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I will do python data cleaning, pandas eda, and outlier removal with visualization
Python Data Analyst and EDA Specialist
Greetings, This is Arsal Khan. I’m a Python Data Analyst specializing in data cleaning and Exploratory Data Analysis (EDA). I have a great interest in Data Analysis. I help businesses turn messy, unor...
About this Gig
Is your raw data messy, missing crucial values, or riddled with hidden outliers that skew your business metrics?
As a dedicated Data Analyst, I build Python data cleaning and EDA pipelines to transform messy datasets into structured, ready-to-use business assets.
With deep expertise in relational databases, mathematical anomaly detection, and visual debugging, I ensure your data tells an accurate story.
What I Will Do:
- Advanced Data Cleaning: Handle missing values, structural formatting, duplicates, and text normalization using Pandas & NumPy.
- Mathematical Outlier Detection: Pinpoint and isolate anomalies using statistical logic (IQR vs. Z-Score).
- Data Shape Analysis: Deep dives into data asymmetry using skewness computation (.skew()) and statistical summaries (.describe()).
- Visual Analytics: Deliver interactive Box Plots, Scatter Plots, and Histograms to visually verify data integrity.
Why Work With Me?
- Clean, Documented Code: Delivered via modular Python scripts or structured Jupyter Notebooks.
- Mathematical Precision: Outliers and distributions managed using rigorous statistical standards.
- Lets unlock the true potential of your data. Contact me today to discuss your project!
FAQ
Q: What do you deliver at the end of the project?
A: You will receive the fully cleaned dataset (CSV/Excel/SQL) along with a professionally structured, documented Python script (.py) or Jupyter Notebook (.ipynb) so you can run the pipeline again anytime.
Q: How do you decide whether to use IQR or Z-Score for my outliers?
A: I check your data distribution shape using .skew(). For normal (symmetric) distributions, I apply Z-Score. For skewed or non-normal data, I use the Interquartile Range (IQR) to avoid mathematical bias.
