Data Science Summer Program
As I said in my first blog, I learned much of the basics of data science through a summer program. The program lasted 2 weeks, and every day I was taught how to analyze data sets and observe certain present patterns. In the first week, our instructors taught us how to code in Python and view different data sets to look at patterns among them. Each day of the first week, we would dive deep into different, small-world issues and how we can use data to help create a solution. As we transitioned into the second week, we focused our learning on a specific world problem. In my case, I focused on world hunger and trying to find a way to feed our overpopulated Earth. We would work in teams of 5 and at the end, we presented our findings.
Throughout the first week, our instructors taught us the fundamentals of data science. They initially helped us with algorithmic thinking to get us to create out-of-the-box solutions. Once we were able to find our solutions for different problems, they introduced us to data science through Python programming. We were given data sets and a problem, and we had to use the data sets to find a pattern and produce a solution. Then, we would arrange the data sets in order of importance to our solution. As the week progressed, we were taught different types of regressions to try and predict if our solution would work, mainly linear regression and logistic regression. All this work we completed during the first week helped prepare us for our second week, where we would use this knowledge and attempt to create a solution for a crucial world problem.
Going into the second week, we were given the option to choose between a variety of different problems. I chose sustainable farming and tried to figure out a way to grow enough crops to feed all 8 billion people on Earth. The lack of efficient farming is the main reason for the demand not being met. Modern-day farming focuses on the expansion of crops, but not the efficiency of them. Our dataset presented us with multiple farms and their microbe count and crop yield. We then trained our program to sort all the farms in the dataset by microbe count and crop yield to find which farms have the highest microbe count and will yield the most crops. Then, we researched many different crops to pinpoint the best possible crop to grow. We looked at the time needed to grow, calories per portion, and the greatest amount per harvest. In the end, the data indicated it was potatoes. Then, through linear regression, we were able to create a model and a line of best fit for our data to predict our crop yield per year if we were to grow potatoes on said farms. Ultimately, we designed a clear and concise plan to slow down the effects of world hunger. At the end of the week, all groups presented their findings and their solutions, and each person voted for which presentation they liked the most. Our group ended up winning and it showed me what power data science has to offer.
Ultimately, this summer program helped build my foundation in data science. The hands-on project we took part in helped show me what the work of a data scientist is. With these basics, I plan to expand my current knowledge and learn more about different ways to interpret data sets and use different regression models. I look forward to learning more about data science and broadening my perspective to use these skills in different fields of work.
Sustainable Farming Project: https://docs.google.com/presentation/d/1oeUVsMjstbw82_Vcy6UNJj5LA85FTLTDbDuCn4pKt0Q/edit?usp=sharing
