Embedded Files
A Musical Journey: The Story Hidden in 141,000 streams
Music isn’t just music; it tells a story about you, what you love, what resonates with you, who you are, and sometimes, it tells a story of where you are. Each song carries a memory, and each artist marks a season.
Maven Analytics released a dataset chronicling the musical journey of an individual from 2013–2024, I was able to journey in the shoes of another through music. Want to take a look into what the music says about this individual? Dashboard
A Musical Journey: The Story Hidden in 141,000 streams
Music isn’t just music; it tells a story about you, what you love, what resonates with you, who you are, and sometimes, it tells a story of where you are. Each song carries a memory, and each artist marks a season.
Maven Analytics released a dataset chronicling the musical journey of an individual from 2013–2024, I was able to journey in the shoes of another through music. Want to take a look into what the music says about this individual? Dashboard
A sporting goods e-commerce store with a customer base in Europe, North America, and the Pacific has experienced tremendous growth in terms of profit, revenue, number of products sold each year, and a consistently growing customer base. Irrespective of this, the store is not satisfied and believes there are ways to optimize its sales and rake in more profit. To achieve this goal, the store provides data on customers' purchases, regions and countries sales were made, customer biodata, etc., from July 2005 to July 2008.
To help the store achieve its aim, I had to carry out ETL process, data cleaning, and modelling before further analysis. My analysis showed patterns within the data, customer purchase behaviour using cross-sell analytics, and customer segmentation using RFM analysis. In recommending the best course of action to optimize sales, I leveraged on my investigative research skills to provide data backed recommendations for the store and possible untapped markets
View dashboard here
Tool Used: Power BI
As a part of the PwC virtual internship, this contains a dataset of a telecommunications customers data and the telecommunications company seeks to understand its customers and avoid a reactionary approach to customers churn in the organization.
This analysis looks into customers information while providing insight into the types of customers that are more likely to leave based on certain data points. This analysis also provides recommendations on how the telecommunication can improve its services in other to reduce its churn rate.
Tool Used: Power BI
Socialblaze launched a new social media app called Ribbon. A month after the launch of the Ribbon app, the CEO provided a dataset which contains app usage information such as total number of installs, total number of signups, daily active users, posts seen, etc., and the CEO is interested in finding out the following from the data provided If
the app is a success and if it is, how can the success of the app launch be leveraged to further enhance Socialblaze's products and services?
Are there any opportunities for expansion or improvements in user acquisition and engagement?
Tool used: Microsoft Excel
This project involves cleaning a FIFA 21 dataset gotten from Kaggle. The dataset contains data about players information and statistics according to the FIFA 21 game. The information contained in this dataset includes but is not limited to Player name, Nationality, Team name and contract, Attacking attributes, defending attributes, Position, Height, Weight, etc.
The aim of this project was to ensure the dataset is properly prepared and suitable for analysis.
Tool used: Microsoft Excel Power Query.
This dashboard is part of the PwC virtual Internship by Forage and I was tasked with creating a dashboard in Power BI for Claire who is a call center manager at PhoneNow. The dashboard has to reflects all relevant Key Performance Indicators (KPIs) and metrics in the dataset. The dashboard showed some metrics which includes:
Overall customer satisfaction
Overall calls answered/abandoned
Calls by time
Average speed of answer
Agent’s performance quadrant -> average handle time (talk duration) vs calls answered
Tool used: Power BI
I used PostgreSQL to explore the fictional Parch and Posey database. The questions attempted are from the SQL course on Udacity.
All questions from Basic SQL to Data Cleaning have been answered using PostgreSQL.
The United Nations Development Programme (UNDP) Composite Indices are used to help countries measure and track progress towards the Sustainable Development Goals (SDGs) and assess the impact of policies and programs. These indices are designed to provide a comprehensive understanding of the interlinkages between the three dimensions of sustainable development: economic, social, and environmental.
The most well-known of these indices is the Human Development Index (HDI). Other indicators include life expectancy, education, and Gross National Income (GNI).
This visualization takes a look at these indices at national levels, reflecting how well each country is performing.
Tool used: Tableau
Foresight Pharmaceuticals is a top distributor in the pharmaceutical landscape and have operations in two countries, Germany and Poland.
This analysis gives a deep insight into the company's sales data which spans across 3 years (2017-2020). This report provides insights such as YoY sales growth, top selling products and what percentage of sales they contribute to, highest selling city and for each city, its highest selling sales agent and channel, etc.
Recommendations for improving sales growth was provided based on the data at hand.
Tool used: Tableau
This dataset contains information on the time spent in picking up a passenger and dropping them off at their desired location.
This analysis focuses on exploring and visualizing NYC taxi trip duration via questioning the data, cleaning, working through the data to remove outliers and anomalies, generating new data from already existing data for further analysis, and visualization to better understand the data
Tool used: Python
Udemy is an online learning platform with 35M Learners, 57K Instructors, 130K Courses, 400M Course enrollments, 110M Minutes of video, courses taught in 65+ Languages. It was founded in May 2010 by Eren Bali, Gagan Biyani, and Oktay Caglar and it has consistently catered to the needs of those willing to improve on their existing skill or pick up a new skill.
In this analysis, we take a look at various courses offered by udemy between 2011 and 2017 and make a number of analysis based on the dataset answering questions such as:
What are the best free courses by subject?
What are the most popular courses?
What are the most engaging courses?
How are courses related?
Which courses offer the best cost benefit?
Tool used: Python
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