I’m glad to see you here. My name is Payam Bahreyni and I will be talking about interesting topics here, which usually happen to be mostly quantitative. Although I love number crunching, I’d like to do it in a practical way and in general, I prefer understanding concepts by intuition and learning them by doing, rather than deep diving into the math equations behind them. So, if you would prefer to apply concept to solve problems instead of working on the proof for the math theorems and algorithms you have come to the right place.
I have always been interested in computer programming and have had good relationships with Math and Physics courses in middle schools and high school. I love hands-on learning and after a couple software applications and programming languages got admitted into Computer Science program at one of the best universities of Iran, IUST for my undergrad, and then into Sharif University of Technology, known as Stanford of Iran, to get my master’s.
Later on, after doing a couple projects in software engineering, I felt almost ready to learn business hands-on. So, formed a team to work on an enterprise software project. It was a great learning opportunity, but it didn’t go too far. The second company I co-founded provided vehicle tracking systems and is still around.
After getting some understanding of how businesses work, I started my MBA at University of Arizona, with a concentration in Entrepreneurship and Finance. Once again my love for number crunching gave me the opportunity to work on a couple interesting projects.
The first one was about demand forecasting for an interpretation call center, CyraCom, where we forecasted daily volume of the calls with 90+ % accuracy. I was the Data Analyst of the team. On my second assignment, I worked with the CEO of a SaaS startup, LawLytics, and gathered company financial data from multiple sources and forecasted financial performance of the company. For my next quantitative project, I analyzed the performance of an investment portfolio managed by our class, to separate the effects of sector weighting, stock selection, and timing using attribution analysis.
At this point, it was pretty clear to me that I would love to combine my technical knowledge, business acumen, and love for numbers to bring business value. Continuous challenge and hands-on learning have been a natural part of all these projects, and the main reason that I opted into them in the first place.
It’s been a while that I’m doing diverse projects in quantitative analysis and data science, mostly as part of my coursework in Coursera Data Science Specialization. I follow two main goals for this blog. The first one is to keep track of what I have done and the decisions made along the way. The code will be available through GitHub, and I always seek your comments and advices on these projects. Please feel free to reach out if you had any questions or comments on these projects.
The second and more important goal is to give back to the community. I hope to pave the road for aspirant data scientists and quantitative analysts to get started in their journey, by sharing my experience and pointers to the resources I deemed valuable.
I provide the description and discussions around the Projects I have worked on. You will find step-by-step instructions in the Tutorials section and quick fixes and workarounds in the Tips and Tricks.
Once again, I’m glad you’re here with me and look forward to your comments.