Mastering Statistics Fundamentals: Key to Success for Machine Learning Engineers and Data Scientists

A year ago, I stumbled upon a link in /r/dataisbeautiful on Reddit. I can't recall the exact topic of the article, but it was from a site called 538. Being an avid reader, I started exploring other posts on the site and was pretty much blown away by the analyses.

The data analysis fascinated me, so I began researching it. After a couple of days, I realized the power of data analysis, and the geek in me couldn't wait to be one of those people who can do cool stuff with data. And so, my journey to become an awesome data scientist began.

Throughout my pursuit of becoming a data scientist, I've learned a ton of concepts and discovered new things every day. Stepping out of your comfort zone can make you pretty smart, you know. Of course, some days can be overwhelming, and you might find yourself slacking off, leading to stress and frustration. But hey, you gotta bounce back! I've designated one day a week as my "chill out day," when I come home from work and spend time with family or play video games. Wednesdays are my go-to chill days, while I prefer to work the rest of the week.

In any case, after diving into data science, I realized that strong math and stats skills are essential. As a math geek, this was music to my ears. Doing math again, woohoo! So, after much thought and research, I decided to pick up 'Think Stats' by Allen B. Downey. You can find his website here Think Stats A.Downey. I chose to study statistics at this point because I'm taking a course on edx called 'Analytics Edge' by MITx, which requires a good grasp of statistics, math, and R.

Once I finish 'Think Stats,' I plan to tackle 'Statistical Learning' by Stanford. From what I gather, these two courses should provide a solid foundation in statistics. I'm also posting my solutions to the exercise challenges from 'Think Stats' on my GitHub account for future reference. You can find the repository here Think Stats Solutions.