Membrane separations have long been recognized as energy-efficient processes with a rapidly growing market. In particular, organic solvent nanofiltration (OSN) technology has shown considerable potential when applied to various industries, such as petrochemicals, pharmaceuticals and natural products. The energy consumed by these industries accounts for 10 to 15 percent of the world's entire energy consumption. Nevertheless, difficulties in predicting the separation performance of OSN membranes have hindered smooth transition from lab discovery to industry implementation. Predicting the performance of membranes is a challenging task because of the complex nature of solvent, solute and membrane interactions.
Learn methods of data analysis and their application to real-world data sets This updated second edition serves as an introduction to data mining methods and models, including association rules, clustering, neural networks, logistic regression, and multivariate analysis. The authors apply a unified white box approach to data mining methods and models. This approach is designed to walk readers through the operations and nuances of the various methods, using small data sets, so readers can gain an insight into the inner workings of the method under review. Chapters provide readers with hands-on analysis problems, representing an opportunity for readers to apply their newly-acquired data mining expertise to solving real problems using large, real-world data sets. Data Mining and Predictive Analytics, Second Edition: * Offers comprehensive coverage of association rules, clustering, neural networks, logistic regression, multivariate analysis, and R statistical programming language * Features over 750 chapter exercises, allowing readers to assess their understanding of the new material * Provides a detailed case study that brings together the lessons learned in the book * Includes access to the companion website, www.dataminingconsultant.com,
Every package you'll see is free and open source software. Thank you to all the folks who create, support, and maintain these projects! If you're interested in learning about contributing fixes to open source projects, here's a good guide. And If you're interested in the foundations that support these projects, I wrote an overview here. Pandas is a workhorse to help you understand and manipulate your data.
Data science is an attractive field. It's lucrative, you get opportunities to work on interesting projects, and you're always learning new things. Hence, breaking into the world of data science is extremely competitive. One of the best ways to start your data science career is through a data science internship. In this article, we'll look at the general level of knowledge that's required, the components of a typical interview process, and some example interview questions.
While these statements are humorous, it's not at all obvious what data science encompasses. There have been many data science Venn diagrams and many definitions over the years. However, in my research, the ones I found were either convoluted or missing one of the three core data science functions. In this article, you'll learn about the three primary parts of data science. You'll also learn about an emerging type of data science project. Finally, you'll see two other areas that are important to data science, but not quite part of the core.
I work for Icon Solutions. We work in instant payments. What I want to talk about is applying machine learning to fraud detection. When we first started researching it, we found two themes that were going on. We found these hype type things. I'm sure you've all seen this, when will we bow to our machine overlord? By 2025, robots will be playing symphonies and all that stuff. Then we found the other extreme as well, which was the fairly wacky math. What we were looking at is how can we actually apply this technology to our requirements and to those of our clients? I'm going to talk about payments. Then I'm going to do a demonstration. In terms of payments, the way it worked, if you wanted to interact with the bank through most of 20th centuries, you had to go into a branch. That was the only way you could interact with the bank. If somebody wanted to steal money from a bank, they had to rob it. That was basically the only option they had, which is why you can see the big security barriers that they had in the branches at that point in time. Then, moving on to about 1960s, the bank started employing new technologies. They took things like the IBM 360 series, and they actually started using it. Even then it was pretty secure. The people who were using it were people who worked for the bank. It was a closed network. If you wanted to actually get into the systems, you had to go into the bank's offices, and you had to be an employee. The potential for fraud was fairly small.
The chi-square statistic is a useful tool for understanding the relationship between two categorical variables. For the sake of example, let's say you work for a tech company that has rolled out a new product and you want to assess the relationship between this product and customer churn. In the age of data, tech or otherwise, many companies undergo to risk of taking evidence that is either anecdotal or perhaps a high level visualization to indicate certainty of a given relationship. The chi-square statistic gives us a way to quantify and assess the strength of a given pair of categorical variables. Let's explore chi-square from this lens of customer churn.
Research published recently by CSE investigators can make training machine learning (ML) models fairer and faster. With a tool called AlloX, Prof. Mosharaf Chowdhury and a team from Stony Brook University developed a new way to fairly schedule high volumes of ML jobs in data centers that make use of multiple different types of computing hardware, like CPUs, GPUs, and specialized accelerators. As these so-called heterogeneous clusters grow to be the norm, fair scheduling systems like AlloX will become essential to their efficient operation. This project is a new step for Chowdhury's lab, which has recently published a number of tools aimed at speeding up the process of training and testing ML models. Their past projects Tiresias and Salus sped up GPU resource sharing at multiple scales: both within a single GPU (Salus) and across many GPUs in a cluster (Tiresias).