Most Viewed, Apr 12-18
- The Most In-Demand Skills for Data Scientists in 2021, by Terence Chin
- Top 3 Statistical Paradoxes in Data Science, by Francesco Casalegno
- A/B Testing: 7 Common Questions and Answers in Data Science Interviews, Part 2, by Emma Ding
- ETL in the Cloud: Transforming Big Data Analytics with Data Warehouse Automation, by Nitin Kumar
- Essential Math for Data Science: Linear Transformation with Matrices, by Hadrien Jean
Most Shared, Apr 12-18
- How to deploy Machine Learning/Deep Learning models to the web, by Ahmad Anis
- Interpretable Machine Learning: The Free eBook, by Matthew Mayo
- Why machine learning struggles with causality, by Ben Dickson
- Awesome Tricks And Best Practices From Kaggle, by Bex T.
- How to Dockerize Any Machine Learning Application, by Arunn Thevapalan
Previous weeks top stories:
- Apr 5-11: Awesome Tricks And Best Practices From Kaggle; How to deploy Machine Learning/Deep Learning models to the web
- Mar 29 - Apr 4: Top 10 Python Libraries Data Scientists should know in 2021; Shapash: Making Machine Learning Models Understandable
- Mar 22-28: How to Succeed in Becoming a Freelance Data Scientist
- Mar 15-21: Top Stories, Mar 15-21: More Data Science Cheatsheets
- Mar 8-14: How To Overcome The Fear of Math and Learn Math For Data Science
2021 Top stories each month
- March: Are You Still Using Pandas to Process Big Data in 2021? Here are two better options; How To Overcome The Fear of Math and Learn Math For Data Science
- February: We Don't Need Data Scientists, We Need Data Engineers; How to create stunning visualizations using python from scratch
- January: How I Got 4 Data Science Offers and Doubled My Income 2 Months After Being Laid Off; Best Python IDEs and Code Editors You Should Know
Top stories in 2020
- 24 Best (and Free) Books To Understand Machine Learning;
If I had to start learning Data Science again, how would I do it?;
Know What Employers are Expecting for a Data Scientist Role in 2020;
Top Python Libraries for Data Science, Data Visualization & Machine Learning.
2020 Top stories each month
- December: Why the Future of ETL Is Not ELT, But EL(T); 20 Core Data Science Concepts for Beginners
- November: Top Python Libraries for Data Science, Data Visualization & Machine Learning; The Best Data Science Certification You’ve Never Heard Of
- October: Data Science Minimum: 10 Essential Skills You Need to Know to Start Doing Data Science; fastcore: An Underrated Python Library
- September: Free From MIT: Intro to Computer Science and Programming in Python; Best Online MS in AI, Analytics, Data Science, Machine Learning
- August: Know What Employers are Expecting for a Data Scientist Role in 2020; If I had to start learning Data Science again, how would I do it?
- July: Data Science MOOCs are too Superficial; Free MIT Courses on Calculus: The Key to Understanding Deep Learning
- June: How Much Math do you need in Data Science? Easy Speech-to-Text with Python
- May: The Best NLP with Deep Learning Course is Free
- April: Mathematics for Machine Learning: The Free eBook; The Super Duper NLP Repo: 100 Ready-to-Run Colab Notebooks
- March: 24 Best (and Free) Books To Understand Machine Learning; COVID-19 Visualized: The power of effective visualizations; 20 AI, Data Science, ML terms you need to know
- February: The Death of Data Scientists – will AutoML replace them?
- January: How to land a Data Scientist job at your dream company; I wanna be a data scientist, but ... how?
Top stories in 2019
- Top 10 Technology Trends of 2019;
How to select rows and columns in Pandas;
Your AI skills are worth less than you think;
Another 10 Free Must-See Courses for Machine Learning and Data Science.
2019 Top stories each month
- December: What is a Data Scientist Worth? AI, Machine Learning, Data Science, Deep Learning Research Main Developments in 2019 and Key Trends for 2020
- November: How to Speed up Pandas by 4x with one line of code
- October: How to Become a (Good) Data Scientist; Everything a Data Scientist Should Know About Data Management; The Last SQL Guide for Data Analysis
- September: I wasn’t getting hired as a Data Scientist. So I sought data on who is.
