Top Stories
Most Viewed, Jun 3 – 9
7 Steps to Mastering Intermediate Machine Learning with Python — 2019 Edition, by Matthew Mayo- A Step-by-Step Guide to Transitioning your Career to Data Science — Part 1, by Manu Jeevan
- How to choose a visualization
- Math for Programmers
- PyViz: Simplifying the Data Visualisation Process in Python
- Random Forest vs Neural Network: Which is Better, and When?
- Top 10 Statistics Mistakes Made by Data Scientists
Most Shared, June 3 – June 9
- 7 Steps to Mastering Intermediate Machine Learning with Python — 2019 Edition, by Matthew Mayo - Jun 03, 2019.
Jupyter Notebooks: Data Science Reporting, by Asel Mendis - Jun 06, 2019.- Top 10 Statistics Mistakes Made by Data Scientists - Jun 07, 2019.
- Random Forest vs Neural Network: Which is Better, and When? - Jun 07, 2019.
- How to choose a visualization - Jun 04, 2019.
- PyViz: Simplifying the Data Visualisation Process in Python - Jun 06, 2019.
- The Whole Data Science World in Your Hands - Jun 05, 2019.
Previous weeks top stories:
- May 27 - June 2: A Step-by-Step Guide to Transitioning your Career to Data Science — Part 1; Python leads the 11 top Data Science, Machine Learning platforms: Trends and Analysis
- May 20 - 26: 7 Steps to Mastering SQL for Data Science; The Data Fabric for Machine Learning
- May 13- 19: 7 Steps to Mastering SQL for Data Science — 2019 Edition; Mathematical programming — Key Habit to Build Up for Advancing Data Science
- May 6 - 12: The Third Wave Data Scientist; The 3 Biggest Mistakes on Learning Data Science
- Apr 29 - May 5: The most desired skill in data science; Top Data Science and Machine Learning Methods Used in 2018, 2019
2019 Top stories each month
- 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
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
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
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
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
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