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tiannaparris / repository
This is a repository that I have created to showcase skills, share projects and track my progress in Data Analytics / Data Science related topics.
Hi, I'm Tianna! I have an analytical background in Biochemistry and currently, I am on track to completing my degree in Data Science (MS. Data Science). I have developed a strong foundation in the life sciences and a passion for using data to uncover meaningful insights. I am excited to bring my technical and analytical skills to the field of data science as an entry-level data specialist.
During my studies, I honed my ability to work with complex data and developed a keen eye for identifying patterns and trends. I also gained experience in laboratory techniques, data management, and statistical analysis, which I believe will be valuable assets in my role as a data specialist.
In my free time, I enjoy exploring new data analysis tools and techniques, and I am always looking for opportunities to expand my knowledge and skills. Whether working on a team or independently, I am driven by the thrill of discovering new insights and the satisfaction of using data to solve complex problems.
My CV in pdf.
This is a repository to showcase skills, share projects and track my progress in Data Analytics / Data Science related topics.
In this section I will list data analytics projects briefly describing the technology stack used to solve cases.
Code: Analyzing the Factors Contributing to the Success of a Movie.ipynb
Goal: To determine what factors contribute the most to a movie's success.
Description: The project focused on analyzing a dataset of movies released between 1980 and 2022. The dataset included movie titles, ratings, genres, release dates, budgets, gross earnings, and other relevant information. The project involved loading the data, cleaning and preprocessing it, performing exploratory data analysis (EDA), analyzing the correlation between budget and gross earnings, and implemented Pearson’s correlation statistical analysis.
Skills: data cleaning, data analysis, correlation matrices, hypothesis testing, data visualization.
Technology: Python, Pandas, Numpy, Seaborn, Matplotlib, SciPy.
Results: Using Python functions the analysis revealed that votes and budget have the highest correlation with gross earnings, while the company has no significant correlation.
Goal: To examine the sales history of the store and extract insights on its performance, as well as to identify potential improvements that can be implemented.
Code: Tech Store Sales Analysis.ipynb
Description: The dataset contains a list of sales records. The records include the products for sale and order information(order id, order date, price, quantity ordered and purchase address). The project includes the following steps: data loading, data cleaning and preprocessing, EDA (exploratory data analysis), analyzing sales data and hypothesis testing.
Skills: data cleaning, data analysis, hypothesis testing, data visualization.
Technology: Python, Pandas, Matplotlib.
Results: Python functions that calculated and visually presented the sales data by month, city, and the most commonly sold items. Additionally, the reasons for the high frequency of these items being sold were analyzed and provided as insights.
Code: Data Cleaning Project Queries: Nashville Housing.sql
Description: The dataset contains a list of houses that have been sold in Nashville between 2013 and 2019. This project includes the following steps: data loading, data cleaning and preprocessing.
Skills: DML(Data Manipulation Language), DQL (Data Query Language), DDL (Data Definition Language).
Technology: SQL Server
Code: COVID Portfolio Project.sql
Description: The dataset contains records of Covid-19 cases, deaths and vaccine records by country in 2020-2021. This project includes the following steps: data loading, data cleaning and preprocessing and EDA (exploratory data analysis).
Skills: Joins, CTE's, Temp Tables, Windows Functions, Aggregate Functions, Creating Views, Converting Data Types
Technology: SQL Server
Goal: To predict Pokémon status based on their characteristics and rank their importance in determining whether a Pokémon is classified as legendary.
Code: Legendary Pokémon Analysis (Study Project)
Description: The dataset contains a list of Pokémon. The records include their characteristics such as attack, defense, type and size. The project includes the following steps: data loading, data cleaning EDA (exploratory data analysis), analyzing characteristics of different Pokémon.
Skills: data cleaning, data analysis, data visualization.
Technology: Tidyverse
University of Colorado, Boulder: Master of Science - MS, Data Science, Dec 2022 - Dec 2024
The University of the West Indies, Mona: Bachelor's degree, Biochemistry and Molecular Biology, 2019 - 2022
Pre University School: Associate's degree, Science and Mathematics, 2017 - 2019
The best way to showcase skills is by doing and sharing your job done but sometimes certificates appear to be as an indirect result. Here's a list of the ones I have (in reverse-chronological order, with the date of completion in brackets):
Selected from shared topics, language and repository description—not editorial ratings.
weijie-chen /
Tutorials of econometrics featuring Python programming. This is a crash course for reviewing the most important concepts and techniques of basic econometrics, the theories are presented lightly without hustles of derivation and Python codes are straightforward.
recodehive /
Stack overflow is a professional community for developers. This repo analysis 3 years of developer Survey done by Stackoverflow and do visualization and predict the salary of Data Scientist in future.
archd3sai /
This Portfolio is a compilation of all the Data Science and Data Analysis projects I have done for academic, self-learning and hobby purposes. This portfolio is updated on the regular basis.
nktnlx /
This is a repository that I have created to showcase skills, share projects and track my progress in Data Analytics / Data Science related topics.
noahgift /
[Course-2020-2023] taught at Duke MIDS. This is also a Coursera Course that covers MLOps, ML Engineering and the foundations of Cloud Computing for Data Science.
kfultz07 /
A simple package to abstract away the process of creating usable DataFrames for data analytics. This package is heavily inspired by the amazing Python library, Pandas.