Logo freelancer
Đăng Nhập Đăng Ký
Đăng dự án
KHÁM PHÁ
Data Entry
Excel
Excel VBA
Training
Visual Basic
Ảnh bìa hồ sơ
Bạn đang theo dõi
Lỗi khi theo dõi người dùng.
Người dùng này không cho phép người dùng khác theo dõi họ.
Bạn đã theo dõi người dùng này rồi.
Gói thành viên của bạn chị cho phép 0 lượt theo dõi. Hãy nâng cấp lên ở đây.
Bỏ theo dõi thành công
Lỗi khi bỏ theo dõi người dùng.
Bạn đã đề xuất thành công
Lỗi khi đề xuất người dùng.
Đã xảy ra lỗi. Hãy làm mới trang và thử lại.
Đã xác thực email thành công.
Avatar người dùng

Christine Z.

@christinezekrian

5,0
4
2,5
2,5
100%

Data Analyst / Data Visualization Expert

$50 USD / giờ
・
Cờ của
Egypt (2:32 SA)
・
Đã tham gia vào tháng 1 4, 2023
$50 USD / giờ
・
Hi EveryOne, I'm Christine Zekrian I'm a Fulltime ERP system Admin, Freelancer Data Analyst Specialist and an Excel Expert, I Have:- • Diploma in Computer Science and Operations Research • B.A.A - Management Information Systems • [login to view URL] - Computer Science Skills: ✔ Python/ Pandas/ NumPy ✔ Microsoft Excel ✔ Tableau ✔ Power BI ✔ SQL ✔ VBA ✔ MS Access ✔ Data Wrangling ✔ Data Visualization If you are looking for a Highly professional Data Scientist and excellent communicator then I am your choice. I'm ready to start now.
Xác thực
Đúng thời hạn
100%
Đúng ngân sách
100%
Tỉ lệ chấp nhận
83%
Tỉ lệ thuê lại
20%
Freelancer Excel Experts Egypt
Xem tất cả về Excel Experts tại Egypt

