6 Steps to Consider When Starting a Career in Data Science
Staring a career in data science can be exciting. However, this field will not be that satisfying if you don’t have an idea of what you need to do at a given time. When I say this I don’t mean you need experience for you to succeed in the business. If you are eager to know where you need to start in your data science career then here are the 6 steps to follow.
The first thing is to know what you need. This is step is very important for you because that where you get the basis for your career. This simply means that you have to know where you are and what you need. For you to complete with that step you will need to explain the meaning of data science. I and you know that data science is a process of getting answers using numeric data for the asked questions. Nevertheless, you need to have a program to help you in solving the huge data that you will be working on. The program will be responsible for collecting data, clean and analyze it to give the answers to the questions. Working with a scientist that can write programs and being mathematically fluent is a key to success in your data science career. The flowing of the coding language that you intend to use is very important.
Python and R are the first the second step to consider. The use of R is to compute statistical data like data manipulation, storage and also graphing. Wide range of people prefers to learn data science with python because it makes work easy for them. Its good that you get used to one language before you use several languages. Semantics, structures and basic functions should be at your fingertips before you think of adding another language.
It’s also good that you pursue a degree. A degree in either information technology, computer science mathematics or statistics will be an advantage to your data science career because you will get into details of your career and you will also be close to experts in the field hence giving you a chance to ask any question that you may have.
Then, you should learn about specialization. Since the data science is an umbrella of many specializations you should find the direction to take depending on your interest.
Consider practical applications. It’s good to learn the working of the program and why it reacts in a certain way but also it necessary to study practically how to work with the program.
Working on what you have learned is important and it works better if you start on an independent project.