Breaking into the world of data science is not an easy task, especially with the surge of data and analytics jobs. Regardless of competition in the job market, many fresh graduates are looking to take that career leap.
No doubt data science skills are skyrocketing and there’s increased demand for data science skills.
Based on a report, it is said the data science jobs are bound to increase by about 364,000 openings to 2,720,000 by 2020. A recent report by the US Bureau of Labor Statistics predicted new job openings of about 11.5 million to be created by 2026.
It is quite evident the data science industry will continue to grow extensively, thus, it is safe to say that data science skills will shape up.
Data science professionals are individuals who sit at the intersection of programming and mathematics. This is the basic skill a data scientist need to first grasp. Needless to say, if you have the knack for mathematics, then your base foundation is set right in place. You do not need to specifically remember every formula you learned at school. However, having strong knowledge in statistics and probability such as mean, median, normal distributions, the standard deviation is an added advantage.
Here’s what you need to do: –
Gain hands-on experience in programming languages and build your statistical foundation
Python and R programming languages are the basic languages every aspiring data science professional needs to learn. According to a data released by LinkedIn, near about 24,697 job openings were seen in the US alone. And with the help of data mining, they were being able to analyze the topmost data science skills that were in demand. However, as on April 14, 2019, these were the top data science skills that were trending for a data scientist job role – SQL, R, Python, Jupyter Notebooks, AWS, Tensorflow, and Unix Shell/Awk, etc.
Besides programming languages, the candidate needs to gain hands-on training. This is one of the ideal ways recommended for one to improve their programming skills along with building their statistical foundation.
The best way to get your hands dirty is by taking up projects and solving statistical problems.
Build your online portfolio, work on open source projects in data science and machine learning
While this may seem tough and challenging, remember this is the only way potential employers will be interested in hiring a fresher.
Portfolio building has become the norm today, especially for aspiring tech professionals looking to work in new technologies.
Having a portfolio is one way of self-proving your potentiality in the field of data science. It is proof to employers stating you’re eligible and competent enough to take up a job in the given field. Most employers are skeptical in handing over the company’s data to a fresher since data can be sensitive and crucial to the organization. They need to understand you’re capable enough of handling a project. A portfolio demonstrates the employer regarding the projects you’ve worked upon assuring them that you, as a fresher is eligible to handle the job.
While you may find datasets available online, you might want to consider data science certifications to upgrade your data science skills. Since most certification programs often stay updated with the ongoing market trends, it is ideal to give it a second thought.
Certain skills you need to highlight in your portfolio – R and Python programming, statistics, SQL, data visualization, deployment of an application, and story-telling.