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$5 USD / hora
Bandera de EGYPT
kafr el dawwar, egypt
$5 USD / hora
Aquí son las 6:03 a. m.
Se unió el octubre 3, 2020
1 Recomendación

Abdelaziz N.

@Abdelaziz2352000

 4,9 (14 comentarios)
3,9
3,9
90%
90%
$5 USD / hora
Bandera de EGYPT
kafr el dawwar, egypt
$5 USD / hora
95 %
Trabajos finalizados
100 %
Dentro del presupuesto
94 %
A tiempo
5 %
Tasa de recontratación

Data Engineer & Data Analyst

As a data professional and a Full-Time Freelancer, I'm passionate about delivering successful, efficient, and winning solutions for your business through transparency, reliability, trust, and outstanding customer care. I'd love to have the opportunity to discuss the project with you. Skills: - Python - Excel - SQL - Data Analysis - Machine learning - Dashboard Design (Power Bi) - Web Scraping - Data Entry - Data Engineering ✔ Full-Time Freelancer ✔ 24/7 Service ✔ Unlimited Revisions until client satisfaction ✔ Hourly Rate not Fixed ✔ LinkedIn Account: [login to view URL]
Freelancer Web Scraping Specialists Egypt
Ver todo Web Scraping Specialists en Egypt

Contacta Abdelaziz N. sobre tu trabajo

Inicia sesión para comentar cualquier detalle por chat.

