If you are a frequent internet or social media user, then seeing dozens of advertisements in a single sitting has most likely become a daily, even normal, occurrence for you. Some articles go as far to estimate that the average person is presented with 6,000 to 10,000 ads per day.
Because I like math, I went ahead and calculated (using the aforementioned statistic) how many ads a person might encounter in his/her lifetime. Assuming that the person will be a frequent internet or social media user for 40 years, he/she would encounter anywhere from 87 to 146 million ads in his/her lifetime.
The art of advertising began in London around the 17th century. Since then, advertising has evolved to a level that no one could have predicted.
Today's advertising is dominated by algorithms. These algorithms collect data based on a user's search history, social media interactions, etc. The aim of these algorithms is to present a user with a targeted ad so that whatever product or service is being advertised will have a higher likelihood of being purchased.
Personally, I am not in favor of these algorithms, mostly due to the fact that they are collecting my personal data; however, some people actually prefer receiving targeted ads over generalized ads.
Luckily, not all algorithms are bad.
Actually, most algorithms are beneficial. From performing rigorous calculations to autonomously driving a car, algorithms have endless potential when it comes to making our lives easier.
With that, I encourage you to react differently the next time that you hear the word "algorithm".
Day #6 (11/20/2022)
"A person who never made a mistake never tried anything new."
— Albert Einstein
Accomplishments
- Learned about the physical and mathematical constants available in SciPy
- Learned how to perform simple statistical tests using SciPy
- Finished writing my PotW
Weekly Goals
- Learn the basics of NumPy, Matplotlib, and SciPy (80%)
- Write a program to calculate the volumes of common geometric shapes (90%)
Closing Thoughts
Taking the step from NumPy to SciPy was like going from riding a bike to driving a car.
There are infinitely more variables, things to keep an eye out for, and hours that need to be spent practicing.
With that being said, I enjoyed the challenge of learning (the basics) of such a complex library. I do not foresee myself using everything that SciPy has to offer, but it will definitely come in handy for intensive tasks like statistical analysis, optimization, etc.