Are you wondering to switch your career in Data Science? But confused about what is Data Science exactly and what Data Science work involves. There are almost all the domains or industries where you can step ahead in your career as the data is only getting bigger with each day and companies need professionals with relevant skill sets to handle their data.
If you’re a Non-IT professional and willing to move into science and work as a Data Scientist, Good news for you, you can switch your career to a Data Analyst, Data Engineer, or Data Scientist based on your skills & expertise.
Let’s dive into this topic by understanding what is data science and why is it important to pursue a career in Data Science within your niche. It does not matter what your background is or what experience you have right now, as Data Science is used in almost every field such as cyber security, healthcare, Finance, marketing, sports, manufacturing, retail, e-commerce, solar energy, electrical & electronics, and many more.
You’re reading the article, What is Data Science and Why it is Useful in 2023?
Let’s understand this concept in detail by starting with the meaning of Data Science.
What is Data Science with an example?
Data Science is the process of extracting useful insights and valuable information from the given data.
Data + Science = Data Science
(any kind of information) (the state of knowing) (study of data)
Above mentioned formula clearly shows the basic meaning of Data and Science. But what type of data, what type of information is called data?
To understand well, let’s assume an example, hospitals record patients’ information such as medical history, diagnostic tests, medical records, etc. Data Scientists use this information to predict models that can identify patients at risk of certain medical conditions.
With time-to-time, the demand for Data Scientists or Data Analysts has increased in every field. According to the Bureau of Labor Statistics, “the Employment of data, scientists is estimated to be 36 percent from 2021 to 2031, significantly higher than the average for all occupations.”
You’re reading the article, What is Data Science and Why it is Useful in 2023?
Can AI (Artificial Intelligence) Take Over Data Science Jobs?
The answer is NO. Yes, you heard it right. Even with the growing demand for AI, the demand for Data Professionals is also increasing in the tech industry. Data Science job offers a high-paying salary, job security, continuous growth and learning, entrepreneurial opportunities, etc.
Skillset and Experience Required For Different Data Science Job Roles
Data Analyst: If you are a fresher or Non-IT professional, you can start your Data Science journey by learning programming languages like Python, R, Java, and Scala. As the majority of companies use Python for their data science tasks, there are multiple job openings in the field related to Python For Data Analytics. If you’re willing to explore your career as a data analyst, you can move ahead by learning Python, data analysis libraries like Numpy and Pandas, and data visualization libraries like Matplotlib and Seaborn. To become a data analyst, you don’t really need any experience and you can smoothly switch your job to data analyst as a fresher.
You’re reading the article, What is Data Science and Why it is Useful in 2023?
Data Engineer: To become a Data Engineer, a candidate must have technical skills like data analysis, data visualization, ETL, and knowledge of cloud tools like AWS or Azure. There are many fresher jobs available in data engineering but in most cases, one must have 2-3 years of relevant experience in the data field to become a data engineer. The work of a data engineer involves technical aspects like managing databases by ensuring performance, handling large volumes of data by working with big data technologies, implementing data quality checks, etc.
You’re reading the article, What is Data Science and Why it is Useful in 2023?
Data Scientist: The role of data science is the most reputed and senior-level role in the data field. A Data Scientist is a professional who has all the skillsets that every other data professionals have. Data science has skills in data analysis, data visualization, data engineering, database management, cloud engineering, big data analysis, and everything that the data field involves. To become a data scientist, one must have at least 3-5 years of relevant experience.
However, it varies from company to company requirement. The skillset for the Data Scientist role is proficiency in Python, R, SQL, advanced machine learning concepts, big data tools like Pyspark & Databricks, and in-depth domain knowledge.
You’re reading the article, What is Data Science and Why it is Useful in 2023?
How Can Data Science Benefit You
The first and foremost advantage you will get is job security and a future-proof career. Due to the increasing availability of data and advancement in technology, there are a plethora of companies that require Data Science Professionals who can work on the company data and extract useful insights for company growth.
You can see in the image below that there are more than 85,000 jobs available for the position of data scientist in India only, and that too is only on one job portal, i.e. LinkedIn.
You’re reading the article, What is Data Science and Why it is Useful in 2023?
Another major upside of becoming a Data Scientist is the handsome Data Science salary package.
You’re reading the article, What is Data Science and Why it is Useful in 2023?
According to Glassdoor, the average package salary of a Data Scientist is Rs. 14,00,000 per year with experience of 5-10 years.
To conclude, making a career as Data Scientist is a fruitful decision with a great deal of potential for future growth.
To make your career easy, ConsoleFlare helps you with learning Python programming, Python libraries like Numpy, Pandas, Matplotlib, and Seaborn, SQL for data analysis, bigdata tools like Pyspark and Databricks, and BI reporting tool Microsoft Power BI.\
Hope you liked reading the article, What is Data Science used for? Please share your thoughts in the comments section below.