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What is data science?

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 organization’s 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. 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 ingestion: The lifecycle begins with the data collection—both raw structured and unstructured data from all relevant sources using a variety of methods. These methods can include manual entry, web scraping, and real-time streaming data from systems and devices. Data sources can include structured data, such as customer data, along with unstructured data like log files, video, audio, pictures, the Internet of Things (IoT), social media, and more.
  • Data storage and data processing: Since data can have different formats and structures, companies need to consider different storage systems based on the type of data that needs to be captured. Data management teams help to set standards around data storage and structure, which facilitate workflows around analytics, machine learning and deep learning models. This stage includes cleaning data, deduplicating, transforming and combining the data using ETL (extract, transform, load) jobs or other data integration technologies. This data preparation is essential for promoting data quality before loading into a data warehouse, data lake, or other repository.
  • Data analysis: Here, data scientists conduct an exploratory data analysis to examine biases, patterns, ranges, and distributions of values within the data. This data analytics exploration drives hypothesis generation for a/b testing. It also allows analysts to determine the data’s relevance for use within modeling efforts for predictive analytics, machine learning, and/or deep learning. Depending on a model’s accuracy, organizations can become reliant on these insights for business decision making, allowing them to drive more scalability.
  • Communicate: Finally, insights are presented as reports and other data visualizations that make the insights—and their impact on business—easier for business analysts and other decision-makers to understand. A data science programming language such as R or Python includes components for generating visualizations; alternately, data scientists can use dedicated visualization tools.

  • Data science versus data scientist

    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 engineers—but 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, it’s 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 scientist’s 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:

    • Know enough about the business to ask pertinent questions and identify business pain points.
    • Apply statistics and computer science, along with business acumen, to data analysis.
    • Use a wide range of tools and techniques for preparing and extracting data—everything from databases and SQL to data mining to data integration methods.
    • Extract insights from big data using predictive analytics and artificial intelligence (AI), including machine learning models, natural language processing, and deep learning.
    • Write programs that automate data processing and calculations.
    • Tell—and illustrate—stories that clearly convey the meaning of results to decision-makers and stakeholders at every level of technical understanding.
    • Explain how the results can be used to solve business problems.
    • Collaborate with other data science team members, such as data and business analysts, IT architects, data engineers, and application developers.

    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.

  • Data science versus business intelligence

    It may be easy to confuse the terms “data science” and “business intelligence” (BI) because they both relate to an organization’s 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 exclusive—digitally savvy organizations use both to fully understand and extract value from their data.

    Data science tools

    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?"):

    • R Studio: An open source programming language and environment for developing statistical computing and graphics.
    • Python: It is a dynamic and flexible programming language. The Python includes numerous libraries, such as NumPy, Pandas, Matplotlib, for analyzing data quickly.

    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:

    • SAS: A comprehensive tool suite, including visualizations and interactive dashboards, for analyzing, reporting, data mining, and predictive modeling.
    • IBM SPSS: Offers advanced statistical analysis, a large library of machine learning algorithms, text analysis, open source extensibility, integration with big data, and seamless deployment into applications.

    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 project’s 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.

    Data science and cloud computing

    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 user—whether they are a large enterprise or a small startup.

    Open source technologies are widely used in data science tool sets. When they’re hosted in the cloud, teams don’t 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.

    Data science use cases

    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:

    • An international bank delivers faster loan services with a mobile app using machine learning-powered credit risk models and a hybrid cloud computing architecture that is both powerful and secure.
    • An electronics firm is developing ultra-powerful 3D-printed sensors to guide tomorrow’s driverless vehicles. The solution relies on data science and analytics tools to enhance its real-time object detection capabilities.
    • A robotic process automation (RPA) solution provider developed a cognitive business process mining solution that reduces incident handling times between 15% and 95% for its client companies. The solution is trained to understand the content and sentiment of customer emails, directing service teams to prioritize those that are most relevant and urgent.
    • A digital media technology company created an audience analytics platform that enables its clients to see what’s engaging TV audiences as they’re offered a growing range of digital channels. The solution employs deep analytics and machine learning to gather real-time insights into viewer behavior.
    • An urban police department created statistical incident analysis tools to help officers understand when and where to deploy resources in order to prevent crime. The data-driven solution creates reports and dashboards to augment situational awareness for field officers.
    • Shanghai Changjiang Science and Technology Development used IBM® Watson® technology to build an AI-based medical assessment platform that can analyze existing medical records to categorize patients based on their risk of experiencing a stroke and that can predict the success rate of different treatment plans.
Authors: IBM, T. C. Okenna
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What is Data Science: Lifecycle, Applications and Prerequisites

Introduction

Data science is an essential part of many industries today, given the massive amounts of data that are produced, and is one of the most debated topics in IT circles. Its popularity has grown over the years, and companies have started implementing data science techniques to grow their business and increase customer satisfaction. In this article, we’ll learn what is data science, its applications, and how you can become a data scientist.

