Do you know that Data Science and Analysis positions are the toughest to fill?

The global revenues for big data and business analytics are set to reach new heights. Naturally, because of increased demand, and data surge data science professionals are much in demand and enterprises are welcoming them with open arms to embrace the industry and join the workforce.

One question which is shared by everyone interested in pursuing a career in analytics is how to get a career into Analytics?

The gap between existing skill sets and required skills is very broad, which makes it hard to find a good fit for the analytics job. A lucrative job opportunity to fathom, data analytics and data science is all set to grow and permeate into other industries regardless of how distant they may have once been. To address the skill gap and headcount challenge, enterprises are all set to recruit from the non-technical background to data science and related fields. These tips will guide you on how to get a foot in the door and stay at the top of recruiter's minds when new and competitive job opportunities pop up.

Why Work in Data Analytics?

According to projections from IBM and Burning Glass Insights, it's estimated there will be more than 2.7 million job openings for professionals with data skills by 2020.

If you're considering a career in the analytics field, you may be wondering about the specific opportunities available to you. While the title of data analyst is a popular one (and likely the first to come to mind), it is not the only option.

Here are other in-demand data-oriented careers that  may align well with your interest and skills:

• Big Data Engineer: US$127,250– US$219,500

• Database Manager: US$108,000– US$183,000

• Database Developer: US$98,250– US$167,750

• Database Administrator: US$77,000– US $159,250

• Data Analyst/Report Writer: US$81,750– U$138,000

• Data Architect: US$111,500– US$187,750

• Data Modeler: US$79,000– US$164,500

• Data Scientist: US$102,750– US$175,000

• Data Warehouse Analyst: US$77,750– US$160,000

• Business Intelligence Analyst: US$85,750– US$178,000

Towards a Data Analytics Career

1. Assume an Analytical mindset

If you haven't had much exposure to working with data or using data to draw conclusions, one way to gain some practice is by focusing on the everyday statistics and numbers in your life. Next time you take a stance on a topic at a company meeting or out with friends, ask yourself:

• From what am I deriving this information?

• What numbers support my position?

• What numbers contradict my position?

2. Research how analytics is adopted in industries

Analytics isn't a stand-alone field. Analytics can be applied wherever data is collected, and analytical roles can vary depending on the context of the industry, department, and role. Thus, great analysts come from a range of different professional backgrounds.

. Imagine an analyst role in your chosen field, and try to answer these questions:

• How is success defined in this field?

• If success is defined qualitatively, how can it also be measured quantitatively?

• Picture a common challenge or problem being faced in this field. What metrics would you look at in order to diagnose and resolve it? For example, the food industry suffers from produce spoiling in transit. In that scenario, you might consider distance travelled, type of produce, method of transportation, crop yield, etc.

• Imagine you have multiple new opportunities in this field (i.e. expansions, partnerships). What metrics would you look at in order to decide what to pursue and what not to pursue?

• What companies or organizations do you admire? Look at their blogs, hiring policies, social media accounts, and mission statements. What value do they place on data and analytics?

3. Develop your coding skills

Some of the most in-demand technical analytical skills include machine learning, predictive analytics, data visualization, MapReduce, and a general understanding of big data and data science. Additionally, many employers seek to hire individuals who have expertise working with specific tools, such as Apache Pig, Apache Hive, Apache Hadoop, and MongoDB.