Data Science in business

Data Science consists of a huge collection of Big Data tools and Machine Learning algorithms, which apply various operations to huge amounts of data in a very efficient way and predict unseen future scenarios. 

The main goal here is to train a machine by feeding it with enough pieces of information on how to make decisions when scenarios are met that are not yet present in the previously stored data.

In business, knowledge is power, and data is the ingredient that adds fuel to the fire. Fortunately, these two are the main things Data Science is good at. Whether it's a bank, a retailer or a regular store owner, periodically collecting data and applying different hypotheses to that data is their routine. 

With the introduction of Data Science, these things that used to require a lot of manpower are being done as fast as you read this, and completely automatically. Pretty interesting, right? 

Well, an even more interesting fact is that Data Science isn't just beneficial to businesses; it's a two-way street that simultaneously helps both the customer and the business, providing a more personalized experience. 

For example, if you used a smart-movie-recommender system or just Netflix, you would have seen personalized movie recommendations based on your interest and what you have watched before. On the other hand, it helps Netflix determine the genre of movies that you usually watch, and so it would recommend movies to you that are closest to that. 

So, you get the idea. Customers need products, and if a company can offer them exactly the product they need, it helps save the customers time. If you were to do all this work manually, do you know how hectic it would be to collect, process, clean up and finally analyze data from all the customers to use it to improve the customer experience?

Let's go through an abstract concept of how data science helps companies foresee future events. 

Once you have developed a machine-learning model and fed it with the right dataset, some high-level calculations are performed; certain parameters are calculated by the model based on the dataset. This is called the training phase of the machine-learning model, in which the model learns the relationship between the different characteristics of the dataset and the target property. 

Once the machine learning model is trained, you can simply feed new information to the algorithm, and it will provide output based on the parameters it learned earlier. The process is iterative, and the parameters get better and more accurate as more data is fed to the system. 

As an example, you can feed the model your daily routine and let it know under what conditions you should go jogging. The system will then recommend that you go jogging on a day with the appropriate conditions. This is a fairly simple example to easily grasp der basic building blocks. But real-life systems are much more complex, with hundreds of features to consider.