from data for business decision-making, strategic planning, and other uses. It is becoming increasingly crucial for businesses: Data science insights, among other things, help organizations increase operational efficiency, identify new business opportunities, and improve marketing and sales programs. They can eventually lead to competitive advantages over business rivals.

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Data science encompasses a wide range of disciplines, including data engineering, data preparation, data mining, predictive analytics, machine learning, and data visualization, in addition to statistics, mathematics, and software programming. Skilled data scientists mainly do it, but lower-level data analysts may also be involved. Furthermore, many organizations now rely on citizen data scientists, including BI professionals, business analysts, data-savvy business users, data engineers, and other workers who do not have a formal data science background.

Why is data science important?

Data science is essential in almost all aspects of business operations and strategies. For example, it provides information about customers, enabling businesses to create more effective marketing campaigns and targeted advertising to increase product sales. It aids in financial risk management, detecting fraudulent transactions, and preventing equipment breakdowns in manufacturing plants and other industrial settings. It aids in the prevention of cyber-attacks and other security threats in IT systems.

Data science initiatives can improve the operational management of supply chains, product inventories, distribution networks, and customer service. More fundamentally, they point to increased efficiency and cost savings. It also enables businesses to develop plans and strategies based on an in-depth analysis of customer behavior, market trends, and competition. Without it, companies risk missing out on opportunities and making poor decisions.

Data science is also important in areas other than regular business operations. Its applications in healthcare include medical condition diagnosis, image analysis, treatment planning, and medical research. It is used by academic institutions to monitor student performance and improve their marketing to prospective students. Sports teams use data science to analyze player performance and plan game strategies. Government and public policy organizations are also frequent users.

Data science process and lifecycle

A series of data collection and analysis steps are involved in data science projects. Donald Farmer, principal of analytics consultancy TreeHive Strategy, outlined the following six primary steps in an article describing the data science process:

Determine a business hypothesis to test.
Collect and prepare data for analysis.
Experiment with various analytical models.
Choose the best model and test it on the data.
Inform business executives of the findings.
Deploy the model for continuous use with new data.

According to Farmer, the process turns data science into a scientific endeavor. However, he wrote that data science work in corporate enterprises “will always be most usefully focused on straightforward commercial realities” that can benefit the business. As a result, he suggests that data scientists work on projects with business stakeholders throughout the analytics lifecycle.

Advantages

One of the most significant advantages of data science is that it empowers and facilitates better decision-making. Companies that invest in it can incorporate quantifiable, data-driven evidence into their business decisions. Such data-driven decisions should ideally result in improved business performance, cost savings, and smoother business processes and workflows.

Data science’s specific business benefits vary depending on the company and industry. Data science, for example, assists customer-facing organizations in identifying and refining target audiences. Customer data can be mined by marketing and sales departments to improve conversion rates and create personalized marketing campaigns and promotional offers that increase sales.

Reduced fraud, more effective risk management, more profitable financial trading, increased manufacturing uptime, better supply chain performance, stronger cybersecurity protections, and improved patient outcomes are some of the benefits in other cases. Data science also allows for real-time analysis of data as it is generated; read about the benefits of real-time analytics, such as faster decision-making and increased business agility, in another Farmer article.

Challenges

Because of the progressive nature of the analytics involved, data science is inherently difficult. The massive amounts of data that are typically analyzed add to the complexity and lengthen the time it takes to complete projects. Furthermore, data scientists frequently work with pools of big data that may contain a mix of structured, unstructured, and semistructured data, complicating the analytics process even further.

One of the most difficult challenges is removing bias from data sets and analytics applications. This includes both problems with the underlying data and problems that data scientists unconsciously build into algorithms and predictive models. Preferences that are not identified and addressed can skew analytics results, resulting in flawed findings and poor business decisions. Worse, they can have a negative impact on specific groups of people, as in the case of racial bias in AI systems.

Another challenge is locating relevant data to analyze. Gartner analyst Afraz Jaffri and four of his colleagues at the consulting firm cited choosing the right tools, managing deployments of analytical models, quantifying business value, and maintaining models as significant hurdles in a report published in January 2020.

Data science applications and use cases

Predictive modeling, pattern recognition, anomaly detection, classification, categorization, and sentiment analysis are typical applications for data scientists, as is the development of technologies such as recommendation engines, personalization systems, and artificial intelligence (AI) tools such as chatbots and autonomous vehicles and machines.

These applications power a wide range of business use cases, including the following:

customer analytics
fraud detection
risk management
stock trading
targeted advertising
website personalization
customer service
predictive maintenance
logistics and supply chain management
image recognition
speech recognition
natural language processing
cybersecurity
medical diagnosis

Data science technologies, techniques, and methods

Machine learning algorithms are heavily used in data science. Machine learning is advanced analytics in which algorithms learn about data sets and search them for patterns, anomalies, or insights. It employs a mix of supervised, unsupervised, semisupervised, and reinforcement learning methods, with algorithms receiving varying degrees of training and supervision from data scientists.

Deep learning is a more advanced subset of machine learning that primarily employs artificial neural networks to analyze large amounts of unlabeled data. In a separate article, Cognilytica’s Schmelzer discusses the relationship between data science, machine learning, and AI, describing their various characteristics and how they can be combined in analytics applications.

