What is Data Science? The Complete Guide

What is data science?


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.

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.

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What is Data Science?

Data science is a field that uses math, statistics, programming, and AI to find valuable information in data. This information helps organizations make better decisions and plans.

With the growing amount of data from various sources, data science has become a rapidly growing field in many industries. Harvard Business Review even called the role of a data scientist the “sexiest job of the 21st century.” Companies rely on data scientists to interpret data and provide recommendations to improve their operations.

Data Science Process and Lifecycle

Data science involves various roles, tools, and processes to help analysts extract actionable insights. A typical data science project goes through the following stages:

1. Data Ingestion: This stage involves collecting raw data from various sources. The data can be both structured (like customer data) and unstructured (like log files, videos, audio, images, IoT data, and social media content). Methods for data collection include manual entry, web scraping, and real-time streaming from systems and devices.

2. Data Storage and Processing: Since data comes in different formats and structures, companies use different storage systems tailored to the type of data they handle. Data management teams establish standards for data storage and structure, which support workflows in analytics, machine learning, and deep learning. This stage includes cleaning, deduplicating, transforming, and combining data using ETL (extract, transform, load) jobs or other data integration technologies. Proper data preparation ensures high data quality before loading it into a data warehouse, data lake, or other repositories.

3. Data Analysis: In this stage, data scientists perform exploratory data analysis to identify biases, patterns, ranges, and value distributions. This exploration helps generate hypotheses for A/B testing and determines the data’s relevance for modeling efforts in predictive analytics, machine learning, and deep learning. Depending on the accuracy of these models, organizations can rely on the insights to make business decisions and drive scalability.

4. Communication: The final stage involves presenting the insights through reports and data visualizations. These visualizations make it easier for business analysts and decision-makers to understand the insights and their impact on the business. Data scientists use programming languages like R or Python, which have components for generating visualizations, or dedicated visualization tools for this purpose.

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.

Read more: The Advantages of Data Science Outsourcing

Data Science vs. Data Scientist

use case of big data in healthcare - data scientist

Data science is the field of study, while data scientists are the people who work in that field. Data scientists aren’t responsible for every part of the data science process. For example, data engineers handle data pipelines, but data scientists might suggest which data to collect. Data scientists can create machine learning models, but making these models run efficiently often requires help from software engineers. Therefore, data scientists often work with machine learning engineers to make their models scalable.

Data scientist responsibilities often overlap with those of data analysts, especially in exploring data and creating visualizations. However, data scientists typically have a broader skill set, using programming languages like R and Python to perform more advanced statistical analyses and create visualizations.

Data scientists need skills in computer science, pure science, and an understanding of the business they work in, whether it’s automotive, eCommerce, or healthcare.

In summary, a data scientist should be able to:

  • Understand the business to identify important questions and problems.
  • Apply statistics and computer science, combined with business knowledge, to analyze data.
  • Use various tools and techniques to prepare and extract data from databases, perform data mining, and integrate data.
  • Use predictive analytics and AI, including machine learning, natural language processing, and deep learning, to extract insights from big data.
  • Write programs to automate data processing and calculations.
  • Create and present clear visualizations to explain results to decision-makers with varying technical backgrounds.
  • Explain how these results can solve business problems.
  • Work with other team members, such as data analysts, IT architects, data engineers, and developers.

These skills are in high demand. Many people entering a data science career pursue certification programs, courses, and degrees from educational institutions.

Data Science vs. Business Intelligence

Data science and business intelligence (BI) both involve analyzing an organization’s data, but they have different focuses.

Business Intelligence (BI) is a broad term for technologies that help with data preparation, data mining, data management, and data visualization. BI tools and processes help users identify useful information from raw data, making it easier for organizations to make data-driven decisions. BI focuses mainly on past data and provides descriptive insights to understand what happened previously and guide future actions. It typically deals with static, structured data.

Data Science, while it uses some of the same tools as BI, goes further by focusing on predicting future trends and behaviors. Data science uses past data to find predictive variables, which help categorize data and make forecasts. It involves advanced techniques like machine learning and AI to uncover deeper insights and make predictions.

Although they have different focuses, both data science and BI are essential for digitally savvy organizations to fully understand and extract value from their data. BI helps understand past performance, while data science helps predict future outcomes.

