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KDnuggets News: 2021 News | Schedule | Archive
Top stories: Most viewed and Most shared
How to submit a blog to KDnuggets
KDnuggets News: 2021 News | Schedule | Archive
Latest:
Six Times Bigger than GPT-3: Inside Google’s TRILLION Parameter Switch Transformer Model
, by Jesus Rodriguez - Jan 25, 2021.
Google’s Switch Transformer model could be the next breakthrough in this area of deep learning.
Top Stories, Jan 18-24: How I Got 4 Data Science Offers and Doubled my Income 2 Months After Being Laid Off; Cloud Computing, Data Science and ML Trends in 2020–2022: The battle of giants
- Jan 25, 2021.
Also: Data Engineering — the Cousin of Data Science, is Troublesome; Build a Data Science Portfolio that Stands Out Using These Platforms; K-Means 8x faster, 27x lower error than Scikit-learn in 25 lines; Popular Machine Learning Interview Questions- The Ultimate Scikit-Learn Machine Learning Cheatsheet
, by Andre Ye - Jan 25, 2021.
With the power and popularity of the scikit-learn for machine learning in Python, this library is a foundation to any practitioner's toolset. Preview its core methods with this review of predictive modelling, clustering, dimensionality reduction, feature importance, and data transformation. 
Null Hypothesis Significance Testing is Still Useful
, by Nicole Janeway Bills - Jan 25, 2021.
Even in the aftermath of the replication crisis, statistical significance lingers as an important concept for Data Scientists to understand.- Building a Deep Learning Based Reverse Image Search
, by Vegard Flovik - Jan 22, 2021.
Following the journey from unstructured data to content based image retrieval. -
Data Engineering — the Cousin of Data Science, is Troublesome
, by Lissie Mei - Jan 22, 2021.
A Data Scientist must be a jack of many, many trades. Especially when working in broader teams, understanding the roles of others, such as data engineering, can help you validate progress and be aware of potential pitfalls. So, how can you convince your analysts to realize the importance of expanding their toolkit? Examples from real life often provide great insight. -
Cloud Computing, Data Science and ML Trends in 2020–2022: The battle of giants
, by George Vyshnya - Jan 22, 2021.
Kaggle’s survey of ‘State of Data Science and Machine Learning 2020’ covers a lot of diverse topics. In this post, we are going to look at the popularity of cloud computing platforms and products among the data science and ML professionals participated in the survey. - How to Use MLOps for an Effective AI Strategy
- Jan 21, 2021.
The need to deal with the challenges and other smaller nuances of deploying machine learning models has given rise to the relatively new concept of MLOps. – a set of best practices aimed at automating the ML lifecycle, bringing together the ML system development and ML system operations. - Going Beyond the Repo: GitHub for Career Growth in AI & Machine Learning
, by Martin Isaksson - Jan 21, 2021.
Many online tools and platforms exist to help you establish a clear and persuasive online profile for potential employers to review. Have you considered how your go-to online code repository could also help you land your next job? - Top 5 Artificial Intelligence (AI) Trends for 2021
, by Kevin Vu - Jan 21, 2021.
From voice and language driven AI to healthcare, cybersecurity and beyond, these are some of the key AI trends for 2021. 
Travel to faster, trusted decisions in the cloud
, by SAS - Jan 20, 2021.
Join technology experts, partners and analysts in the industry to see what is taking off in AI, cloud computing and putting models into production for better outcomes and trusted results. Register today!- Mastering TensorFlow Variables in 5 Easy Steps
, by Orhan G. Yalçın - Jan 20, 2021.
Learn how to use TensorFlow Variables, their differences from plain Tensor objects, and when they are preferred over these Tensor objects | Deep Learning with TensorFlow 2.x. - Popular Machine Learning Interview Questions
, by Mo Daoud - Jan 20, 2021.
Get ready for your next job interview requiring domain knowledge in machine learning with answers to these eleven common questions. - Loglet Analysis: Revisiting COVID-19 Projections
, by Dennis Ganzaroli - Jan 20, 2021.
We will show that the decomposition of growth into S-shaped logistic components also known as Loglet analysis, is more accurate as it takes into account the evolution of multiple covid waves. - KDnuggets™ News 21:n03, Jan 20: K-Means 8x faster, 27x lower error than Scikit-learn in 25 lines; Essential Math for Data Science: Information Theory
- Jan 20, 2021.