Portafolio

9621711
Get self-service analytics at enterprise scale Reduce the added cost, complexity, and security risks of multiple solutions with an analytics platform that scales from individuals to the organization as a whole. Use smart tools for strong results Find and share meaningful insights with hundreds of data visualizations, built-in AI capabilities, tight Excel integration, and pre-built and custom data connectors. Help protect your analytics data Keep your data secure with industry-leading data security capabilities including sensitivity labeling, end-to-end encryption, and real-time access monitoring.
power pi
9427535
Hello, I am a Data Entry Expert. I have good experience working with Data Entry: • Research • Market Research • Python. • SQL. • creating business reports and Dashboards. • Excel / CSV / XML / JSON Processing I will use some Tools and Different search Engines for your project like: • Google. • Bing. • Yahoo • Ask • Aol • Ecosia Regards, Abdelaziz.
Web search for Emails In different search Engines
Hello, I am a Data Entry Expert. I have good experience working with Data Entry: • Research • Market Research • Python. • SQL. • creating business reports and Dashboards. • Excel / CSV / XML / JSON Processing I will use some Tools and Different search Engines for your project like: • Google. • Bing. • Yahoo • Ask • Aol • Ecosia Regards, Abdelaziz.
Web search for Emails In different search Engines
9416506
Table of Contents Introduction Data Wrangling Exploratory Data Analysis Conclusions Introduction To complete my Data Analysis project I am using TMDb movies dataset. This data set contains information about 10,000 movies collected from The Movie Database (TMDb), including user ratings and revenue. Certain columns, like ‘cast’ and ‘genres’, contain multiple values separated by pipe (|) characters. There are some odd characters in the ‘cast’ column. The final two columns ending with “_adj” show the budget and revenue of the associated movie in terms of 2010 dollars, accounting for inflation over time. From this dataset we could try to solve many different questions such as : Is there an improvement in the quality of movies from year to year? What kinds of properties are associated with movies that have high revenues? Is the number of movies increase from year to year? Is there a relation between a movie runtime and the vote average? Who are the top 5 directors according to high rev
TMDb movie data analysis
Table of Contents Introduction Data Wrangling Exploratory Data Analysis Conclusions Introduction To complete my Data Analysis project I am using TMDb movies dataset. This data set contains information about 10,000 movies collected from The Movie Database (TMDb), including user ratings and revenue. Certain columns, like ‘cast’ and ‘genres’, contain multiple values separated by pipe (|) characters. There are some odd characters in the ‘cast’ column. The final two columns ending with “_adj” show the budget and revenue of the associated movie in terms of 2010 dollars, accounting for inflation over time. From this dataset we could try to solve many different questions such as : Is there an improvement in the quality of movies from year to year? What kinds of properties are associated with movies that have high revenues? Is the number of movies increase from year to year? Is there a relation between a movie runtime and the vote average? Who are the top 5 directors according to high rev
TMDb movie data analysis
Table of Contents Introduction Data Wrangling Exploratory Data Analysis Conclusions Introduction To complete my Data Analysis project I am using TMDb movies dataset. This data set contains information about 10,000 movies collected from The Movie Database (TMDb), including user ratings and revenue. Certain columns, like ‘cast’ and ‘genres’, contain multiple values separated by pipe (|) characters. There are some odd characters in the ‘cast’ column. The final two columns ending with “_adj” show the budget and revenue of the associated movie in terms of 2010 dollars, accounting for inflation over time. From this dataset we could try to solve many different questions such as : Is there an improvement in the quality of movies from year to year? What kinds of properties are associated with movies that have high revenues? Is the number of movies increase from year to year? Is there a relation between a movie runtime and the vote average? Who are the top 5 directors according to high rev
TMDb movie data analysis
Table of Contents Introduction Data Wrangling Exploratory Data Analysis Conclusions Introduction To complete my Data Analysis project I am using TMDb movies dataset. This data set contains information about 10,000 movies collected from The Movie Database (TMDb), including user ratings and revenue. Certain columns, like ‘cast’ and ‘genres’, contain multiple values separated by pipe (|) characters. There are some odd characters in the ‘cast’ column. The final two columns ending with “_adj” show the budget and revenue of the associated movie in terms of 2010 dollars, accounting for inflation over time. From this dataset we could try to solve many different questions such as : Is there an improvement in the quality of movies from year to year? What kinds of properties are associated with movies that have high revenues? Is the number of movies increase from year to year? Is there a relation between a movie runtime and the vote average? Who are the top 5 directors according to high rev
TMDb movie data analysis
Table of Contents Introduction Data Wrangling Exploratory Data Analysis Conclusions Introduction To complete my Data Analysis project I am using TMDb movies dataset. This data set contains information about 10,000 movies collected from The Movie Database (TMDb), including user ratings and revenue. Certain columns, like ‘cast’ and ‘genres’, contain multiple values separated by pipe (|) characters. There are some odd characters in the ‘cast’ column. The final two columns ending with “_adj” show the budget and revenue of the associated movie in terms of 2010 dollars, accounting for inflation over time. From this dataset we could try to solve many different questions such as : Is there an improvement in the quality of movies from year to year? What kinds of properties are associated with movies that have high revenues? Is the number of movies increase from year to year? Is there a relation between a movie runtime and the vote average? Who are the top 5 directors according to high rev
TMDb movie data analysis
7751104
scraping large data from https://www.local.ch/ website
Total around 10-15.000 entries
scraping large data from https://www.local.ch/ website
Total around 10-15.000 entries
7514546
Scrape data from yellow pages Australia website 300 industries, approximately 340,000 records. but records keep changing continuously. it could be little more or less now. I need 9 pieces (columns) of information for each company. As a sample, 1) Accountants & Auditors
Scrape data from yellow pages australia website
Scrape data from yellow pages Australia website 300 industries, approximately 340,000 records. but records keep changing continuously. it could be little more or less now. I need 9 pieces (columns) of information for each company. As a sample, 1) Accountants & Auditors
Scrape data from yellow pages australia website
Scrape data from yellow pages Australia website 300 industries, approximately 340,000 records. but records keep changing continuously. it could be little more or less now. I need 9 pieces (columns) of information for each company. As a sample, 1) Accountants & Auditors
Scrape data from yellow pages australia website
Scrape data from yellow pages Australia website 300 industries, approximately 340,000 records. but records keep changing continuously. it could be little more or less now. I need 9 pieces (columns) of information for each company. As a sample, 1) Accountants & Auditors
Scrape data from yellow pages australia website
7466926
download some data like images of Huawei protects and its descriptions in form excel file
scraping data from amazon like images and descriptions
download some data like images of Huawei protects and its descriptions in form excel file
scraping data from amazon like images and descriptions
download some data like images of Huawei protects and its descriptions in form excel file
scraping data from amazon like images and descriptions
