With the data revolution in full swing, there is more information on the internet than a human can remember and process in his/her lifetime. Data Science is a demanding platform, where every forward looking enterprise and startup wants to increase their productivity with the help of intelligent systems. It is an interdisciplinary platform that involves numerous techniques and skills such as, analysis, programming, math and statistics. Now, it is commonly believed that a person with a hacker mindset can come up with an easier solution compared to an orthodox approach.

Let us look at some of the little-known hacks in data science field which aren’t extensively talked about.

“The job of the data scientist is to ask the right questions. If I ask a question like ‘how many clicks did this link get?’ which is something we look at all the time, that’s not a data science question. It’s an analytics question. If I ask a question like, ‘based on the previous history of links on this publisher’s site, can I predict how many people from France will read this in the next three hours?’ that’s more of a data science question.”

―Hilary Mason, Founder, Fast Forward Labs

Hacker Mindset

When dealing with data, a hacker’s mindset always wins hands down. Data science is not all about building models, plotting graphs to analyse the attributes, training and testing by tuning the parameters but a person who finds an easy way to deal with data rather than making use of complex tools to process a data is definitely a hacker. Let us consider an example which just reduces the code to just one line,

From this,

list1 = range(0, 10)
for i in list1:
print(i)

To this,

[print(i) for i in range(0 ,10)]

Data Cleaning Tricks

Let us say you are cleaning data for language processing tasks, and a simple models might give you the best result. Cleaning is one of the most complex processes involved in data science, since almost every data available or extracted for language processing tasks  is unstructured. It is a fact that a highly processed and neatly structured data will yield better results than a noisy one. But the cleaning task can be accomplished with simple regular expression rather than making use of a complex tool.

Domain Knowledge

When a Data Scientist is asked to build a model with a given data, understanding what the data is about is a key aspect. Irrespective of the structure and the type of the data, knowing and understanding the domain knowledge of where the data is from, let us say, from a finance, tech, agriculture, manufacturing industries. A data scientist with knowledge of industry will be able to give better insights and analysis about the data compared to just build a model from A to Z. Domain knowledge also helps to develop better insights and understand the analysis processes.

Never say “No More Learning”

“Data Science is a journey, not a destination”

This line gives us an insight about how huge the data science domain is and why constant learning is as important as build intelligent models. Practitioners who keep themself updated with the new tech being developed everyday, are able to implement and solve business problems faster.  With all the resources available on the internet like MOOCs, one can easily make use of these to be updated. Also showcasing your skill on your blog or Github is an important hack which most of us are unaware of. This not only benefits their

“The man who is too old to learn was probably always too old to learn.”

             – Henry S. Haskins

Cheat Sheets

Machine learning cheat sheets are a way to keep your mind on things which one may tend to forget, as there is a lot to remember in Data Science. There are a lot of machine learning cheat sheets available on the internet. Some of which are also available on the Scikit-learn website, also we have found a github repo which can be found here. This is a cheat sheet provided by the Stanford University to keep oneself updated. This is definitely a hack which no one thinks of when it comes to building a pipeline to solve a problem, a must-have in every data scientist’s toolkit.

In Conclusion

These are some of the hacks which have gone unnoticed, when it comes to machine learning and analytics Hacks can make one’s life easier and give a better result compared to a complex approach of solving.

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