Data Analysis using Python

Mohamed Ramy Mahjoub

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Starting at

/hr

About this service

Summary

I offer a comprehensive data analysis services tailored to your specific needs, utilizing advanced techniques and industry-standard tools like Seaborn, Matplotlib, NumPy, and Pandas.

Process

Initial Consultation: We'll discuss your specific data analysis needs, objectives, and timelines to ensure alignment.

Data Collection: Acquiring datasets either from diverse sources or utilizing data provided by the client.

Data Cleaning and Preparation: Scrubbing, formatting, and organizing data for analysis, ensuring accuracy and consistency.

Exploratory Data Analysis (EDA): Employing statistical techniques and visualization tools to understand the underlying patterns and trends in the data.

Advanced Analysis Techniques: Applying advanced statistical methods, machine learning algorithms, or predictive modeling as per project requirements.

Interpretation and Insights: Extracting meaningful insights from the analysis to inform decision-making and strategy.

Reporting and Visualization: Creating clear and concise reports, summaries, and visualizations to communicate findings effectively.

Feedback and Revisions: Incorporating client feedback and making necessary revisions to refine the analysis.

What's included

Data Analysis Report
Description: A comprehensive report detailing the analysis performed on the dataset provided by the client. Format: PDF format. Quantity: 1 final report. Revisions: Up to 2 rounds of revisions based on client feedback.
Machine Learning Model:
Description: A predictive model developed using machine learning algorithms to address the specific problem defined in the project scope. Format: Python code (Jupyter Notebook) and a trained model file (e.g., pickle or joblib). Quantity: 1 final version of the model. Revisions: 1 round of model refinement based on client feedback, if necessary.
Data Visualization Dashboard:
Description: An interactive dashboard showcasing key insights and visualizations derived from the data analysis. Format: Web-based dashboard (e.g., using Plotly Dash or Tableau) or static visualizations (e.g., in PDF or image format). Quantity: 1 final version of the dashboard. Revisions: Up to 2 rounds of revisions based on client feedback

Skills and tools

Data Scientist

Data Visualizer

Data Analyst