- August: How to Become More Marketable as a Data Scientist
- July: The Death of Big Data and the Emergence of the Multi-Cloud Era
- June: 5 Useful Statistics Data Scientists Need to Know; 7 Steps to Mastering Intermediate Machine Learning with Python – 2019 Edition
- May: A Step-by-Step Guide to Transitioning your Career to Data Science; 7 Steps to Mastering SQL for Data Science – 2019 Edition
- April: The most desired skill in data science; Top 10 Coding Mistakes Made by Data Scientists
- March: Another 10 Free Must-Read Books for Machine Learning and Data Science
- February: Data Scientists: Why are they so expensive to hire? Artificial Neural Network Implementation using NumPy and Image Classification
- January: Your AI skills are worth less than you think.
Top stories in 2018
- 9 Must-have skills you need to become a Data Scientist, updated; Python eats away at R: Top Software for Analytics, Data Science, Machine Learning; 5 Data Science Projects That Will Get You Hired in 2018; Top 20 Python AI and Machine Learning Open Source Projects;
2018 Top stories each month
- December: Why You Shouldn’t be a Data Science Generalist
- November: The Most in Demand Skills for Data Scientists; What is the Best Python IDE for Data Science?
- October: 9 Must-have skills you need to become a Data Scientist, updated; 10 Best Mobile Apps for Data Scientist / Data Analysts
- September: Essential Math for Data Science: Why and How; Machine Learning Cheat Sheets
- August: Data Visualization Cheat Sheet; Basic Statistics in Python
- July: Cartoon: Data Scientist was the sexiest job of the 21st century until ...; Does PCA really improve classification outcome? Causation in a nutshell
- June: 5 Data Science Projects That Will Get You Hired in 2018; Data Lake – the evolution of data processing
- May: Python eats away at R: Top Software for Analytics, Data Science, Machine Learning in 2018; Data Science vs Machine Learning vs Data Analytics vs Business Analytics
- April: Why so many data scientists are leaving their jobs? 7 Books to Grasp Math Foundations of Data Science and Machine Learning
- March: Will GDPR Make Machine Learning Illegal?
- February: Neural network AI is simple. So... Stop pretending you are a genius
- January: Docker for Data Science; Top 10 TED Talks for Data Scientists and Machine Learning Engineers
Top stories in 2017
- 10 Free Must-Read Books for Machine Learning and Data Science; Python overtakes R, becomes the leader in Data Science, Machine Learning platforms; Top 10 Machine Learning Algorithms for Beginners; 30 Essential Data Science, Machine Learning & Deep Learning Cheat Sheets
2017 Top stories each month
- December: Computer Vision by Andrew Ng – 11 Lessons Learned; Top Data Science and Machine Learning Methods Used in 2017
- November: The 10 Statistical Techniques Data Scientists Need to Master
- October: Top 10 Machine Learning Algorithms for Beginners
- September: 30 Essential Data Science, Machine Learning & Deep Learning Cheat Sheets
- August: Python overtakes R, becomes the leader in Data Science, Machine Learning platforms
- July: The 4 Types of Data Analytics
- June: Top 15 Python Libraries for Data Science in 2017
- May: KDnuggets Poll: Software for Analytics, Data Science, Machine Learning; How to Learn Machine Learning in 10 Days
- April: 10 Free Must-Read Books for Machine Learning and Data Science
- March: 7 More Steps to Mastering Machine Learning With Python; 50 Companies Leading The AI Revolution, Detailed
- February: 17 More Must-Know Data Science Interview Questions and Answers; 5 Career Paths in Big Data and Data Science, Explained
- January: The Most Popular Language For Machine Learning and Data Science Is ...
