Becoming a Data Scientist: What You Need to Know
Data scientists are one of the most in-demand professionals in the world we are living in today. In fact, the demand for this job is higher than the supply. Data scientists are usually responsible for carrying out research, analyzing data and communicating conclusive information that will help an enterprise to make an informative decision.
Becoming a data scientist is one of the best moves anyone can make due to the high demand for data scientists, and it is not likely to change in the near future. Unfortunately, there are so many individuals out there who would like to become scientists, but fail to chase this promising career due to the misleading information available on the internet. Perhaps you may have also read a how to become a data scientist blog, and you got very discouraging information.
The good news is that with the right information and resources, you can also become a data scientist if you are aspiring to become one. Do not let the false information on the internet that indicate that it is almost impossible to become a data scientist mislead you. Here are the skills you need to learn to become a data scientist that every company would hire.
Becoming a Data Scientist
No matter which company you will be working for, you will have to know how to use computing tools. You need to have an understanding of statistical programming language and data query language. When looking for a data scientist position, the interviewer will need to know whether you understand how statistics tools work.
Every data scientist is required to have a basic understanding of basic statistics. Most individuals who are looking for jobs as data scientists fail to pass the interview stage due to the lack of basic knowledge in statistics. Before applying for a job in any enterprise, you should ensure that you understand some statistics, such as distributions, statistical tests, and distribution among others. Statistics is vital in all companies, especially where the main product is not data. Companies rely on data scientists to evaluate experiments, design strategies and come up with estimation for conclusive decision making.
Article continues after jobs recommendation
If you intend to work in a large company that processes huge data, then it is crucial that you have knowledge in machine learning techniques. This means you need to understand things like the random forest, k-nearest neighbors and ensemble methods among all other machine learning techniques. However, these techniques can be implemented using machine programming languages such as Python; thus you do not necessarily need to be a machine learning expert. The key point is to understand how these techniques work and when to use them.
Linear algebra and multivariate calculus:
A data scientist position interview you may be asked to derive some statistics or machine learning results you employ. In other instances, your interviewer may pose some multivariate calculus or linear algebra questions, for they compose the basis of most statistics techniques. The reason why you need to understand this information is because at some point it may become necessary for data science team to come up with tailored implementations. In the case where company’s product is data driven, these techniques are very useful in predicting performance.
In most cases, data handled by data scientist is always unorganized and therefore difficult to work with. Due to such factors, you need to be able to work with imperfect data. Some of the factors that can make data imperfect include inaccurate formatting, missing values, and poor classification among other. This skill will help you to handle your work when working in a new company or working in a company where the product is not data driven.
Data communication and visualization:
Visualizing and communicating data is one of the major roles of a data scientist. If you are working in a new company that has not made a data- driven decision before, or a company that relies on solely on data scientists to make data-driven decisions, then you must be able to visualize and communicate data information appropriately. Data communication involves providing information from your data.
Visualization can be done using tools like ggplot to help the audience understand your findings much better. Understanding how visualization tools work is not enough, but you also need to understand the principles applicable in encoding data visually and information communication.
Software engineering can be viewed as the backbone of data science. Software engineering is a skill that every data scientist should possess, especially if you are working in a company that has never hired a data scientist before. A software engineer helps in creating all the company’s data-driven products as well as data logging, thus having a software engineering background increases your chances of being hired as a data scientist.
Think like a data scientist:
Being a data scientist is very demanding, thus you to be thinking like one. This is normally tested in an interview where an interviewer may pose a high-level problem such as their intention to develop a data-driven product in the future.
You should be able to figure out the important factors surrounding any problem in order to solve it efficiently. You need to be able to know how data analytical models work and always be a problem solver to be useful in a company. Many companies offer very good data scientist salary; thus you need to add value to them by helping make profitable decisions.
Learning the basic skills is not enough, but you need to practice as you study the basic data science skills. Practicing brings about perfection. To become a great data scientist, you need both theoretical information and practical experience. During your study, you can intern in companies that handle huge data. Interning as you study gives you an idea of what you will be dealing with in the real world. Practicing helps you to polish your knowledge that you have acquired through reading.