Miga Budaasuren
Data Analytics Portfolio

In this project, I have cleaned the raw data of diet from The World Health Organization's website using PostgreSQL and then transformed the cleaned data for the purpose of the analysis. Also, I have created data schema for relational database.

Tools/Languages : ETL, Relational Database, Table Schema, PostgreSQL

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In this excel challenge, I completed the data analysis by using the below methods.

• Conditional formatting
• Pivot table
• Excel built-in functions like COUNTIFS() etc
• Statistical analysis like mean, median, max, min, variance, and standard deviation
• Manuplating the datatypes
• Filtering with various fields
• Visualizing the results using various types of graphs

Tools/Languages : Excel functions, Pivot table

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In the data modeling section, inspected the CSV files, and then sketched an Entity Relationship Diagram (ERD) of the tables using QuickDBD.
In the data engineering section, used the provided information to create a table schema for each of the six CSV files.

• Specifing the data types
• Specifing primary keys.For the primary keys, verified that the column is unique. Otherwise, created a composite key which takes two primary keys to uniquely identify a row.
• Specifing foreign keys, and other constraints.
• Created the tables in the correct order to handle the foreign keys
• Imported each CSV file into its corresponding SQL table

Tools/Languages : ERD (entity relationship diagram), Relational Database, Table Schema

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Created a script that loops through all the stocks for one year and outputs the following information:

• Yearly change from the opening price at the beginning of a given year to the closing price at the end of that year
• The percentage change from the opening price at the beginning of a given year to the closing price at the end of that year
• The total stock volume of the stock
• Make the appropriate adjustments to the VBA script to enable it to run on every worksheet (that is, every year) at once

Tools/Languages : VBA Scripting

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Used Python and SQLAlchemy to do a basic climate analysis and data exploration of the climate database. Specifically, used SQLAlchemy ORM queries, Pandas, and Matplotlib. After completing the EDA, designed a Flask API based on the queries that I developed.

Tools/Languages: SQLAlchemy (ORM Queries), Flask API, Jason, Python, Pandas, Matplotlib

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Created a database using MongoDB, then updated the database and completed EDA.

Tools/Languages: NoSQL,MongoDB, PyMongo, Python

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Identified HTML elements on a page by using inspect, identified their id and class attributes, and used this knowledge to extract information via both automated browsing with Splinter and HTML parsing with Beautiful Soup. Scraped various types of information from the web including HTML tables and recurring elements.

Tools/Languages: HTML, Splinter, Beautiful Soup, Python

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Used Python requests, APIs, and JSON traversals to complete the analysis. Used the OpenWeatherMap API to retrieve weather data from the cities. Then, visualized the results using holoviews and hvplot.pandas.

Tools/Languages: API, api_keys, JSON traversals, holoviews, hvplot.pandas

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Created a Python script to analyze the two datasets.

Tools/Languages: python, os, csv

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Analyzed the district-wide standardized test results using student's math and reading scores, as well as various information on the schools they attend. Then, aggregated the data to showcase obvious trends in school performance

Tools/Languages: Pandas, Jupyter notebook

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