“DATA IS THE NEW OIL, WE NEED TO FIND IT, EXTRACT IT, REFINE IT, DISTRIBUTE IT AND MONETIZE IT.” – DAVID BUCKINGHAM

Data science is the process of extracting insights and trends that are hiding behind the data. Machine learning, a method of data analysis uses statistical techniques and predictive models which gives the system the ability to learn with data. A data scientist’s job has been acclaimed as the sexiest job of the 21st century. So what do data scientists exactly do? They collect data from various sources, clean it for uniformity and then apply various algorithms & statistical models. Finally, they identify patterns, trends and provide business solutions to their clients. Sounds cool, doesn’t it?

We all use products or services based on machine learning or in short ML in our day to day life such Google search engine, ad placement, stock trading, computer vision, drug design, Face Detection – Facebook photo tagging, Span email detection, Recommendation system by E-commerce giants such as Amazon and Ebay. Every tech company is making use of these ML Algorithm to provide a perfect user friendly experience and simultaneously multiply profits by increasing business.

The basic entity – data:

Data is in structured and unstructured form. Structured data refers to information with a high degree of organization, such that it can be included in a database to readily perform analysis; whereas unstructured data is essentially the opposite. For example of an unstructured data, an email holds information such as the time sent, subject, and sender but the content of the message is not so easily broken down and categorized. This can introduce some compatibility issues with the structure of a relational database system.

Deliverables of data science:

By this time, you must be wondering about the applications of this booming field. So read on to find how data science has been a viable resource to companies who are ruling our minds and hearts.

1.Optimization of Search Engine Results

Data Collection : Data is to be collected first ,it can be any in any form such as comma separated values(.csv), excel sheets, spread sheets, etc. (Note:It is not necessary in ML that the larger the data faster the learning.)

2.Mental Health Care

Ginger.io performs behavioral analytics on their users to determine how they are feeling. According to Ginger’s website, their “behavioral analytics engine, built from years of research at the MIT Media Lab, aggregates, encrypts, and anonymizes patient data before running it through statistical analysis to create meaningful insights.”

3.Recommendations systems

Tech giants like Amazon, Google and Netflix are using users’ viewing history to suggest new products/ services/ movies, etc. This is also known as Content Based Recommendation (CBR)

4.Genetics and Genomics

Genomics is closely related to the field of precision medicine, which is a process that encompasses genetics, behavior, and environment to predict proper treatment; in contrast to a one-size-fits-all approach. Researchers are predicting whether a child will develop serious health issues before his/her birth. DNAs of parents are studied and based on the data, predictions are made.

5.Detecting Financial Frauds

Credit card companies are using customers’ transaction details such as amount, merchant, location, time and others to classify transactions into fraudulent or legit.

6.Facebook’s data leak and Cambridge Analytica Fallout

This was in the recent news that the British political consulting firm, Cambridge Analytica had been alleged to perform analytics on Facebook’s data to strategize the win of Donald Trump in the presidential elections. The company has, in fact, revealed that they ran the digital & television campaign and that their data provided all the strategy.

These are only some of the applications. There are a hundred others and almost every other domain is using data science to develop and flourish.

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