Data Scientist| Data Visualization| SQL

Joyce Oluwasegun

(0)

Starting at

/hr

About this service

Summary

Leveraging a profound understanding of statistical modeling, data analysis, and machine learning, I am a seasoned Data Scientist adept at utilizing Power BI and Tableau for impactful Data Visualization. My expertise extends to providing individuals and companies with precise, lucid, and empowering analyses and predictions derived from their datasets. Having collaborated with diverse clients worldwide, my experience spans a wide array of projects.

Process

1) Defining the Question

2) Collecting the dataset

3)Cleaning the dataset

4) Exploratory Data Analysis

5) Statistical Prediction/modeling

6) Interpret results

FAQs

How long does a typical data science project take?
Project timelines vary based on complexity and goals. I provide a detailed project plan with milestones and timelines during the initial consultation, ensuring transparency and alignment with your expectations.
How do you communicate insights derived from data to non-technical stakeholders?
I use clear, jargon-free language and visually appealing data visualizations through tools like Power BI or Tableau. Regular updates and collaborative discussions ensure that insights are easily understood and actionable.
What type of data do I need for a data science project?
The data needed depends on your project goals. It could include customer data, sales figures, website interactions, or any relevant information. We'll work together to define the most important data sources.

What's included

Comprehensive Analysis Report
A detailed document outlining key findings, trends, and insights derived from the data
Predictive Models
Developed models for forecasting future trends or outcomes based on the data patterns identified.
Interactive Dashboards
User-friendly interfaces allowing clients to explore and visualize data dynamically.
Data Cleaning and Preprocessing Documentation
A clear record of steps taken to clean and preprocess the raw data for transparency and reproducibility.
Algorithmic Recommendations
Guidance on the most effective algorithms used in the project and their applications.
Codebase
Providing the codebase for the developed models and analytics tools for future reference or integration.
Model Evaluation Metrics
Clear metrics to assess the performance of developed models and their reliability.