Statistics Projects in Warsaw
Statistics Projects in Warsaw
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Renate Oniuna
Metabolite Changes in Dialysis Effluent by Modality
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Nicholas Canfield
Retool App For Medical Insurance Billing
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Leonardo Gonzalez
Case Study: Pricing Strategy for a New Premium Hair Serum
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Olha Arkusha
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American Football Stats Tracking Mobile App
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Mintesnot Mengesha
Global Superstore: Strategic Sales & Profitability Analysis I performed a comprehensive analysis of the Global Superstore dataset to uncover hidden trends in sales performance, customer behavior, and regional profitability. This project demonstrates my ability to transform raw data into actionable business intelligence using modern data science techniques. Key Technical Features: Data Wrangling: Performed extensive data cleaning and preprocessing using Pandas to handle missing values and inconsistent formatting. Exploratory Data Analysis (EDA): Conducted deep-dive analysis to identify top-performing product categories and high-growth geographic regions. Visual Intelligence: Created advanced visualizations using Matplotlib and Seaborn to represent complex sales trends and profit margins clearly. Business Impact: Developed a data-driven model to identify the key drivers of customer churn and profitability bottlenecks.
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AFRID SHAIK
what i have achieved
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Shayne Cuffy
Giving a Voice to Breast Cancer Patients at Work
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7
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Anthony Remichris
Analyzing COVID-19 World Data with SQL
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18
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Rajajayaruban T
Virtual Textiles Shop
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24
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Ahmad Ali
SEO key words Assignments Dissertation
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10
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Marshal Davis
: Supply Chain, Delay, Delivery, & Risk Example Plots
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25
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Kunal Khatri
🧠 Title: Statistical Analysis of 325 Survey Responses for Research Study 📌 Problem: The client required a complete statistical analysis of survey data (325 respondents) to examine relationships between multiple factors such as organizational support, job involvement, and employee engagement. They needed: 1. Correct statistical test selection 2. Accurate analysis 3. Clear interpretation for research reporting ⚙️ Approach: I followed a structured analytical process: 1. Data cleaning and preprocessing 2. Reliability testing (Cronbach’s Alpha) 3. Descriptive statistics 4. Correlation analysis 5. Regression analysis 6. Hypothesis testing using SPSS 🛠 Tools Used: SPSS, Excel, R, Python 📊 Results 1. Identified significant relationships between key variables 2. Established impact of organizational support on engagement 3. Generated clear statistical outputs with interpretation 🎯 Outcome: The client successfully completed their research project with strong statistical backing and well-structured results for their report/dissertation. 💡 Key Value Delivered 1. Accurate and reliable statistical analysis 2. Easy-to-understand interpretation 3. Research-ready outputs
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Eric Holton
Non-metric MultiDimensional Scaling (NMDS) analysis of a publicly-available legume dataset for sorting machine development
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22
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Harsh Mudgal
Presents the exposure of global tariff wars
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17
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Ihtisham Mehmood
A/B Testing For an E-commerce Store
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10
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Léon Sharon Geisler
Employee surveys Museum for russian german cultural history
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