Shazam! statue Charles Schwab with MSBA students

Transforming into an Analytics Shazam!
Winter quarter defined my MSBA student experience

There is one quarter in the M.S. in Business Analytics program that transformed my thought process and prepared me to excel in this field.

These five winter quarter courses satiated my analytical soul in several ways: Machine Learning, Advanced Statistics, Big Data, Organizational Issues in Implementing Analytics Behavior, as well as the year-long Practicum.


With the MSBA program located in San Francisco—a global analytics and tech innovation hub—we have been fortunate to meet, network and hear from top industry experts at meet-ups, during our practicum projects and as guest speakers in class.

Transforming into an Analytics Shazam!

In our Practicum course, for example, lecturer Sanjay Saigal recently invited industry experts from the analytics team at Charles Schwab. Senior Vice President of Analytics and Business Insights John Carter was joined by Manager of Strategy Analytics Sonic Prabhudesai, an alum of the inaugural MSBA class in 2018 who worked on the Practicum project for Charles Schwab and then joined the financial services firm.

For two hours, the Charles Schwab executives guided us through a deep dive into practical analytics in the finance industry. Carter emphasized that asking the right questions is key to establishing the correct logic, gathering the required data, and building models using business context.

They outlined four steps to approach an analytical problem:

  1. List all possible scenarios that can influence the decision.
  2. Brainstorm in a team to figure out what is most significant and what can be eliminated.
  3. Start asking questions and gather information about the available data.
  4. Repeat as much as needed.


The Machine Learning core course taught by industry expert and lecturer Noah Gift builds a strong foundation in problem solving using a variety of emerging analytical techniques. It helped me establish a technical toolkit, enabling me to conquer challenging problems.

Some classmates had non-technical backgrounds, so initially the class seemed daunting. That quickly faded as Gift dispelled any unknown fear in us, and, more importantly, taught us how to adopt new technologies through repetition.

My favorite quote from Noah Gift in Machine Learning:
 “Practice more now to bleed less later.”

With practice, we ramped up on Python and machine learning techniques, allowing us to leverage our new techniques to quickly develop insights and recommendations.

Two big takeaways from this class:

  • Any technology can be daunting, but only when it is unknown. Once we jump in and start practicing, it becomes a tool to solve our needs.
  • Storytelling is more important than analysis. This became evident when we relayed our solutions to our classmates and received feedback from industry professionals.


One of the most challenging analytical skills in our field is observing trends and patterns in data sets. It’s difficult to take a bird’s eye approach, or a deep dive, and ultimately choose the right model with sound judgment. Data scientists must appreciate the value of experience.

Linear regression analysis

Professor Prasad Naik’s Advanced Statistics class helped us establish new problem-solving techniques as he introduced us to mind-blowing concepts in mathematics. Starting with linearity in linear regression (sample picture) and moving into advanced time-series analysis using the ARIMA model, this class proved challenging and useful in applying the statistical concepts while building our model.

Using real-world cases, Professor Naik explained how companies decide the price-point of a new product using conjoint analysis, and he showed us how to predict the customer churn and discover seasonal patterns in sales.

He can explain any complicated statistics topic to a 15-year-old. I bet he’s one of the rare few who can explain Kalman Filter in a simple manner. My 100-page notebook was not sufficient for his class.

My two biggest takeaways:

  • Use sound judgment to select the appropriate model for each analytical case. Deconstruct the data to reveal the hidden parameters, then make your selection carefully. The use of the wrong model could lead to a disastrous prediction.
  • With dimensionality reduction and shrinkage methods, linear regression could turn out to be one of the most powerful prediction methods we use in statistics. That’s because we are eliminating unwanted parameters and reducing our chance for errors.


Big data is a business buzz-word de jure, but not well defined. How big is big data?

MSBA lecturer Andy Barkett, a UC Davis MBA 2009 alum, describes it simply: “Big data is any data that cannot fit into a single computer.”

With this in mind, Barkett exposed our class to techniques such as Hadoop and Apache Spark that are used to handle big data’s volume and velocity.

My biggest takeaways from the Big Data class:

  • Use cloud-based, or distributed systems when your data can’t be analyzed on a single computer.
  • Before choosing any big data technology, be cognizant of the alternatives. List each alternative’s pros and cons and choose the one that best fits your requirements.


In my childhood, history class taught me a vital truth: “when we don’t learn from the mistakes of the past, we repeat them.”

Associate Professor Gina Dokko’s course on Organizational Issues in Implementing Analytics took our group on a deep rewind through history so that we too could learn from past mistakes. Our journey saw kings and kingdoms morph into CEOs and organizations. Canons and muskets quickly turned into an invisible war in the 20th century as companies sought advantages in data capture to create new market revenues.

Professor Dokko’s highly interactive class delved into the strategy, the culture and the practices of several organizations, and we saw what led to their successes or failures. This became an essential learning experience.

My two biggest takeaways:

  • In order to avoid future pitfalls, be aware of an organization’s successes, and understand their failures too. Sometimes the greatest lessons come from near-failures.
  • Data has a strong potential to influence business trends, yet analytics without ethics could lead to disruption or corruption. Such unethical analysis could be a cancer to the entire organization.

Looking back at the winter quarter in the rear view mirror, I feel like I transformed into some sort of superhero, testing my newfound powers. I’ve emerged as a data-conquering, real-world problem-solver ready to take on industry’s toughest challenges.