Probability is one of the few topics that I actually find somewhat interesting. After all, it's all about chances and what could happen, which can be pretty funny sometimes. Here are some of the funniest things I've found about probability (a thread):
#DataScience#Math
3๏ธโฃ Early Career Exploration: The college provided a nurturing environment to explore and identify my career path. Taking advantage of the available resources and networking opportunities allowed me to carve out my desired trajectory.
Key Learnings from My College Journey:
1๏ธโฃ Academic Focus: Investing in my academics proved crucial as a reflection of my dedication and work ethic. A strong GPA served as an indicator of my commitment and opened doors to numerous opportunities.
#learning#GetHired
2๏ธโฃ Priorities and Balance: While college societies offered exciting experiences, I learned to strike a balance. Prioritizing academics and skill development ensured a solid foundation for my future.
Looking for a statistical pickup line to make you laugh? "Hi, is your P-value > .5 because I always fail to reject you." Let's hear your best statistical pickup lines in the replies below! ๐๐ #StatisticLove#FunnyPickupLines#TwitterHumor
In the pioneer days they used oxen for heavy pulling, and when one ox couldnโt budge a log,
they didnโt try to grow a larger ox. We shouldnโt be trying for bigger computers, but for
more systems of computers.
โGrace Hopper
#hadoop#distributedsystems#BigData
Most data scientists use linear/logistic regression to figure out which features are important in a dataset.
I almost never do this.
Instead, I generally use leave-one-out feature importance (LOFO) + LightGBM.
Here's why:
Just spent the morning feature engineering for my machine learning model and it's amazing how much impact it can have on model performance! It's all about finding the right data to feed into your algorithm. #machinelearning#featureengineering
๐ซ ๐ ๐๐ข๐ฌ๐ญ๐ซ๐ข๏ฟฝ๏ฟฝ๐ฎ๐ญ๐ข๐จ๐ง: Probability distribution that is often used in stats analysis when the sample size is small or the population variance is unknown, shaped like a bell curve & is used to calculate the probability that the T-statistic falls within a certain range.
๐ซ ๐-๐ฌ๐ญ๐๐ญ๐ข๐ฌ๐ญ๐ข๐: This is a measure of the size of the difference between two sample means, relative to the variability within the samples. It is used to test whether the means of the two samples are significantly different from each other.
#DataScience#Stats