Delight Ogwor

Detail-focused Data Analyst with knowledge in data warehousing, data analysis, process validation and business needs analysis. Proven to understand customer requirements and translate into actionable project plans. Dedicated and hard-working with passion for Data.

Google Data Analytics Professional Certificate

Data science jobs/salary exploration with PowerBI:

• Explored the job market and salaries in data science using a dataset that included employer location, employment types, and work types anad other variables.
• Cleaned the dataset using data cleaning techniques to ensure accuracy and remove errors and inconsistencies.
• Created an interactive and visually appealing dashboard using Power BI, a data visualization tool.
• The dashboard included various visualizations such as charts, graphs, and tables to provide insights into the job market and salaries for data science professionals.
• Valuable insights were gained into the distribution of jobs and salaries across different locations and types of employment in the field of data science.

Data Analysis of New York AirBNB dataset (2008-2015) with Tableau:

• Conducted a data analysis of the New York AirBNB dataset from 2008 to 2015 using Tableau, a data visualization tool.
• Cleaned the dataset to ensure data accuracy and completeness.
• Created calculated fields in Tableau to derive new variables and metrics to aid in analysis.
• Used data analysis techniques to gain insights into the AirBNB market in New York, such as trends in listings and pricing over time, popular neighborhoods and property types, and the impact of external factors on AirBNB bookings (e.g., seasonality, major events in the city).
• Created charts, graphs, and other visualizations using Tableau to communicate findings.

Uber Trips Analysis with Python:

• I conducted an analysis of Uber trip data using Python, which included information about Uber trips such as pickup and drop-off locations, dates and times, and trip distances. To ensure accuracy and completeness, I used Python data manipulation and cleaning libraries such as Pandas and NumPy to clean and preprocess the data.
• Next, I used data analysis techniques such as data visualization and statistical analysis to gain insights into the Uber trips market. Some of the insights I uncovered included popular pickup and drop-off locations, trends in trip demand over time, and average trip distances and durations.
• To communicate my findings, I created visualizations such as charts, graphs, and maps using Python data visualization libraries such as Matplotlib and Seaborn. My analysis provided valuable insights for Uber and other transportation companies, such as trends and patterns that can inform pricing, marketing, and business strategies.

Analysis of the world population in 2015 with SQL:

• I conducted an analysis of world population in 2015 using SQL, which included information about population counts for different countries or regions in the world, as well as other relevant information such as geographical location, economic indicators, and demographic data.
• To gain insights into the distribution and characteristics of the world population in 2015, I used SQL queries to filter, aggregate, and manipulate the data. Some of the analysis techniques I used included joins to combine different tables, grouping and summarization to aggregate data by categories, and subqueries to perform more complex analyses.
• Through my analysis, I was able to gain insights into the world population distribution by geographic regions, country-wise population trends, age or gender demographics, and other related factors. These insights can help inform policymakers, researchers, and other stakeholders in making informed decisions about issues related to population, demographics, and public policy.