Developing the right skillset for digital economies

One major characteristic of the information age is just as the name suggests – there is a lot of information at the disposal of a user, to an extent that a lack of knowledge on a particular subject such as trending topics would be considered blatant ignorance.

Inarguably, exposing oneself to endless information available on the Internet and platforms is necessary as it allows you to keep re-engineering and re-sharpening your skills as per the latest market trends.

You remain at an enviable distance ahead of the business curve when you have soft skills that can help you easily acquire Certified Data Science Programs. Yet even so, becoming an expert is not a walk in the park. Just like any other hard-earned deal, developing a desirable maturity in the programming field will need your individual effort and proper guidance from a certified instructor.

While it is easy to fancy getting equipped with programming, Machine Learning or data science skills fast enough to make you the next employable ‘demigod’, learning the ropes takes a considerable amount of time.

After attending the full sessions and successfully mastering the programming languages such as Python or R, you have to keep practicing for a while to learn it well especially if they are your first languages.

You probably feel the pressure to meet the already growing business demand for relevant skills needed for a digital economy. Organisations have dramatically transformed their skill-set requirements and you feel the need to reskill.

Do not opt to drown yourself in endless online tutorials and books on Machine Learning or other topics.

It could probably take you two months to learn the essential rules and procedures of Python programming and another three months of practice using Kaggle datasets. Gaining the theoretical knowledge of basic Machine Learning can possibly happen in a week but getting a hang of it in practical applications will take a while.

The Internet is a sea of data at our disposal. Thousands of online tutors bombard tailored ads, each recommending programs they think would give you a market edge. How do you settle on the appropriate one?

From my experience, self-taught programs from the Internet could be unstructured, thus proving less beneficial value for someone who would genuinely want to learn programming.

But certified institutions, approved by National Industrial Training Authority (NITA) to be training providers for Data Science should be top on your list as they offer more synthesised content, tailored for individual needs.

Jumping over to the next advertised Machine Learning course will not properly equip you for the job market. Take time to analyse the skill gap you need filled, do enough research on how you can bridge that gap and finally find out what available institution will help you with your specific pain point.

The basic excel skills of creating and tabulating graphs are not enough. You will need to advance if you intend to be close to a Data Science guru. Learn some advanced techniques like v-lookup, pivot table, Macros and visual basic.

Learn how to use a good data visualisation tool like Tableau. Tableau will allow you make complex visualisations with a simple drag and drop. You don't have to write any programming logic or any code.

Learning SQL can be easier than learning a programming language as SQL queries are just like a regular language which makes it easy to grasp. Moreover it is an invaluable skill in the job market as I usually meet so many people in different conferences who are working as SQL developers for the last 10 years.

Another great way of growing professionally is to frequently attend data science forums, lab guests, bootcamps and conferences. Aside from building your skills, this networking will a help you learn about new developments there are in Artificial Intelligence and the latest business intelligence apps in the market.

Above all, constantly learn what works and what doesn't as this will help keep you afloat.

Mr Oriedo is the CEO of Predictive Analytics Lab.