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.

• About

• Portfolio Projects

  • Python
    • Analyzing the Factors Contributing to the Success of a Movie
    • Tech Store Sales Analysis
  • SQL
    • Covid 19 Data Exploration
    • Nashville Housing Data Cleaning
  • R
    • Legendary Pokémon Analysis (Study Project)
  • Excel / Google Sheets
  • Tableau---> go to Tableau..
  • Power BI

• Education

• Certificates

• Contact

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):

• Google Data Analytics Professional Certificate (Sep 2022) (Coursera - Google)
• Tableau (Oct 2022) (Coursera - University of California, Davis)
• Python for Data Science (Feb 2023) (Coursera - University of Colorado Boulder)

• LinkedIn: @tiannaparris
• Email: [email protected]