Here is a clever method of getting K-Means 8x faster, 27x lower error than Scikit-learn; Understand information theory you need for Data Science; Learn how to do cleaner data analysis with pandas using pipes; What are the four jobs of the data scientist? and more - Graph Representation Learning: The Free eBook
, by Matthew Mayo - Jan 19, 2021.
This free eBook can show you what you need to know to leverage graph representation in data science, machine learning, and neural network models. - Build a Data Science Portfolio that Stands Out Using These Platforms
, by Benjamin Obi Tayo - Jan 19, 2021.
Making your big break into the data science profession means standing out to potential employers in a crowd of tough competition. An important way to showcase your skills and experience is through the presentation of a portfolio. Following these recommendations for developing your portfolio will help you network effectively and stay on top of an ever-changing field. - How I Got 4 Data Science Offers and Doubled my Income 2 Months After Being Laid Off
, by Emma Ding - Jan 19, 2021.
In this blog, I shared my story on getting 4 data science job offers including Airbnb, Lyft and Twitter after being laid off. Any data scientist who was laid off due to the pandemic or who is actively looking for a data science position can find something here to which they can relate. - Data Science and Analytics Career Trends for 2021
, by Great Learning - Jan 18, 2021.
Let's check out what are the new data science and analytics career trends for 2021 that may also shape the career options in the future. - Microsoft Uses Transformer Networks to Answer Questions About Images With Minimum Training
, by Jesus Rodriguez - Jan 18, 2021.
Unified VLP can understand concepts about scenic images by using pretrained models. - Top Stories, Jan 11-17: K-Means 8x faster, 27x lower error than Scikit-learn in 25 lines; My Data Science Learning Journey So Far
- Jan 18, 2021.
Also: Essential Math for Data Science: Information Theory; Cleaner Data Analysis with Pandas Using Pipes; The Four Jobs of the Data Scientist - Can Data Science Be Agile? Implementing Best Agile Practices to Your Data Science Process
, by Jerzy Kowalski - Jan 18, 2021.
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. - Comprehensive Guide to the Normal Distribution
, by Nicole Janeway Bills - Jan 18, 2021.
Drop in for some tips on how this fundamental statistics concept can improve your data science. - Snowflake and Saturn Cloud Partner To Bring 100x Faster Data Science to Millions of Python Users
, by Saturn Cloud - Jan 15, 2021.
Snowflake the cloud data platform, is partnering, integrating products, and pursuing a joint go-to-market with Saturn Cloud to help data science teams get 100x faster results. Read more about developments and how to get started here. - Essential Math for Data Science: Information Theory
, by Hadrien Jean - Jan 15, 2021.
In the context of machine learning, some of the concepts of information theory are used to characterize or compare probability distributions. Read up on the underlying math to gain a solid understanding of relevant aspects of information theory. - K-Means 8x faster, 27x lower error than Scikit-learn in 25 lines
, by Jakub Adamczyk - Jan 15, 2021.
K-means clustering is a powerful algorithm for similarity searches, and Facebook AI Research's faiss library is turning out to be a speed champion. With only a handful of lines of code shared in this demonstration, faiss outperforms the implementation in scikit-learn in speed and accuracy. - Cleaner Data Analysis with Pandas Using Pipes
, by Soner Yıldırım - Jan 15, 2021.
Check out this practical guide on Pandas pipes. - 8 New Tools I Learned as a Data Scientist in 2020
, by Ben Weber - Jan 14, 2021.
The author shares the data science tools learned while making the move from Docker to Live Deployments. - Data Cleaning and Wrangling in SQL
, by Antonio Badia - Jan 14, 2021.
SQL is a foundational skill for data analysts but its application is sometimes limited within the data pipeline. However, SQL can be successfully used for many pre-processing tasks, such as data cleaning and wrangling, as demonstrated here by example. - Unsupervised Learning for Predictive Maintenance using Auto-Encoders
, by Kundaliya & Aggarwal - Jan 14, 2021.
This article outlines a machine learning approach to detect and diagnose anomalies in the context of machine maintenance, along with a number of introductory concepts, including: Introduction to machine maintenance; What is predictive maintenance?; Approaches for machine diagnosis; Machine diagnosis using machine learning - My Data Science Learning Journey So Far
, by Arnuld on Data - Jan 13, 2021.
