One of my final projects for the Data Analysis and Knowledge Discovery course consisted of a side-by-side comparison tool to explore the 2019 ratings of any two schools or districts in Texas. It was originally created with Excel using advanced look up functions and formulas, etc. but has now been transferred to Google sheets. My plan is to make an improved, web based version of this tool in the not too distant future.

The course utilized the book Microsoft Excel: Data Analysis and Business Modeling and it was significantly utilized throughout the course. The course began with an introduction to data analysis and then then covered data mining, text mining and knowledge discovery principles, concepts, theories and practices.

For the second part of the course (Text Mining and Classification), the primary tool utilized was RapidMiner. The no code (node based) environment was new and different for me.

My final project for this part of the course was a text analysis of Google’s Python Crash Course Coursera Reviews for the year 2020. The original data set file for this project was obtained from Kaggle and it was collected by Muhmammad Nakhaee. The text analysis techniques utilized for this project were association analysis and cluster (k-means) analysis. Here is the detailed report of the analysis which details the process for both techniques using RapidMiner and the conclusions of the analysis.

Tools Utilized: Excel, RapidMiner
Skills Acquired/Developed: Spreadsheet Modeling Basics - Lookup, Index, Match Functions, Pivot Tables, Array Formulas, Charts and Dashboards, Data Mining Basics - Data Prep, Correlation Methods, Association Rules, K-Means Clustering, Discriminant Analysis, k-nearest neighbors, Naive Bayes, Text Mining, Decision Trees, Neural Networks

Apart from that, my interest in data analytics and data science continues to grow so I completed another quick intro/review of the basics – (Learning Data Science: Understanding the Basics). I feel a bit conflicted. I don’t see myself fully in information science but I don’t think I would survive the data science. I’d like to continue to explore working with data but in a more applied path/role.