What Is Data Science?

Data science is the domain of study that deals with vast volumes of data using modern tools and techniques, including essential data science skills, to find unseen patterns, derive meaningful information, and make business decisions. Data science uses complex machine learning algorithms to build predictive models. The data used for analysis can come from many different sources and presented in various formats.

The Data Science Lifecycle

Now that you know what is data science, next up let us focus on the data science lifecycle. Data science’s lifecycle consists of five distinct stages, each with its own tasks:

  1. Capture: Data Acquisition, Data Entry, Signal Reception, Data Extraction. This stage involves gathering raw structured and unstructured data.
  2. Maintain: Data Warehousing, Data Cleansing, Data Staging, Data Processing, Data Architecture. This stage covers taking the raw data and putting it in a form that can be used.
  3. Process: Data Mining, Clustering/Classification, Data Modeling, Data Summarization. Data scientists take the prepared data and examine its patterns, ranges, and biases to determine how useful it will be in predictive analysis.
  4. Analyze: Exploratory/Confirmatory, Predictive Analysis, Regression, Text Mining, Qualitative Analysis. Here is the real meat of the lifecycle. This stage involves performing the various analyses on the data.
  5. Communicate: Data Reporting, Data Visualization, Business Intelligence, Decision Making. In this final step, analysts prepare the analyses in easily readable forms such as charts, graphs, and reports.

Data Science Prerequisites

Here are some of the technical concepts you should know about before starting to learn what is data science.

1. Machine Learning: Machine learning is the backbone of data science. Data Scientists need to have a solid grasp of ML in addition to basic knowledge of statistics.

2. Modeling: Mathematical models enable you to make quick calculations and predictions based on what you already know about the data. Modeling is also a part of Machine Learning and involves identifying which algorithm is the most suitable to solve a given problem and how to train these models.

3. Statistics: Statistics are at the core of data science. A sturdy handle on statistics can help you extract more intelligence and obtain more meaningful results.

4. Programming: Some level of programming is required to execute a successful data science project. The most common programming languages are Python, and R. Python is especially popular because it’s easy to learn, and it supports multiple libraries for data science and ML.

5. Database: A capable data scientist needs to understand how databases work, how to manage them, and how to extract data from them.

Who Oversees the Data Science Process?

1. Business Managers

The business managers are the people in charge of overseeing the data science training method. Their primary responsibility is to collaborate with the data science team to characterise the problem and establish an analytical method. A data scientist may oversee the marketing, finance, or sales department, and report to an executive in charge of the department. Their goal is to ensure projects are completed on time by collaborating closely with data scientists and IT managers.

2. IT Managers

Following them are the IT managers. If the member has been with the organisation for a long time, the responsibilities will undoubtedly be more important than any others. They are primarily responsible for developing the infrastructure and architecture to enable data science activities. Data science teams are constantly monitored and resourced accordingly to ensure that they operate efficiently and safely. They may also be in charge of creating and maintaining IT environments for data science teams.

3. Data Science Managers

The data science managers make up the final section of the tea. They primarily trace and supervise the working procedures of all data science team members. They also manage and keep track of the day-to-day activities of the three data science teams. They are team builders who can blend project planning and monitoring with team growth.

What is a Data Scientist?

If learning what is data science sounded interesting, understanding what does this job roles is all about will me much more interesting to you. Data scientists are among the most recent analytical data professionals who have the technical ability to handle complicated issues as well as the desire to investigate what questions need to be answered. They're a mix of mathematicians, computer scientists, and trend forecasters. They're also in high demand and well-paid because they work in both the business and IT sectors. On a daily basis, a data scientist may do the following tasks:

  1. Discover patterns and trends in datasets to get insights
  2. Create forecasting algorithms and data models
  3. Improve the quality of data or product offerings by utilising machine learning techniques
  4. Distribute suggestions to other teams and top management
  5. In data analysis, use data tools such as R, SAS, Python, or SQL
  6. Top the field of data science innovations

What Does a Data Scientist Do?