These are some obstacles the author faced in their data science learning journey in the past year, including how much time it took to overcome each obstacle and what it has taught the author. - The Four Jobs of the Data Scientist
, by Roger Peng - Jan 13, 2021.
So, what do you do for a living? Sometimes, the answer to that question can feel like, "everything!" Well, for the Data Scientist, an extreme sense of being a "jack of all trades" is common. In fact, four such trades can be defined that a top-quality Data Scientist will iterate through during any one project. - The Best Tool for Data Blending is KNIME
, by Dennis Ganzaroli - Jan 13, 2021.
These are the lessons and best practices I learned in many years of experience in data blending, and the software that became my most important tool in my day-to-day work. - KDnuggets™ News 21:n02, Jan 13: Best Python IDEs and Code Editors; 10 Underappreciated Python Packages for Machine Learning Practitioners
- Jan 13, 2021.
Best Python IDEs and Code Editors You Should Know; 10 Underappreciated Python Packages for Machine Learning Practitioners; Top 10 Computer Vision Papers 2020; CatalyzeX: A must-have browser extension for machine learning engineers and researchers - Creating Good Meaningful Plots: Some Principles
, by Vikrant Dogra - Jan 12, 2021.
Hera are some thought starters to help you create meaningful plots. - Working With Sparse Features In Machine Learning Models
, by Arushi Prakash - Jan 12, 2021.
Sparse features can cause problems like overfitting and suboptimal results in learning models, and understanding why this happens is crucial when developing models. Multiple methods, including dimensionality reduction, are available to overcome issues due to sparse features. - Cloud Data Warehouse is The Future of Data Storage
, by Nitin Kumar - Jan 12, 2021.
Today, cloud data storage accounts for 45% of all enterprise data and by Q2 2021, that number could grow to 53%. Now is the time to embrace cloud than now. - Top Stories, Jan 04-10: Best Python IDEs and Code Editors You Should Know; All Machine Learning Algorithms You Should Know in 2021
- Jan 11, 2021.
Also: DeepMind’s MuZero is One of the Most Important Deep Learning Systems Ever Created; 10 Underappreciated Python Packages for Machine Learning Practitioners; Six Tips on Building a Data Science Team at a Small Company - 5 Tools for Effortless Data Science
, by Nicole Janeway Bills - Jan 11, 2021.
The sixth tool is coffee. - Attention mechanism in Deep Learning, Explained
, by Nagesh Chauhan - Jan 11, 2021.
Attention is a powerful mechanism developed to enhance the performance of the Encoder-Decoder architecture on neural network-based machine translation tasks. Learn more about how this process works and how to implement the approach into your work. - OpenAI Releases Two Transformer Models that Magically Link Language and Computer Vision
, by Jesus Rodriguez - Jan 11, 2021.
OpenAI has released two new transformer architectures that combine image and language tasks in an fun and almost magical way. Read more about them here. - JupyterLab 3 is Here: Key reasons to upgrade now
, by Matthew Mayo - Jan 8, 2021.
Read about these 3 reasons for checking out JupyterLab 3 today. - Best Python IDEs and Code Editors You Should Know
, by Claire D. Costa - Jan 8, 2021.
Developing machine learning algorithms requires implementing countless libraries and integrating many supporting tools and software packages. All this magic must be written by you in yet another tool -- the IDE -- that is fundamental to all your code work and can drive your productivity. These top Python IDEs and code editors are among the best tools available for you to consider, and are reviewed with their noteworthy features. - Top 10 Computer Vision Papers 2020
, by Louis (What’s AI) Bouchard - Jan 8, 2021.
The top 10 computer vision papers in 2020 with video demos, articles, code, and paper reference. - Top December Stories: Why the Future of ETL Is Not ELT, But EL(T); 20 Core Data Science Concepts for Beginners
- Jan 7, 2021.
Also: A Rising Library Beating Pandas in Performance; 15 Free Data Science, Machine Learning & Statistics eBooks for 2021 - 11 Industrial AI Trends that will Dominate the World in 2021
, by Swati Giri - Jan 7, 2021.
These trends broadly cover the three themes of: Where will businesses adopt AI in 2021? How will AI become more accessible? How will AI capabilities evolve? - Advice to aspiring Data Scientists – your most common questions answered
, by Roman Orac - Jan 7, 2021.
