- 5 Mistakes I Wish I Had Avoided in My Data Science Career - Jul 26, 2021.
Everyone makes mistakes, which can be a good thing when they lead to learning and improvements over time. But, we can also try to first learn from others to expedite our personal growth. To get started, consider these lessons learned the hard way, so you don’t have to.
- How a Single Mistake Wasted 3 Years of My Data Science Journey - Jun 9, 2021.
Self-paced courses are just sleeping pills; Industry experts are the right choice.
- 6 Mistakes To Avoid While Training Your Machine Learning Model - Apr 15, 2021.
While training the AI model, multi-stage activities are performed to utilize the training data in the best manner, so that outcomes are satisfying. So, here are the 6 common mistakes you need to understand to make sure your AI model is successful.
- Learning from machine learning mistakes - Mar 19, 2021.
Read this article and discover how to find weak spots of a regression model.
- 15 common mistakes data scientists make in Python (and how to fix them) - Mar 3, 2021.
Writing Python code that works for your data science project and performs the task you expect is one thing. Ensuring your code is readable by others (including your future self), reproducible, and efficient are entirely different challenges that can be addressed by minimizing common bad practices in your development.
- 6 Common Mistakes in Data Science and How To Avoid Them - Sep 10, 2020.
As a novice or seasoned Data Scientist, your work depends on the data, which is rarely perfect. Properly handling the typical issues with data quality and completeness is crucial, and we review how to avoid six of these common scenarios.
- Data Scientists think data is their #1 problem. Here’s why they’re wrong. - Sep 4, 2020.
We tend to think it's all about the data. However, for real data science projects at real organizations in real life, there are more fundamental aspects to consider to do data science right.
- Were 21% of New York City residents really infected with the novel coronavirus? - May 6, 2020.
Understanding the types of statistical bias that pop up in popular media and reporting is especially important during this pandemic where the data -- and our global response to the data -- directly impact peoples' lives.
- How (not) to use Machine Learning for time series forecasting: The sequel - Mar 30, 2020.
Developing machine learning predictive models from time series data is an important skill in Data Science. While the time element in the data provides valuable information for your model, it can also lead you down a path that could fool you into something that isn't real. Follow this example to learn how to spot trouble in time series data before it's too late.
- Predicting the President: Two Ways Election Forecasts Are Misunderstood - Mar 27, 2020.
With election cycles always seeming to be in season, predictions on outcomes remain intriguing content for the voting citizens. Misinterpretation of election forecasts also runs rampant, and can impact perceptions of candidates and those who post these predictions. A better fundamental understanding of probability can help improve our collective notion of futurism, and how we monitor elections.
- Learning from 3 big Data Science career mistakes - Feb 25, 2020.
Thinking of data science as merely a technical profession, like programming, may take you away from your goals. We explain big mistakes to avoid, including not understanding the 2 cultures of statistics, and not understanding the shift to industrial focus.
- KDnuggets™ News 19:n42, Nov 6: 5 Statistical Traps Data Scientists Should Avoid; 10 Free Must-Read Books on AI - Nov 6, 2019.
Learn about statistical fallacies Data Scientists should avoid; New and quite amazing Deep Learning capabilities FB has been quietly open-sourcing; Top Machine Learning tools for Developers; How to build a Neural Network from scratch and more.
- Top 10 Statistics Mistakes Made by Data Scientists - Jun 7, 2019.
The following are some of the most common statistics mistakes made by data scientists. Check this list often to make sure you are not making any of these while applying statistics to data science.
- How (not) to use Machine Learning for time series forecasting: Avoiding the pitfalls - May 10, 2019.
We outline some of the common pitfalls of machine learning for time series forecasting, with a look at time delayed predictions, autocorrelations, stationarity, accuracy metrics, and more.
- Top KDnuggets tweets, May 01-07: The 3 Biggest Mistakes in Learning Data Science; ReinforcementLearning vs. Differentiable Programming; XGBoost Reign - May 8, 2019.
Also XGBoost Algorithm: Long May She Reign; CycleGANs to Create Computer-Generated #Art - #GANs #DeepLearning; Another 10 Free Must-See Courses for Machine Learning and Data Science.
- KDnuggets™ News 19:n18, May 8: What Data Science/Machine Learning software you used – KDnuggets Poll; The Third Wave Data Scientist - May 8, 2019.
Vote in KDnuggets 20th annual poll: What software you used for Analytics, Data Mining, Data Science, Machine Learning projects? Also, what skills are needed for the 3rd wave Data Scientist?; The 3 biggest mistakes in learning Data Science; What makes XGBoost so successful; The ranking of best Masters in Analytics/Data Science in US/Canada; and more.
