There seem to be abundance of data science training programs available online and offline, however they all seem to jump into technical aspects far too quick without laying proper foundation of data science and everything related.
Data science concepts such as methodology, feature engineering, data science applications, its value proposition, best practices – are often ignored in favour of how to code specific technique or how algorithm function mathematically.
In data science, there are many concepts to understand and it is big area to cover. And if your map doesn’t cover whole area you will get lost. Let me give you example of just how one important concept – such as “missing values in data” is not well understood in wider data science community and many are getting misled:
Let’s imagine you have missing values in your data and you are unsure what you should do, and you decide to google on this problem. So, in matter of seconds you get many links telling you what you should do. And guess what? If you follow what has been prescribed that you should do - you would be doing wrong thing at about 80% of the time. If you want to know why am I saying this please contact me directly or read my article: How to deal with missing values in your data? This is just one example of many that tells me that there is flaw in how data science is thought all around.
Reality is that data science field is growing too fast due to unprecedented demand, and that feeds to lot of cut-and-paste skills from university of Google and from numerous data science crash-courses that teach whatever technology or method is “flavour of the month” while fundamentals are ignored. And data scientists are happy to oblige stacking up their CV’s with in-demand skills and everything is fine until reality bites and someone ask – how to connect the dots and where is the value?
So, shortcomings of current data science educational stack is what prompted me to create pretty unique data science course titled “Foundational 4-pillar ™ data science training” which aims in giving future data scientists rock-solid foundation as starting point on your journey of mastering it.
This course has several formats and aim is to give you solid in-debt overview of data science landscape which rests on four underlying pillars methodology, data enrichment, techniques and applications. Once you have that holistic helicopter view – wherever you land – you will be able to navigate along best path possible in given scenario.
For more information contact me on email@example.com