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I will perform statistical data analysis on big data using python and rstudio
Applied Mathematician and Data Scientist
Hi, I’m Mohiz, a Master’s graduate in Mathematics specializing in Data Science, Machine Learning, and Applied AI. I help businesses turn raw, complex data into actionable insights and deployment-ready...
About this Gig
Are you struggling to turn massive datasets into actionable business decisions? I offer professional statistical data analysis on big data using python and rstudio to transform raw, unstructured information into clear, data-driven insights.
Whether you need deep statistical testing or large-scale data handling, I deliver precise, reproducible analytical solutions tailored to your technical requirements.
Services Covered:
Data Cleaning & Wrangling: Missing value handling, outlier detection, and data transformation.
Statistical Analysis: Hypothesis testing (t-tests, ANOVA, Chi-Square), correlation, and regression models.
Big Data Handling: Scalable data pipelines using Python (Pandas, PySpark, Dask) and RStudio (tidyverse, data.table).
Predictive Analytics: Supervised and unsupervised machine learning algorithms.
Data Visualization: High-resolution charts and interactive plots (Matplotlib, Seaborn, ggplot2).
What You Receive:
Fully commented Python Jupyter Notebooks or R scripts, clean output datasets, and a comprehensive summary report highlighting key findings.
Ready to unlock the power of your data? Message me today or order directly to get started!
FAQ
Q: Do you provide the source code with the final delivery?
A: Yes, every order includes fully documented, clean, and reproducible source code (Jupyter Notebook .ipynb or RStudio .R/.Rmd scripts) alongside raw outputs.
Q: Which programming language should I choose: Python or RStudio?
A: Python is ideal for large-scale data processing, PySpark workflows, and machine learning models. RStudio excels at complex hypothesis testing, bio-statistics, and custom statistical visual graphics. If you're unsure, send me a message and I will recommend the best fit for your dataset.
Q: How do you handle large datasets (big data) that exceed standard memory?
A: I utilize distributed and chunk-based processing tools such as PySpark, Dask, and data.table in R to efficiently process and analyze multi-gigabyte or streaming datasets without data loss.
Q: Can you work with custom data formats or direct API/database connections?
A: Absolutely. I work with CSV, Excel, JSON, SQL databases, BigQuery, and custom API extractions. Please contact me prior to ordering if connection credentials are required.
Buyer Requirements
1. Please upload your dataset(s) or provide secure access/links to the database, along with a brief description of the data structure. 2. What are the specific goals, key research questions, or statistical tests you need performed on this data? (Include any preferences for Python vs. RStudio).
