With an explosive growth in the number of transactions, detecting fraud cannot be done manually and Machine Learning-based methods are required. We examine what are the main challenges for using Machine Learning for Trust.
Data Science projects involve iterative processes and may need changes in data at every iteration. But Data versioning, data pipelines and data workflows make Data Scientist’s life easy, let’s see how.
There are elements of what we do which are AI complete. Eventually, Artificial General Intelligence will eliminate the data scientist, but it’s not around the corner.
NLG tools automate the analysis and enhance traditional BI platforms by explaining in plain English the significance of visualizations and findings – here is an overview of the market.
Ready to embark on an exciting and in-demand career? Here’s what you need to know about what a data scientist does—and how you can become competitive in this in-demand field.
Moving to Hadoop is not without its challenges—there are so many options, from tools to approaches, that can have a significant impact on the future success of a business’ strategy. Data management and data pipelining can be particularly difficult.
Understanding the process requires not only wide technical background in machine learning but also basic notions of businesses administration; here I will share my experience on teaching the data science process.
Data science projects may often fail due to a lack of clear definition of the business goal and not because data scientists technical abilities. We examine the connection between data science and research design to help address this issue.
ML modeling is an iterative process and it is extremely important to keep track of all the steps and dependencies between code and data. New open-source tool helps you do that.
This report, created by analyzing millions of job postings using advanced technology, divides Data Science and Analytics roles into 6 broad categories, and answers many questions, including cities, industries, job roles with most growth.
A/B testing is key to improving results in any marketing campaign. We examine the issues involved in its 3 main components: message variants, user group selection, and choosing the winning version.
Datakind, in collaboration with Microsoft, completed significant data-driven projects to improve traffic safety and help save lives in New York City, Seattle, and New Orleans.
A resilient Data Science Platform is a necessity to every centralized data science team within a large corporation. It helps them centralize, reuse, and productionize their models at peta scale.
Is Machine Learning is overtaking Big Data?! We also examine trends for several more related and popular buzzwords, and see how BD, ML. Artificial Intelligence, Data Science, and Deep Learning rank.
42 illuminating quotes you need to read if you’re a data scientist or considering a career in the field – insights from industry experts tackling the tough questions that every data scientist faces.
This post summarizes nine creative ways to condemn almost any AI startup to bankruptcy. I focus on data science and machine learning startups, but the lessons on what to avoid can easily be applied to other industries.
We know Big Data & Analytics are new & cutting edge technologies; but actually, human started using data & analytics techniques 5000 years ago. Let’s take a look.
There is a lot of buzz around deep learning technology. First developed in the 1940s, deep learning was meant to simulate neural networks found in brains, but in the last decade 3 key developments have unleashed its potential.
While programming languages will never be completely obsolete, a growing number of programmers (and data scientists) prefer working with frameworks and view them as the more modern and cutting-edge option for a number of reasons.
Getting Into Data Science: What You Need to Know
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