The Bureau Labor of Statistics (BLS) prediction projects to the fact that data related jobs such as management analysts, financial analysts, business intelligence analysts, database developer, data analysts, and statisticians will shoot up at a rapid pace. The prediction also states that by 2026, there will be 33%, 14%, and 11% increase in these job roles respectively.
For those heading to break into data science and data related career, the job market is still hot. What’s more, technology professionals with the new age technology skills are in demand. Data science is here to stay and is becoming critical for most businesses today.
According to Indeed, an employment portal states that now is a great time for professionals to enter the data science industry. The job of a data scientist has not only grown sexier but companies these days are more than ever looking to hire skilled expert professionals. Indeed also reports that the demand for data scientists has grown 344% since 2013. The demand for data scientist continues to rise sharply, but the talent in data science remains at 14% (there is a gap between the demand and the supply).
The IT market is in the realization that implementing data science can make their businesses more profitable for which many career-minded professionals are now upgrading their skills and preparing for these job roles. ‘Upskilling’ is a trend in today’s technology market. We all know that due to digitization and emergence of technology skills such as AI, data science, machine learning, etc. over more than 375 million workers will need to change their skill sets by 2030, states a report by McKinsey Global Institute.
Although, one of the biggest challenge the industry still faces: hiring a data scientist with relevant skills.
Companies are still looking to cherry pick talents skilled in data science. There are certain tools and technologies that these companies will look for from their candidates. In terms of technical skills, Python showed a lot of traction in the past years followed by R and SAS. These tools and technologies are seen to be the most relevant skills that the recruiters will look for from their candidates.
A data science professional is also expected to have experience in technology tools such as machine learning, data visualization tools like Tableau, ggplot2, Plotly, RapidMiner etc, NoSQL databases, and AWS.
Most organizations these days are in a hunt for candidates who can handle a huge amount of data. In short, data science professionals who are able to put these theoretical into practical usage will be most sought after. Gaining expertise in these tools will prove that you’re competent enough to handle projects making you a desirable candidate.
Why data science matters today?
Well, the role of a data scientists did not just evolve overnight. However, we should be thankful for the fast computing and cheap storage we are now able to predict things within minutes. This generally used to take hours together for a human brain to process.
Have you wondered while browsing through the internet for certain words or news that you wish to read, the internet delivers several other related data? How does the internet do that? Or how does YouTube recognize what would be the exact next video the user would view or would choose to view?
For instance, let us take Google as an example, when we need to search for anything on the internet, the first thing that strikes our mind is Google or Yahoo, etc. Well, all these search engines use data science in their delivery process. They make use of data science algorithms to deliver the best results that we’re in search for.
There are many other applications that are built upon the concept of data science. To name a few are healthcare, internet search, speech recognition, gaming, augmented reality, fraud and risk detection, advanced image recognition, virtual assistance, and website recommendation, etc. These are the technological applications of data science.
Who does not want a job in the data science industry where an avalanche of job postings is seen daily. If you’re looking to establish a career in this field then you might need to consider these in-demand job roles – data scientists, data analysts, data architect, data engineer, statistician, database administrator, business analyst, and data analytics manager, etc.
The core responsibilities of a data scientists
Being a data scientist one is expected to manage, interpret the data and solve problems using predictive analysis. These experts are generally involving with exceptional analytical skills, problem-solving skills, data modeling, coupled with strong business acumen. On a daily basis, these are the responsibilities a data scientist might carry:
- Extracting and cleaning data using R and Python programming
- Analysis of data using statistics
- Data visualization using tools such as Tableau, Excel, ggplot2, etc.
- Machine learning algorithms for building predictive models that help predict the future
Being a data scientist can be a great game changer for organizations offering insight and predictions that can illuminate the business goals of an organization. Currently, data science is an expansive field, however, landing a job as a data scientist can give you heads up in the IT industry today.