Embarking on a new career path can be daunting with many unknowns about how to get started and how to be successful. If you are aspiring to become a Data Scientist, then the answers to these common questions can help set you off on the right foot. - 10 Underappreciated Python Packages for Machine Learning Practitioners
, by Vinay Uday Prabhu - Jan 7, 2021.
Here are 10 underappreciated Python packages covering neural architecture design, calibration, UI creation and dissemination. - CatalyzeX: A must-have browser extension for machine learning engineers and researchers
, by Himanshu Ragtah - Jan 6, 2021.
CatalyzeX is a free browser extension that finds code implementations for ML/AI papers anywhere on the internet (Google, Arxiv, Twitter, Scholar, and other sites). - Learn Data Science for free in 2021
, by Ahmad Anis - Jan 6, 2021.
If you are considering starting a career path in machine learning and data science, then there is a great deal to learn theoretically, along with gaining practical skills in applying a broad range of techniques. This comprehensive learning plan will guide you to start on this path, and it is all available for free. - MLOps: Model Monitoring 101
, by Saha & Bose - Jan 6, 2021.
Model monitoring using a model metric stack is essential to put a feedback loop from a deployed ML model back to the model building stage so that ML models can constantly improve themselves under different scenarios. - KDnuggets™ News 21:n01, Jan 6: All machine learning algorithms you should know in 2021; Monte Carlo integration in Python; MuZero – the most important ML system ever created?
- Jan 6, 2021.
The first issue in 2021 brings you a great blog about Monte Carlo Integration - in Python; An overview of main Machine Learning algorithms you need to know in 2021; SQL vs NoSQL: 7 Key Takeaways; Generating Beautiful Neural Network Visualizations - how to; MuZero - may be the most important Machine Learning system ever created; and much more! - Where is Marketing Data Science Headed?
, by Kevin Gray - Jan 5, 2021.
Marketing data science - data science related to marketing - is now a significant part of marketing. Some of it directly competes with traditional marketing research and many marketing researchers may wonder what the future holds in store for it. - How to Get a Job as a Data Engineer
, by Anna Anisienia - Jan 5, 2021.
Data engineering skills are currently in high demand. If you are looking for career prospects in this fast-growing profession, then these 10 skills and key factors will help you prepare to land an entry-level position in this field. - Model Experiments, Tracking and Registration using MLflow on Databricks
, by Dash Desai - Jan 5, 2021.
This post covers how StreamSets can help expedite operations at some of the most crucial stages of Machine Learning Lifecycle and MLOps, and demonstrates integration with Databricks and MLflow. - DeepMind’s MuZero is One of the Most Important Deep Learning Systems Ever Created
, by Jesus Rodriguez - Jan 4, 2021.
MuZero takes a unique approach to solve the problem of planning in deep learning models. - Top Stories, Dec 21 – Jan 03: Monte Carlo integration in Python; 15 Free Data Science, Machine Learning & Statistics eBooks for 2021
- Jan 4, 2021.
Also: SQL vs NoSQL: 7 Key Takeaways; Generating Beautiful Neural Network Visualizations; Meet whale! The stupidly simple data discovery tool; Key Data Science Algorithms Explained: From k-means to k-medoids clustering - All Machine Learning Algorithms You Should Know in 2021
, by Terence Shin - Jan 4, 2021.
Many machine learning algorithms exits that range from simple to complex in their approach, and together provide a powerful library of tools for analyzing and predicting patterns from data. If you are learning for the first time or reviewing techniques, then these intuitive explanations of the most popular machine learning models will help you kick off the new year with confidence. - Six Tips on Building a Data Science Team at a Small Company
, by Zbar & Vallejo - Jan 4, 2021.
When a company decides that they want to start leveraging their data for the first time, it can be a daunting task. Many businesses aren’t fully aware of all that goes into building a data science department. If you're the data scientist hired to make this happen, we have some tips to help you face the task head-on. -
Meet whale! The stupidly simple data discovery tool
, by Robert Yi - Dec 31, 2020.
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. -
15 Free Data Science, Machine Learning & Statistics eBooks for 2021
, by Matthew Mayo - Dec 31, 2020.
We present a curated list of 15 free eBooks compiled in a single location to close out the year. - Data Science as a Product – Why Is It So Hard?
, by Tad Slaff - Dec 30, 2020.
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. - Generating Beautiful Neural Network Visualizations
, by Matthew Mayo - Dec 30, 2020.
