🗺 Portfolio Guide
Hello
This is my Portfolio Summary where I provide a walkthrough to all of my forays into data analytics projects, courses, problem-solving, interviews, etc... I'll track everything here with a quick note on what each project does.
Feel free to chat with me on LinkedIn about my projects!
📚 Table of Contents
Click on the project's title (bold and coloured in Blue) to view my projects! Thank you!
Python
Skills: Data cleaning, wrangling, visualisation, analysis
Libraries: pandas, numpy, matplotlib, seaborn, scikit-learn
Project Name | Area | Description | Libraries |
---|---|---|---|
Data Wrangling, EDA |
A project close to |
pandas, seaborn, matplotlib | |
Data Wrangling, EDA, Machine Learning |
Analysis using NLP techniques to interpret text data that I was working with in my PhD and to learn some techniques I was going to use in other context. The text used to perform this analysis was a transcript of a chemistry class for undergraduation students. |
pandas, scikit-learn, pickle, textblob, spacy, re, string, numpy, wordcloud, matplotlib |
R
Functions:
Libraries: ggplot2
Project Name | Area | Description | Libraries |
---|---|---|---|
Data Visualization | This was developed during my PhD. I will keep it private until fully prublished. | ggplot2, dplyr, lubridate |
SQL
Click HERE for my SQL specific summary. I'll keep here just the more relevant stuff.
Knowledge: handling NULLs functions, subqueries, nested subqueries, inline views, subquery factoring (WITH clause), row limiting clause, aggregate functions (count, avg, sum, min/max, etc..), CASE WHEN statements, analytic functions (windows, partitions, rankings, listagg, lag, lead, first, last, first_value and last_value), set operators (union, union all, intersect and minus), joins (inner, outer, cross, self, equijoins, non-equijoins), hierarchical queries (level, connect_by_isleaf, connect_by_root, sys_connect_by_path), procedural functions, procedures.
Project Name | Description | SQL 'Flavour' |
---|---|---|
Documentation of a SQL course I took. Most courses, if not all, focus too much on “telling” or “demonstrating” how to do things, but don’t give the student the opportunity to test and practice what is being taught. In this course, every lesson has a coding task to practice what I was learning. You will notice that some of the queries are a little off topic because I was exploring learning possibilities within the lesson. Course content: 19 sections, 210 lectures, 16 h and 5 m total length, XX exercises. |
Oracle SQL | |
I've analyzed this dataset which is the historical sales of a supermarket company in Myanmar which has recorded data from 3 different branches for 3 months data. I've draw conclusions in which I believe there are meaningful opportunities in Naypyitaw (Branch B) to increase revenue. |
MySQL Tableau |
Power BI
Click HERE for my BI specific summary.
Functions:
Libraries:
Project Name | Area | Description | Placeholder |
---|---|---|---|
Power BI for Business Intelligence | [Placeholder] | [Placeholder] | [Placeholder] |