Search results for Value At Risk Development Programming

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• DataLang: A New Programming Language for Data Scientists… Created by ChatGPT?

  I recently tasked ChatGPT-4's to come up with a new programming language appropriate for data scientists in their day to day tasks. Let's look at the results, and the process of getting there.

  https://www.kdnuggets.com/2023/04/datalang-new-programming-language-data-scientists-chatgpt.html

• Object-oriented programming for data scientists: Build your ML estimator">Object-oriented programming for data scientists: Build your ML estimator

  Implement some of the core OOP principles in a machine learning context by building your own Scikit-learn-like estimator, and making it better.

  https://www.kdnuggets.com/2019/08/object-oriented-programming-data-scientists-estimator.html

• How a simple mix of object-oriented programming can sharpen your deep learning prototype

  By mixing simple concepts of object-oriented programming, like functionalization and class inheritance, you can add immense value to a deep learning prototyping code.

  https://www.kdnuggets.com/2019/08/simple-mix-object-oriented-programming-sharpen-deep-learning-prototype.html

• AI, Data Science, Analytics Main Developments in 2018 and Key Trends for 2019">AI, Data Science, Analytics Main Developments in 2018 and Key Trends for 2019

  Review of 2018 and Predictions for 2019 from our panel of experts, including Meta Brown, Tom Davenport, Carla Gentry, Bob E Hayes, Cassie Kozyrkov, Doug Laney, Bill Schmarzo, Kate Strachnyi, Ronald van Loon, Favio Vazquez, and Jen Underwood.

  https://www.kdnuggets.com/2018/12/predictions-data-science-analytics-2019.html

• Data Science, Machine Learning: Main Developments in 2017 and Key Trends in 2018">Data Science, Machine Learning: Main Developments in 2017 and Key Trends in 2018

  The leading experts in the field on the main Data Science, Machine Learning, Predictive Analytics developments in 2017 and key trends in 2018.

  https://www.kdnuggets.com/2017/12/data-science-machine-learning-main-developments-trends.html

• Exploring the Potential of Transfer Learning in Small Data Scenarios

  This article has shown how transfer learning can be used to help you solve small data problems, while also highlighting the benefits of using it in other fields.

  https://www.kdnuggets.com/exploring-the-potential-of-transfer-learning-in-small-data-scenarios

• Best Practices for Building ETLs for ML

  This article talks about several best practices for writing ETLs for building training datasets. It delves into several software engineering techniques and patterns applied to ML.

  https://www.kdnuggets.com/best-practices-for-building-etls-for-ml

• A Comprehensive Guide to MLOps

  Machine Learning Operations (MLOps) is a relatively new discipline that provides the structure and support necessary for machine learning (ML) models to thrive in production environments.

  https://www.kdnuggets.com/2023/08/comprehensive-guide-mlops.html

• Introduction to Data Science: A Beginner’s Guide

  This article is a guide for new data scientists, and it's designed to help you get started quickly. It's meant to be a starting point, but if you're already in the market for a new job, you may want to read this article more.

  https://www.kdnuggets.com/2023/07/introduction-data-science-beginner-guide.html

• AI: Large Language & Visual Models

  This article discusses the significance of large language and visual models in AI, their capabilities, potential synergies, challenges such as data bias, ethical considerations, and their impact on the market, highlighting their potential for advancing the field of artificial intelligence.

  https://www.kdnuggets.com/2023/06/ai-large-language-visual-models.html

• How To Calculate Algorithm Efficiency

  In this article, we will discuss how to calculate algorithm efficiency, focusing on two main ways to measure it and providing an overview of the calculation process.

  https://www.kdnuggets.com/2022/09/calculate-algorithm-efficiency.html

• Machine Learning Metadata Store

  In this article, we will learn about metadata stores, the need for them, their components, and metadata store management.

  https://www.kdnuggets.com/2022/08/machine-learning-metadata-store.html

• 6 Ways Businesses Can Benefit From Machine Learning

  Machine learning is gaining popularity rapidly in the business world. Discover the ways that your business can benefit from machine learning.

  https://www.kdnuggets.com/2022/08/6-ways-businesses-benefit-machine-learning.html

• Why are More Developers Using Python for Their Machine Learning Projects?

