Shouldn’t You Become A Data Scientist Data science has become a popular career path in the last few years. The development of AI and the growing number of data has made this field a destination for many.
It involves various aspects of statistics, machine learning, and computer science. It is a relatively new field. But the high salary, diverse opportunities, and bright future are making it an attractive career.
What is data?
You may have heard about data. But let us repeat – data is a collection of facts, information and statistics. It can be in various forms such as numbers, letters, sounds and images.
Now every organization/institution has a large amount of such data. But they are not in a consistent or fixed format. Some experts are needed to understand such data.
Skilled human resources extract important information from such data. Using this, organizations/companies get practical solutions to business problems.
As a result, those companies grow and succeed in a short time. This is why every company, business and sector today needs skilled human resources in data science.
Moreover, those who do well in this also get job opportunities in multinational companies like Google, Amazon, and IBM. This means that the field of data science is only going to grow in the coming years.
There are career options in this field such as data scientist, data analyst, data administrator, data engineer, marketing analyst, quality analyst, product operations associate, machine learning engineer, data architect, data manager, etc. Having written all this, let’s now discuss how to enter this field:
-Getting a related degree
To start your career in the field of data science, it is necessary to get a bachelor’s degree in subjects like data science, statistics, and computer science. This will strengthen your foundation.
It is not mandatory to study IT to make a career in this. In Nepal, some colleges that run courses from foreign universities, including Tribhuvan University, Kathmandu University, offer data science.
-Hard and soft skills
Programming languages like Python, R, and SQL play a special role in data science. Similarly, it is necessary to have in-depth knowledge of data visualization, machine learning, deep learning, natural language processing (NLP), big data, and cloud computing. Also, one should gain expertise in data science tools like TensorFlow, Pandas, and Git.
– Practical experience
In this field, theoretical knowledge alone is not enough. Because the work done here has an impact on the practical world. This means that you have to improve your skills every time you do something. A good way to do this is through internships. Internships help you learn a lot while working.
-Portfolio and Networking
There is a point to developing data science skills. Along with this, you also need to show your online portfolio. The reason for this is job opportunities.
For example, if your portfolio is good, you can get a chance to work in world-class companies. To create a portfolio that provides such opportunities, you need to include the projects you have done and the skills you have.
Similarly, you can also benefit from joining data science communities. You can find such groups on social media such as Facebook, and LinkedIn.
Overall, after completing these steps, you can get the job you want in the field of data science.
