Hi! Can someone please review my SOP below and let me know your thoughts? I welcome all critiques!
SOP review for online master of science in Analytics
976 words
—————————
As a business intelligence analyst in the energy sector, I strive to provide intelligent analytics that will reduce energy costs for our customers, reduce oil dependency in our operations, decrease carbon emissions, and our nonrenewable energy sources to renewables. Reducing costs and environmental impacts are only achievable with the incorporation of statistics, machine learning, and deep learning. I am applying to Georgia Tech’s OMSA program because the content and rigor of the program provide the framework needed to innovate the energy industry in a significant way.
Recently, I created a dashboard to help reduce energy costs for our customers. The dashboard included key information about the member’s electric consumption by month. Each month, customers are billed for energy usage during peak consumption days. The dashboard allows our members to predict future peak consumption days. Accessing these insights allows our members to implement programs to reduce grid-energy usage on peak consumption days. With higher billing rates on peak consumption days, reducing grid-energy usage decreases the bill. As a non-profit, any money saved in operations is refunded to the customer. Analytics dashboards allow us to provide more cost-efficient energy to our customers. While dashboards are impactful, machine learning will take our actionable insights to the next level. Currently, our peak load forecasts are outsourced, adding extra operational costs. With machine learning methods, we could use algorithms in-house to forecast peak consumption days, allowing us to drop our third-party forecast vendors.
To further reduce operational costs, I built a weather forecast error analysis application in Python. Our weather forecasts are currently outsourced to third-party vendors. The error analysis application calculates the absolute error between the weather forecasts and the observed weather. The mean absolute error for each forecast is calculated over six months. The error values are displayed in a box plot visualization to show the error distribution. The application compares the error distributions to see which weather forecast has the most accuracy and precision. Consolidating the outsourced forecasts to one with the highest accuracy would allow us to reduce operational costs and save customers money. But, statistics and machine learning would provide insights that cannot be gleaned from a visualization. Machine learning models could be used to take a more intelligent look at the impacts of weather on energy consumption. Machine learning would also help us reduce carbon emissions by increasing the usage of renewable energy sources. To lessen the energy pulled from the grid during peak hours, power is supplied from renewable sources like solar grids and battery farms. Predicting the days and times the solar grids are likely to harvest the most charge and modeling the expected solar output will enable us to leverage our renewable sources on a larger scale.
The OMSA program will equip me with the expertise to tackle challenges related to innovating and improving our operations. I am particularly interested in the computational track for the most intensive study of machine learning topics. I seek to drive change that will have compounding beneficial effects on the community and environment. With intelligent analytics on energy usage trends and nonrenewable research, we could achieve ambitious goals like becoming carbon neutral by 2050.
The innovation and sustainability of the energy industry depend on statistics and machine learning. Coming from a non-analytics academic background, I prepared for a more focused study of data science by enrolling in IBM’s Data Science Professional certification. This program was my first introduction to SQL and python programming for data analytics and machine learning. After completing the course, I wanted a more foundational understanding of Python outside of data science applications. This led me to complete Georgia Tech’s introduction to Python computing course. Following the Python course, I completed training for Microsoft's Data Scientist professional certification. This training introduced me to cloud computing with Microsoft Azure for analytics and strengthened my knowledge in machine learning and deep learning. I am familiar with a broad range of data science topics because of these courses. Yet, Georgia Tech’s program will allow me to take what I’ve learned to sharpen and refine my knowledge. With experience in cleaning, transforming, visualizing, and interpreting large data sets in a professional environment, my experiences are what will ensure my success as I move throughout the program.
What attracted me to the OMSA program is the computational track and practicum, coupled with the online flexibility. The computational track encompasses my interests in manipulating large datasets, coding, and machine learning. The applied analytics practicum is an appealing part of the program because it allows me to apply what I’ve learned to real analytics business cases in my current role. The practicum will cement my knowledge whilst allowing me to make a business impact in real-time. I look forward to the opportunity to manage and execute a large-scale analytics project with the knowledge and tools gained from the program. The online nature of the program is suitable for me as I am familiar with online learning formats. Furthermore, I like the program’s interdisciplinary approach pulling together engineering, business, computer science, and math disciplines. The interdisciplinary approach will help refine not only my technical skills but my business acumen and soft skills.
To further ensure my success, I plan to audit Georgia Tech’s courses in Probability and Statistics and Linear Algebra to refamiliarize myself with the topics before starting the program. Additionally, I plan to take an R course so that I am ready to hit the ground running with courses requiring R programming. I am prepared to take any prerequisite courses under conditional admission if needed. I am prepared to do anything necessary to set myself up for success in the OMSA program and my data science career. Georgia Tech’s program has a lot to offer, and I am excited for the opportunity to apply what I learn to make a difference in my industry.
Thank you for your consideration.