Portfolio

Portfolio

11615949
I conducted a comprehensive analysis of a dataset focusing on the performance evaluation of preservice teachers. Utilizing a nuanced rating system spanning from Emergent Candidate to Accomplished Candidate, I explored how teachers align with various standards. The dataset included student-related factors such as GENDER, SAT scores, ACT scores, and CUM GPA. 1. Data Understanding: Thoroughly understood each dataset attribute, especially the nuanced rating system for teacher performance. 2. Data Cleaning: Ensured data integrity by addressing missing or inconsistent entries, guaranteeing a reliable foundation for analysis. 3. Descriptive Statistics: Calculated key statistics, shedding light on the distribution of teacher performance levels and student attributes. 4. Correlation Analysis: Explored relationships between student attributes (SAT, ACT, GPA) and teacher performance ratings, uncovering potential influencing factors. 5. Teacher Performance Analysis: Evaluated teacher performance
Teacher Performance Evaluation Correlation Analysis
I conducted a comprehensive analysis of a dataset focusing on the performance evaluation of preservice teachers. Utilizing a nuanced rating system spanning from Emergent Candidate to Accomplished Candidate, I explored how teachers align with various standards. The dataset included student-related factors such as GENDER, SAT scores, ACT scores, and CUM GPA. 1. Data Understanding: Thoroughly understood each dataset attribute, especially the nuanced rating system for teacher performance. 2. Data Cleaning: Ensured data integrity by addressing missing or inconsistent entries, guaranteeing a reliable foundation for analysis. 3. Descriptive Statistics: Calculated key statistics, shedding light on the distribution of teacher performance levels and student attributes. 4. Correlation Analysis: Explored relationships between student attributes (SAT, ACT, GPA) and teacher performance ratings, uncovering potential influencing factors. 5. Teacher Performance Analysis: Evaluated teacher performance
Teacher Performance Evaluation Correlation Analysis
10864503
The "Exploring Cinema: Data Analysis of TMDB Movie Database with Python" project aims to analyze and gain insights from the TMDB movie dataset using Python programming language. By leveraging Python's data analysis libraries and techniques, the project will explore various aspects of the dataset, including movie genres, ratings, revenue, and popularity. It will involve data cleaning, visualization, and statistical analysis to uncover patterns, trends, and correlations within the movie data. The project aims to provide valuable insights for filmmakers, producers, and movie enthusiasts, helping them make informed decisions and understand the factors that contribute to a movie's success.
Exploring Cinema: DataAnalysis of TMDB Movie Database Python
The "Exploring Cinema: Data Analysis of TMDB Movie Database with Python" project aims to analyze and gain insights from the TMDB movie dataset using Python programming language. By leveraging Python's data analysis libraries and techniques, the project will explore various aspects of the dataset, including movie genres, ratings, revenue, and popularity. It will involve data cleaning, visualization, and statistical analysis to uncover patterns, trends, and correlations within the movie data. The project aims to provide valuable insights for filmmakers, producers, and movie enthusiasts, helping them make informed decisions and understand the factors that contribute to a movie's success.
Exploring Cinema: DataAnalysis of TMDB Movie Database Python
The "Exploring Cinema: Data Analysis of TMDB Movie Database with Python" project aims to analyze and gain insights from the TMDB movie dataset using Python programming language. By leveraging Python's data analysis libraries and techniques, the project will explore various aspects of the dataset, including movie genres, ratings, revenue, and popularity. It will involve data cleaning, visualization, and statistical analysis to uncover patterns, trends, and correlations within the movie data. The project aims to provide valuable insights for filmmakers, producers, and movie enthusiasts, helping them make informed decisions and understand the factors that contribute to a movie's success.
Exploring Cinema: DataAnalysis of TMDB Movie Database Python
The "Exploring Cinema: Data Analysis of TMDB Movie Database with Python" project aims to analyze and gain insights from the TMDB movie dataset using Python programming language. By leveraging Python's data analysis libraries and techniques, the project will explore various aspects of the dataset, including movie genres, ratings, revenue, and popularity. It will involve data cleaning, visualization, and statistical analysis to uncover patterns, trends, and correlations within the movie data. The project aims to provide valuable insights for filmmakers, producers, and movie enthusiasts, helping them make informed decisions and understand the factors that contribute to a movie's success.
Exploring Cinema: DataAnalysis of TMDB Movie Database Python
10866990
COVID-19 Analytics: Real-time Data and Trends is a comprehensive project aimed at providing up-to-date insights and analysis on the COVID-19 pandemic. Utilizing Microsoft Excel, we have developed a dynamic and interactive dashboard that offers a detailed view of the virus's impact globally and locally. The dashboard collects real-time data from reliable sources, including official health organizations and reputable data repositories. It presents the information in a visually appealing and easily understandable format, allowing users to explore trends, patterns, and correlations related to the pandemic.
COVID-19 Analytics: Real-time Data and Trends
