Why Biotech Needs the Power of Data Analytics

Why Biotech Needs the Power of Data Analytics

The Human Genome Project, that aimed to map and sequence the entire human genome, began in 1990 and ended in 2003 with a starting budget of over $1.5 million. It provided us, for the first time, a means to access invaluable data through genes – evolution patterns, diseases and their treatments, gene mutations and their effects, anthropological information, etc. Now, powerful software and analysis tools are being built that can decode an entire genome in a matter of hours. Data analytics is quickly becoming one of the most important branches of science that can be applied in the biotech industry. 


DNA sequencing generates a huge amount of data that needs to be analyzed with care, as the information and conclusions drawn are applicable in a whole range of industries from medicine to forensic science. It involves data science at various levels:


The first step is storage of DNA sequencing data. If we were to sequence the genome of every living thing from a microbe to a human, then we need to have powerful data science tools that help us store, track and retrieve relevant information.


Annotation is the process of adding notes to specific genes in the sequence. Tools are being built to put ...

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5 Big Mistakes To Avoid While Using Big Data

5 Big Mistakes To Avoid While Using Big Data

Trying to figure out how you can employ Big Data to grow your business? Make sure you’re not jumping onto the Big Data bandwagon unprepared. The best of technologies and systems can fail if not implemented properly. Big data experts shed light on a few critical aspects to look out for.

We’ve tried to enlist some important pointers below that you need to keep in mind while dealing with Big Data. Avoiding these five big mistakes will help you avoid some Big Data blunders.

1) Choosing The Wrong Sources

Focusing on the wrong source can lead to huge misunderstandings and wrong conclusions. Big Data can be garnered from a multitude of sources such as website analytics, social media data, sensor data, machine log data, media, business apps, and the Internet of course! So, it’s easy to get drowned in this sea of data. One of the common mistakes is to choose a dataset that is easily available and doesn’t require cleaning up. However, it is of critical importance that you select the right source depending on what you need to solve for, even if that dataset requires a lot of digging or cleaning up. This also brings us to the next important aspect.

2) Not Defining ...

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How Data Science Can Help You Select Your Coffee

How Data Science Can Help You Select Your Coffee

Behind every successful person, there is tons of coffee.

Successful or not or on your way to success, coffee is the beverage of choice for many of us. Today a coffee drinker faces a ‘choice overload’ problem – there are too many varieties, blends and roasts to choose from. Is there a way to simplify this problem? How can data science help us to streamline our choices without sacrificing individual preferences?

Data science refers to knowledge discovery from large volumes of structured or unstructured data. Through advancements in computing, and thanks to the Internet, data science continues to inform us about patterns and trends observed in our behaviors. With the help from data science, we can get an insight into our own subjective likes and dislikes (regarding coffee of choice) by continuous, quantified observation of our behaviors.

A five year old startup company from Brooklyn, ‘Coffee Craft’ has harnessed the power of data science to provide their customers coffee beans of their choice. The Coffee Craft is a subscription service that offers the flexibility of buying coffee beans based on previous choices or trying new coffee beans matching to individual preferences.

Michael Horn, founder of Coffee Craft, and his team undertook the ‘Coffee DNA Project’ where ...

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