Another key data science technology is predictive models. Data scientists develop them by applying machine learning, data mining, or statistical algorithms to data sets to predict business scenarios and likely outcomes or behavior. Data sampling is a data mining technique used in predictive modeling and other advanced analytics applications to analyze a representative subset of data. It is designed to make the analytics process more manageable and time-consuming.

Common statistical and analytical techniques that are used in data science projects include the following:

classification, which separates the elements in a data set into different categories;
regression, which plots the optimal values of related data variables in a line or plane; and
clustering, which groups together data points with an affinity or shared attributes.

Data science tools and platforms

There are numerous tools available for data scientists to use in the analytics process, both commercial and open source:

data platforms and analytics engines, such as Spark, Hadoop and NoSQL databases;
programming languages, such as Python, R, Julia, Scala and SQL;
statistical analysis tools like SAS and IBM SPSS;
machine learning platforms and libraries, including TensorFlow, Weka, Scikit-learn, Keras and PyTorch;
Jupyter Notebook, a web application for sharing documents with code, equations and other information; and
data visualization tools and libraries, such as Tableau, D3.js and Matplotlib.

Furthermore, software vendors provide diverse data science platforms with varying features and functionality. This includes analytics platforms for skilled data scientists, automated machine learning platforms that citizen data scientists can use, and data science workflow and collaboration hubs. Alteryx, AWS, Databricks, Dataiku, DataRobot, Domino Data Lab, Google, H2O.ai, IBM, Knime, MathWorks, Microsoft, RapidMiner, SAS Institute, Tibco Software, and others are among the vendors.

How data science is used in industries

Google, Amazon, and other internet and e-commerce companies like Facebook, Yahoo, and eBay were early users of data science and big data analytics for internal applications before becoming technology vendors. Here are some examples of its application in various industries:

Entertainment. Data science enables streaming services to track and analyze what users watch, which aids in creating new TV shows and films. Data-driven algorithms are also used to generate personalized recommendations based on a user’s viewing history.
Finance services. Banks and credit card companies mine and analyze data to detect fraudulent transactions, manage financial risks on loans and credit lines, and assess customer portfolios to identify upselling opportunities.
Healthcare. Hospitals and other healthcare providers use machine learning models and other data science components to automate X-ray analysis and assist doctors in diagnosing illnesses and planning treatments based on previous patient outcomes.
Manufacturing. Data science applications in manufacturing include supply chain management, distribution optimization, and predictive maintenance to detect potential equipment failures in plants before they occur.
Retail. Retailers analyze customer behavior and purchasing patterns to provide personalized product recommendations and targeted advertising, marketing, and promotions. Data science also assists them in managing product inventories and supply chains to keep items in stock.
Transportation. Delivery companies, freight carriers use data science, and logistics service providers to optimize delivery routes and schedules and the best modes of transport for shipments.
Travel. Airlines use data to optimize flight routes, crew scheduling, and passenger loads. Algorithms also drive variable pricing for flights and hotel rooms.

Other data science applications are typical across industries, including cybersecurity, customer service, and business process management. For example, analytics can help with employee recruitment and talent acquisition by identifying common characteristics of top performers, measuring how effective job postings are, and providing other information to aid in the hiring process.

Data science vs. business intelligence

Essential business intelligence and reporting, like data science, aims to aid in operational decision-making and strategic planning. However, BI is primarily concerned with descriptive analytics: what happened or is happening now to which an organization should respond or address? BI analysts and self-service BI users typically work with structured transaction data extracted from operational systems, cleansed and transformed to ensure consistency, and loaded into a data warehouse or data mart for analysis. A typical BI use case is monitoring business performance, processes, and trends.

Data science entails more advanced analytics applications. In addition to descriptive analytics, it includes predictive analytics, which predicts future behavior and events, and prescriptive analytics, which attempts to determine the best course of action to take on the issue under consideration.

Along with structured data, unstructured or semistructured data, such as log files, sensor data, and text, is common in data science applications. Furthermore, data scientists frequently require access to raw data before it has been cleaned up and consolidated to analyze the entire data set or filter and prepare it for specific analytics uses. As a result, raw data may be stored in a Hadoop-based data lake, a cloud object storage service, a NoSQL database, or another big data platform.

Data science’s Future

Citizen data scientists are expected to play a larger role in analytics as data science becomes more prevalent in organizations. In its 2020 Magic Quadrant report on data science and machine learning platforms, Gartner stated that the need to support a diverse set of data science users is becoming “increasingly the norm.” One likely outcome is an increase in the use of automated machine learning, particularly by skilled data scientists looking to streamline and accelerate their work.

Gartner also mentioned the emergence of machine learning operations (MLOps). This concept adapts DevOps practices from software development to better manage machine learning model development, deployment, and maintenance. MLOps methods and tools aim to standardize workflows so that models can be scheduled, built, and deployed more efficiently.

Other trends that will impact data scientists’ work in the Future include an increasing emphasis on explainable AI, which provides information to help people understand how AI and machine learning models work and how much to trust their findings in making decisions, as well as a related focus on responsible AI principles, which are designed to ensure that AI technologies are fair, unbiased, and transparent.