The Data Science Learning Path for an Upwards Career Graph

21-Dec-2020

What is the Need for Data Scientists?

Data Science is transforming the way businesses and organizations run their operations daily. It has completely changed how they view and evaluate their clients and customers. Now the behaviour of the customers, and their purchasing patterns hold a paramount importance in the companies’ collective minds. Before it was simply a matter of marketing the products and services to the customers in creative ways, but now with the advent of data science the playing field has shifted to a new paradigm.

Now the most competitive companies are those who possess the most data and know how to evaluate and analyze it best.  For this purpose they require data scientists who can bring their unsorted and unstructured data into a recognizable and usable form. Then these data scientists can use their sophisticated tools to analyze and process the data and extract meaningful insights which the companies can use to make more informed decisions. This will result in better business outcomes.

Here is an informative article on learning Python for beginners.

So it should come as no surprise to those in the know that data scientists are in high demand. The demand for data scientists is much larger than the number of skilled and competent data scientists available in the job market. This is the reason that today many individuals are trying to gain the skills and knowledge needed to become a data scientist. The best way to do so is to enroll in a post graduate program in data science course.

Which Educational Qualifications do Data Scientists Need?

There are various ways to become qualified to be hired as a data scientist. Some of them are listed below -

• Graduate Degrees – One can get a full-blown degree by attending a graduate data science course at a reputed and distinguished college which is sure to offer a well-designed course. One big advantage of going to a college is that one is sure to meet and be taught by very experienced and skilled teachers. Colleges tend to have a very strict requirements for teachers and so the students will be able to learn from teachers with a lot of experience from working in the industry. They will also be able to work with and interact with other students who have similar or different backgrounds and thus be exposed to a lot of diversity.

• MOOCs and Self-Guided Learning Courses – Massive Open Online Courses (MOOCs) are another way to become qualified data scientists. The big advantage of taking MOOCs or similar self-guided learning courses is that one can progress through the course and engage with the learning materials at one’s own pace. There is no need to keep pace with a classroom setting and become limited to the speed of other students. The one disadvantage of MOOCs is that instructors are rarely able to give one on one attention and guidance to the learners as there are hundreds or even thousands of students enrolled in the MOOC.

• Bootcamps – Bootcamps are shorter than a graduate data science course and typically last for a couple of months or lesser. While they are based in a classroom setting the pace of the course is much faster than a graduate data science course or an MOOC. They are only suitable for learners who are somewhat familiar with the field of data science and want a refresher or a quick overview of it since the fast pace of the coursemeans that the learner will not be able to spend much time on studying the materials alone for his own understanding.

• Data Science Certification Courses – This is the most recommended way for becoming a fully qualified data scientist. Data science certification courses are usually online, and can be taken from the comfort of your home. They tend to have very well designed course curriculums and provide very comprehensive and meticulously created course materials, such as videos, lectures, notes, surprise quizzes, assignments, capstone projects etc. The course providers host the course of advanced Learning Management Systems which are packed with helpful features such as student forums, doubt clearing sections, assignment submission links, project submission links, and live instructor video, phone, and email support.

The instructors are hired after being put through a rigorous screening process which tests their capabilities as a teacher, their subject matter expertise, and their experience from working in the industry. So the instructors teach using only the latest teaching methodologies and teaching practices. The learners will be in very good hands and will be able to test their newly gained skills and knowledge on capstone projects at the end of the course.

What Knowledge Does an Aspiring Data Scientist Need?

A data scientist should have an expertise in a vast domain of knowledge. The most important requirement is that he should be well-versed with the Python programming language.

Python

The Python programming language is the most widely used programming language in the field of Data Science. So any aspiring data science should make it his first business to learn Python. But it is easy to become sidetracked in the course of learning the language and forget the main objective.  Many learners start to learn the programming techniques and start solving programming exercises, making small software projects, and memorizing software algorithms and methodologies.

But this is not the optimal approach to learning Python if one intends to use it in their work as a data scientist. While it is true that Python is used for data science because it is an easy to read and parse language with minimal programmatic syntax, there are other more important reasons to learn and use Python. The first and the most prominent one is that there are a huge number of libraries and modules available for data science in the Python community. Libraries like Numpy, Pandas, and Matplotlib are extremely popular among data scientists. There are also a large number of bindings for major languages written for Python. This makes it easy for data scientists to offload the heavy resource-intensive calculations and large data sets to languages which are well-suited to them, such as Fortran and C.

So data scientists should learn Python with a focus not on the software paradigms, but on familiarizing themselves with the large number of data science modules and libraries available in the language’s community.

Data Scientist Salaries Based on Experience

Entry Level Data Scientist in India

Mid-Level Data Dcientist in India

Experienced Data Scientist in India

Now let us take a look at data scientist salaries in the US -

Entry-Level Data Scientist in the US

Mid-Level Data Scientist in the US

Experienced Data Scientist in the US

Data Scientist Salaries According to the Location

Conclusion

So one can see that there are many learning paths available for an aspiring data scientist. The best one out of them however is to take Careerera’s data science certification online course which is a proven and time-tested course. Careerera provides a placement guarantee after course completion so that learners can have a seamless and smooth start to their careers. The course carries a very affordable and competitive price. So enroll in Careerera’s data science certification online now.

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4. Can You Become a Data Scientist Without any Experience?
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