Finding data and understanding its meaning represents the traditional "daily grind" of a Data Scientist. With whale, the new lightweight data discovery, documentation, and quality engine for your data warehouse that is under development by Dataframe, your data science team will more efficiently search data and automate its data metrics.
Developing machine learning models as products that deliver business value remains a new field with uncharted paths toward success. Applying well-established software development approaches, such as agile, is not straightforward, but may still offer a solid foundation to guide success.
As a core method in the Data Scientist's toolbox, k-means clustering is valuable but can be limited based on the structure of the data. Can expanded methods like PAM (partitioning around medoids), CLARA, and CLARANS provide better solutions, and what is the future of these algorithms?
The Poisson distribution, named after the French mathematician Denis Simon Poisson, is a discrete distribution function describing the probability that an event will occur a certain number of times in a fixed time (or space) interval.
So much happened in the world during 2020 that it may have been easy to miss the great progress in the world of AI. To catch you up quickly, check out this curated list of the latest breakthroughs in AI by release date, along with a video explanation, link to an in-depth article, and code.
By Louis (What's AI) Bouchard on Dec 28, 2020 in AI, Research, Trends
Why data catalogs aren’t meeting the needs of the modern data stack, and how a new approach – data discovery – is needed to better facilitate metadata management and data reliability.
Also: A Rising Library Beating Pandas in Performance; 20 Core Data Science Concepts for Beginners; How to Create Custom Real-time Plots in Deep Learning; 10 Python Skills They Don’t Teach in Bootcamp
Machine learning models deployed today -- as will many more in the future -- impact people and society directly. With that power and influence resting in the hands of Data Scientists and machine learning engineers, taking the time to evaluate and understand if model results are fair will become the linchpin for the future success of AI/ML solutions. These are critical considerations, and using a recently developed fairness module in the dalex Python package is a unified and accessible way to ensure your models remain fair.
Automated Machine Learning, or AutoML, tries hundreds or even thousands of different ML pipelines to deliver models that often beat the experts and win competitions. But, is this the ultimate goal? Can a model developed with this approach be trusted without guarantees of predictive performance? The issue of overfitting must be closely considered because these methods can lead to overestimation -- and the Winner's Curse.
XGBoost is a tree based ensemble machine learning algorithm which is a scalable machine learning system for tree boosting. Read more for an overview of the parameters that make it work, and when you would use the algorithm.
Our recent survey of over 130 top data engineers, data architects, and executives uncovered details and trends of the current state of data engineering and DataOps.Read our survey report to learn more about these trends as well as our predictions for future obstacles and our recommendations for avoiding them.
A feature store is a data warehouse of features for machine learning. Differently from a data warehouse, it is dual-database: one serving features at low latency to online applications and another storing large volumes of features. Learn how Data Scientists leverage this capability in production-deployed models.
While it is important for enterprises to continue solving the past challenges in a machine learning pipeline (manage, monitor, track experiments and models) in 2021 enterprises should focus on strategies to achieve scalability, elasticity and operationalization of machine learning.
A practical deep dive on production monitoring architectures for machine learning at scale using real-time metrics, outlier detectors, drift detectors, metrics servers and explainers.
Delivering machine learning solutions is so much more than the model. Three key concepts covering version control, testing, and pipelines are the foundation for machine learning operations (MLOps) that help data science teams ship models quicker and with more confidence.
Creating an model that works well is only a small aspect of delivering real machine learning solutions. Learn about the motivation behind MLOps, the framework and its components that will help you get your ML model into production, and its relation to DevOps from the world of traditional software development.
Deploying AI ethically and responsibly will involve cross-functional team collaboration, new tools and processes, and proper support from key stakeholders.
Also: Know What Employers are Expecting for a Data Scientist Role in 2020; Top Python Libraries for Data Science, Data Visualization & Machine Learning.
We've gathered best practices for data science and engineering teams to create an efficient framework to monitor ML models. This ebook provides a framework for anyone who has an interest in building, testing, and implementing a robust monitoring strategy in their organization or elsewhere.
In classification problems, the proportion of cases in each class largely determines the base rate of the predictions produced by the model. Therefore if you use sampling techniques that change this proportion, there is a good chance you will want to rescale / calibrate your predictions before using them in the wild.
SQL is an essential programming language for data analysis and processing. So, SQL questions are always part of the interview process for data science-related jobs, including data analysts, data scientists, and data engineers. Become familiar with these common patterns seen in SQL interview questions and follow our tips on how to neatly handle each with SQL queries.
Also: Data Science and Machine Learning: The Free eBook; CatBoost vs. Light GBM vs. XGBoost; 10 Python Skills They Don’t Teach in Bootcamp; MIT @techreview read the paper that forced @TimnitGebru out of Google. It presents the history of #NLP and an overview of four main #risks of large language models - here are the details
This article explains the goals of anomaly detection and outlines the approaches used to solve specific use cases for anomaly detection and condition monitoring.
We bring you industry predictions from 12 innovative companies - what key trends they expect in 2021 in AI, Analytics, Data Science, and Machine Learning?
In recent times, a large number of businesses have begun realising the potential of Data Science. Business analytics and data science applications are far and wide. So let us have a look at them in detail.
Check out the newest addition to our free eBook collection, Data Science and Machine Learning: Mathematical and Statistical Methods, and start building your statistical learning foundation today.
Increased capabilities in screening and early testing for a disease can significantly support quelling its spread and impact. Recent progress in developing deep learning AI models to classify cough sounds as a prescreening tool for COVID-19 has demonstrated promising early success. Cough-based diagnosis is non-invasive, cost-effective, scalable, and, if approved, could be a potential game-changer in our fight against COVID-19.
Kaggle recently released its State of Data Science and Machine Learning report for 2020, based on compiled results of its annual survey. Read about 3 key findings in the report here.
Also: A Rising Library Beating Pandas in Performance; Main 2020 Developments and Key 2021 Trends in AI, Data Science, Machine Learning Technology; R or Python? Why Not Both?; Artificial Intelligence in Modern Learning System : E-Learning; Essential Math for Data Science: Probability Density and Probability Mass Functions
Blaize’s novel stream processor for Edge AI offers a case study of new opportunities for smaller companies to leverage semiconductor industry resources in pursuit of their goals.
As is the potential for any "trending hot" career, the reality of a position in the field may not be all that you initially expected. Data Science is no exception, and being still a young field, its evolving definition can offer some surprises that you should know about before accepting that dream offer.
This article covers matrix decomposition, as well as the underlying concepts of eigenvalues (lambdas) and eigenvectors, as well as discusses the purpose behind using matrix and vectors in linear algebra.
No matter the field in which you hold some expertise, sharing your skills to benefit the lives of others or supporting non-profit organizations that try to make the world a better place is a noble and time-worthy personal pursuit. Many opportunities exist in data science to contribute to meaningful projects and crucial needs from your local community to a global scale.
This article compares the performance of the well-known pandas library with pypolars, a rising DataFrame library written in Rust. See how they compare.
Many data scientists have implemented machine or deep learning algorithms on static data or in batch, but what considerations must you make when building models for a streaming environment? In this post, we will discuss these considerations.
AdaBoost technique follows a decision tree model with a depth equal to one. AdaBoost is nothing but the forest of stumps rather than trees. AdaBoost works by putting more weight on difficult to classify instances and less on those already handled well. AdaBoost algorithm is developed to solve both classification and regression problem. Learn to build the algorithm from scratch here.
Lift the curse of dimensionality by mastering the application of three important techniques that will help you reduce the dimensionality of your data, even if it is not linearly separable.
In this blog post, the author explains his journey from Software Engineer to Machine Learning Engineer. The focus of the blog post is on the areas that the author wished he'd have focused on during his learning journey, and what should you look for outside of books and courses when pursuing your Machine Learning career.