If you are looking to easily generate visualizations of neural network architectures, PlotNeuralNet is a project you should check out. -
Key Data Science Algorithms Explained: From k-means to k-medoids clustering
, by Arushi Prakash - Dec 29, 2020.
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? - Essential Math for Data Science: The Poisson Distribution
, by Hadrien Jean - Dec 29, 2020.
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. - 2020: A Year Full of Amazing AI Papers — A Review
, by Louis (What's AI) Bouchard - Dec 28, 2020.
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. - Data Catalogs Are Dead; Long Live Data Discovery
, by Debashis Saha & Barr Moses - Dec 28, 2020.
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. - Monte Carlo integration in Python
, by Tirthajyoti Sarkar - Dec 24, 2020.
A famous Casino-inspired trick for data science, statistics, and all of science. How to do it in Python? - Top Stories, Dec 14-20: Crack SQL Interviews; State of Data Science and Machine Learning 2020: 3 Key Findings
- Dec 24, 2020.
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 - How to easily check if your Machine Learning model is fair?
, by Jakub Wisniewski - Dec 24, 2020.
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. - SQL vs NoSQL: 7 Key Takeaways
, by Alex Williams - Dec 23, 2020.
People assume that NoSQL is a counterpart to SQL. Instead, it’s a different type of database designed for use-cases where SQL is not ideal. The differences between the two are many, although some are so crucial that they define both databases at their cores. - Can you trust AutoML?
, by Ioannis Tsamardinos - Dec 23, 2020.
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: What it is, and when to use it
, by Harish Krishna - Dec 23, 2020.
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. - KDnuggets™ News 20:n48, Dec 23: Crack SQL Interviews; MLOps – Why and How; 2021 AI, Data Science, ML Predictions
- Dec 23, 2020.
In this last issue of the year learn how to crack SQL interviews, find why and how of MLOps, check top online courses Data Science, and read the predictions for AI, Data Science, and Machine Learning from our panel of experts and a group of innovative companies. - The Future of Cloud is Now
- Dec 22, 2020.
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. - Resampling Imbalanced Data and Its Limits
, by Maarit Widmann - Dec 22, 2020.
Can resampling tackle the problem of too few fraudulent transactions in credit card fraud detection? - Feature Store vs Data Warehouse
, by Jim Dowling - Dec 22, 2020.
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. - 5 strategies for enterprise machine learning for 2021
, by Leah Kolben - Dec 22, 2020.
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. - Top 9 Data Science Courses to Learn Online
, by Simplilearn - Dec 21, 2020.
Learn Data Science from these top courses. Details like cost and course duration are included. - Production Machine Learning Monitoring: Outliers, Drift, Explainers & Statistical Performance
, by Alejandro Saucedo - Dec 21, 2020.
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. - MLOps Is Changing How Machine Learning Models Are Developed
, by Henrik Skogstrom - Dec 21, 2020.
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. - Fast and Intuitive Statistical Modeling with Pomegranate
, by Tirthajyoti Sarkar - Dec 21, 2020.
Pomegranate is a delicious fruit. It can also be a super useful Python library for statistical analysis. We will show how in this article. - Optimization Algorithms in Neural Networks
, by Nagesh Singh Chauhan - Dec 18, 2020.
This article presents an overview of some of the most used optimizers while training a neural network. - MLOps – “Why is it required?” and “What it is”?
, by Bose & Aggarwal - Dec 18, 2020.
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. - Navigate the road to Responsible AI
, by Ben Lorica - Dec 18, 2020.
Deploying AI ethically and responsibly will involve cross-functional team collaboration, new tools and processes, and proper support from key stakeholders. - Top 2020 Stories: 24 Best (and Free) Books To Understand Machine Learning; If I had to start learning Data Science again, how would I do it?
- Dec 17, 2020.
Also: Know What Employers are Expecting for a Data Scientist Role in 2020; Top Python Libraries for Data Science, Data Visualization & Machine Learning. - ebook: Fundamentals for Efficient ML Monitoring
- Dec 17, 2020.
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. - Undersampling Will Change the Base Rates of Your Model’s Predictions
, by Bryan Shalloway - Dec 17, 2020.
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. - Crack SQL Interviews
, by Xinran Waibel - Dec 17, 2020.
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. - 8 Places for Data Professionals to Find Datasets
, by Devin Partida - Dec 17, 2020.
Here is a curated list of sites and resources invaluable for data professionals to acquire practice datasets.