10864497
"Visualizing Waste: Exploring Australian State Waste Management with Excel" is a project that utilizes Microsoft Excel's data visualization capabilities to analyze and present waste management metrics in various Australian states. By integrating data from different sources, the project provides interactive dashboards, charts, and geospatial visualizations to showcase waste generation, recycling rates, landfill usage, and trends over time. The insightful and user-friendly reports aim to support evidence-based decision-making and promote transparency, ultimately driving positive changes in waste management practices nationwide.
Visualizing Waste: Exploring Australian State Waste Manageme
11130920
The Power BI Interactive Dashboard for Real Estate project offers a comprehensive and visually appealing solution for analyzing and exploring real estate data. This interactive dashboard provides users with a dynamic platform to gain valuable insights into various aspects of the real estate market, enabling informed decision-making. Key Features: Data Visualization: The dashboard presents real estate data in an intuitive and interactive manner, utilizing visually appealing charts, graphs, maps, and other visual elements to effectively convey information. Geographic Analysis: Explore real estate data geographically, with the ability to view property locations on an interactive map. The map feature allows users to filter data by specific regions, counties, or neighborhoods, providing a comprehensive understanding of the real estate landscape.
Real Estate Insights: Interactive Power BI Dashboard
10864495
The Sales Data Dashboard project aims to streamline the analysis and visualization of sales data using Microsoft Excel. By leveraging Excel's powerful features and functionalities, the project aims to provide an intuitive and user-friendly interface for analyzing sales data, generating insights, and making data-driven decisions. Benefits: Streamlined analysis: The Sales Data Dashboard will simplify the process of analyzing sales data, reducing the time and effort required to generate meaningful insights. Data-driven decision-making: With interactive visualizations and KPIs, decision-makers can make informed choices based on accurate sales data and trends. Improved efficiency: Automating data integration, cleansing, and visualization tasks will save time and allow sales teams to focus on strategic activities. Enhanced collaboration: The dashboard can be shared among team members, facilitating collaboration and enabling real-time data sharing and updates.
Sales Data Dashboard: Streamlining Insights with Microsoft E
11615949
I conducted a comprehensive analysis of a dataset focusing on the performance evaluation of preservice teachers. Utilizing a nuanced rating system spanning from Emergent Candidate to Accomplished Candidate, I explored how teachers align with various standards. The dataset included student-related factors such as GENDER, SAT scores, ACT scores, and CUM GPA. 1. Data Understanding: Thoroughly understood each dataset attribute, especially the nuanced rating system for teacher performance. 2. Data Cleaning: Ensured data integrity by addressing missing or inconsistent entries, guaranteeing a reliable foundation for analysis. 3. Descriptive Statistics: Calculated key statistics, shedding light on the distribution of teacher performance levels and student attributes. 4. Correlation Analysis: Explored relationships between student attributes (SAT, ACT, GPA) and teacher performance ratings, uncovering potential influencing factors. 5. Teacher Performance Analysis: Evaluated teacher performance
Teacher Performance Evaluation Correlation Analysis
I conducted a comprehensive analysis of a dataset focusing on the performance evaluation of preservice teachers. Utilizing a nuanced rating system spanning from Emergent Candidate to Accomplished Candidate, I explored how teachers align with various standards. The dataset included student-related factors such as GENDER, SAT scores, ACT scores, and CUM GPA. 1. Data Understanding: Thoroughly understood each dataset attribute, especially the nuanced rating system for teacher performance. 2. Data Cleaning: Ensured data integrity by addressing missing or inconsistent entries, guaranteeing a reliable foundation for analysis. 3. Descriptive Statistics: Calculated key statistics, shedding light on the distribution of teacher performance levels and student attributes. 4. Correlation Analysis: Explored relationships between student attributes (SAT, ACT, GPA) and teacher performance ratings, uncovering potential influencing factors. 5. Teacher Performance Analysis: Evaluated teacher performance
Teacher Performance Evaluation Correlation Analysis
10864503
The "Exploring Cinema: Data Analysis of TMDB Movie Database with Python" project aims to analyze and gain insights from the TMDB movie dataset using Python programming language. By leveraging Python's data analysis libraries and techniques, the project will explore various aspects of the dataset, including movie genres, ratings, revenue, and popularity. It will involve data cleaning, visualization, and statistical analysis to uncover patterns, trends, and correlations within the movie data. The project aims to provide valuable insights for filmmakers, producers, and movie enthusiasts, helping them make informed decisions and understand the factors that contribute to a movie's success.
Exploring Cinema: DataAnalysis of TMDB Movie Database Python
The "Exploring Cinema: Data Analysis of TMDB Movie Database with Python" project aims to analyze and gain insights from the TMDB movie dataset using Python programming language. By leveraging Python's data analysis libraries and techniques, the project will explore various aspects of the dataset, including movie genres, ratings, revenue, and popularity. It will involve data cleaning, visualization, and statistical analysis to uncover patterns, trends, and correlations within the movie data. The project aims to provide valuable insights for filmmakers, producers, and movie enthusiasts, helping them make informed decisions and understand the factors that contribute to a movie's success.
Exploring Cinema: DataAnalysis of TMDB Movie Database Python
The "Exploring Cinema: Data Analysis of TMDB Movie Database with Python" project aims to analyze and gain insights from the TMDB movie dataset using Python programming language. By leveraging Python's data analysis libraries and techniques, the project will explore various aspects of the dataset, including movie genres, ratings, revenue, and popularity. It will involve data cleaning, visualization, and statistical analysis to uncover patterns, trends, and correlations within the movie data. The project aims to provide valuable insights for filmmakers, producers, and movie enthusiasts, helping them make informed decisions and understand the factors that contribute to a movie's success.
Exploring Cinema: DataAnalysis of TMDB Movie Database Python
The "Exploring Cinema: Data Analysis of TMDB Movie Database with Python" project aims to analyze and gain insights from the TMDB movie dataset using Python programming language. By leveraging Python's data analysis libraries and techniques, the project will explore various aspects of the dataset, including movie genres, ratings, revenue, and popularity. It will involve data cleaning, visualization, and statistical analysis to uncover patterns, trends, and correlations within the movie data. The project aims to provide valuable insights for filmmakers, producers, and movie enthusiasts, helping them make informed decisions and understand the factors that contribute to a movie's success.
Exploring Cinema: DataAnalysis of TMDB Movie Database Python
10866990
COVID-19 Analytics: Real-time Data and Trends is a comprehensive project aimed at providing up-to-date insights and analysis on the COVID-19 pandemic. Utilizing Microsoft Excel, we have developed a dynamic and interactive dashboard that offers a detailed view of the virus's impact globally and locally. The dashboard collects real-time data from reliable sources, including official health organizations and reputable data repositories. It presents the information in a visually appealing and easily understandable format, allowing users to explore trends, patterns, and correlations related to the pandemic.
COVID-19 Analytics: Real-time Data and Trends
10864497
"Visualizing Waste: Exploring Australian State Waste Management with Excel" is a project that utilizes Microsoft Excel's data visualization capabilities to analyze and present waste management metrics in various Australian states. By integrating data from different sources, the project provides interactive dashboards, charts, and geospatial visualizations to showcase waste generation, recycling rates, landfill usage, and trends over time. The insightful and user-friendly reports aim to support evidence-based decision-making and promote transparency, ultimately driving positive changes in waste management practices nationwide.
Visualizing Waste: Exploring Australian State Waste Manageme
11130920
The Power BI Interactive Dashboard for Real Estate project offers a comprehensive and visually appealing solution for analyzing and exploring real estate data. This interactive dashboard provides users with a dynamic platform to gain valuable insights into various aspects of the real estate market, enabling informed decision-making. Key Features: Data Visualization: The dashboard presents real estate data in an intuitive and interactive manner, utilizing visually appealing charts, graphs, maps, and other visual elements to effectively convey information. Geographic Analysis: Explore real estate data geographically, with the ability to view property locations on an interactive map. The map feature allows users to filter data by specific regions, counties, or neighborhoods, providing a comprehensive understanding of the real estate landscape.
Real Estate Insights: Interactive Power BI Dashboard
10864495
The Sales Data Dashboard project aims to streamline the analysis and visualization of sales data using Microsoft Excel. By leveraging Excel's powerful features and functionalities, the project aims to provide an intuitive and user-friendly interface for analyzing sales data, generating insights, and making data-driven decisions. Benefits: Streamlined analysis: The Sales Data Dashboard will simplify the process of analyzing sales data, reducing the time and effort required to generate meaningful insights. Data-driven decision-making: With interactive visualizations and KPIs, decision-makers can make informed choices based on accurate sales data and trends. Improved efficiency: Automating data integration, cleansing, and visualization tasks will save time and allow sales teams to focus on strategic activities. Enhanced collaboration: The dashboard can be shared among team members, facilitating collaboration and enabling real-time data sharing and updates.
Sales Data Dashboard: Streamlining Insights with Microsoft E
Đã lưu thay đổi
5.0 · 4 Reviews
Nhận xét
T
Excel Management for Airbnb Rental Control • €15 EUR
Christine made a great job.
Nuno A.
@tatankov
•
Cờ của
Lisboa, Portugal
•
7 tháng trước
Chia sẻ
Avatar người dùng
Project for Christine Z. • $120 AUD
ive worked with many times and always delivers
Adrian P.
@pattoplus
•
Cờ của
Heathwood, Australia
•
9 tháng trước
Chia sẻ
Avatar người dùng
CRM excel spreadsheet using macros with VB code • $70 AUD
Great to work with, I will use again