  To support the creation of new and exciting ML and artificial intelligence (AI) applications, developers need a robust programming language. That's where the Python programming language comes in.

  https://www.kdnuggets.com/2022/01/developers-python-machine-learning-projects.html

• The Chatbot Transformation: From Failure to the Future

  The all-knowing chatbots we once thought to be the future have been replaced by specialized bots, and the results are outstanding.

  https://www.kdnuggets.com/2021/12/chatbot-transformation-failure-future.html

• 10 Key AI & Data Analytics Trends for 2022 and Beyond

  What AI and data analytics trends are taking the industry by storm this year? This comprehensive review highlights upcoming directions in AI to carefully watch and consider implementing in your personal work or organization.

  https://www.kdnuggets.com/2021/12/10-key-ai-trends-for-2022.html

• 5 Data Science Career Mistakes To Avoid

  Everyone makes mistakes, which can be a good thing when they lead to learning and improvements over time. But, we can also try to first learn from others to expedite our personal growth. To get started, consider these lessons learned the hard way, so you don’t have to.

  https://www.kdnuggets.com/2021/08/5-data-science-career-mistakes-avoid.html

• 5 Mistakes I Wish I Had Avoided in My Data Science Career

  Everyone makes mistakes, which can be a good thing when they lead to learning and improvements over time. But, we can also try to first learn from others to expedite our personal growth. To get started, consider these lessons learned the hard way, so you don’t have to.

  https://www.kdnuggets.com/2021/07/5-mistakes-data-science-career.html

• Awesome list of datasets in 100+ categories

  With an estimated 44 zettabytes of data in existence in our digital world today and approximately 2.5 quintillion bytes of new data generated daily, there is a lot of data out there you could tap into for your data science projects. It's pretty hard to curate through such a massive universe of data, but this collection is a great start. Here, you can find data from cancer genomes to UFO reports, as well as years of air quality data to 200,000 jokes. Dive into this ocean of data to explore as you learn how to apply data science techniques or leverage your expertise to discover something new.

  https://www.kdnuggets.com/2021/05/awesome-list-datasets.html

• Build an Effective Data Analytics Team and Project Ecosystem for Success

  Apply these techniques to create a data analytics program that delivers solutions that delight end-users and meet their needs.

  https://www.kdnuggets.com/2021/04/build-effective-data-analytics-team-project-ecosystem-success.html

• Who is fit to lead data science?

  Data science success depends on leaders, not the latest hands-on programming skills. So, we need to start looking for the right leadership skills and stop stuffing job postings with requirements for experience in the most current development tools.

  https://www.kdnuggets.com/2021/02/fit-lead-data-science.html

• Can Data Science Be Agile? Implementing Best Agile Practices to Your Data Science Process

  Agile is not reserved for software developers only -- that's a myth. While these effective strategies are not commonly used by data scientists today and some aspects of data science make Agile a bit tricky, the methodology offers plenty of benefits to data science projects that can increase the effectiveness of your process and bring more success to your outcomes.

  https://www.kdnuggets.com/2021/01/data-science-agile-best-practices.html

• Monte Carlo integration in Python">Monte Carlo integration in Python

  A famous Casino-inspired trick for data science, statistics, and all of science. How to do it in Python?

  https://www.kdnuggets.com/2020/12/monte-carlo-integration-python.html

• A Tour of End-to-End Machine Learning Platforms

  An end-to-end machine learning platform needs a holistic approach. If you’re interested in learning more about a few well-known ML platforms, you’ve come to the right place!

  https://www.kdnuggets.com/2020/07/tour-end-to-end-machine-learning-platforms.html

• Deep Learning in Finance: Is This The Future of the Financial Industry?

  Get a handle on how deep learning is affecting the finance industry, and identify resources to further this understanding and increase your knowledge of the various aspects.

  https://www.kdnuggets.com/2020/07/deep-learning-finance-future-financial-industry.html

• A Layman’s Guide to Data Science. Part 3: Data Science Workflow">A Layman’s Guide to Data Science. Part 3: Data Science Workflow

  Learn and appreciate the typical workflow for a data science project, including data preparation (extraction, cleaning, and understanding), analysis (modeling), reflection (finding new paths), and communication of the results to others.

  https://www.kdnuggets.com/2020/07/laymans-guide-data-science-workflow.html

• Faster machine learning on larger graphs with NumPy and Pandas

  One of the most exciting features of StellarGraph 1.0 is a new graph data structure — built using NumPy and Pandas — that results in significantly lower memory usage and faster construction times.

  https://www.kdnuggets.com/2020/05/faster-machine-learning-larger-graphs-numpy-pandas.html

• Peer Reviewing Data Science Projects">Peer Reviewing Data Science Projects

  In any technical development field, having other practitioners review your work before shipping code off to production is a valuable support tool to make sure your work is error-proof. Even through your preparation for the review, improvements might be discovered and then other issues that escaped your awareness can be spotted by outsiders. This peer scrutiny can also be applied to Data Science, and this article outlines a process that you can experiment with in your team.

  https://www.kdnuggets.com/2020/04/peer-reviewing-data-science-projects.html

• Python for data analysis… is it really that simple?!?">Python for data analysis… is it really that simple?!?

