I joined the MSBA program at UC Davis in Aug 2021, with a lot of preconceived notions of what constitutes being an analyst. In hindsight, most of my options were based on my own Hubris, having spent two years at an early-stage startup. Over the last 6 months, I’ve had…

There are two significant stages of team building. The first is to form a rockstar team. Our practicum project here at the MSBA program of UC Davis has given me a team full of individuals willing to learn and grow together. …

Standardization or Normalization of quantitative features is a standard step in a machine learning project. But, we seldom worry about what kind of feature scaling technique to use in our project. This article by Shay Geller discusses in detail how choosing an appropriate scaling technique can increase the accuracy of…

In this article, I will briefly describe the process of application lifecycle management. I will also talk about the role analytics can play in the process and the advantages of including analytics stakeholders early in the process. Application lifecycle management (ALM) combines all the resources required to manage an application…

In this day and age where data rules the world, being a data analyst is like changing the world in the middle of a storm. The field is getting more sophisticated day by day, and that is why I decided to join the UC Davis MSBA program last year. This…

In the part 1 of this article series, I went over some basic methods of forecasting utilizing time-series data. In this article, we will be going over some of the more mathematically advanced methods of time-series forecasting. Holt’s linear trend method This method, developed in 1957, is an extension of…

Interpretation of a Machine Learning model has been a longstanding issue. Many methods have been proposed over the last few years to use alternative approaches to the interpretation issue. I recently came across an article which uses SHAP (or Shapley Values), first introduced in 2017 in this paper. SHAP or…

Time series data is ordered data points collected at regular intervals. It can also be described as discrete-time data. Time-series data is ubiquitous and is used in various fields like statistics, finance, earthquake prediction, astronomy e.t.c. Time-series analysis is a specific way of analyzing time-series data. The most common form…

I have written about Quasi-experiment designs in my earlier articles. Difference-in-Differences (DiD) is a kind of quasi-experiment design, utilizing time-series data to analyze the change in outcome over time between a population in the treatment group vs a population in the control group. DiD is generally implemented in settings where…