2.3 Distribution-and-Data-Modeling

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Distribution and Data Modeling
Understanding Data Distributions and Their Role
in Analytics
by Jitendra Tomar
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Introduction to Data Modeling
What is Data Modeling?
Key Components
Representation of real-world data using
mathematical and statistical methods

• Data Types Categorical, Numerical, Time-series

• Distributions Normal, Binomial, Poisson, etc.

• Relationships Correlations, dependencies

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2. Types of Data Distributions
Normal Distribution
Binomial Distribution
Poisson Distribution
Exponential Distribution
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2. Types of Data Distributions
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2. Types of Data Distributions
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2. Types of Data Distributions
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2. Types of Data Distributions
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3. Data Modeling Techniques
Descriptive Modeling
Predictive Modeling
Summarizing data
Using historical data for future predictions
Prescriptive Modeling
Recommending actions based on predictions
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3. Data Modeling Techniques
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3. Data Modeling Techniques
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3. Data Modeling Techniques
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4. Choosing the Right Distribution
Nature of data
Symmetry vs. Skewness
Underlying assumptions
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4. Choosing the Right Distribution
Nature of data
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4. Choosing the Right Distribution
Nature of data
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4. Choosing the Right Distribution
Symmetry vs. Skewness
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4. Choosing the Right Distribution
Symmetry vs. Skewness
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4. Choosing the Right Distribution
Underlying assumptions
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5. Applications in Business Analytics
Customer Behavior Analysis
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Risk Assessment in Finance
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Quality Control in Manufacturing
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Predicting Demand in Supply Chain
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5. Applications in Business Analytics
Customer Behavior Analysis Understanding Buying
Patterns Businesses use probability distributions
to analyze customer purchase behavior,
preferences, and spending patterns over time. By
modeling factors like purchase frequency, basket
size, and seasonal trends, companies can optimize
marketing strategies and personalize
recommendations. Example An e-commerce platform
uses a Poisson distribution to predict how many
customers will make a purchase within a given hour
Customer Behavior Analysis
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Risk Assessment in Finance
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Quality Control in Manufacturing
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Predicting Demand in Supply Chain
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5. Applications in Business Analytics
Risk Assessment in Finance Modeling Stock Price
Movements Financial analysts apply statistical
distributions to model stock prices, volatility,
and portfolio risk. The log-normal distribution
is often used to describe stock price movements,
while the normal distribution helps in
value-at-risk (VaR) calculations. Example
Investment firms use Monte Carlo simulations with
normal and log-normal distributions to estimate
future stock performance.
Customer Behavior Analysis
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Risk Assessment in Finance
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Quality Control in Manufacturing
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Predicting Demand in Supply Chain
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5. Applications in Business Analytics
Quality Control in Manufacturing Detecting
Defects Manufacturing processes rely on
statistical distributions to monitor defect rates
and process variations. The binomial
distribution is commonly used to analyze the
probability of defective items in a sample, while
the normal distribution helps assess deviations
from ideal product specifications. Example A
car manufacturer applies Six Sigma techniques
using normal distribution to detect variations in
engine component dimensions.
Customer Behavior Analysis
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Risk Assessment in Finance
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Quality Control in Manufacturing
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Predicting Demand in Supply Chain
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5. Applications in Business Analytics
Supply Chain Demand Forecasting Predicting
Inventory Needs Businesses use probability
distributions to forecast product demand,
supplier lead times, and inventory levels. The
Poisson and normal distributions help predict
demand fluctuations, while the exponential
distribution is used for modeling lead times and
delays. Example A retail store applies Poisson
distribution to estimate the number of daily
customer orders and plan restocking accordingly.
Customer Behavior Analysis
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Risk Assessment in Finance
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Quality Control in Manufacturing
3
Predicting Demand in Supply Chain
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6. Conclusion Key Takeaways
Understanding data distributions is crucial
Choosing the right model depends on the data type
and business need
Real-world applications span across multiple
industries
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Thats all folks

• Learning is an ART