Covering Data Science, Business Intelligence, Technology Industry News Updates and Trends
Common data science questions and answers for refreshing your data science knowledge with the help of most common data science interview questions.
Building great data science models is key to success in business. Understanding the difference between data science and business intelligence is key to using these to succeed in your business…
Spark is a fast, easy-to-use and flexible data processing framework. It has an advanced execution engine supporting cyclic data flow and in-memory computing. Spark can run on Hadoop, standalone or…
What is Sentiment Analysis ? Sentiment analysis and opinion mining technology is an important tool for businesses, and governments to judge and evaluate the reaction of people for their services.…
Deep learning is a field with intense computational requirements, and your choice of GPU will fundamentally determine your deep learning experience. As discussed previously a CPU is divided into multiple…
Greenplum Architecture The main reason behind adaptation of massively parallel processing(MPP), data warehouse(DWH) solution is MPP architectural principles. These principles aim at removing main drawbacks of traditional DWH, and make…
Exploratory data analysis is an important step in any data science and even business intelligence project, which gives you adequate understanding of underlying data.
Database programming in Python using SQLAlchemy library that provides uniform interface for connecting with different databases.
This article provides a good overview of exploratory data analysis techniques, and then provides code examples using seaborn library.
Understanding Greenplum architecture and use cases for various big data and data science scenarios for business and industry.
Understanding payment fraud is key to successfully building a payment fraud detection system. In this article, we provide a comprehensive overview of payment fraud analysis.
This article will introduce open source data science datasets for learning and exploration.
Data Scientists often turn to famous deep neural networks such as ALEXNET and LENET to gain inspiration for designing neural networks for real world problems.
One of the key reasons that we often use deep learning is because we need to extract features that would effectively solve a machine learning problem.
Create an artificial neural network using Perceptron model, and use it for digit recognition and iris recognition.
Matplotlib is a cross-platform, data visualization and graphical plotting library for Python and its numerical extension NumPy. As such, it offers a viable open source alternative to MATLAB. Developers can…
NumPy – A Replacement for MatLab NumPy is often used along with packages like SciPy (Scientific Python) and Matplotlib (plotting library). This combination is widely used as a replacement for…
Business leaders or any one responsible for making decisions related to application of data science and business intelligence in any field need to have high level understanding about these, and…