MPP Data Science Final Project

Recently I wrote about my experience working through the Microsoft Professional Program in Data Science in my blog post Data Science Breakdown. I am so happy to announce that I successfully completed the capstone! More-so, I achieved the MPP Data Science Certificate!

“Was it difficult?”

The number one question I get asked is if the capstone was difficult. The short answer is “yes.” The capstone is designed to test the knowledge and skills gained from the classes in the course, as well as expand your horizons as to how you would deal with “real-world” projects. The next question I get is “Do you think I can pass it?” Absolutely… if you give it your all.

The capstone I completed was divided into three parts.
* data analytics
* building and testing a machine learning model
* write a professional report

The Sum of Three Parts

Part 1, Analyzing Data

The capstone really put me to work. Reviewing and analyzing data is very easy and straight forward for me. I really enjoy looking between the lines and finding the patterns that emerge. The capstone allows the student use of any analytics program they desire. For this portion, Excel was my choice, and it proved to be a wise one. Part 1 took mere minutes for me to complete. It was the second part of the capstone where the majority of my time and effort was spent.

Part 2, Machine Learning

In order to create a successful machine learning model, you will need to be proficient in your Google-Fu. Building the machine learning model took more knowledge and experience than I had gained in the course work alone. A large amount of my time was spent exploring various approaches and algorithms. Many hours were spent researching algorithms and trying to figure out the best ways to go about training the unruly model.

I was frustrated, tired, and admittedly ready to give up. REALLY AND TRULY. A thought occurred: “This is why they call it ‘Data Science’. I am sitting here trying to find answers along an untrodden path.” In that moment, I imagined this is what it feels like to be a true scientist. Just as in physical science, data science requires time, patience, research, trial and error.

Not being one to give in, I persisted until the best combination of algorithms was found. It was a profound relief to test the model against my data and to see it be successful.

Giving Thanks

So many people in the community helped guide me in the direction to find answers. Thank you to all who wrote blogs, tweets, whitepapers, and produced videos. Sharing your unique views and understanding with others makes us a stronger community.

Part 3, Professional Report

Armed with my data, my experiences, and my successful machine learning model, the final step in the capstone is to put it all together in a written report! Holy goodness. This was much more difficult than I had expected it to be. Your grade for the third part is dependent on other students and their assessment of your report. Yet another real-life experience to get you ready for a career as a Data Scientist!

The report took a few days to complete; the last day I worked a solid 30 hours straight on it. I could not sleep; was excited, terrified, stressed, and also just REALLY ready to get this completed. Once my report was submitted, I began reading the other students’ reports as assigned. The reports were outstanding. Those students brought to light information that had not shown itself in my project. Each report was vastly different; the story the data told the other three varied from each other and mine as well. How interesting that we all had the same data sets and somehow all four of us presented completely different stories!

Lessons Learned

Undoubtedly, the classes in the MPP Data Science course taught me a number of valuable skills while also having the unintended consequence of teaching me some things about…well…me.

When faced with something new and extremely difficult, I learned that I have the ability to rise up and learn and be successful. That the willingness to learn something new can set you apart from others.

At the end of the capstone, while reviewing other students’ reports, I saw opportunities for more learning as their approaches to the same material differed from mine.

Finally, I learned that when we work together for a common goal, we are stronger and smarter together.

We are stronger and smarter together.

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