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    What Is Data Science? The Opportunity In The Field, and How To Get Started

    What Is Data Science? The Opportunity In The Field, and How To Get Started

    What Is Data Science? The Opportunity In The Field, and How To Get Started

    Posted by Tech.us Category: Data Science

    One of the things that has been making quite the buzz in the world of business — and in nonprofit industries too — is artificial intelligence and data science. With various organizations all over the world aiming to integrate artificial intelligence processes into their business model, people are starting to ask questions. This curiosity only increases when you realize that tech and industry giants like Google, Apple, Netflix, and Uber are among the companies investing millions of dollars annually into data science specialists and AI technicians.

    If there is a digital transformation upon us in the business world, fueling exponential business growth in various sectors, you’re not wrong in wanting a slice of the pie. But, who are these data scientists that everyone keeps talking about, and what’s so special about them? Is this a career worth pursuing, or is this just the latest fad that’s sure to die out soon, leaving everyone silly enough to follow it in ruins? 

    Everyone in the world of business has accepted by now that AI technology is here to stay, and so are the data scientists trained to run it.

    What Is Data Science?

    At its core, data science is the study of data. A data scientist is an expert in collecting, organizing, structuring, and making predictions based on a raw set of data. Until a decade ago, most data was structured, low in volume, and easy enough to manage. Now, data collected through various places online is semi-structured at best, and some of it is in a state of absolute chaos. The need to organize, manage, and use that data eventually gave birth to a whole new disciple, called data science.

    With the advent of data science, it actually became possible for businesses to use AI technology and effectively train it with the help of the data extracted and organized by data scientists. Entrepreneurs were able to try and integrate it into their business models.

    Now, data science makes it possible for industries all over the world to make use of the insurmountable data they all collect every day to drive their business growth, make accurate future predictions, and even ascertain how to allocate limited business resources to effectively meet demand.

    What Does a Data Scientist Do?

    A data scientist is tasked with the job of leveraging his knowledge in statistics, mathematics, science, and many other fields to derive actionable predictions and insights from unstructured, raw data. They reach these predictions and insights with the help of machine learning algorithms and find solutions to problems that previously didn’t have any, or help use that data to streamline business processes.

    Data scientists can perform either predictive analysis or prescriptive analysis on any given set of data, both useful in their own ways. Predictive analysis uses a set of data and analyses its history to determine the probability of a particular event happening in the future. In the case of prescriptive analysis, that data is used to develop a system that not only predicts future trends but also offers advice and actionable solutions, sometimes helping to implement them in real-time as well.

    Is a Data Scientist Similar To a Data Analyst?

    Data science and data analysis may sound the same, but they couldn’t be more different. Where a data analyst can only study a set of data and explain it, sometimes offering insight into usual problems that are occurring, or pointing out certain trends in that data, a data scientist’s work goes far beyond that.

     A data scientist will take a set of data, structure it, and then employ the help of machine learning and deep learning algorithms to derive results and insights from it that you couldn’t even imagine.

    AI technology and data science have now permeated into the world of healthcare, customer service, social media, journalism, traveling and hospitality, the food industry, and the automobile industry too.

    Why Is Data Science So Popular?

    In the last decade, more companies have started using AI technology than ever before. Experts have stated time and time again that it is a disruptive piece of technology that will soon become the norm and businesses that were formerly reluctant to automate all of or part of their business model will be forced to do so in order to compete.

     A data scientist is perhaps the only expert appropriately trained in the science of artificial intelligence technology and managing data efficiently enough to help businesses establish, run, and manage any artificial intelligence algorithm that a business might want to use. The popularity of data science is partially owed to the fact that artificial intelligence — a revolutionary piece of technology that everyone now needs to stay ahead of the competition in the world of business — needs to be fed high-quality data in order for it to work efficiently. In some cases, even handling and structuring high volumes of unstructured data (data that is so vast a human being can’t manage it themself) requires the help of AI software.

    Practical Uses of Data Science

    Data science utilizes the vast amount of data available to certain industries to establish and run artificial intelligence algorithms that help boost business growth across industries, sometimes giving birth to whole new technology and disciplines.


    Credit and Insurance

    Data scientists working in the credit and insurance industry have helped multiple credit providers and insurance companies in developing algorithms that use previous customer history and data to predict the probability of a particular customer paying the company on time. The AIs developed by data scientists can usually even predict the chances of a particular customer committing fraud or being dishonest.