- KDnuggets™ News 19:n13, Apr 3: Top 10 Data Scientist Coding Mistakes; Explaining Random Forest®; Which Face is Real? - Apr 3, 2019.
Do you know when is using "for" loop a mistake? Read 10 top coding mistakes by Data Scientists; Understand Random Forests and Linear Regression with scikit-learn; Find how to choose the right chart type; and see if you can guess which face is real.
- Top 10 Coding Mistakes Made by Data Scientists - Apr 2, 2019.
Here is a list of 10 common mistakes that a senior data scientist — who is ranked in the top 1% on Stackoverflow for python coding and who works with a lot of (junior) data scientists — frequently sees.
- KDnuggets™ News 18:n47, Dec 12: Common mistakes when doing machine learning; Here are the most popular Python IDEs / Editors - Dec 12, 2018.
Common mistakes when carrying out machine learning and data science; Most popular Python IDEs/Editors; Machine Learning / AI Main Developments in 2018 and Key Trends for 2019; Machine Learning Project checklist.
- Common mistakes when carrying out machine learning and data science - Dec 6, 2018.
We examine typical mistakes in Data Science process, including wrong data visualization, incorrect processing of missing values, wrong transformation of categorical variables, and more. Learn what to avoid!
- 10 Mistakes to Avoid When Adopting Advanced Analytics - Jul 10, 2018.
Download this report for a list of 10 mistakes to avoid when adopting advanced analytics, learn how you can improve your own implementation, and get a taste of premium membership.
- KDnuggets™ News 18:n13, Mar 28: Where did you apply Data Science/ML? 12 Essential Command Line Tools for Data Scientists - Mar 28, 2018.
Also: 8 Common Pitfalls That Can Ruin Your Prediction; Text Data Preprocessing: A Walkthrough in Python; CatBoost vs. Light GBM vs. XGBoost.
- Top 6 errors novice machine learning engineers make - Oct 30, 2017.
What common mistakes beginners do when working on machine learning or data science projects? Here we present list of such most common errors.
- The 5 Common Mistakes That Lead to Bad Data Visualization - Oct 10, 2017.
Here are 5 common mistakes that lead to bad data visualization. Avoid these to get the most out of your data visualizations.
- How to Lie with Data - Apr 20, 2017.
We expect data scientists to be objective, but intentionally or not, they can produce results that mislead. We examine three common types of “lies” that Data Scientists should be aware of.
- KDnuggets™ News 17:n15, Apr 19: Forrester vs Gartner on Data Science/Analytics Platforms; 5 Machine Learning Projects You Can No Longer Overlook - Apr 19, 2017.
Also Top mistakes data scientists make when dealing with business people; New Online Data Science Tracks for 2017; Cartoon: Why AI needs help with taxes.
- Top mistakes data scientists make when dealing with business people - Apr 13, 2017.
There are no cover articles praising the fails of the many data scientists that don’t live up to the hype. Here we examine 3 typical mistakes and how to avoid them.
- What we can learn from AI mistakes - Dec 19, 2016.
Because of recent innovations and research in AI, we have seen AI performing best in some very important tasks and even worst in even simple tasks. So the question is, Why is it that AI can look so brilliant and so stupid at the same time?
- Avoid These Common Data Visualization Mistakes - Feb 8, 2016.
Data Visualization is a handy tool which can lead to interesting discoveries about the data, which otherwise wouldn’t have been possible. But, there are common mistakes which could produce the misdirecting results. Learn what are they and how you can avoid them.
- 7 Common Data Science Mistakes and How to Avoid Them - Jan 26, 2016.
Data scientist in business is as similar as to that of a detective: discovering the unknown. But, while venturing onto this journey they do tend to fall into the pitfalls. Understand, how these mistakes are made and how you can avoid them.
- Sentiment Analysis & Predictive Analytics for trading. Avoid this systematic mistake - Jan 25, 2016.
The financial market is the ultimate testbed for predictive theories. With this post we want to highlight the common mistakes, observed in the world of predictive analytics, when computer scientists venture into the field of financial trading and quantitative finance.
- KDnuggets™ News 15:n08, Mar 11: 7 common Machine Learning mistakes; Statistical Reasoning - Mar 11, 2015.
7 common mistakes when doing Machine Learning; 10 Predictive Analytics Influencers; Kaiser Fung on Why Statistical Reasoning is more important than Number Crunching; The Elements of Data Analytic Style - checklist; KDD-2017.
- 7 common mistakes when doing Machine Learning - Mar 7, 2015.
In statistical modeling, there are various algorithms to build a classifier, and each algorithm makes a different set of assumptions about the data. For Big Data, it pays off to analyze the data upfront and then design the modeling pipeline accordingly.
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