Data Science Applications and Use Cases

Enterprises can gain numerous benefits from data science, including process optimization and enhanced customer targeting and personalization. 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. Here are some specific examples of how data science and artificial intelligence (AI) are being applied:

  1. Faster Loan Services: An international bank uses a mobile app with machine learning-powered credit risk models and a hybrid cloud computing architecture to deliver faster and secure loan services.
  2. Driverless Vehicle Sensors: An electronics firm is developing ultra-powerful 3D-printed sensors for driverless vehicles. These sensors use data science and analytics tools to improve real-time object detection capabilities.
  3. Incident Handling Optimization: A robotic process automation (RPA) provider created a cognitive business process mining solution that reduces incident handling times by 15% to 95% for clients. This solution understands the content and sentiment of customer emails, helping service teams prioritize urgent and relevant messages.
  4. Audience Analytics: A digital media technology company developed an audience analytics platform to help clients understand what engages TV audiences across digital channels. This platform uses deep analytics and machine learning to provide real-time insights into viewer behavior.
  5. Crime Prevention: An urban police department implemented statistical incident analysis tools to determine when and where to deploy resources for crime prevention. The solution generates reports and dashboards to enhance situational awareness for officers in the field.
  6. Medical Assessment Platform: Shanghai Changjiang Science and Technology Development built an AI-based medical assessment platform using IBM® Watson® technology. This platform analyzes medical records to categorize patients based on their risk of stroke and predicts the success rates of different treatment plans.

These examples illustrate the diverse applications of data science and AI across various industries, showcasing how these technologies can drive innovation and efficiency.

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

Data science tools

Data scientists use various tools and programming languages to analyze data and build predictive models. Here are some of the key tools and languages they rely on:

Programming Languages

  • R Studio: An open-source programming language and environment for statistical computing and graphics.
  • Python: A versatile and dynamic programming language. Popular libraries include:
    • NumPy: For numerical operations.
    • Pandas: For data manipulation and analysis.
    • Matplotlib: For creating static, animated, and interactive visualizations.

To share code and information, data scientists often use:

  • GitHub: A platform for version control and collaboration.
  • Jupyter Notebooks: An open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text.

Enterprise Tools

Some data scientists prefer user interfaces provided by enterprise tools:

  • SAS: A comprehensive suite for data analysis, reporting, data mining, and predictive modeling, featuring visualizations and interactive dashboards.
  • IBM SPSS: Offers advanced statistical analysis, a wide range of machine learning algorithms, text analysis, open-source extensibility, integration with big data, and easy deployment into applications.

Big Data Processing Platforms

  • Apache Spark: An open-source unified analytics engine for big data processing.
  • Apache Hadoop: An open-source framework for distributed storage and processing of large data sets.
  • NoSQL Databases: Databases designed for large-scale data storage and real-time web applications.

Data Visualization Tools

Data scientists use a variety of tools for data visualization, ranging from simple to advanced:

  • Microsoft Excel: For basic data visualization and analysis.
  • Tableau: A commercial data visualization tool for creating interactive and shareable dashboards.
  • IBM Cognos: A business intelligence tool for performance management and data visualization.
  • D3.js: An open-source JavaScript library for creating complex, interactive data visualizations.
  • RAW Graphs: An open-source data visualization framework.

Machine Learning Frameworks

  • PyTorch: An open-source machine learning library based on the Torch library.
  • TensorFlow: An open-source library for dataflow and differentiable programming.
  • MXNet: An open-source deep learning framework.
  • Spark MLib: A scalable machine learning library integrated with Apache Spark.

Multi person Data Science and Machine Learning (DSML) Platforms

To address the shortage of data science talent and accelerate AI projects, companies are turning to multipersona DSML platforms. These platforms offer:

  • Automation: Tools that simplify complex data science processes.
  • Self-Service Portals: Interfaces that allow users with minimal technical expertise to create data science solutions.
  • Low-Code/No-Code Interfaces: Enabling business users (citizen data scientists) to leverage data science and machine learning without deep technical knowledge.
  • Collaboration: Facilitating teamwork across different roles within an organization.

These platforms support both novice and expert data scientists, fostering collaboration and helping organizations maximize the potential of their data science projects.

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 and Cloud Computing

Cloud computing enhances data science by providing scalable access to processing power, storage, and essential tools for data science projects.

Data science often involves handling large data sets, making it crucial to have tools that can scale with the data size, especially for time-sensitive projects. Cloud storage solutions, such as data lakes, offer robust storage infrastructure capable of ingesting and processing vast amounts of data efficiently. These systems are flexible, allowing users to create large clusters as needed and add compute nodes to speed up data processing tasks. This flexibility lets businesses make short-term trade-offs to achieve significant long-term outcomes. Cloud platforms usually offer various pricing models, like pay-per-use or subscriptions, catering to the needs of both large enterprises and small startups.

Open-source technologies are commonly used in data science toolsets. Hosting these tools in the cloud means teams don’t need to install, configure, maintain, or update them locally. Cloud providers, such as IBM Cloud®, also offer prepackaged toolkits that enable data scientists to build models without coding, making advanced technology and data insights more accessible.

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.

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