Focus area: Data analysis using Python.
Period: Aug - Oct 2021 (estimated completion)
At the end of the course, these are my 🔑 key learnings:
- Learn the data analysis process of wrangling, exploring, analyzing, and communicating data, and work with data in Python, using libraries like NumPy and Pandas.
- Apply inferential statistics and probability to real-world scenarios, such as analyzing A/B tests and building supervised learning models.
- Learn the data wrangling process of gathering, assessing, and cleaning data and use Python to wrangle data programmatically and prepare it for analysis.
- Apply visualization principles to the data analysis process and explore data visually at multiple levels to find insights and create a compelling story.
Click on the project title to view my projects! 🙂
In this time-series analysis, I use moving average method to analyze local and global temperature data and compare the temperature trends where I live to overall global temperature trends.
A study on red and white wine samples and understanding whether certain types of wine and their qualities (alcohol level, sugar content and acidity level) are associated with higher wine quality
Analysis on vehicles’ fuel economy in 2008 and 2018 to understand usage of alternative sources of fuel, changes in greenhouse gas and smog ratings over the decade, and vehicle features associated with better fuel economy.
Analysing more than 10,000 TMDb movies and getting the answers to - Which actor(s) is associated with higher revenue and profit, Does a higher budget constitute to a higher profit, and Which director produced the highest grossing movie?
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent