- Graph Databases, Explained, by Alex Williams - Feb 26, 2021.
Between the four main NoSQL database types, graph databases are widely appreciated for their application in handling large sets of unstructured data coming from various sources. Let’s talk about how graph databases work and what are their practical uses.
- Data Science Learning Roadmap for 2021, by Harshit Tyagi - Feb 26, 2021.
Venturing into the world of Data Science is an exciting, interesting, and rewarding path to consider. There is a great deal to master, and this self-learning recommendation plan will guide you toward establishing a solid understanding of all that is foundational to data science as well as a solid portfolio to showcase your developed expertise.
- Machine Learning Systems Design: A Free Stanford Course, by Matthew Mayo - Feb 26, 2021.
This freely-available course from Stanford should give you a toolkit for designing machine learning systems.
- 6 Web Scraping Tools That Make Collecting Data A Breeze, by Sara Metwalli - Feb 25, 2021.
The first step of any data science project is data collection. While it can be the most tedious and time-consuming step during your workflow, there will be no project without that data. If you are scraping information from the web, then several great tools exist that can save you a lot of time, money, and effort.
- The Difficulty of Graph Anonymisation, by Timothy Lin - Feb 25, 2021.
Lessons from network science and the difficulty of graph anonymization. A data scientist's take on the difficultly of striking a balance between privacy and utility in anonymizing connected data.
- How Reading Papers Helps You Be a More Effective Data Scientist, by Eugene Yan - Feb 24, 2021.
By reading papers, we were able to learn what others (e.g., LinkedIn) have found to work (and not work). We can then adapt their approach and not have to reinvent the rocket. This helps us deliver a working solution with lesser time and effort.
- Pandas Profiling: One-Line Magical Code for EDA, by Juhi Sharma - Feb 24, 2021.
EDA can be automated using a Python library called Pandas Profiling. Let’s explore Pandas profiling to do EDA in a very short time and with just a single line code.
- Using NLP to improve your Resume, by David Moore - Feb 23, 2021.
This article discusses performing keyword matching and text analysis on job descriptions.
- 10 Statistical Concepts You Should Know For Data Science Interviews, by Terence Shin - Feb 23, 2021.
Data Science is founded on time-honored concepts from statistics and probability theory. Having a strong understanding of the ten ideas and techniques highlighted here is key to your career in the field, and also a favorite topic for concept checks during interviews.
- Data Observability, Part II: How to Build Your Own Data Quality Monitors Using SQL, by Moses & Kearns - Feb 23, 2021.
Using schema and lineage to understand the root cause of your data anomalies.
- An overview of synthetic data types and generation methods, by Devaux & Wehmeyer - Feb 22, 2021.
Synthetic data can be used to test new products and services, validate models, or test performances because it mimics the statistical property of production data. Today you'll find different types of structured and unstructured synthetic data.
- Powerful Exploratory Data Analysis in just two lines of code, by Francois Bertrand - Feb 22, 2021.
EDA is a fundamental early process for any Data Science investigation. Typical approaches for visualization and exploration are powerful, but can be cumbersome for getting to the heart of your data. Now, you can get to know your data much faster with only a few lines of code... and it might even be fun!
- Inside the Architecture Powering Data Quality Management at Uber, by Jesus Rodriguez - Feb 22, 2021.
Data Quality Monitor implements novel statistical methods for anomaly detection and quality management in large data infrastructures.
- Evaluating Deep Learning Models: The Confusion Matrix, Accuracy, Precision, and Recall, by Ahmed Gad - Feb 19, 2021.
This tutorial discusses the confusion matrix, and how the precision, recall and accuracy are calculated, and how they relate to evaluating deep learning models.
- Feature Store as a Foundation for Machine Learning, by German Osin - Feb 19, 2021.
With so many organizations now taking the leap into building production-level machine learning models, many lessons learned are coming to light about the supporting infrastructure. For a variety of important types of use cases, maintaining a centralized feature store is essential for higher ROI and faster delivery to market. In this review, the current feature store landscape is described, and you can learn how to architect one into your MLOps pipeline.
- Multidimensional multi-sensor time-series data analysis framework, by Ajay Arunachalam - Feb 19, 2021.
This blog post provides an overview of the package “msda” useful for time-series sensor data analysis. A quick introduction about time-series data is also provided.
- Approaching (Almost) Any Machine Learning Problem, by Matthew Mayo - Feb 18, 2021.
