In the current clime where technology is rapidly overtaking every other sector and industry in both importance and popularity, many businesses and organizations are becoming interested in understanding the operation and results of technology systematically. One good way to do this is to take a data science course.
Data science provides the most suitable and appropriate tools and techniques for accomplishing this task. As a result, businesses and organizations around the world have become extremely interested in data science. Many professionals nowadays are asking the question – “How do I become a data scientist step by step?”
In this article we will try to provide an answer to this question which will be both comprehensive and satisfactory. We will also mention a few points about data science courses.
Steps to become a data scientist –
1. Polish your basic mathematical and statistical skills –
The first thing that an aspiring data scientist should do is that they should polish their basic mathematical and statistical skills. They can do this with the help of textbooks from high school or even by taking a data science certification course.
They should have a strong grasp over mathematical fields such as calculus, linear algebra, numerical methods for optimization etc. They should also have a strong grasp over statistical concepts such as measuring and recording data, analyzing data, drawing and generating graphs of data etc. A data science course teaches all of this and much more.
2. Familiarize yourself with the basic data science toolset –
Data science requires not only knowledge of the mathematical and statistical concepts but also a thorough familiarity with and expertise in the data science toolset. Over the decades, data scientists have identified many common problems and many common needs and requirements which appear again and again with every project.
In order to save themselves and other data scientists the trouble of having to solve those problems afresh every time they appear, they have created and built some tools which have many sophisticated, advanced, and helpful features which solve those problems automatically. Some examples of those tools are – SQL, Apache Spark, BigML, D3, MATLAB, Excel, ggplot2, Excel etc. A data science course teaches all of this and much more.
3. Learn and practice a programming language –
No matter how many sophisticated, advanced, and helpful tools are created by data scientists in order to solve common problems, it is a fact that data scientists will always run into new problems or new needs and new requirements. In order to handle those, the data scientists will have to write customized code which is targeted at their unique problem or need or requirement.
This means that every data scientist should learn and know at least one programming language thoroughly. Some popular programming languages used in the field of data science are Python, SQL, SAS, and R. They are popular among data scientists because they are especially tailored to the unique problems which exist in the field of data science and have many useful and comprehensive data science libraries. All these features and libraries are taught in the data science certification course.
4. Learn applied machine learning and the big data ecosystem pathway –
Every data scientist who wants to advance to even the intermediate level will have to learn and pick up applied machine learning. They will have to thoroughly understand and grasp the concept of teaching machines how to make decisions based on the information they glean from their environment.
Another domain they will have to delve into is the big data ecosystem pathway because big data is growing increasingly popular in the field of data science and is being studied and adopted by big companies such as Google, Microsoft, Facebook, Twitter etc. One can enter these companies by taking a data science course.
5. Build a project portfolio –
A data scientist should start to build a project portfolio as soon as they have acquired sufficient skills and knowledge to embark on their own personal projects. The reason for this is that no matter how much bookish and theoretical learning one acquires, they can never truly engage with the material until and unless they put it into practice.
A project portfolio will also come in as very handy when an aspiring data scientist approaches potential employers for a job. A data science course includes building a project portfolio.
6. Start sharing your work –
A data scientist should always keep sharing and forwarding the work that he does to others in his field. Doing this will bring them several benefits. The first and the foremost is that the data scientist will get seasoned and experienced eyes looking over his work and reviewing it.
They will be able to give him valuable feedback which will prove to be very edifying for him in the long run and which will help him improve the quality of his work.
7. Learn from the work of the masters –
A data scientist should always keep his eyes and ears open and keep looking for new advances and developments in the field of data science. The best way to do this is by studying the work of prominent experts in this field who have been pushing the envelope of data science with their innovation and creativity.
8. Push your boundaries and try new things –
A data scientist should always try to push their boundaries and try new things by learning new concepts and carrying out various experiments with the datasets that they possess. They can try to make new projects with bigger data sets than before, or make projects in domains for which they don’t possess the domain-specific knowledge.
