Becoming a Data Scientist with the new Data Science certification course

Once upon a time, transactions happened only face-to-face and things like costumer satisfaction, gross and net profit, and sales trends were relatively easy to track. But we have gone a long way from a consumer direct business model: nowadays people buy goods across continents and share opinions about them in the virtual world. Every Internet search, every purchase, every comment and every product review left on social media produces valuable information which companies collect in order to increase their insight into consumer behaviour and market trends. Companies that are able to process the vast amounts of data collected from multiple sources quickly and effectively, can adapt their marketing strategy and be at the top of their game. This is why learning how to become a data scientist has never been as crucial as now!

(Read more about the data science course for new data scientists)


Whether you already belong to a company or you are seeking employment, being able to make sense of the huge quantity of data daily collected will both make you a valuable asset to your company and earn your company the pole position. But how can you learn to carry out big data analysis? Should the fact that you don’t have a data science degree put you off? What are the prerequisites for learning big data analysis? And how long will you need to acquire the full toolkit of data scientist skills? The reassuring thing is that, with the plethora of data science online courses, it has never been easier than today to get your big data certification.


Whilst the decision to enroll on a data analytics course is a no-brainer, here are a few tips that will help you master this skill.


1.    Big data analysis is a hands-on skill: you need to learn it in context; reading theory books will not suffice. Whether you want to predict how a product will perform on the market, currency fluctuations or a company’s turnover, make sure you apply the theory to your specific task. This will make is easier for you to memorize how to carry out the analyses and will motivate you as you progress in your learning because you have to find an answer to the task you need to carry out.


2.    Big data analysis is a work-in-progress: if you have found an algorithm that successfully predicts the aspect you are interested in, well done! But don’t be too smug about it: you should continue to work to perfect your algorithm so that it accounts for a greater percentage of variance in the data. Also, the nature of markets is changeable: an algorithm that worked last year might no longer be a good fit this year.


3.    Big data analysis is an organization task: a lot of the data you will have to deal with is unstructured. Because of this, it is necessary to organize the data in a meaningful way before doing anything else. Develop a systematic way of allocating data and deal with each subset accordingly to its characteristics.


4.    Big data analysis is time-sensitive: due to the large amounts of data that constantly stream into a company’s banks, these need to be dealt with in a timely manner. If it take too long to obtain an analysis output, it might no longer be useful. Once you have learnt how to analyze big data, you will have to learn how to process it in near-real time!


5.    Big data analysis is like a composition: data comes from a variety of sources and in different formats. It is the task of the data scientist to extract meaning from each data set and then put all the pieces together to achieve an overall picture of the situation.

June 18, 2016

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