This folder contains projects from my BDA-594 course at San Diego State University (SDSU). The course summary is listed in the SDSU Fall 2020 catalog as:
BDA 594 - Big Data Science and Analytics Platforms
Big data science to include analysis, data collection, filtering, GIS, machine learning, processing, text analysis, and visualization. Computational platforms, skills, and tools for conducting big data analytics with real world case studies and examples.
- Matplotlib_Demo: This folder contains two IPython notebooks that demostrates the Matplotlib data visualization tool.
- Matplotlib_Demo.ipynb Contains the Matplotlib demo with no answers to practice on.
- Matplotlib_Demo_Answers.ipynb: Contains the Matplotlib demo with the answers.
- Web_Exercises: This folder contains the web exercises for this course.
- WebExercise_1_GitHub: This web exercise contains files related to learning GitHub.
- WebExercise_2_R: This web exercise contains files related to learning R and R Studio.
- WebExercise_3_Tableau: This web exercise contains Tableau workbooks and images exported from them. To view my published Tableau workbooks, check out my Tableau Public account.
- WebExercise_4_AWS: This web exercise contains files related to using AWS, MongoDB, and the Twitter API.
- WebExercise_5_ArcGISOnline: This web exercise explores practicing ArcGIS along with creating HTML pages and websites.
- WebExercise_6_Gephi: This web exercise explores practicing Gephi, a network analysis visualization tool.
- WebExercise_7_Video&TopicModeling: This web exercise has two parts: (1) practice creating screen recorded videos and (2) generating topics using Latent Dirichlet Allocation (LDA) model.
- WebExercise8_ArcGISInsights&JupyterNotebook: This web execise looks into using ArcGIS Insights and Jupyter Notebooks (specifically ArcGIS API for Python).
With this day and age, data is increasing exponentially. Big data simply can be described as the process in collecting, processing, and analyzing data.
The size of "big data" can be subjective. However, collecting data may not be an easy task. A method in collecting data needs to be considered when dealing with cases like population and geography.
"Grooming" data will be necessary too before analyzing it. This could mean cleaning up databases or sythesizing data together from different sources.
Lastly, analyzing the data is necessary to gain a conclusion. Trends may be observed and more questions may come up to continue studying further.
Big data definitions gathered from the BDA-594 class:
