Data science is a field that deals with the extraction of insights and knowledge from data. Simply put, it's a way to make sense of large amounts of data and use it to make decisions or predictions.
Think of data science as detective work, where data scientists collect data, analyze it, and use it to find answers to important questions. This could be anything from figuring out what products people are buying, to finding patterns in medical data that could help prevent diseases.
The goal of data science is to extract insights from data for making decisions based on predictions. It is rapidly growing and plays a crucial role in many industries.
Data science is the domain of study that deals with vast volumes of data using modern tools and techniques 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.
DATA SCIENCE MAKES EVERYTHING BETTER
Data Science help industries in automating redundant tasks. It is enabling companies to use historical data to train machines to perform repetitive tasks without being explicitly programmed (Machine Learning). This has thereby simplified the tiring jobs that humans performed earlier! Since it involves machine learning, it has enabled the industries and businesses to create products and services that are not only profitable but also consumer-centric. Thus, it has helped industries achieved the magical space where both the customers and the businesses are in a happy place.
What are The Benefits of Employing a Data Scientist? Fueling data enablement and operations in an organization is one of the biggest benefits of employing data scientists. However, the benefits of data scientists are not limited to data science or analytics; they play an imperative role in the different organizations across various industries. Below, we enlist the benefits of employing a data scientist in an organization.
Data science is a scientific process that relies on mathematical and statistical formulas to extract data and make sense of it. Data scientists are trained professionals who convert unstructured and raw data into deep insights that benefit business organizations. Therefore, data scientist empowers business decisions and helps leaders during complex business scenarios.
By using different data science and analytical process, data scientists help businesses identify different work opportunities. They also use data extracted to forecast future market conditions that help business makes fundamental changes and prepare for adversities.
Data scientists are believed to challenge employees to perform better and successfully overcome the digital transition across various industries. Moreover, they prepare the workforce to use data analytics and scientific applications to increase automation and innovation in various business processes.
Data scientists are trained professionals who change the central operating system of the business organization by introducing automation and modern technologies like artificial intelligence, machine learning, and deep learning. These data science applications help automate recruitment, customer feedback, market research, etc. Thus, making it imperative for organizations to recruit data scientists, rendering the benefits of employing them.
Data scientist helps make data-driven business decision that reduces the risks of failure. Hence, making them an inevitable part of the organization, and adding to the benefits of employing a data scientist. It also helps in forecasting and budgeting, which further reduces financial risk. Similarly, the scientific process implements its unique processes in different departments to ensure minimal risk and increase efficiency and productivity.
There are a variety of applications of data science. It is being widely used in healthcare, banking sector, financial services, Insurance services, E-commerce, retail, entertainment, education and where not! Therefore, data science is a very versatile field.
Students who are either undergraduate or graduate in data science, or related fields like computer science, business information, technology, engineering, economics, information management, mathematics, or statistics meet the Data Science Course Eligibility.
If you are a beginner or a professional and want to learn Data Science, you can! Marketing experts, Software/IT professionals, and engineers can pursue external data science programs online/offline. To opt for a regular course in Data Science, you must have knowledge of school-level subjects like Mathematics, Statistics, and more.
The minimum data science eligibility criteria in India is anyone who is from a STEM background (Science, technology, engineering, and mathematics). Such students who had studied computer programming in high school have greater chances of getting selected for the data science course.
If you are a candidate belonging to business studies, you’re also eligible for relevant courses in Data Science. Business professionals with a basic degree in Business Administration - BBA or MBA, can always opt for courses in the Data Science domain. The basic requirement for someone opting for a PhD in Data Science is a percentile of 55% in the post-graduation program.
What is the scope of data science as career?
Especially in a developing country like India, there is scope for huge data-related operations such as data scientists, data analytics, big data engineers, big data managers, and data architects. Check out our Data Science Course in one of the top data science institute in Mumbai which provides the best out of the course to students so that they can become experts. This course covers foundational topics in data science, as follows
• How to deal with Data Manipulation
• How to analyze Data with Statistics and dealing with various data
• Data Communication
• Working with Big Data and Hadoop
You can also check out our website to know more about Data Science.
Scope of Data Science in India
With data being termed as the future oil for organizations, analytics have become an engine that drives it to arrive at meaningful insights. The powerful combination of both is what is driving the future scope of data science. All across the globe, organizations are innovating multiple methods to harness data and use this powerful tool to drive their businesses.
The confluence of massive data influx and the need to harness this huge amount of data has together built a big job market in data science. Termed as “one of the sexiest jobs in 2021” by the Harvard Business Review, data science has introduced a whole new array of jobs to the market. Especially in a developing country like India, there is scope for huge data-related operations such as data scientists, data analytics, big data engineers, big data managers, and data architects.
Data Scientist salary in India with less than 1 year of experience to 8 years ranges from ₹ 3.8 Lakhs to ₹ 26 Lakhs with an average annual salary of ₹ 10 Lakhs based on 22k latest salaries.
Executive Data Scientist salary in India ranges between ₹ 3.4 Lakhs to ₹ 8.5 Lakhs with an average annual salary of ₹ 6.8 Lakhs. Salary estimates are based on 29 latest salaries received from Executive Data Scientists.
Average salary for an Executive Data Scientist in India is 6.8 Lakhs per year (₹56.7k per month). Salary estimates are based on 29 latest salaries received from various Executive Data Scientists across industries.
What is data science course all about?
Data science is the domain of study that deals with vast volumes of data using modern tools and techniques to find unseen patterns, derive meaningful information, and make business decisions. Data science uses complex machine learning algorithms to build predictive models.
Why Choose Data Science As A Career Alternative?
A LinkedIn Workforce report estimated the demand for skilled data scientists to rise by 15% between 2019 and 2029. Moreover, with established businesses implementing modern technologies that automate traditional business processes and bring about innovation, efficiency, and productivity across all business functions, the demand for data scientists and analysts is expected to increase in forthcoming years substantially. Therefore, if you plan to make a career in data science, now is the right time to make big strides in the right direction.
You can take different professional courses that help you understand the pros and cons of data science, different applications, etc. Interestingly, Emeritus India has some of the best data science certification courses that help you learn appropriate skills and deep knowledge about the fundamentals. Moreover, our certification courses help build a strong foundation in the field and prepare you to reach new heights in your career. As a result, Emeritus India has partnered with renowned universities to offer the best curriculum.
Data science is a scientific process that uses mathematical and statistical formulas to extract data and use it to make business decisions or impact different industries. Data scientists are trained professionals who perform the data science process. There are many advantages of data science that we will discuss below.
Innumerable career opportunities
Helps in solving complex business problemsAutomates customer interaction, recruitment, and other processes
Now that you are aware of the advantages of data science, here are some of the disadvantages of data science:
The data Science process is considered to be an expensive affairData Scientist salary in India with less than 1 year of experience to 8 years ranges from ₹ 3.8 Lakhs to ₹ 26 Lakhs with an average annual salary of ₹ 10 Lakhs based on 22k latest salaries.
A data science salary in India is Rs.10,00,000. This qualitative job market kept evolving with predominance across different industries, relying heavily on business and data intelligence and offering high data scientist salaries.
Benefits of using Data Science
∙ Extracting insights and knowledge from data that can inform decision-making and optimize business processes.
∙ Making predictions about future trends and patterns in data.
∙ Identifying new opportunities and areas for growth.
∙ Improving efficiency and automating repetitive tasks.
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