9621711
Get self-service analytics at enterprise scale Reduce the added cost, complexity, and security risks of multiple solutions with an analytics platform that scales from individuals to the organization as a whole. Use smart tools for strong results Find and share meaningful insights with hundreds of data visualizations, built-in AI capabilities, tight Excel integration, and pre-built and custom data connectors. Help protect your analytics data Keep your data secure with industry-leading data security capabilities including sensitivity labeling, end-to-end encryption, and real-time access monitoring.
power pi
9427535
Hello, I am a Data Entry Expert. I have good experience working with Data Entry: • Research • Market Research • Python. • SQL. • creating business reports and Dashboards. • Excel / CSV / XML / JSON Processing I will use some Tools and Different search Engines for your project like: • Google. • Bing. • Yahoo • Ask • Aol • Ecosia Regards, Abdelaziz.
Web search for Emails In different search Engines
Hello, I am a Data Entry Expert. I have good experience working with Data Entry: • Research • Market Research • Python. • SQL. • creating business reports and Dashboards. • Excel / CSV / XML / JSON Processing I will use some Tools and Different search Engines for your project like: • Google. • Bing. • Yahoo • Ask • Aol • Ecosia Regards, Abdelaziz.
Web search for Emails In different search Engines
9416506
Table of Contents Introduction Data Wrangling Exploratory Data Analysis Conclusions Introduction To complete my Data Analysis project I am using TMDb movies dataset. This data set contains information about 10,000 movies collected from The Movie Database (TMDb), including user ratings and revenue. Certain columns, like ‘cast’ and ‘genres’, contain multiple values separated by pipe (|) characters. There are some odd characters in the ‘cast’ column. The final two columns ending with “_adj” show the budget and revenue of the associated movie in terms of 2010 dollars, accounting for inflation over time. From this dataset we could try to solve many different questions such as : Is there an improvement in the quality of movies from year to year? What kinds of properties are associated with movies that have high revenues? Is the number of movies increase from year to year? Is there a relation between a movie runtime and the vote average? Who are the top 5 directors according to high rev
TMDb movie data analysis
Table of Contents Introduction Data Wrangling Exploratory Data Analysis Conclusions Introduction To complete my Data Analysis project I am using TMDb movies dataset. This data set contains information about 10,000 movies collected from The Movie Database (TMDb), including user ratings and revenue. Certain columns, like ‘cast’ and ‘genres’, contain multiple values separated by pipe (|) characters. There are some odd characters in the ‘cast’ column. The final two columns ending with “_adj” show the budget and revenue of the associated movie in terms of 2010 dollars, accounting for inflation over time. From this dataset we could try to solve many different questions such as : Is there an improvement in the quality of movies from year to year? What kinds of properties are associated with movies that have high revenues? Is the number of movies increase from year to year? Is there a relation between a movie runtime and the vote average? Who are the top 5 directors according to high rev
TMDb movie data analysis
Table of Contents Introduction Data Wrangling Exploratory Data Analysis Conclusions Introduction To complete my Data Analysis project I am using TMDb movies dataset. This data set contains information about 10,000 movies collected from The Movie Database (TMDb), including user ratings and revenue. Certain columns, like ‘cast’ and ‘genres’, contain multiple values separated by pipe (|) characters. There are some odd characters in the ‘cast’ column. The final two columns ending with “_adj” show the budget and revenue of the associated movie in terms of 2010 dollars, accounting for inflation over time. From this dataset we could try to solve many different questions such as : Is there an improvement in the quality of movies from year to year? What kinds of properties are associated with movies that have high revenues? Is the number of movies increase from year to year? Is there a relation between a movie runtime and the vote average? Who are the top 5 directors according to high rev
TMDb movie data analysis
Table of Contents Introduction Data Wrangling Exploratory Data Analysis Conclusions Introduction To complete my Data Analysis project I am using TMDb movies dataset. This data set contains information about 10,000 movies collected from The Movie Database (TMDb), including user ratings and revenue. Certain columns, like ‘cast’ and ‘genres’, contain multiple values separated by pipe (|) characters. There are some odd characters in the ‘cast’ column. The final two columns ending with “_adj” show the budget and revenue of the associated movie in terms of 2010 dollars, accounting for inflation over time. From this dataset we could try to solve many different questions such as : Is there an improvement in the quality of movies from year to year? What kinds of properties are associated with movies that have high revenues? Is the number of movies increase from year to year? Is there a relation between a movie runtime and the vote average? Who are the top 5 directors according to high rev
TMDb movie data analysis
Table of Contents Introduction Data Wrangling Exploratory Data Analysis Conclusions Introduction To complete my Data Analysis project I am using TMDb movies dataset. This data set contains information about 10,000 movies collected from The Movie Database (TMDb), including user ratings and revenue. Certain columns, like ‘cast’ and ‘genres’, contain multiple values separated by pipe (|) characters. There are some odd characters in the ‘cast’ column. The final two columns ending with “_adj” show the budget and revenue of the associated movie in terms of 2010 dollars, accounting for inflation over time. From this dataset we could try to solve many different questions such as : Is there an improvement in the quality of movies from year to year? What kinds of properties are associated with movies that have high revenues? Is the number of movies increase from year to year? Is there a relation between a movie runtime and the vote average? Who are the top 5 directors according to high rev
TMDb movie data analysis
7751104
scraping large data from https://www.local.ch/ website
Total around 10-15.000 entries
scraping large data from https://www.local.ch/ website
Total around 10-15.000 entries
7514546
Scrape data from yellow pages Australia website 300 industries, approximately 340,000 records. but records keep changing continuously. it could be little more or less now. I need 9 pieces (columns) of information for each company. As a sample, 1) Accountants & Auditors
Scrape data from yellow pages australia website
Scrape data from yellow pages Australia website 300 industries, approximately 340,000 records. but records keep changing continuously. it could be little more or less now. I need 9 pieces (columns) of information for each company. As a sample, 1) Accountants & Auditors
Scrape data from yellow pages australia website
Scrape data from yellow pages Australia website 300 industries, approximately 340,000 records. but records keep changing continuously. it could be little more or less now. I need 9 pieces (columns) of information for each company. As a sample, 1) Accountants & Auditors
Scrape data from yellow pages australia website
Scrape data from yellow pages Australia website 300 industries, approximately 340,000 records. but records keep changing continuously. it could be little more or less now. I need 9 pieces (columns) of information for each company. As a sample, 1) Accountants & Auditors
Scrape data from yellow pages australia website
7466926
download some data like images of Huawei protects and its descriptions in form excel file
scraping data from amazon like images and descriptions
download some data like images of Huawei protects and its descriptions in form excel file
scraping data from amazon like images and descriptions
download some data like images of Huawei protects and its descriptions in form excel file
scraping data from amazon like images and descriptions

