In a world that has undergone a digital revolution, Data Science and Artificial Intelligence have now become two of the most sought-after fields of study in the 21st century. With huge chunks of data being generated and analyzed every minute, the need for professionals who can analyze and interpret this data has seen a tremendous increase. But, What exactly is Data Science?
Data Science is the field of study that uses huge volumes of structured and unstructured data to extract meaningful and valuable business insights from it. In other words, Data Science can be defined as a multidisciplinary branch that integrates ideas and techniques from various domains including mathematics, statistics, computer science engineering, and Artificial intelligence. A data scientist essentially performs a combination of tasks including analyzing and processing data followed by modeling it for theorizing and forecasting. The results are then interpreted and deployed to come up with workable plans that would benefit the organization or company and also to answer questions like why has something happened, what might happen in the future, and what can be done with the outcomes, all based on previous information at hand.
Although the phrase "data science" is not new, its associations and implications have hugely evolved through time. The term first appeared as a synonym for statistics in the 1960s to aid in understanding and analyzing enormous amounts of data being generated at that time. Later, professionals in computer science formalized the phrase in the late 1990s to include methods to gather knowledge and produce insightful forecasts across a range of industries. Data design, collecting, and analysis were the three components of a proposed definition of data science, which saw it as a distinct field. But it still took another decade for the term data science to be frequently outside academic circles.
Statistics forms the backbone of Data Science and hence a good Data Scientist must have a strong foundation in it. Through the application of complex machine learning algorithms, data science depends on statistics to identify and transform data patterns into relevant evidence. Having a firm grasp of statistics can also help you get greater insight and produce significant results.
It is very crucial to impart some level of programming knowledge in order to carry out a successful data science project properly. Some of the most commonly used programming languages while working on any data science project include python, R, and SQL. Amongst these, python leads the pack by being the most commonly used language, with about 57% of data scientists and machine learning developers using it and 33% prioritizing it for development.
To make accurate predictions and estimates using data, it is necessary to learn to use Machine learning making it a very crucial component of the Data Science workflow. This makes it necessary for one to have a firm understanding of machine learning to succeed in the field of data science, in addition to statistics.
To be a competent data scientist, one must be familiar with the concept of database operations to be able to extract, manage and organize data that would be used to make crucial decisions.
The part of Machine learning that deals with finding the best-suited algorithm that is to be used to solve the particular problem in consideration and also involves how to train the models is Modelling. This knowledge of mathematical modeling enables you to calculate quickly and make accurate predictions based on the prior knowledge we have about the data.
Despite being connected and often used interchangeably, there are many key meaningful differences between Artificial intelligence, Machine Learning, and Data Science. In short, Artificial Intelligence deals with giving machines the ability to think and act like humans.
Machine Learning is the area of computational science that enables a machine to learn from past data insights to produce the best possible results and as discussed above, Data Science is the study of data to gain insights from it. Machine Learning is hence a subset of Artificial Intelligence and Data Science is a broader term that encompasses both Artificial intelligence and Data Science including the process of collecting, organizing, and analyzing the data to get meaningful results from it.
Data Science today is being used for various tasks including those which need predictive analysis, diagnostic analysis, descriptive analysis, and prescriptive analysis. Some examples of the ones mentioned below are.
Data Science allows businesses to
A data scientist essentially performs a combination of tasks including analyzing and processing data followed by modeling it for theorizing and forecasting. The results are then interpreted and deployed to come up with workable plans that would benefit the organization or company.
Below listed are a few of the various tasks that a Data Scientist might do on a day-to-day basis:
Now that you have gained some basic knowledge of what data science is, let’s look at another compelling argument in favor of why you should choose data science as your area of expertise. Given the durability and endurance of the field, data science provides you with the opportunity to have a stable career. According to Glassdoor and Forbes, demand for data scientists will rise by 28 percent by 2026 with data scientists being named amongst the top three best jobs in America according to the United States bureau of labor statistics. Additionally, data scientists have an average base pay of about USD 127,500.
It has also been noted that over the next ten years, almost 5,400 new employment are anticipated in the field of Data Science. According to LinkedIn, there are currently more than 20,000 job openings for data scientists. And with the amount of data being generated continuously increasing at an exponential rate, these numbers are only expected to go up in the coming years.
Data Science today offers some of the best-paying jobs in the world which also offer growth opportunities. Some such jobs include Data Analysts who examine data and use it to aid businesses in decision-making, Data scientists who study and interpret complex data to get insights from it, data engineers who prepare the data along with creating, building, testing, and maintaining the entire architecture, data architects who develop and maintain the formal description of data types and data, business analysts who help identify business problems and propose solutions by analyzing the business needs of their clients, and machine learning engineers who focus on writing code and deploying machine learning products.
The journey of becoming a Data Scientist is not an easy one but one whose results are definitely worth it. A step-by-step guide to becoming a data scientist includes:
Univ.AI offers certificate programs in AI and ML that are focused on tangible learning and professional outcomes at various stages of your learning journey. Founded by professors from Harvard and UCLA, we provide short free preparatory courses to help you learn the fundamentals and prepare for our programs. All of our programs are delivered LIVE online, with intensive mentorship, and emphasize hands-on learning.
Some of our programs include
AI0: The Basics of Data Science course will train you to program in python, and at the same time, teach you the concepts of mathematics and statistics that you will need to further progress on this journey of becoming a Data Scientist. Not only that, it is a great way to get up to speed with the prerequisites required for any course or self-study of machine learning and AI.
Data Science Leaders Program which is our 43-week master's program that provides beginners with a wide and deep background in Machine Learning and Artificial Intelligence. The programs start with the fundamentals of Machine Learning and Data Science and then progress into Advance modules that enable you to develop deep expertise in topics like natural language processing, reinforcement learning, and generative models.
Certificate in Data Science Program begins with the fundamentals of data science and machine learning. In order for you to get extensive experience in Data Science and Engineering, we cover topics including large data, databases, cluster development, and performant applications with advanced Modules that later expand on this. Additionally, you will put your training to use on difficult module-end projects.
Deep Learning Leaders Program for advanced learners that provide you with in-depth knowledge in machine learning and artificial intelligence. With the help of this certification program, you will gain expertise in Natural Language Processing, Generative Models, and Reinforcement Learning in addition to Deep Learning proficiency. This program also prepares you for top AI and Deep Learning positions as well as for research opportunities and graduate school.