Data Analysis
Data Analysis
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Ahsan Khan – Turning Data into Decisions: A Journey Through Data Analysis
Operations Research Analyst | Master’s in Mathematics | Expert in Analytical Problem Solving
The Story Begins…
The Story Begins…
In the fast-paced world of uncertainty, where decisions shape industries and numbers often hold the truth, stands Ahsan Khan—an Operations Research Analyst with a sharp mind and a Master’s in Mathematics.
But Ahsan’s true strength?
Data.
He believes every dataset tells a story, and his mission is to listen, analyze, and guide organizations to act with precision and confidence.
Why Data Analysis Matters in Operations Research
Why Data Analysis Matters in Operations Research
Operations Research is built on structure, models, and optimization—but none of it works without high-quality, insightful data. That’s where Ahsan Khan shines.
He uses data analysis not just to understand the present, but to forecast the future, diagnose problems, and power intelligent decisions across industries.
Ahsan Khan’s Data Analysis Approach
Ahsan Khan’s Data Analysis Approach
Step 1: Data Cleaning & Preprocessing
Step 1: Data Cleaning & Preprocessing
“Dirty data leads to dirty decisions,” Ahsan often says.
Handling missing values
Removing duplicates and noise
Standardizing units and formats
Feature engineering for modeling
🛠 Tools: Python (Pandas), R, Excel
Step 2: Exploratory Data Analysis (EDA)
Step 2: Exploratory Data Analysis (EDA)
Finding patterns and trends
Detecting outliers and anomalies
Visualizing relationships using charts and plots
📊 Tools: Power BI, Tableau, Python (Matplotlib, Seaborn)
Example:
While analyzing hospital admission data, Ahsan discovered that peak ER congestion occurred 2 hours earlier than previously thought—helping the staff plan shifts more effectively.
Step 3: Statistical Analysis & Hypothesis Testing
Step 3: Statistical Analysis & Hypothesis Testing
Correlation & regression analysis
ANOVA and t-tests
Confidence intervals and p-values
🧪 Use Case: Determining if a new logistics route actually reduced cost significantly, or if it was just random chance.
Step 4: Predictive Modeling
Step 4: Predictive Modeling
Time Series Forecasting (ARIMA, Exponential Smoothing)
Machine Learning Models (Linear Regression, Decision Trees)
🛠 Tools: Python (Scikit-learn, Statsmodels), Excel Forecast, R
Success Story:
For an e-commerce startup, Ahsan used ARIMA forecasting to predict product demand 4 weeks in advance, reducing stockouts by 30%.
Step 5: Optimization through Data
Step 5: Optimization through Data
Once the data told its story, Ahsan would move to what he does best—applying optimization techniques such as:
Linear & Integer Programming
Network Flow Optimization
Supply Chain Modeling
All of it data-driven, not assumption-driven.
Real Impact Through Data Analysis
Real Impact Through Data Analysis
Manufacturing:
Manufacturing:
Analyzed machine downtime logs to identify root causes.
Result: 15% improvement in equipment uptime
Healthcare:
Healthcare:
Analyzed patient flow and triage data.
Result: 40% reduction in ER wait times through better shift planning.
Logistics:
Logistics:
Studied delivery time variability across routes.
Result: 20% improvement in on-time delivery using route realignment.
Core Skills in Data Analysis
Core Skills in Data Analysis
Data wrangling & transformation
Multivariate analysis
Forecasting & predictive analytics
Optimization modeling
Data storytelling & dashboarding
Evidence-based decision-making
Final Thoughts
Final Thoughts
For Ahsan Khan, data analysis isn’t just about numbers—it’s about finding meaning, solving problems, and creating measurable value.
"Data tells the truth—but only if you know how to listen."
— Ahsan Khan
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