You know what is data science, and you must be wondering what exactly is this job role like - here's the answer. A data scientist analyzes business data to extract meaningful insights. In other words, a data scientist solves business problems through a series of steps, including:

  • Before tackling the data collection and analysis, the data scientist determines the problem by asking the right questions and gaining understanding.
  • The data scientist then determines the correct set of variables and data sets.
  • The data scientist gathers structured and unstructured data from many disparate sources—enterprise data, public data, etc.
  • Once the data is collected, the data scientist processes the raw data and converts it into a format suitable for analysis. This involves cleaning and validating the data to guarantee uniformity, completeness, and accuracy.
  • After the data has been rendered into a usable form, it’s fed into the analytic system—ML algorithm or a statistical model. This is where the data scientists analyze and identify patterns and trends.
  • When the data has been completely rendered, the data scientist interprets the data to find opportunities and solutions.
  • The data scientists finish the task by preparing the results and insights to share with the appropriate stakeholders and communicating the results.

Why Become a Data Scientist?

You learnt what is data science. Did it sound exciting? Here's another solid reason why you should pursue data science as your work-field. According to Glassdoor and Forbes, demand for data scientists will increase by 28 percent by 2026, which speaks of the profession’s durability and longevity, so if you want a secure career, data science offers you that chance. So, if you’re looking for an exciting career that offers stability and generous compensation, then look no further!

Uses of Data Science

  1. Data science may detect patterns in seemingly unstructured or unconnected data, allowing conclusions and predictions to be made.
  2. Tech businesses that acquire user data can utilise strategies to transform that data into valuable or profitable information.
  3. Data Science has also made inroads into the transportation industry, such as with driverless cars. It is simple to lower the number of accidents with the use of driverless cars. For example, with driverless cars, training data is supplied to the algorithm, and the data is examined using data Science approaches, such as the speed limit on the highway, busy streets, etc.
  4. Data Science applications provide a better level of therapeutic customisation through genetics and genomics research.

Where Do You Fit in Data Science?

Now that you know the uses of Data Science and what is data science in general, let's see all the opportunity that this feild offers to focus on and specialize in one aspect of the field. Here’s a sample of different ways you can fit into this exciting, fast-growing field.

Data Scientist

  • Job role: Determine what the problem is, what questions need answers, and where to find the data. Also, they mine, clean, and present the relevant data.
  • Skills needed: Programming skills (SAS, R, Python), storytelling and data visualization, statistical and mathematical skills, knowledge of Hadoop, SQL, and Machine Learning.

Data Analyst

  • Job role: Analysts bridge the gap between the data scientists and the business analysts, organizing and analyzing data to answer the questions the organization poses. They take the technical analyses and turn them into qualitative action items.
  • Skills needed: Statistical and mathematical skills, programming skills (SAS, R, Python), plus experience in data wrangling and data visualization.

Data Engineer

  • Job role: Data engineers focus on developing, deploying, managing, and optimizing the organization’s data infrastructure and data pipelines. Engineers support data scientists by helping to transfer and transform data for queries.
  • Skills needed: NoSQL databases (e.g., MongoDB, Cassandra DB), programming languages such as Java and Scala, and frameworks (Apache Hadoop).

Applications of Data Science

There are various applications of data science, including:

1. Healthcare

Healthcare companies are using data science to build sophisticated medical instruments to detect and cure diseases.

2. Gaming

Video and computer games are now being created with the help of data science and that has taken the gaming experience to the next level.

3. Image Recognition

Identifying patterns is one of the most commonly known applications of data science. in images and detecting objects in an image is one of the most popular data science applications.

4. Recommendation Systems

Next up in the data science applications list comes Recommendation Systems. Netflix and Amazon give movie and product recommendations based on what you like to watch, purchase, or browse on their platforms.

5. Logistics

Data Science is used by logistics companies to optimize routes to ensure faster delivery of products and increase operational efficiency.

6. Fraud Detection

Fraud detection comes the next in the list of applications of data science. Banking and financial institutions use data science and related algorithms to detect fraudulent transactions.   

7. Internet Search

Internet comes the next in the list of applications of data science. When we think of search, we immediately think of Google. Right? However, there are other search engines, such as Yahoo, Duckduckgo, Bing, AOL, Ask, and others, that employ data science algorithms to offer the best results for our searched query in a matter of seconds. Given that Google handles more than 20 petabytes of data per day. Google would not be the 'Google' we know today if data science did not exist.

8. Speech recognition

Speech recognition is one of the most commonly known applications of data science. It is a technology that enables a computer to recognize and transcribe spoken language into text. It has a wide range of applications, from virtual assistants and voice-controlled devices to automated customer service systems and transcription services.