Comentarios

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5,0
$16,00 USD
Pro-level Educational PowerPoint Designer Needed
he's soo cooperative
Excel
Powerpoint
Word
Presentations
Avatar del usuario
Bandera de Raya A.
@roozzzz8
•
hace 26 días
5,0
$25,00 USD
Script for Web Scrapping from rarity.tools
Abdelaziz did a great job scrapping the data i needed and he did it in a short time, I highly recommend him.
Python
Web Scraping
MySQL
BeautifulSoup
Selenium
Avatar del usuario
Bandera de Leonardo G.
@leonardogutie
•
hace 2 años
5,0
$3,00 USD
Process data online
Very professional freelancer. Work delivered 100% genuine.
Data Entry
Web Search
H
Bandera de Hubert L.
@hubertlis
•
hace 2 años
5,0
₹600,00 INR
Project 31529549 has been deleted
Satisfied with work perfect work delivers on time.
Avatar del usuario
Bandera de Tushar J.
@tusharjoshi948
•
hace 3 años
4,0
$25,00 USD
Maps labeling (Arabic is required)
Abdelaziz labelled my map very well and I may hire him for a similar project. Recommended.
Data Entry
JSON
Geographical Information System (GIS)
Avatar del usuario
Bandera de Abdulaziz J.
@aziog91
•
hace 3 años

Experiencia

web scraper

upwork
ene 2018 - Presente
the web scraping is considered the best resource of leads, it contains billions of data for all businesses categories, if you would like to take benefits from it, then you are at the right place! We will work together until you get the leads that fit your needs. My spiders are ready for collecting the leads! Just contact me to trigger the start button.

Educación

Bachelor Degree in electronic and communications Engineering

Alexandria University, Egypt 2018 - 2023
(5 años)

Calificaciones

Data Analysis Professional Nanodegree Program

Udacity
2021
✅ Develop programs in Python ✅ Manipulate data using NumPy ✅ Load and process data using Pandas ✅ Use Anaconda to manage your programming environment ✅ Investigate a dataset using Python data analysis packages ✅ Perform the entire data analysis process on a dataset ✅ Data Wrangling ✅ Gather data from multiple sources in a variety of formats ✅ Assess the quality and tidiness of data visually and programmatically ✅ Clean data using Python and Pandas

Ask Questions to Make Data-Driven Decisions

Coursera
2021
✅Explain how each step of the problem-solving road map contributes to common analysis scenarios. ✅Discuss the use of data in the decision-making process. ✅Demonstrate the use of spreadsheets to complete basic tasks of the data analyst including entering and organizing data. ✅Describe the key ideas associated with structured thinking. Skills acquired: ▶️Spreadsheet ▶️Questioning ▶️Decision-Making ▶️Problem Solving ▶️Data Analysis

Data Analysis Challenger

Udacity
2021
✅Calculate summary statistics using spreadsheets ✅Create data visualizations using spreadsheets ✅Ask questions and answer them using data ✅Calculate key business metrics in financial analysis and interpret values ✅Forecast financial metrics using scenario analysis

Contacta Abdelaziz N. sobre tu trabajo

Inicia sesión para comentar cualquier detalle por chat.

Verificaciones

Freelancer preferente
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Email verificado
Facebook conectado

Principales habilidades

Web Scraping 14
Data Entry 8
PHP 6
Excel 6
Web Search 6

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