Data Science
While the field of data science is not tied directly to Big Data, advances in one tends to produce advances in the other. Big Data increases our ability to harvest and process data, while data science allows us to dig into it for insights.
How Faulty Data Breaks Your Machine Learning Process
This article is part of a media partnership with PyData Berlin, a group helping support open-source data science libraries and tools. To learn more about this topic, please consider attending our fourth annual PyData Berlin conference on June 30-July 2, 2017. Miroslav Batchkarov and other experts will be giving talks
How Deep Learning is Personalizing the Internet
Deep learning is a subfield of machine learning and it comprises several approaches to tackling the single most important goal of AI research: allowing computers to model our world well enough to exhibit something like what we humans call intelligence. On a basic conceptual level, deep learning approaches share a
Preventing Fraud and Saving Costs with Machine Learning
Whilst most businesses don’t earn revenue by processing data, they do spend a large amount of their hard earned revenue in manually processing data, validating it and ultimately performing manual tasks that don’t scale. But at what point does this manual involvement become a burden of cost upon your business?
Big Data Is Revolutionizing The Way We Develop Life-Saving Medicine
Big data sets are so complex and large that common data processing tools and technologies cannot cope with them. The process of inspection of such data and uncovering patterns is called big data analytics. The basic question which arises in our mind is, “In what way is the drug discovery
Deduplicating Massive Datasets with Locality Sensitive Hashing
This article is part of a media partnership with PyData Berlin, a group helping support open-source data science libraries and tools. To learn more about this topic, please consider attending our fourth annual PyData Berlin conference on June 30-July 2, 2017. Matti Lyra and other experts will be giving talks
Frequency Distribution Analysis using Python Data Stack – Part 2
This is the continuation of the Frequency Distribution Analysis using Python Data Stack – Part 1 article. Here we’ll be analyzing real production business surveys for your review. Application Configuration File The configuration (config) file config.py is shown in Code Listing 3. This config file includes the general settings for
3 ways BI is changing the game for fintech
Fintech is becoming an increasingly competitive market. A KPMG analysis saw investments decline in 2016 and investors are now more cautious about betting on segments that are becoming saturated. Lending and payments are two segments that saw increased participation over the past two years. Competitors come in all forms. We
Springboard Launches Course To Help You Build A Data Science Career
Springboard is a leader in data science education. Thousands of data science learners complete their education in Springboard’s mentored data science courses and they’ve given Springboard courses an average rating of 4.9/5 on CourseReport and Switchup. Springboard is the proven option in data science training you’ve been looking for if
Frequency Distribution Analysis using Python Data Stack – Part 1
During my years as a Consultant Data Scientist I have received many requests from my clients to provide frequency distribution reports for their specific business data needs. These reports have been very useful for the company management to make proper business decisions quickly. In this paper I would like to
Machine Learning using Spark and R
R is ubiquitous in the machine learning community. Its ecosystem of more than 8,000 packages makes it the Swiss Army knife of modeling applications. Similarly, Apache Spark has rapidly become the big data platform of choice for data scientists. Its ability to perform calculations relatively quickly (due to features like in-memory