  The article addresses a simple data analytics problem, comparing a Python and Pandas solution to an R solution (using plyr, dplyr, and data.table), as well as kdb+ and BigQuery solutions. Performance improvement tricks for these solutions are then covered, as are parallel/cluster computing approaches and their limitations.

  https://www.kdnuggets.com/2020/04/python-data-analysis-really-that-simple.html

• Building a Mature Machine Learning Team

  After spending a lot of time thinking about the paths that software companies take toward ML maturity, this framework was created to follow as you adopt ML and then mature as an organization. The framework covers every aspect of building a team including product, process, technical, and organizational readiness, as well as recognizes the importance of cross-functional expertise and process improvements for bringing AI-driven products to market.

  https://www.kdnuggets.com/2020/03/mature-machine-learning-team.html

• Graph Machine Learning Meets UX: An uncharted love affair

  When machine learning tools are developed by technology first, they risk failing to deliver on what users actually need. It can also be difficult for development teams to establish meaningful direction. This article explores the challenges of designing an interface that enables users to visualise and interact with insights from graph machine learning, and explores the very new, uncharted relationship between machine learning and UX.

  https://www.kdnuggets.com/2020/01/graph-machine-learning-ux.html

• Why is Machine Learning Deployment Hard?">Why is Machine Learning Deployment Hard?

  Developing an excellent machine learning model is one thing. Deploying it to production is another. Consider these lessons learned and recommendations for approaching this important challenge to help ensure value from your AI work.

  https://www.kdnuggets.com/2019/10/machine-learning-deployment-hard.html

• The Last SQL Guide for Data Analysis You’ll Ever Need">The Last SQL Guide for Data Analysis You’ll Ever Need

  This is it: the last SQL guide for data analysis you'll ever need! OK, maybe it’s actually the first. But it’ll give you a solid head start.

  https://www.kdnuggets.com/2019/10/last-sql-guide-data-analysis-ever-need.html

• The Data Fabric for Machine Learning – Part 2: Building a Knowledge-Graph

  Before being able to develop a Data Fabric we need to build a Knowledge-Graph. In this article I’ll set up the basis on how to create it, in the next article we’ll go to the practice on how to do this.

  https://www.kdnuggets.com/2019/06/data-fabric-machine-learning-building-knowledge-graph.html

• On Building Effective Data Science Teams

  We take a look at the qualities that make a successful data team in order to help business leaders and executives create better AI strategies.

  https://www.kdnuggets.com/2019/03/building-effective-data-science-teams.html

• Modern Graph Query Language – GSQL

  This post introduces the prospect of fulfilling the need for a modern graph query language with GSQL

  https://www.kdnuggets.com/2018/06/modern-graph-query-language-gsql.html

• How to Organize Data Labeling for Machine Learning: Approaches and Tools

  The main challenge for a data science team is to decide who will be responsible for labeling, estimate how much time it will take, and what tools are better to use.

  https://www.kdnuggets.com/2018/05/data-labeling-machine-learning.html

• Jupyter Notebook for Beginners: A Tutorial

  The Jupyter Notebook is an incredibly powerful tool for interactively developing and presenting data science projects. Although it is possible to use many different programming languages within Jupyter Notebooks, this article will focus on Python as it is the most common use case.

  https://www.kdnuggets.com/2018/05/jupyter-notebook-beginners-tutorial.html

• The Artificial ‘Artificial Intelligence’ Bubble and the Future of Cybersecurity

  What’s going on now in the field of ‘AI’ resembles a soap bubble. And we all know what happens to soap bubbles eventually if they keep getting blown up by the circus clowns (no pun intended!): they burst.

  https://www.kdnuggets.com/2017/06/kaspersky-artificial-intelligence-bubble-future-cybersecurity.html

• How to Become a Data Scientist – Part 1">How to Become a Data Scientist – Part 1

  Check out this excellent (and exhaustive) article on becoming a data scientist, written by someone who spends their day recruiting data scientists. Do yourself a favor and read the whole way through. You won't regret it!

  https://www.kdnuggets.com/2016/08/become-data-scientist-part-1.html

• Big Data and Data Science for Security and Fraud Detection

  We review big data analytics tools and technologies that combine text mining, machine learning and network analysis for security threat prediction, detection and prevention at an early stage.

  https://www.kdnuggets.com/2015/12/big-data-science-security-fraud-detection.html

• 5 Warning Signs that Turn Off Data Science Hiring Managers

  Here are some warning signs that will prevent managers from hiring you for a Data Science position. If your resume has one or more of them, make an effort to remove the risk factors.

  https://www.kdnuggets.com/2015/11/warning-signs-data-science-hiring-managers.html

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• Software Suites/Platforms for Analytics, Data Mining, Data Science, and Machine Learning

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