    In the world of automated vehicles and machines, data scientists help make intelligent machinery that can be trained to perform particular tasks and even use past data to decide what the best course of action would be when faced with a new problem.

    The most famous example of this kind of machinery is Tesla’s self-driving cars. Current models are capable of parking themselves into designated parking spaces and driving themselves to the location of their owner when prompted. They even integrate AI technology into the amenities inside the cars, and the cars are always collecting data and storing it to determine in the future where to speed up, slow down, and when to change lanes.


    In the retail world, many grocery store chains now employ AI technology developed by data scientists to keep track of inventory and use that data to predict future demand, and what to restock in what amounts in different branches. This allows retail outlets to avoid overstocking a particular product in one branch of their stores while another branch faces a shortage of the same product.


    AI technology is now being used in the world of healthcare to detect diseases with more efficiency, predict future outbreaks, and even help in making medicinal formulas more effective. AI would be able to detect diseases in a particular sample or a scan faster than the human eye would, and with more efficiency.

    Amazon has recently announced the launch of its Telehealth service across all 50 states, which was previously only available to its employees in Washington. The service claims to be able to connect Amazon employees to a healthcare professional via in-app chat and video call features in under 60 seconds. It’s hard to imagine technology like that working effectively without the help of AI in the current state of healthcare infrastructure.


    Data scientists have helped develop mechanisms that aid businesses in targeting social media users who are either already looking for their products or have more chances of being interested in them. This has helped countless businesses lower marketing costs and increase the efficiency of their marketing campaigns.

    These are just some of the examples of data scientists being the drivers for exponential business growth across various industries.

    The High Demand for Data Scientists

    Given the amazing business growth results achieved by businesses who have invested in employing data scientists and developing AI-powered automated business processes, and the importance placed by tech giants like Google, Apple, and IBM in their data science talent, the demand for their expertise has never been higher.

    With the demand for data science experts growing this much, entrepreneurs have struggled in the past years to hire quality data science talent. It doesn’t help matters when the people who do the hiring themselves have a limited understanding of what data science is. If you’re an entrepreneur looking to hire a data scientist for your business, it might be a good idea to delve a little into data science yourself and find out what skills make a good data scientist.

    If you’re looking for guidelines and help with hiring, you can read our blog on how to hire a data scientist which is available below.


    Importance of Data Science in the Future

    Experts have determined that data science is here to stay. There is a demand for data scientists in every field of business and every industry, which means a data scientist wouldn’t have much problem seeking employment. On the contrary, they might have to choose between different job offers. The reason for this is that universities have only recently introduced certifications and degrees in this field, and many students are reluctant to opt for a lesser-known field in IT.

    Many businesses can no longer compete in their industries without employing the help of artificial intelligence technology, and the ongoing need to manage, train, retrain, and supervise a business’s AI program provides a need for you to hire an in-house data science team.

    How To Get Started

    Integrating data scientists and AI technology in your business model is time-consuming and requires lots of effort, dedication, and learning and relearning. However, the payoff is more than worth it. Certain steps need to be taken before you can start the process of changing how your organization does things.

    Get Your Staff On Board

    Before you start the integration process, make sure your employees are on board with the idea. Address any misgivings and ethical objections they might have about using this technology in your business processes, provide them with the necessary training to work with AI technology efficiently, and consider holding a seminar or workshop to give your employees the opportunity to interact with an actual expert in the field.

    Set Realistic Goals

    Evaluate your business performance and keep in mind that your business has limitations and capabilities it can’t exceed, no matter how advanced the AI you employ for your business model is. Set realistic goals for your organization and consider what it can realistically achieve in a set amount of time.

    Employ a Trial and Error Model

    Your new AI technology is going to be only as good as the data you use to train it, but given how there’s no correct way to develop a machine learning algorithm, it will take lots of trial and error for a data scientist to get the new technology up and running. Be patient, and leave room for learning and relearning business processes, not just in your AI algorithm but also in your business.

    Final Message

    In the coming years, it will be harder and harder for you to run your business without integrating AI technology into your business model. Employing a data scientist now will give your business ample time to learn how to operate with AI software’s help, and by the time other businesses catch on, you will have gotten ahead.


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