If you are a movie lover, you will surely know the two popular websites called Box Office Mojo and IMDb, the former tracks movie revenue across the globe while the latter is an online database information related to film, televisions, home videos etc.,. In this project, I use Python and Power BI to ingest, transform and analyze data from these real-world use cases. Particularly, the focus is on the analysis of all movies shown in Vietnam during the period from 2009 and 2024.
Sydney schools analysis with Tableau in two weeks
Having worked with several BI software and tools for couple years ago, and mostly with Power BI from my recent job, I have seen data visualization technologies growing incredibly fast, and to my knowledge, Power BI and Tableau are the first and second most popular business intelligence tool on the market. To be honest, I haven't even tried Tableau either for my work or study, but now I finally took some time to try my hands on with this software, and it's actually two weeks creating a dashboard from scratch.
How to create a DIY comment component in streamlit apps
Normally when we develop a Machine Learning model and use Streamlit to turn it into a web application, comment section is practically useful for us to get interactive discussion or feedbacks from the viewers. Unfortunately, at the time of writing this blog, it seemed like Streamlit does not offer any component to suit this purpose.
Building a resume scanning system using Python
Job seekers who are usually looking for advertising jobs sometimes overwhelmed with the jobs posted on the websites. A simple search mechanism in these web pages do not really respond a good match between their resume and the job descriptions. The job seekers would need to seriously study each job description and use the same language as the job posting if they want to be shorted for an interview.
Quick report using Pivot and Grouping sets in SQL
Consider a scenario you want to create a quick report from a table product in SQL showing total product sold over the last 12 months, in which each column represents the month, last column could be the YTD sales. There will be the horizontal sub-total for each category for example sale by products, regions etc.
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