what is Data science

What is Data Science? Why is This Career Path in Demand? Find Out Now!

Companies are shifting their focus to data-driven goals and decision-making. This is a global phenomenon. In fact, according to the International Data Corporation (IDC 2021) reports, by 2025 that worldwide data will grow 61% to 175 zettabytes. So, what is data science and why is it so important? The field of data science has gained prominence because it enables organizations to efficiently process and interpret data. Leaders, in turn, use this data to make informed business decisions, drive growth, optimize spending, and push performance.

Let us dive in and understand why it is necessary now more than ever to learn how to process and understand data that can be used to drive better decisions within every organization.

Brief History of Data Science

Data science has become the popular field it is today, all thanks to the rise of technology especially programming languages, and techniques for collecting, analyzing, and interpreting data. In 1962-63, an American mathematician, John W. Tukey predicted the rise of a new field in his paper “The Future of Data Analysis”. Another pioneer, Peter Naur, who was a computer engineer, gave the earliest definition of data science in his book “Concise Survey of Computer Methods”. 

In two decades technology advanced, data collection exploded, and personal computers were launched by IBM in 1981. Apple followed suit in 1983. Throughout the 80s, computing evolved at an exponential rate, giving companies the opportunity to digitally transform and collect data easily. In the 90s, technology made huge strides by making internet connectivity, communication, and (of course) data collection practically widespread. 

By the mid 2000s, data became more important and organizations became more interested in finding patterns and making better business decisions. The demand for data scientists grew dramatically in various parts of the world and data science still continues to be one of the most thriving fields today. 

Next promotion is a course away

Data Science Lifecycle

​​Wondering what steps are taken to build, deliver, and maintain any data science product? Thought not all companies approach data science in the same way, the general lifecycle of most products includes the same common steps. A general data science lifecycle process incorporates the use of statistical practices, number crunching, machine learning algorithms, and prediction models. The five most commonly involved steps are: 

  1. Data extraction
  2. Preparation
  3. Cleansing
  4. Modeling
  5. Evaluation

This process is commonly referred to as the Cross-industry Standard Process for Data Mining and Analysis. Using these steps data experts harness the power of data science to uncover actionable insights. This in turn helps transform an organization’s decision making. 

Why is Data Science Important?

Data is the most powerful tool that businesses currently possess. It has the power to tell a compelling story as well as guide and influence decision making. Swift business actions can be taken now based on the right kind of data. No matter the industry, every organization must be in the data business now if they wish to stay relevant. Research has shown that data-driven organizations are more profitable and likely to retain customers. Here are a few ways in which data can help a company grow and become future-proof:

  1. By leveraging the right data, companies can create and implement business strategies to stay ahead of the competition
  2. Leaders can make data-driven decisions to solve business problems using trends and data insights 
  3. Recommendations for business growth and expansion can be made by combining experiments with analytics 
  4. It is a good opportunity for companies to achieve a sustainable competitive advantage by evaluating the current data strategy approach
  5. Leaders can also drive business improvement and re-evaluate the company’s needs by analyzing data sets and future trends

Why Become a Data Scientist?

Did you know? The average annual salary for a Data Scientist in the US is $140,772. As per the U.S. Bureau of Labor Statistics, the field is expected to grow by 22% by 2030 which is thrice the rate of other average professions. 

There is a huge gap in the demand for data scientists and their supply! As per a survey conducted in 2021, 92% hiring managers feel that there is a shortage of talent when it comes to data scientist careers. It is a great opportunity for aspirants to find and bag their dream roles. Also, it is estimated that 55% of businesses have started using data analytics to improve efficiency as a result of COVID-19. It is only natural that these enterprises and industries demand talent skilled in data science. 

A recent LinkedIn report also shows that data science specialists, Machine Learning (MI) engineers, and Artificial Intelligence (AI) specialists are some of the top 15 in-demand and fastest-growing jobs right now. Here are some other career paths that require data science skills.

What skills must a data scientist have?

  1. Data analysis and modeling
  2. Problem-solving
  3. Intellectual curiosity
  4. Critical thinking
  5. Math and statistics
  6. Basic knowledge of coding: Java/ Python is an added advantage
  7. Market understanding
  8. Business acumen
  9. Communication and collaboration
  10. Visualization and presentation skills

Also read: Top 9 Roles to Pursue for a Career in Data Science in 2022

Real-world Data Science Applications

A successful data science career would require individuals to be a jack of all trades. This could be a programmer, analyst, engineer, mathematician, statistician, and strategist. But above all, a data scientist needs to love data and be able to visualize it. A curious mind that revolves around information, formulates patterns, identifies trends, analyzes data, and solves business challenges is a perfect fit in the world of data science. Nearly 80% of data scientists spend most of their time discovering, analyzing, and preparing data.  A career in data science requires a master’s degree in computer science, engineering or mathematics, and statistics.

Data science in the world of healthcare

Medical science has revolutionized the healthcare industry globally. Doctors, researchers, and other healthcare professionals have realized the role technology and data can play in this equation. There are new treatments and remedies being developed on a daily basis. With the help of the right data scientists, healthcare professional can improve diagnosis, research, patient data management, post-op care, and much more. Here are the top four areas where data science is being applied in the real-world of healthcare: 

  1. Pharmaceutical drug research 
  2. Monitor patient health 
  3. Manage patient information and history
  4. Medical image-based diagnosis 

Data science in the world of banking and finance

Data is disrupting the banking sector like never before. Banks are sitting on piles of data. Over the years as data has become more prominent, banks have started harnessing this data to ensure process automation, explore new delivery models, and introducing new digital banking services. Here’s are the five ways in which data science in banking is creating new processes, models, and changing the way we bank:

  1. Detecting frauds and preventing mishaps
  2. Creating new risk management models
  3. Customer data analysis and insights
  4. Personalized marketing & sales
  5. AI chatbots & virtual assistants

Data science in the world of marketing

Big data in marketing provides companies, both big and small, an opportunity to understand their target audiences and the market much better. Also, new ways to apply data and analytics in marketing are emerging every day, thanks to technological advancement. The main task of the marketing team is to create an efficient, customer-focused, efficient marketing campaign dedicated to delivering the right message to the right people at the right time. Data can help at every stage of such decision making. Data is helping marketers make prudent and effective decisions. Here are a few instances:

  1. Customer segmentation
  2. Real-time and predictive analytics
  3. Recommendation engines
  4. Campaign monitoring and optimization
  5. Lead collection and management

Harness the power of data science & accelerate your career

Are you ready to enter the world of data science? If you are looking to learn further before you dive in, then check out the data science and analytics courses offered by Emeritus in partnership with the world’s leading universities. Choose from a host of courses and master critical processes, tools, and concepts in data analytics. As a part of the course you learn to prepare data for modeling, use analytical expressions to perform time-based analyses, and visualize data. Get access to a diverse network, learn from top-ranked faculty, complete capstone projects, and master skills that are in demand. Happy learning.

Contact us at content@emeritus.org

By Manasa Ramakrishnan

Data science banner CTA

Share This

Copy Link to Clipboard

Copy