Top stories in 2016
- 21 Must-Know Data Science Interview Questions and Answers; 10 Algorithms Machine Learning Engineers Need to Know; Software used for Analytics, Data Science, Machine Learning projects; Top Algorithms and Methods Used by Data Scientists
2016 Top stories each month
- December: 50+ Data Science, Machine Learning Cheat Sheets; Machine Learning/AI: Main 2016 Developments, Key 2017 Trends
- November: Trump, Failure of Prediction, and Lessons for Data Scientists
- October: 5 EBooks to Read Before Getting into A Machine Learning Career; Top 10 Data Science Videos on YouTube
- September: Top Algorithms and Methods Used by Data Scientists
- August: The 10 Algorithms Machine Learning Engineers Need to Know; How to Become a Data Scientist
- July: Bayesian Machine Learning, Explained; Why Big Data is in Trouble: They Forgot About Applied Statistics
- June: The Difference Deep Learning and "Regular" Machine Learning? R, Python duel as top Data Science tools
- May: Poll: What software you used for Analytics, Data Mining, Data Science? How to Explain Machine Learning to a Software Engineer
- April: 10 Essential Books for Data Enthusiast; When Deep Learning is better than SVMs or Random Forests?
- March: R Learning Path: From beginner to expert in 7 steps; R or Python? Consider learning both
- February: 21 Must-Know Data Science Interview Q&A; Gartner 2016 MQ for Advanced Analytics: gainers and losers
- January: 20 Questions to Detect Fake Data Scientists, Machine Intelligence vs. Machine Learning vs. Deep Learning vs. AI
Top stories in 2015
- R vs Python for Data Science: The Winner is ...; 60+ Free Books on Big Data, Data Science, Data Mining Top 20 Python Machine Learning Open Source Projects; 50+ Data Science and Machine Learning Cheat Sheets.
2015 Top stories each month
- December: Top 10 Machine Learning Projects on Github; 50 Useful Machine Learning, Prediction APIs
- November: TensorFlow Disappoints - Google Deep Learning falls shallow; 5 Best Machine Learning APIs for Data Science
- October: Top 5 arXiv Deep Learning Papers, Explained; R vs Python: head to head data analysis
- September: 60+ Free Books on Big Data, Data Science; The one language a Data Scientist must master
- August: How to become a Data Scientist for Free; Data is Ugly - Tales of Data Cleaning
- July: 50+ Data Science and Machine Learning Cheat Sheets; Deep Learning and the Triumph of Empiricism
- June: Top 20 Python Machine Learning Projects; Which Big Data, Data Mining Tools go together?
- May: Most popular Predictive Analytics, Data Mining, Data Science software; R vs Python
- April: Awesome Public Datasets on GitHub; Forrester Wave Big Data Predictive Analytics - Gainers and Losers
- March: 7 common Machine Learning mistakes; Deep Learning for Text Understanding from Scratch
- February: 10 things statistics taught about big data; Gartner Analytics MQ: gainers and losers
- January: (Deep Learning Deep Flaws) Deep Flaws; Research Leaders on key trends, papers
Top stories in 2014
- Does Deep Learning Have Deep Flaws? Is Data Scientist the right career path for you? Four main languages for Analytics, Data Mining, Data Science; Cartoon: Big Data and World Cup Football
2014 Top stories by month
- December: If programming languages were vehicles; Cartoon: Unexpected Data Science Recommendations
- November: 9 Must-Have Skills for a Data Scientist; IBM Watson Analytics replacing a data scientist?
- October: Ebola Analytics and Data Science Lessons; Will Deep Learning take over Machine Learning?
- September: Data Science is mainly a Human Science; Hiring Data Scientists: What to look for?
- August: Four main languages for Analytics, Data Mining, Data Science
- July: Cartoon: Facebook data science experiment and Cats; Data Mining/Data Science "Nobel Prize"
- June: Does Deep Learning Have Deep Flaws? Cartoon: Big Data and World Cup
- May: New Poll - Analytics, Data Mining Software; Data Science Cheat Sheets
- April: Apache Spark, the hot new trend in Big Data; Data Analytics Handbook, free download
- March: Machine Learning in 7 Pictures; How Many Data Scientists?
- February: 3 Ways to test the accuracy; Exclusive Interview with Yann LeCun; One Page R
- January: Tutorial: Data Science in Python; Learning from Data, Caltech free online course