- 10 Must-Know Statistical Concepts for Data Scientists - Apr 21, 2021.
Statistics is a building block of data science. If you are working or plan to work in this field, then you will encounter the fundamental concepts reviewed for you here. Certainly, there is much more to learn in statistics, but once you understand these basics, then you can steadily build your way up to advanced topics.
Bayes Theorem, Correlation, Normal Distribution, P-value, Sampling, Statistics, Variance
- 10 Statistical Concepts You Should Know For Data Science Interviews - 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.
Bayes Theorem, Interview Questions, Linear Regression, Logistic Regression, P-value, Sampling, Statistics
- Null Hypothesis Significance Testing is Still Useful - Jan 25, 2021.
Even in the aftermath of the replication crisis, statistical significance lingers as an important concept for Data Scientists to understand.
Hypothesis Testing, P-value, Statistical Significance, Statistics
- 5 Must-Read Data Science Papers (and How to Use Them) - Oct 20, 2020.
Keeping ahead of the latest developments in a field is key to advancing your skills and your career. Five foundational ideas from recent data science papers are highlighted here with tips on how to leverage these advancements in your work, and keep you on top of the machine learning game.
Data Science, Machine Learning, P-value, Research, Software, Technical Debt, Transformer
- Hypothesis Test for Real Problems - Aug 14, 2020.
Hypothesis tests are significant for evaluating answers to questions concerning samples of data.
Hypothesis Testing, P-value, Statistics
- Demystifying Statistical Significance - Jul 17, 2020.
With more professionals from a wide range of less technical fields diving into statistical analysis and data modeling, these experimental techniques can seem daunting. To help with these hurdles, this article clarifies some misconceptions around p-values, hypothesis testing, and statistical significance.
P-value, Statistical Significance, Statistics
- P-values Explained By Data Scientist - Jul 30, 2019.
This article is designed to give you a full picture from constructing a hypothesis testing to understanding p-value and using that to guide our decision making process.
Data Science, Data Scientist, Hypothesis Testing, P-value, Statistics
- Comparing Machine Learning Models: Statistical vs. Practical Significance - Jan 18, 2019.
Is model A or B more accurate? Hmm… In this blog post, I’d love to share my recent findings on model comparison.
Machine Learning, Model Performance, P-value, Statistical Modeling, Statistical Significance
Data Scientist Interviews Demystified - Aug 2, 2018.
We look at typical questions in a data science interview, examine the rationale for such questions, and hope to demystify the interview process for recent graduates and aspiring data scientists.
Data Science Skills, Hiring, Interview Questions, P-value, random forests algorithm, XGBoost
- Big Idea To Avoid Overfitting: Reusable Holdout to Preserve Validity in Adaptive Data Analysis - Aug 17, 2015.
Big Data makes it all too easy find spurious "patterns" in data. A new approach helps avoid overfitting by using 2 key ideas: validation should not reveal any information about the holdout data, and adding of a small amount of noise to any validation result.
Pages: 1 2
Holdout, Model Performance, Overfitting, P-value, Vitaly Feldman
