Everything You Need To Know About Data Science

Artificial intelligence is all the rage on the internet these days. Machine learning, neural networks, deep learning, the Turing Test, all of these terms are on everyone’s lips and keyboards. But one thing always seems to miss out on all the attention, always seeming to be in the background for some reason. We are talking, of course, about data science. Now, we all know what data is, it being the building block for all things digital. But what do we mean by data science? On first glance, it just appears to mean the study of data. However, when speaking in terms of AI, it goes much deeper, no pun intended.

Put as simply as possible, data science is a blend of various tools, algorithms, and machine learning principles with the goal to discover hidden patterns from raw data. Now, that may still appear to be gibberish to some, and if you’re looking to define it in caveman terms, data science is just a new, much more intelligent approach to analyzing and using data.

Back to the present, many consider data science to be the future of artificial intelligence. With the exponential increase in the volume of data that needs to be analysed in recent times, the need for new and improved data analysis techniques was quite apparent. Enter big data and data science, two of the biggest names in the data analysis world right now. More specifically, the need for data science techniques arose due to the following reasons:

• Traditionally, data used to be small in size and structured, which could be analysed by traditional, simpler tools. Data today is much larger and semi- or unstructured.
• Data science can train business models more effectively using the vast variety and amount of data.
• Due to the increase in the amount of ‘autonomous’ electronic devices, we needed a data analysis system which could maximize efficiency without compromising performance.
• Traditional forecasting techniques are being rendered obsolete and predictive analytics have ushered in a new era with the help of data science.

So how does data science achieve all of this? Using these 4 very-complex-sounding-yet-simple-yet-brilliant (do we have a word for that?) techniques:

• Predictive causal analytics: Not to be confused with ‘casual’, predictive causal analytics are used to design models which can predict the possibilities of a particular event in the future.
• Prescriptive analytics: Need an intelligent model with the capacity to make its own decisions and modify them on-the-go with dynamic parameters? Prescriptive analytics are the way to go.
• Predictions using machine learning: Need to train a model using already-collected data? Data science has you covered.
• Pattern discovery using machine learning: Pattern discovery is used when you don’t have the required parameters for making predictions so you need to discover hidden patterns within your dataset to fulfill this purpose.

Now, the all-important question: what does this mean for the job market? There’s a new profession in town, called the data scientist, and it has all the traditional data analysts scrambling for their bytes. Well, the last part may not be entirely true, but armed with all this new insight and tools, data scientists can explore realms of analysis not accessible to data analysts and it is one of the fastest-emerging job markets in the information technology world. Stay tuned for more on data science and why you should care about it.

Author Bio: Ghazi Tiwana is a Software Engineering Student from National University of Science and Technology (NUST), Islamabad. Follow him on LinkedIn.