Dennis Rush
Data Analyst Portfolio

Project Examples:

What being a data analyst means to me:

Any business with a product, service, or customer, generates data. The data that is produced is valuable, but, it can only be "of value" if the data is knowable and interpretable.

The roles of a data analyst are to:

1. Know the data and how that data can be utilized 

2. Organize the data in a way that is easy to understand and pass to others

3. Make the data available so others are able to explore the data independently and make their own discoveries 

4. Ensure that the data is reliable

5. Clean the data to increase accuracy and ease of exploration

6. Present the data in ways that provide valuable information (reports, dashboards and visualizations)

If you entered the world of data analysis within the last few years, you may notice that the roles of data analyst and data scientist are blurred.  You may assume that the role of a data analyst is mostly about predictive analysis using machine learning.  Although that can be a task of an analyst, an overwhelming majority of the role is knowing your organization's existing data so well that you can find KPIs, gaps, and trends that you then organize (through visualizations) in a way that communicates information in simple ways to others. 

A data analyst should be able to create effective visualizations that communicate effectively on their own, however, the role of an analyst is also to enhance the visualizations through verbal and written explanation. 

It is not enough for an analyst to create, for example, an accurate trendline. It is critical to be able to explain the influences upon that trendline and suggestions on how to manipulate the direction of the trend. The true value in a visualized trajectory is the ability to change your current actions to change the trajectory if needed. The only way to do that is to thoroughly understand the data behind the prediction.

Lastly, a good data analyst makes EVERYTHING look much simpler than it is.

Fields of Interest:

• Customer "in-store" behavior 

  • Market Basket Analysis

  • Event Data Analysis

• Agriculture data

• Biological data collections

• Human behavior data

• General survey data