Data‐driven science is heralded as a new paradigm in materials science. In this field, data is the new resource, and knowledge is extracted from materials datasets that are too big or complex for traditional human reasoning—typically with the intent to discover new or improved materials or materials phenomena. Multiple factors, including the open science movement, national [...]
Everyone knows that there is a need for math for data science. But which part of math topics should one be aware of? And where to learn math for data science. Let’s find out. Every science field needs mathematics as they are the fundamentals of any science. All the trending technologies such as data science, machine [...]
Machine Learning is a part of Data Science, an area that deals with statistics, algorithmics, and similar scientific methods used for knowledge extraction. Engineers can use ML models to replace complex, explicitly-coded decision-making processes by providing equivalent or similar procedures learned in an automated manner from data. ML offers smart solutions for organizations that want to [...]
A data scientist needs to know the concept of statistics more than software engineering and also programming knowledge more than a statistician, Josh Wills said. Important analyses in descriptive statistics include normal distribution (bell curve), central tendency (the mean, median, and mode), variability (25%, 50%, 75% quartiles), variance, standard deviation, modality, skewness and kurtosis, according to Towards Data Science, [...]
Hadoop is a framework that allows you to first store Big Data in a distributed environment, so that, you can process it parallel ... What is Hadoop? Apache Hadoop is an open-source software framework used to develop data processing applications that are executed in a distributed computing environment. Applications built using HADOOP are run on large [...]
