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Data Mining
Python
Statistical Analysis
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Noureldin A.

@noureldinamr0

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Data Analyst

$10 USD / time
・
s flagg
Egypt (12:49 p.m.)
・
Ble med oktober 26, 2024
$10 USD / time
・
Data Analytics Professional with a petroleum engineering background and hands-on experience in business intelligence. Successfully leveraged Python and SQL to analyze oil field operations data, leading to actionable insights and improved decision-making. Demonstrated expertise in data cleaning, visualization, and statistical analysis across multiple projects and recently completed comprehensive data science certifications from IBM, Google Analytics, and ALX, combining technical proficiency with strong business acumen.
Verifikasjoner
Freelancer Python Developers Egypt
Se alle Python Developers i Egypt

Portefølje

Portefølje

12314463
This map, created with Folium, displays customer order distribution across major regions in South America, with clusters representing the volume of orders per area. The size and color intensity of each marker corresponds to the number of orders, indicating that the majority of orders are concentrated in Brazil, particularly in the cities of São Paulo, Curitiba, and Rio de Janeiro. This geographical analysis helps identify regions with high customer activity, providing insights into regional demand patterns, logistics needs, and potential areas for market expansion within the continent.
Customer Order Distribution Map Across South America
12314446
This chart illustrates the average delivery time over time, highlighting a significant reduction from 2016 to 2018. Initially, there is a steep decline, followed by a stable pattern with delivery times of around 10 days. This trend showcases improvements in the logistics and delivery processes, reflecting enhanced efficiency and reliability. This insight supports the client in understanding delivery performance trends, aiding in further optimizing customer satisfaction through timely delivery improvements.
Average Delivery Time trend Analysis for Marketplace
12314431
This chart presents a comparative analysis of total monthly sales for 2017 and 2018, showcasing seasonal trends and growth patterns for an e-commerce marketplace. Key insights include consistent sales spikes in November and steady growth in other months of 2018 compared to 2017. This bar chart offers valuable insights for strategic planning and targeted marketing efforts, helping the client better understand sales seasonality and identify opportunities for revenue optimization.
Sales performance for E-commerce Marketplace year_month
12313812
In this project, used SQL to extract and analyze sales data from an e-commerce marketplace. The objective was to provide actionable insights into product sales patterns across different cities. Top-Selling Products Per City: I created a report that identified the most popular products in each city. For example, "perfumery" was the highest-selling product in Campos Novos, while cities like Curitiba had "health_beauty" products as top sellers. This breakdown allowed a deeper understanding of product demand by city. Sales Count by City: Another report focused on the total number of sales per city, revealing which cities had the highest purchase frequencies. This data showed where consumer engagement was strongest, helping the business to target high-demand areas effectively.
SQL-Powered Sales Insights for E-Commerce Marketplace
12313780
In this project, I performed web scraping using Python to extract stock and revenue data from Yahoo Finance for Tesla and GameStop. I used BeautifulSoup to scrape the stock prices and revenue information this data included fields such as date, open, high, low, and close prices. I organized the cleaned data into Pandas DataFrames.
Web scraping for yahoo finance stock market companies
12313387
a Web scraping project to extract the table of the top 100 US companies based on revenue from a Wikipedia page. The project will involve identifying the Wikipedia page, inspecting the table, scraping the table data using Python libraries such as requests and BeautifulSoup, formatting the data table, and converting it into a pandas data frame for further analysis.
Web Scraping for the biggest 100 companies in the USA
12314463
This map, created with Folium, displays customer order distribution across major regions in South America, with clusters representing the volume of orders per area. The size and color intensity of each marker corresponds to the number of orders, indicating that the majority of orders are concentrated in Brazil, particularly in the cities of São Paulo, Curitiba, and Rio de Janeiro. This geographical analysis helps identify regions with high customer activity, providing insights into regional demand patterns, logistics needs, and potential areas for market expansion within the continent.
Customer Order Distribution Map Across South America
12314446
This chart illustrates the average delivery time over time, highlighting a significant reduction from 2016 to 2018. Initially, there is a steep decline, followed by a stable pattern with delivery times of around 10 days. This trend showcases improvements in the logistics and delivery processes, reflecting enhanced efficiency and reliability. This insight supports the client in understanding delivery performance trends, aiding in further optimizing customer satisfaction through timely delivery improvements.
Average Delivery Time trend Analysis for Marketplace
12314431
This chart presents a comparative analysis of total monthly sales for 2017 and 2018, showcasing seasonal trends and growth patterns for an e-commerce marketplace. Key insights include consistent sales spikes in November and steady growth in other months of 2018 compared to 2017. This bar chart offers valuable insights for strategic planning and targeted marketing efforts, helping the client better understand sales seasonality and identify opportunities for revenue optimization.
Sales performance for E-commerce Marketplace year_month
12313812
In this project, used SQL to extract and analyze sales data from an e-commerce marketplace. The objective was to provide actionable insights into product sales patterns across different cities. Top-Selling Products Per City: I created a report that identified the most popular products in each city. For example, "perfumery" was the highest-selling product in Campos Novos, while cities like Curitiba had "health_beauty" products as top sellers. This breakdown allowed a deeper understanding of product demand by city. Sales Count by City: Another report focused on the total number of sales per city, revealing which cities had the highest purchase frequencies. This data showed where consumer engagement was strongest, helping the business to target high-demand areas effectively.
SQL-Powered Sales Insights for E-Commerce Marketplace
12313780
In this project, I performed web scraping using Python to extract stock and revenue data from Yahoo Finance for Tesla and GameStop. I used BeautifulSoup to scrape the stock prices and revenue information this data included fields such as date, open, high, low, and close prices. I organized the cleaned data into Pandas DataFrames.
Web scraping for yahoo finance stock market companies
12313387
a Web scraping project to extract the table of the top 100 US companies based on revenue from a Wikipedia page. The project will involve identifying the Wikipedia page, inspecting the table, scraping the table data using Python libraries such as requests and BeautifulSoup, formatting the data table, and converting it into a pandas data frame for further analysis.
Web Scraping for the biggest 100 companies in the USA
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Utdanning
The British University in Egypt
2014 - 2019
•
5 år
Petroleum Engineering
s flagg
Egypt
2014 - 2019
•
5 år
Kvalifikasjoner
IBM Data Science professional Certificate
2024
Coursera & DEPI
IBM Data Science course, where I gained expertise in key skills such as data analysis, visualization, and machine learning. I learned many tools like Python, SQL, Web scraping, and Jupyter Notebooks and I can use them proficiently. The course has sharpened my approach to data science, enhancing my ability to extract insights that inform decision-making. I particularly enjoyed the data-wrangling segment, which strengthened my skills in preparing and cleaning data for deeper analysis.
2024
AiCE - Ai Career Essentials
2024
ALX Africa
I completed an AiCE course that expanded my understanding of AI tools, concepts, and applications across industries. The course highlighted AI's role in the future, as more companies use it for analysis and predictions. I also developed skills in prompt engineering, improving my ability to work with large language models (LLMs) for precise results. This training strengthened my AI knowledge and taught me practical skills to apply AI solutions effectively.
2024
Google Data Analytics Professional Certificate
2023
Coursera
I finished the Google Data Analytics Professional Certificate, which equipped me with the fundamentals of data analysis skills using different industry-standard tools like Excel, Tableau, R, and SQL. I developed expertise in building dashboards and performing in-depth analysis, making me proficient at transforming data into insights that drive decision-making. and I have learned to use Microsoft SQL Server.
2023
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