data science


The most powerful idea in data science

#artificialintelligence

If you take an introductory statistics course, you'll learn that a datapoint can be used to generate inspiration or to test a theory, but never both. Humans are a bit too good at finding patterns in everything. Real patterns, fake patterns, you name it. We're the sort of creatures that find Elvis's face in a potato chip. If you're tempted to equate patterns with insights, remember that there are three kinds of data patterns: Which ones are useful to you?



Workshop on "High Content Imaging and Data Science for Virtual Screening and Drug Discovery", Bled 2019

VideoLectures.NET

High-throughput phenotypic screening, based on high content imaging, is increasingly often used as a tool in the context of drug discovery. Compound screens are used to find hits that produce the desired phenotypes in relevant cellular assays. Genomic screens are used to elucidate the underlying molecular pathways and identify suitable drug targets. Since a wealth of data is produced in the process of high- content screening, data science approaches such as statistics, machine learning and neural networks can play an important role in making the most of the collected data. Much like virtual screening can be performed in more classical chemoinformatic settings by, e.g., learning predictive models for QSAR (quantitative structure-activity relations) from data obtained through compound screens, similar approaches can be taken in the context of high-throughput phenotypic screening.


How did I learn Data Science?

#artificialintelligence

I am a Mechanical engineer by education. And I started my career with a core job in the steel industry. But I didn't like it and so I left that. I made it my goal to move into the analytics and data science space somewhere around in 2013. From then on, it has taken me a lot of failures and a lot of efforts to shift.


An elegant way to represent forward propagation and back propagation in a neural network

#artificialintelligence

Sometimes, you see a diagram and it gives you an'aha ha' moment I saw it on Frederick kratzert's blog Using the input variables x and y, The forwardpass (left half of the figure) calculates output z as a function of x and y i.e. f(x,y) The right side of the figures shows the backwardpass. Receiving dL/dz (the derivative of the total loss with respect to the output z), we can calculate the individual gradients of x and y on the loss function by applying the chain rule, as shown in the figure. This post is a part of my forthcoming book on Mathematical foundations of Data Science. The goal of the neural network is to minimise the loss function for the whole network of neurons. Hence, the problem of solving equations represented by the neural network also becomes a problem of minimising the loss function for the entire network.


Skills Every Ambitious Tech Professional Will Need in 2020

#artificialintelligence

Tech industry employment is a seller's market firmly on the side of top talent, but competition for the best jobs remains fierce. Candidates cannot skate by on common skillsets and expect to secure the lucrative salaries, prestige, and perks for which the tech sector has become known. Companies today use advanced tools and tests to weed out the pretenders and identify the people who bring truly valuable skills to the table. Unfortunately, many tech workers -- even some of the best -- don't know exactly where they stand. To combat that knowledge gap, workers are turning to the same types of advanced tools that employers use on them.


Data Science, the Good, the Bad, and the… Future

#artificialintelligence

How often do you think you're touched by data science in some form or another? Finding your way to this article likely involved a whole bunch of data science (whooaa). To simplify things a bit, I'll explain what data science means to me. "Data Science is the art of applying scientific methods of analysis to any kind of data so that we can unlock important information." If we unpack that, all data science really means is to answer questions by using math and science to go through data that's too much for our brains to process.


Data Science, the Good, the Bad, and the… Future - Knowlab

#artificialintelligence

How often do you think you're touched by data science in some form or another? Finding your way to this article likely involved a whole bunch of data science (whooaa). To simplify things a bit, I'll explain what data science means to me. "Data Science is the art of applying scientific methods of analysis to any kind of data so that we can unlock important information." If we unpack that, all data science really means is to answer questions by using math and science to go through data that's too much for our brains to process.


How to Become More Marketable as a Data Scientist

#artificialintelligence

This headline may seem a bit odd to you. Since data science has a huge impact on today's businesses, the demand for DS experts is growing. At the moment I'm writing this, there are 144,527 data science jobs on LinkedIn alone. But still, it's important to keep your finger on the pulse of the industry to be aware of the fastest and most efficient data science solutions. To help you out, our data-obsessed CV Compiler team analyzed some vacancies and defined the data science employment trends of 2019.


How Artificial Intelligence can transform Education? - CIOL

#artificialintelligence

And sometimes provides better analytics to make wise decisions. It's no more fictional, we now living in a world where machines are intelligent and are easing our lives. Actually, it works with a large amount of data. It processes the data with the help of intelligent algorithms and software to learn automatically from patterns or feature. As much data it will have, that much better insights or decisions it can make.