This freely-available book is a fantastic walkthrough of practical approaches to machine learning problems.
- 6 Data Science Certificates To Level Up Your Career, by Sara Metwalli - Feb 18, 2021.
Anyone looking to obtain a data science certificate to prove their ability in the field will find a range of options exist. We review several valuable certificates to consider that will definitely pump up your resume and portfolio to get you closer to your dream job.
- Forecasting Stories 5: The story of the launch, by Rajneet Kaur - Feb 18, 2021.
New products forecasting can be very difficult - there is no history to start with, and hence no base line. The number of assumptions can be huge. The best way to forecast then, is to try parallel approaches, build different views and triangulate on a common range.
- GPT-2 vs GPT-3: The OpenAI Showdown, by Kevin Vu - Feb 17, 2021.
Thanks to the diversity of the dataset used in the training process, we can obtain adequate text generation for text from a variety of domains. GPT-2 is 10x the parameters and 10x the data of its predecessor GPT.
- 10 resources for data science self-study, by Benjamin Obi Tayo - Feb 17, 2021.
Many resources exist for the self-study of data science. In our modern age of information technology, an enormous amount of free learning resources are available to anyone, and with effort and dedication, you can master the fundamentals of data science.
- Deep Learning-based Real-time Video Processing, by Serhii Maksymenko - Feb 17, 2021.
In this article, we explore how to build a pipeline and process real-time video with Deep Learning to apply this approach to business use cases overviewed in our research.
- Data Observability: Building Data Quality Monitors Using SQL, by Kearns & Moses - Feb 16, 2021.
To trigger an alert when data breaks, data teams can leverage a tried and true tactic from our friends in software engineering: monitoring and observability. In this article, we walk through how you can create your own data quality monitors for freshness and distribution from scratch using SQL.
- Hugging Face Transformers Package – What Is It and How To Use It, by Nagesh Chauhan - Feb 16, 2021.
The rapid development of Transformers have brought a new wave of powerful tools to natural language processing. These models are large and very expensive to train, so pre-trained versions are shared and leveraged by researchers and practitioners. Hugging Face offers a wide variety of pre-trained transformers as open-source libraries, and you can incorporate these with only one line of code.
- Easy, Open-Source AutoML in Python with EvalML, by Dylan Sherry - Feb 16, 2021.
We’re excited to announce that a new open-source project has joined the Alteryx open-source ecosystem. EvalML is a library for automated machine learning (AutoML) and model understanding, written in Python.
- IBM Uses Continual Learning to Avoid The Amnesia Problem in Neural Networks, by Jesus Rodriguez - Feb 15, 2021.
Using continual learning might avoid the famous catastrophic forgetting problem in neural networks.
- Telling a Great Data Story: A Visualization Decision Tree, by Stan Pugsley - Feb 15, 2021.
Pick your visualizations strategically. They need to tell a story.
- Essential Math for Data Science: Scalars and Vectors, by Hadrien Jean - Feb 12, 2021.
Linear algebra is the branch of mathematics that studies vector spaces. You’ll see how vectors constitute vector spaces and how linear algebra applies linear transformations to these spaces. You’ll also learn the powerful relationship between sets of linear equations and vector equations.
- 6 NLP Techniques Every Data Scientist Should Know, by Sara Metwalli - Feb 12, 2021.
Natural language processing has already begun to transform to way humans interact with computers, and its advances are moving rapidly. The field is built on core methods that must first be understood, with which you can then launch your data science projects to a new level of sophistication and value.
- Column-Oriented Databases, Explained, by Alex Williams - Feb 12, 2021.
NoSQL Databases have four distinct types. Key-value stores, document-stores, graph databases, and column-oriented databases. In this article, we’ll explore column-oriented databases, also known simply as “NoSQL columns”.
- How to Speed up Scikit-Learn Model Training, by Michael Galarnyk - Feb 11, 2021.
Scikit-Learn is an easy to use a Python library for machine learning. However, sometimes scikit-learn models can take a long time to train. The question becomes, how do you create the best scikit-learn model in the least amount of time?
- A Critical Comparison of Machine Learning Platforms in an Evolving Market, by Vivek Jain - Feb 11, 2021.
There’s a clear inclination towards the MLaaS model across industries, given the fact that companies today have an option to select from a wide range of solutions that can cater to diverse business needs. Here is a look at 3 of the top ML platforms for data excellence.
