MSBA Students Hit the Ground Running with Foundation of Analytics Bootcamp
Short course gives you all the tools needed to succeed in the MSBA program
It’s a three-week workout for your brain. That’s how I’d describe the UC Davis Master of Science in Business Analytics (MSBA) Foundations of Analytics short course offered to new students before entering the program.
But unlike those intense, 4-hour workouts that you sign up for after Thanksgiving to burn calories, this one is actually fun. At least that was the case for me.
Like many of my classmates, I was curious what my transition to the MSBA program would look like. But I instantly learned many valuable lessons that would give me the boost I would need to kick-start my new journey.
Unlike an intensive coding bootcamp, where you start from scratch, the three-week Foundations of Analytics course assumes that you have already been exposed to programming. And before I arrived at UC Davis, I practiced with open-source programming like R and Python. So I was ready for my new challenge.
In addition to the curriculum, we met a new network of incredibly talented people.
“This class a great segue into a concentrated program because it helps everyone get on the same page.”
– Abhinav Chatterji MSBA 19, Data Analyst, Amazon Web Services
In my opinion, he’s what makes this course so fun. And he’s successful because he’s:
- Approachable and patient, which comes in very handy during the calculus sessions.
- Able to teach you both coding systems, starting in R and giving you the option to code in R or Python, which is amazing when you already have a preference.
- Sharing his knowledge and getting you ready for the course. That includes sharing a lot of content before the class, such as cheat sheets, his slides and hand-written notes.
“What I learned in the first three months of training at my job is the same as what I learned in Mehul Rangwala’s first week of class.”
– Aravind Venugopal, MSBA 2020
This short course covers important industry-appraised topics such as data wrangling, calculus, statistics and linear algebra. Covering these topics early in the program frees us up to take a deeper dive into machine learning and business analytics subjects in future lessons.
Let’s take a look at the benefits of each topic:
Real-world data can be very messy. It can have a lot of missing fields, redundant variables and/or inconsistent formatting. This is why it is crucial to be able to clean complex data sets and arrange it into a form that can be used for analysis—that’s data wrangling. It’s where analytics professionals spend the majority of their time. Fortunately, this MSBA short course made data wrangling an essential part of the bootcamp and we spent a lot of time getting up to speed learning helpful techniques in both R and Python.
Unfortunately for those who don’t enjoy math as much as I do, calculus is essential to understanding and solving real-world problems. From calculating marginal profits to diving into optimization problems, you need to understand how functions work and what they look like in order to solve these types of problems.
Calculus is also critical for understanding how to apply different models. For example, logistic regression can use a method called “gradient descent” to find the minimum loss function. This cannot be done properly without understanding essential concepts such as derivatives, limits, and the chain rule, all of which were covered in our three-week course.
Furthermore, there cannot be analytics without statistics. You may know all of the machine learning models and how to code them in any language of your choice, but you use statistics to approach and solve problems. Statistics also makes you think about your data and question it in a more holistic way. Such as, what does the distribution of your data look like? How does this affect the problem?
The more you dive into the analytical world, the more you rely on your core knowledge of statistics. Rangwala ensured students in his course have the right statistical tools necessary to succeed in the more advanced topics later in the program.
This topic is actually fun. Yes, you read that right: f-u-n.
From matrix operations to vectors and eigenvalues, this was one of my most enjoyable parts of the bootcamp. The visual aspect of these exercises make them more interesting than the typical math problems, and they’re fundamental for understanding neural networks. This is important to know because applications like Spotify and Facebook use neural networks to formulate song recommendations and friend suggestions. See? That’s a cool fact to learn about.
This may seem like a lot, and you’re probably asking yourself, “How am I going to get through all of this in such a short period of time?” Well, don’t worry because you will have a strong network of incredibly talented people to help you along the way.
Most everyone in this program comes from a diverse background, but that’s actually a huge advantage. The best group projects come together when you get to learn from everyone around you. And no matter your experience, this bootcamp will help you create a foundation for success, sharpen your skills and you’ll make new friends and feel supported along the way. It’s truly a great experience.