Project Samples

This project sample examines exploratory data analysis and data cleansing through an introductory project in Natural Language Processing (NLP) Machine Learning (ML). Exploratory data analysis provides the analyst with a good understanding of the dataset they are working with, and can provide valuable insight that can guide the data mining process. For NLP, the data analysis will help highlight some of the areas of the dataset that must be cleansed. Once the process for cleansing the data is completed then a pipeline can be created to automate the text processing and ML steps. This project will explore the competition Natural Language Processing with Disaster Tweets dataset, and will produce a submission for the competition. Additionally, this project highlights the need for data analysts to be continually focused on learning about new research, techniques, and other data science information.

This project sample explores using Python, Python libraries (Pandas and Selenium), and Google Maps to provide a simple but efficient tool for taking the scrapped web content data and turning it into usable business intelligence. Web content scrapping offers access to a wide variety of useful data that can potentially provide a competitive advantage for an organization.

This project sample explores using Python and Python data visualization libraries (Matplotlib and Seaborn) to explore the usefulness of data visualizations within a presentation. To accomplish this, the topic of overfitting predictive models is explored through the usage of hypothetical datasets. Data visualizations within presentations allow complex information to be quickly and effectively communicated to individuals. Data visualizations can play a vital role in business intelligence analysis.

This project example presents the usefulness of data exploration within data analytics and business intelligence. Data exploration can reveal significant amounts of useful information, making it a valuable tool to utilize on a regular basis. Occasionally data exploration can answer the business questions a larger data mining project set out to answer. This essay provides a simple example of how Tableau can be used to quickly and easily explore a dataset to come up with a potentially actionable piece of business intelligence.