9. Targeted Advertising

If you thought Search was the most essential data science use, consider this: the whole digital marketing spectrum. From display banners on various websites to digital billboards at airports, data science algorithms are utilised to identify almost anything. This is why digital advertisements have a far higher CTR (Call-Through Rate) than traditional marketing. They can be customised based on a user's prior behaviour. That is why you may see adverts for Data Science Training Programs while another person sees an advertisement for clothes in the same region at the same time.

10. Airline Route Planning

Next up in the data science and its applications list comes route planning. As a result of data science, it is easier to predict flight delays for the airline industry, which is helping it grow. It also helps to determine whether to land immediately at the destination or to make a stop in between, such as a flight from Delhi to the United States of America or to stop in between and then arrive at the destination.

11. Augmented Reality

Last but not least, the final data science applications appear to be the most fascinating in the future. Yes, we are discussing something other than augmented reality. Do you realise there's a fascinating relationship between data science and virtual reality? A virtual reality headset incorporates computer expertise, algorithms, and data to create the greatest viewing experience possible. The popular game Pokemon GO is a minor step in that direction. The ability to wander about and look at Pokemon on walls, streets, and other non-existent surfaces. The makers of this game chose the locations of the Pokemon and gyms using data from Ingress, the previous app from the same business.

Example of Data Science

Here are some brief example of data science showing data science’s versatility.

  • Law Enforcement: In this scenario, data science is used to help police in Belgium to better understand where and when to deploy personnel to prevent crime. With only limited resources and a large area to cover data science used dashboards and reports to increase the officers’ situational awareness, allowing a police force that’s spread thin to maintain order and anticipate criminal activity.
  • Pandemic Fighting: The state of Rhode Island wanted to reopen schools, but was naturally cautious, considering the ongoing COVID-19 pandemic. The state used data science to expedite case investigations and contact tracing, enabling a small staff to handle an overwhelming number of concerned calls from citizens. This information helped the state set up a call center and coordinate preventative measures.

Challenges of a Data Scientist

Some of the common challenges that a data scientist faces, include:

  • Handling large and messy datasets that require cleaning and organization.
  • Selecting the right tools and techniques for analysis.
  • Ensuring accurate and unbiased results.
  • Communicating complex findings to non-technical stakeholders.
  • Aligning data projects with business goals.
  • Keeping up with rapidly evolving technologies.
  • Managing data privacy and security concerns.

Data Science vs Business Intelligence

Data Science and Business Intelligence (BI) are both data-driven fields but differ in focus and approach. Data Science emphasizes predictive and prescriptive analytics, using advanced techniques like machine learning and AI to forecast trends and provide actionable recommendations. It deals with raw, unstructured, and large datasets to solve complex problems and discover new opportunities.

On the other hand, Business Intelligence focuses on descriptive analytics, analyzing structured data from databases to generate reports, KPIs, and dashboards that summarize past and present performance. While Data Science is exploratory and future-oriented, BI is analytical and operational, helping business managers and executives make informed decisions based on historical data insights.

FAQs

1. What is data science in simple words?

Data science, in simple words, is the field of study that involves collecting, analyzing, and interpreting large sets of data to uncover insights, patterns, and trends that can be used to make informed decisions and solve real-world problems.

2. What is data science used for?

Data science is used for a wide range of applications, including predictive analytics, machine learning, data visualization, recommendation systems, fraud detection, sentiment analysis, and decision-making in various industries like healthcare, finance, marketing, and technology.

3. What’s the difference between data science, artificial intelligence, and machine learning?

Artificial Intelligence makes a computer act/think like a human. Data science is an AI subset that deals with data methods, scientific analysis, and statistics, all used to gain insight and meaning from data. Machine learning is a subset of AI that teaches computers to learn things from provided data.

4. What does a data scientist do?

A data scientist analyzes business data to extract meaningful insights.

5. What kinds of problems do data scientists solve?

Data scientists solve issues like:

  1. Loan risk mitigation
  2. Pandemic trajectories and contagion patterns
  3. Effectiveness of various types of online advertisement
  4. Resource allocation

6. Do data scientists code?

Sometimes they may be called upon to do so.

7. What is the data science course eligibility?

If you wish to know anything about our data science course, please check out Data Science Bootcamp and Data Science master’s program.

8. Can I learn data science on my own?

Data science is a complex field with many difficult technical requirements. It’s not advisable to try learning data science without the help of a structured learning program.

Authors: Avijeet Biswal, T. C. Okenna
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