Data science has become a popular career option these days. With the increasing possibilities of artificial intelligence and other fields, data science has become a relevant field of research and work.
Let us start with the basics of data science and then to other important questions.
What is data science?
As the world entered the period of big data, the need for its storage also increased. Data Science is a blend of different tools, algorithms, and machine learning principles with the goal of identifying hidden patterns from the raw data. The insights gained are later used for problem-solving and research in different scientific and technical areas. It is growing rapidly and has already shown a huge range of chances that a wider definition is necessary to understand it.
What does a data scientist do?
Data scientists study the data given to spot trends or features. They use a number of appropriate methods and research designs to make sense of the data. The outcomes they get can then be used to provide businesses and firms with valuable insights. These results can then be used to design better systems or products. The major responsibilities of data scientists are to study and analyze data, Use and build research tools and algorithms, and lastly, provide companies with useful insights based on the given data.
Besides this, data scientists also help in improving the tools used in data science.
Why build a career in data science?
All sorts of companies today invest in data science to make better decisions for themselves as well as their consumers. Data scientists have grown into an asset to most companies and organizations.
Scope of data science
- In India, sectors like healthcare, pharmaceuticals, banking, telecommunications, and media have a demand for data scientists.
- India comes second to the United States of America when it comes to data science. 1 out of every 10 data science jobs is accounted for by India.
- There are currently up to 60,000 job vacancies in India in this field; hence, there are plenty of opportunities for job seekers to look forward to.
Demand for data science
- The largest employer of data scientists is the banking and finance sector, comprising about 44% of data science jobs.
- Developing technologies mean that it will see significant demand in fields like AI, cybersecurity, space exploration, and driverless transportation too.
Advantages and Disadvantages
- Plenty of job openings
- Lots of demand in major sectors
- Creative utilization of mathematical and statistical skills
- Requires knowledge of multiple disciplines and tools
- Less opportunity for independent research work
- The application of data analysis results are managed by the sector or company’s needs
As data science uses a multidisciplinary approach to problem-solving, you need a strong background in mathematics, statistics, and computer science. Choices are given to candidates with a good understanding of programming languages and some work experience too.
A degree in Maths, Statistics, Computer Science, Engineering, Physics would be of help.
Skills required for a data scientist
- Mathematics-Knowledge of multivariable calculus and linear algebra
- Statistics-Understanding of techniques to find out patterns in data
- Programming and languages-Must be able to use programs like R, Python, Hadoop, SQL, etc.
- Data visualization-Present the information found from data analysis and reporting them to the company
- Machine learning-Working knowledge of algorithms and other aspects
- Data wrangling-Dealing with inconsistent data for analysis and obtaining useful information
Curiosity, creativity, communication, business sense, teamwork, etc.
Tips to get a job
1] Build strong mathematical and statistical skills
A firm base in applied math and statistics can be very effective in data science. It is a key skill for analyzing the large volumes of data.
2] Learn programming languages
Languages like Python and R are very important for getting a head-start. Knowing how to code is also necessary for working with big data.
3] Courses and Certifications
There is a large variety of exciting science courses and programs, both online and offline. These programs involve statistical tools, coding, machine learning, etc. A couple of such certifications will help you increase your employability chances.
4] Work on projects
Learning how to use techniques is not the only way to become a data scientist. Building a portfolio by working on live projects, either as an intern or independently on platforms, can be very helpful.
5] Develop business acumen
Companies hire data scientists for the valuable information they can present during the process of product-building. Knowing how to use the outcomes of data analysis is a brilliant way to sharpen your reliability as a data scientist.
From big organizations to smaller startups, data science is becoming increasingly beneficial in building their infrastructure. With data science and machine learning or AI growing to be the most demanded job, there will only be an increase in opportunities to follow shortly.