Microsoft Certified: Azure Data Scientist Associate Credential
Tableau Desktop Certified Associate
This repository contains data science and machine learning projects that I worked on as part of University of California, Berkeley's MIDS (The Master of Information and Data Science) program.
Link to MIDS Homepage.
These projects cover a range of topics such as:
- Objected-Oriented Programming (OOP),
- data gathering, cleaning, exploration and analysis (EDA), data visualization
- statistical inference, causality analysis and experiment design,
- predictive analytics, time series analysis and sentiment analysis,
- computer vision.
All high-level results and summaries can be found in README.md pages of individual project folders, along with full reports and underlying code.
Please use the table of contents below for quick navigation.
- Data Cleaning and Exploratory Analysis: Exploring MIT's Observatory of Economic Complexity data for long-term EU trade trends.
- Statistical Inference and Exploratory Analysis: Conducting causality and regression analysis of crime stats for North Carolina.
- Experiment Design and Statistical Inference: Designing experiment, collecting data and conducting statistical inference to determine causal relationship between stress measures and watching "stress reducing" clips.
- Predictive Analytics:
- Sentiment Analysis and Time Series Analysis: Using sentiment analysis of tweets to predict specific stocks' performance, based on historic data.
- Computer Vision and Deep Learning: Automated accident detection in CCTV footage to decrease medical help response time.
- Objected Oriented Programming: A simplified version of Texas Hold 'em implemented completely in Python using classes and functions.