- My machine learning model does not learn. What should I do?, by Silipo & Arenas - Feb 10, 2021.
This article presents 7 hints on how to get out of the quicksand.
- 7 Most Recommended Skills to Learn to be a Data Scientist, by Terence Shin - Feb 10, 2021.
The Data Scientist professional has emerged as a true interdisciplinary role that spans a variety of skills, theoretical and practical. For the core, day-to-day activities, many critical requirements that enable the delivery of real business value reach well outside the realm of machine learning, and should be mastered by those aspiring to the field.
- Data Science vs Business Intelligence, Explained, by Stan Pugsley - Feb 10, 2021.
Knowing the differences between the business intelligence and data science is more than just a matter of semantics.
- How to Deploy a Flask API in Kubernetes and Connect it with Other Micro-services, by Rik Kraan - Feb 9, 2021.
A hands-on tutorial on how to implement your micro-service architecture using the powerful container orchestration tool Kubernetes.
- Adversarial Attacks on Explainable AI, by Hubert Baniecki - Feb 9, 2021.
Are explainability methods black-box themselves?
- Microsoft Explores Three Key Mysteries of Ensemble Learning, by Jesus Rodriguez - Feb 8, 2021.
A new paper studies three key puzzling characteristics of deep learning ensembles and some potential explanations.
- Essential Math for Data Science: Introduction to Matrices and the Matrix Product, by Hadrien Jean - Feb 5, 2021.
As vectors, matrices are data structures allowing you to organize numbers. They are square or rectangular arrays containing values organized in two dimensions: as rows and columns. You can think of them as a spreadsheet. Learn more here.
- Deep learning doesn’t need to be a black box, by Ben Dickson - Feb 5, 2021.
The cultural perception of AI is often suspect because of the described challenges in knowing why a deep neural network makes its predictions. So, researchers try to crack open this "black box" after a network is trained to correlate results with inputs. But, what if the goal of explainability could be designed into the network's architecture -- before the model is trained and without reducing its predictive power? Maybe the box could stay open from the beginning.
- Backcasting: Building an Accurate Forecasting Model for Your Business, by Lena Boichuk - Feb 5, 2021.
This article will shed some light on processes happening under the roof of ML-based solutions on the example of the business case where the future success directly depends on the ability to predict unknown values from the past.
- Build Your First Data Science Application, by Naser Tamimi - Feb 4, 2021.
Check out these seven Python libraries to make your first data science MVP application.
- How to create stunning visualizations using python from scratch, by Sharan Kumar R - Feb 4, 2021.
Data science and data analytics can be beautiful things. Not only because of the insights and enhancements to decision-making they can provide, but because of the rich visualizations about the data that can be created. Following this step-by-step guide using the Matplotlib and Seaborn libraries will help you improve the presentation and effective communication of your work.
- Getting Started with 5 Essential Natural Language Processing Libraries, by Matthew Mayo - Feb 3, 2021.
This article is an overview of how to get started with 5 popular Python NLP libraries, from those for linguistic data visualization, to data preprocessing, to multi-task functionality, to state of the art language modeling, and beyond.
- Saving and loading models in TensorFlow — why it is important and how to do it, by Ahmad Anis - Feb 3, 2021.
So much time and effort can go into training your machine learning models. But, shut down the notebook or system, and all those trained weights and more vanish with the memory flush. Saving your models to maximize reusability is key for efficient productivity.
- Adversarial generation of extreme samples, by Lucy Smith - Feb 2, 2021.
In order to mitigate risks when modelling extreme events, it is vital to be able to generate a wide range of extreme, and realistic, scenarios. Researchers from the National University of Singapore and IIT Bombay have developed an approach to do just that.
- Vision Transformers: Natural Language Processing (NLP) Increases Efficiency and Model Generality, by Kevin Vu - Feb 2, 2021.
Why do we hear so little about transformer models applied to computer vision tasks? What about attention in computer vision networks?
- 3 Ways Understanding Bayes Theorem Will Improve Your Data Science, by Nicole Janeway Bills - Feb 1, 2021.
Mastery of the mathematics and applications of this intuitive statistical concept will advance your credibility as a decision maker.
- Beyond the Nash Equilibrium: DeepMind Clever Strategy to Solve Asymmetric Games, by Jesus Rodriguez - Feb 1, 2021.
The method expands the concept of a Nash equilibrium by decomposing an asymmetric game into multiple symmetric games.