My entire data journey so far to remind myself how far I've come and what else I have to learn 😄
coming up: uploading my own project for each concept listed
- Python library- pandas, numpy, matplotlib, seabron, plotly, altair, sklearn, streamlit, prophet, neural prophet, etc.
- Visualization- graph types and interativeness customization with altair, converting altair visuals to html for deployment, streamlit to create quick web applications.
- Machine Learning- Implemented ensemble Time series prediction model to predict ADV with 90% accuracy. Implemented Sentiment analyzer model to assess sentiment from formal and informal English and Malay. Implemented customer segmentation model for targeted marketing purpose.
- Web scraping- Implemented end to end web scraper in AWS, scraping data on an hourly bases with VPN.
- Building data pipelines: Using python, AWS Lambda, AWs Redshift, CRON scheduling, encrypting Personally identifiable information (PII) data columns
- Web developemenmt: HTML, CSS, JavaScript, AWS (compute, storage, network routing, Authorization)
- Algorithms and datatypes: binary search, linear search, how array and linekd list work, big O notations, selection sort algorithm, stacked and queue data structure, quicksort (divide and conquer), hastable and how they work (collisions, load factor, hash function)
- Maths- Bayesian statistics, hypothesis testing, probability sampling, statistical significance, designing tests, inferential statistics
- Others: Data mining, processing text data, understand APIs, filetypes (json, parquet, html, pickle, ...)
- Rust, MLOps, DevOps, Cloud computing, Handling big data hadoop, spark.