Companies only want data scientists who have great skills!

We have learned so much in such a short time in our Data Science Immersive Remote bootcamp. I will list off what we have learned or at least talked about here shortly. But honestly there are a few skills that I think we have learned that aren’t on any list. These are skills that will serve us well for the rest of our days no matter which role we end up playing in the data world.

The first is general problem solving. People who don’t work with computers everyday, or at least with computers that are constantly evolving, would not understand this. They go to work, turn their computers on and they do all the tasks they need them to do without problems or errors. If they encounter an error or problem, they call the IT department and then go get a coffee. Someone who works in data is constantly updating or installing new software and then fixing problems with the new software. I can’t even count how many times I have had to redo my entire environment or wipe and reinstall my jupyter notebook. These problem solving skills are priceless and will serve us well in our career in computers.

The second and third skills are kinda one in the same. That is, learning how to find answers or the right path on the internet to lead us to the code we need. Also, learning how to learn or teach ourselves how to see the path ahead of us we have found. Now this sounds silly, but at first I didn’t believe it myself. I used to talk to computer people about how they do their jobs, and they would reply “I Google it”… And make no mistake, it is a skill to be able to ask Google for the answer you need and actually find it in a reasonable amount of time.

My favorite story from class was about a famous programmer who had written a ludicrous amount of lessons on data this and data that on his webpage. One day he was writing some code and he couldn’t remember how to do a certain task. So he said to himself, “What would Google do?” To his surprise when he looked up his error on Google, the first result was his own homepage which taught him once again how to do said task. This was a huge relief to hear even the best coders can’t remember how to do everything and need to look things up on occasion.

Now for the list of all lists. We started our course actually learning how computers work behind the scenes with the command line or terminal. This was really interesting, because it brought me back to my childhood when you had to use DOS to start a computer game. Next we started learning Python, which is one of the most powerful and useful computer languages currently. While exploring Python, we also started learning statistics. At the end of that week we discussed ethics, which was one of my favorite days. We discussed how we will save the world with data science. But no, seriously, it is good to think about what role you want to play in this world. How can we improve the world for other people, and why it is important to be responsible with our data?

We began to dive into data science next, learning how to explore and clean data. While learning EDA, we also practiced how to present our data in a manner that most anyone could understand with the help of diagrams or charts. Now the fun part! We began to model our data to learn what it is telling us, and we started with Linear Regression. Next we moved into how to model categorical data with Logistic Regression. Along with learning several other models, we learned how to tune them and what to look for to achieve the best results. Following modeling, we explored how webpages are built using HTML and how to collect data from them with APIs and web scraping. Natural language processing was very cool to learn about which is how computers read our minds. Like when you start typing something into Google, and it already knows what you are gonna say next… After learning about all the clouds that are filled with our data, we played with AWS to learn how server farms and the “cloud” actually work. We also learned something most people would pretend to know about these days, like crypto currency. I may never understand crypto currency, or why any money is actually worth whatever everyone seems to think it is worth. We learned a couple other models along the way and spells to create data with magic (bootstraping). This brings us to our current topic, machine learning, which is my favorite topic so far actually. And also the point of this long winded blog post. Of all the things we have learned so far, I think I would like to learn more about this in the future. And if I can get really good at it, I’d like to work in this niche of data science. Another area I really like, which we will cover next week, is SQL. That was the first “language” I learned on this short yet challenging journey. I really think database architecture is very interesting. Maybe this is why I like neural networks. You get to design the shape and size of them. In turn this affects how fast they run and how much data they can process.

I am not sure what I will end up doing in data someday, but I do know that what I have learned at General Assembly will surely help me with wherever I end up!