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Hi šŸ‘‹ , I'm Gal Beeri

About Me:

Data analyst with a startup background and passion for technology, learning, and helping others. I spent the last 1.5 years as the Head of Customer Success and Chief Operating Officer of disability FinTech startup, SpendAble.

I am currently studying data analytics at Monash University, and through self-learning, I developed skills and have experience with MySQL, Python, Power BI, Excel - data models, pivot tables, advanced functions, power query (M language), DAX, JQL, and terminal. This has furthered my desire to pursue a career in data analysis, to improve business processes and achieve results for clients.


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Connect with me:

gal beeri gal beeri gal beeri Kokolipa

  • šŸŒ± Iā€™m currently up-skilling my analysis capabilities at Monash University

  • šŸ’¬ Ask me about Pandas, NumPy, Jupyter, Excel

  • šŸ“« How to reach me [email protected]

Languages and Tools:

Languages

python SQL

Analaysis, Machine Learning, Math, and Database Libraries & tools

pandas PySpark numpy sk-learn scipy excel
mongoDB mysql postgresql SQLite SQLalchemy APIs

Visualisation tools

PowerBI Tableau Dash-Plotly matplotlib seaborn

Webpage Creation and Productivity tools

Flask html5 css3 git bash figma

Ā  kokolipa

Ā  kokolipa

Ā  kokolipa

Gal Beeri's Projects

belly-button-challenge icon belly-button-challenge

Building an interactive dashboard to describe and explore the Belly Button Biodiversity dataset using JavaScript and HTML. The dataset reveals that a small handful of microbial species (also called operational taxonomic units, or OTUs, in the study) were present in more than 70% of people, while the rest were relatively rare.

credit-risk-classification icon credit-risk-classification

Using various techniques to train, evaluate, and identify the creditworthiness of borrowers with logistic regression on the loan and risk historical dataset.

crowdfunding_etl icon crowdfunding_etl

Building an ETL pipeline using Python, Pandas, and other libraries and dictrionary methods.

cryptoclustering icon cryptoclustering

Using unsupervised learning to predict if crypto currencies are affected by 24-hour or 7-day price changes.

fraudulent_transactions icon fraudulent_transactions

Creating a sophisticated web application for transaction analysis, incorporating ML, Bootstrap, Dash, and Plotly. Users can seamlessly upload credit card CSV files, exploring transactions interactively in both tabular and dashboard report formats.

nasdaq_100 icon nasdaq_100

Creating an interactive dashboard to explore the Nasdaq 100 share index using APIs to call the data, Python and SQLAlchemy to clean and transform the data, as well as loading the data to a SQLite database using SQLAlchemy's ORM.

nosql_challange icon nosql_challange

Analysis of the UK Food Standards Agency, in particular food hygiene ratings using NoSQL and Bash. Importing the json data using Bash and using NoSQL to perform CRUD operations on the json data as well as exploritory analysis to answer the questions specified in the readme file

pandas-challenge icon pandas-challenge

This project provide area-wide analysis for school budgeting as well as students scoring (reading & math) for 15 schools. The analysis aggregates the data and showcase obvious trends in school performance.

python-challenge icon python-challenge

This project inlcludes python scripts to analyse two different datasets (PyBank & PyPoll) and provide a text file output containing the results. The first dataset, budget_data.csv, contains financial records of a company in two columns: "Date" and "Profit/Losses".

sql-challenge icon sql-challenge

Design the SQL tables and dependencies to hold the data from the CSV files, import the CSV files into a SQL database, and answer questions about the data.

sqlalchemy-challenge icon sqlalchemy-challenge

Using SQLAlchemy to do basic climate analysis and data exploration. Specifically using SQLAlchemy ORM queries, Pandas, and Matplotlib.

tableau_citibike icon tableau_citibike

Using Tableau, this project analyses CitiBike's July 2023 data to understand user behaviour (Python is employed for ETL processes). Objectives include user segmentation, popular stations, ride characteristics, and geographical concentration. Key findings are summarised in two dashboards for member and casual users.

web_scrapping icon web_scrapping

This project contains two web scrapping methods: scrapping a webpage and scrappping a web page table for analysis purposes

world_weather_analysis icon world_weather_analysis

Creating a Python script to visualise the weather of over 500 cities of varying distances from the equator using citipy Python libraryLinks and OpenWeatherMap APILinks to create a representative model of weather across those cities.

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