Data Cleaning, Preprocessing & Statistical Outlier RemovalMena Hasan
Transform messy or raw datasets into clean, reliable data ready for statistical analysis.
Outliers and uncleaned variables can distort statistical results and lead to inaccurate conclusions. As a junior data analyst, I help clean, structure, and check your datasets using IBM SPSS and Excel so you can perform your analysis with confidence.
What I Can Do For You:
Outlier Detection: Identify extreme data points and potential anomalies using box plots, histograms, and standard deviation checks.
Basic Preprocessing: Handle missing values, remove duplicate entries, and standardize formatting across your variables.
Normality & Distribution Checks: Review basic distributional metrics (skewness, visual histogram checks) to ensure your data fits standard statistical assumptions.
Clean File Delivery: Receive a neatly formatted SPSS file (
.sav) or Excel file (.xlsx) along with a brief summary of the adjustments made.How We Work Together:
Send Your Data: Share your raw dataset along with a brief note on what variables you are focusing on.
Data Cleaning: I screen the dataset, clean errors, and highlight notable outliers.
Delivery: You get your cleaned dataset back quickly, ready for downstream reporting or modeling.
Example work
Data Cleaning, Preprocessing & Statistical Outlier RemovalMena Hasan
Transform messy or raw datasets into clean, reliable data ready for statistical analysis.
Outliers and uncleaned variables can distort statistical results and lead to inaccurate conclusions. As a junior data analyst, I help clean, structure, and check your datasets using IBM SPSS and Excel so you can perform your analysis with confidence.
What I Can Do For You:
Outlier Detection: Identify extreme data points and potential anomalies using box plots, histograms, and standard deviation checks.
Basic Preprocessing: Handle missing values, remove duplicate entries, and standardize formatting across your variables.
Normality & Distribution Checks: Review basic distributional metrics (skewness, visual histogram checks) to ensure your data fits standard statistical assumptions.
Clean File Delivery: Receive a neatly formatted SPSS file (
.sav) or Excel file (.xlsx) along with a brief summary of the adjustments made.How We Work Together:
Send Your Data: Share your raw dataset along with a brief note on what variables you are focusing on.
Data Cleaning: I screen the dataset, clean errors, and highlight notable outliers.
Delivery: You get your cleaned dataset back quickly, ready for downstream reporting or modeling.
Example work
$30