Data science is one of the most in-demand career paths within the technological changes that is shaping the world. Data science is a combination of different things, such as math, statistics, data engineering, visualization, advanced computing, domain expertise and more, and a data scientist's knowledge should combine all these aspects together in order to compete in the market. Machine learning and AI are related to the world of data science, let’s discover more.
Before diving deeply into how you should become a data scientist, it is first important to know what data science is in the first place. Data science is the field of study that combines domain expertise, programming skills, and knowledge of mathematics and statistics in order to extract meaningful insights from data. Data science is a blend of different tools, algorithms, and machine learning principles that all work together in order to help in discovering hidden patterns from raw data.
We could easily sum it up by saying that data science is mainly used in order to make predictions and decisions based on predictive casual analytics, prescriptive analytics, and machine learning. Predictive casual analytics is when you want a model to predict the possibilities of a future event, the prescriptive analysis is when you want a model that has the intelligence of taking its own decisions and the ability to modify it with dynamic parameters, and machine learning is used for both prediction and discovery.
Data scientists are those data experts who are curious enough to explore the different problems that appear as well as the solutions that could help in solving them. Being highly in demand comes for one important reason: being those experts who combine between three different characteristics. Data scientists are considered mathematicians, programmers, computer scientists, and trend spotters, that’s why employers always search for them.
If you give it a second thought, you will realize that a decade ago, data scientists were not placed on the map, but they have recently been taking the top in-demand jobs out there, and this shows one important thing, which is the fact that companies are now paying much attention to big data and need experts for it.
So, what does a data scientist do? Data scientists are the ones who end up extracting the complex data problems using the latest technologies in the field along with their expertise in different fields, such as mathematics, statistics, programming, etc. in order to find a suitable solution and reach the needed conclusion.
There is one question always asked about data scientists: why are they called so? Data scientists' work is always about drawing a lot of information from the scientific fields and applications whether it is statistics or mathematics, and these information are then presented in a more useful form other than the data that they found. They are also called data scientists because they actually have to search for the what, why, when, who, and how of the data that is available for them in order to work among achieving the results needed.
What are the different responsibilities and roles of the data scientist? There are different things that a data scientist should do, and this comes in collaboration with other specialists as well. Data scientists for example work closely with business stakeholders in order to understand their goals and needs and thus know how to determine the data and use it to achieve these goals.
Data scientists are responsible for designing data modelling processes, creating algorithms, and predictive models in order to extract that data needed for the business. From the different job roles that data scientists have to do include acquiring data, processing the data, storing the data, investigating data, choosing one model or algorithm, applying data science techniques such as machine learning and artificial intelligence, measure the results, and finally present the solution for the stakeholder.
In addition to understanding the different roles and responsibilities of the data scientist and understanding what data science is, you should also know why data science is important in today’s world? Data science is important to any organization because it helps business leaders make decisions based on facts, statistical numbers, and trends.
Companies are now turning to machine learning, artificial intelligence, and data science, no matter how big or small they are, that’s why it is important for your company to implement data science in order to avoid being left behind.
Even though holding a bachelor’s degree in data science or any of the other fields that could help you find such a job was one of the most important steps to be taken, now lots of people decide to shift their careers and learn data science with courses and bootcamps. There are three main important things that a data scientist should know about; mathematics, statistics, and programming in order to analyze patterns in data and manipulate it with different treatments, and also deal with data at scale that can take up terabytes of space.
Even though it is possible to learn all these skills through an academic degree, you could still do it on your own; you could enroll yourself in online courses, attend online bootcamps, and keep reading and educating yourself online, what’s important at the end is how you use the skills that you have learned in projects to prove your abilities.
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If you decided to learn data science on your own or even attend a program or bootcamp then you should know important things that will help you.
First of all, it is important to read as much as you can and know what you will need in order to learn data science and what are the skills that you will need to have.
Third, you should at least learn the basics of statistics in order to be able to infer insights from smaller data sets to larger populations.
Fourth, you should know which industry you want to work in in order to know what data means for this particular industry, and if you did not choose yet then you should know what it means for each industry.
Finally, you should build real projects to implement what you have learned and start building your portfolio before you start networking and getting into this community.
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