K-Means 8x faster, 27x lower error than Scikit-learn's in 25 lines; How to do visualization using #Python from scratch; Why the Future of ETL Is Not ELT, But EL(T); NoSQL for Beginners
There has been a considerable shortage in the supply and demand of AI professionals. If you are looking to learn AI or learn machine learning, you can opt for free online courses offered by Great Learning.
Our panel of leading experts reviews 2020 main developments and examines the key trends in AI, Data Science, Machine Learning, and Deep Learning Technology.
Transparency, explainability, and trust are pressing topics in AI/ML today. While much has been written about why they are important and what you need to do, no tools have existed until now.
New book, "Deep Learning Design Patterns" presents deep learning models in a unique-but-familiar new way: as extendable design patterns you can easily plug-and-play into your software projects. Use code kdmath50 to save 50% off.
Join INFORMS community of data, analytics, operations research, and statistics professionals and tackle the future together. With nearly 13,000 members around the world, INFORMS is the largest international association for data science professionals.
With so much to learn and so many advancements to follow in the field of data science, there are a core set of foundational concepts that remain essential. Twenty of these ideas are highlighted here that are key to review when preparing for a job interview or just to refresh your appreciation of the basics.
Also: AI, Analytics, Machine Learning, Data Science, Deep Learning Research Main Developments in 2020 and Key Trends for 2021; Introduction to Data Engineering; Data Science History and Overview; Introduction to Data Engineering; Object-Oriented Programming Explained Simply for Data Scientists
In his latest book, a leading statistician Dr. David Hand explores how we can be blind to missing or unseen data and how, in our rush to be a data-driven society, we might be missing things that matter, leading to dangerous decisions that can sometimes have disastrous consequences. Download this free chapter now.
In this article, we’ll cover probability mass and probability density function in this sample. You’ll see how to understand and represent these distribution functions and their link with histograms.
If you are preparing for data engineering interviews, then follow these technical recommendations regarding your resume, programming skills, SQL acumen, and system design problem-solving, as well as the non-technical aspects of your upcoming interview session.
The well-established technologies and tools around ETL (Extract, Transform, Load) are undergoing a potential paradigm shift with new approaches to data storage and expanding cloud-based compute. Decoupling the EL from T could reconcile analytics and operational data management use cases, in a new landscape where data warehouses and data lakes are merging.
Fast-track your promotion with a degree in data science. The part-time Master of Science in Analytics allows you to balance your personal and professional life while mastering the cutting-edge technology defining the industry today.
2020 is finally coming to a close. While likely not to register as anyone's favorite year, 2020 did have some noteworthy advancements in our field, and 2021 promises some important key trends to look forward to. As has become a year-end tradition, our collection of experts have once again contributed their thoughts. Read on to find out more.
The Q&A for the most frequently asked questions about Data Engineering: What does a data engineer do? What is a data pipeline? What is a data warehouse? How is a data engineer different from a data scientist? What skills and programming languages do you need to learn to become a data engineer?
Also: Best #Python IDEs and Code Editors ; Facebook Is Dead (It Just Doesn’t Know It Yet); Enhance your data science game with these portfolio-worthy projects.; The Online Courses You Must Take to be a Better #DataScientist
As the fields related to AI and Data Science expand, they are becoming complex with more options and specializations to consider. If you are beginning your journey toward becoming an expert in Artificial Intelligence, this roadmap will guide you to find your path along what to learn next while steering clear of the latest hype.
NoSQL can offer an advantage to those who are entering Data Science and Analytics, as well as having applications with high-performance needs that aren’t met by traditional SQL databases.
Data professionals are invited to share their massive data challenges from their own unique perspectives. Learn more about the Massive Data Revolution Video Challenge, get a $50 Amazon gift card, and be sure to submit your entry by December 16th.
There's a lot of data out there and so many data science techniques to master or review. Check out these great project ideas from easy to advanced difficulty levels to develop new skills and strengthen your portfolio.
Meet whale! The stupidly simple data discovery tool
15 Free Data Science, Machine Learning & Statistics eBooks for 2021
Key Data Science Algorithms Explained: From k-means to k-medoids clustering
Nov
