I like to collect blog websites that have nice design or interesting topics. Not only does the website itself attract me, but also people who built the website. I admire them for spending time on designing their own website and publishing contents constantly. I always imagine how nice it will be if I can build a blog website for myself one day. However, I always flinched at the complex knowledge of HTML, CSS, PHP, and JavaScript.
Last week, I finished my final assignment in IBM Data Science course, which is to find an ideal suburb for opening an Italian restaurant based on location data. During the process, I web-scrapped property median price (i.e. House buy/rent and Unit buy/rent) for each suburb in Sydney and plotted them on Choropleth maps, respectively.
However, I was wondering if it is possible to combine all these maps in one and select one of them just by clicking name from a dropdown menu.
I have been studying the IBM Data Science course from Coursera for the past several months. After learning all essential skills and tools in Python for this course, here comes the final assignment. The goal of this assignment is to define a business problem related to the city of my choice and then solve it by using the Foursquare location data.
Although this is an assignment, it closely resembles a real-world problem in which a data analyst would be required to solve in his/her daily job, which involves problem definition, data collection, data cleaning, data analysing, data visualisation, and report forming.
Recently, I started converting from R to Python as I found out that more and more my daily data analysis could be smoothly handled by Python, especially when the data size is getting huge and requiring considerable computing power. I could fully utilise the computing power from remote servers by running Python on them. During the learning process, I learned about Jupyter Lab and impressed by its clean and simple design.