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Inductive and Deductive Methods for Data Analytics Projects
Deductive: Top-down approach. Take existing theories and apply to data
Inductive: Bottom-up approach. Observe data and derive a hypothesis.
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Examples in Data Analytics:
Inductive: Analyzing customer purchase data to identify recurring patterns in buying habits, which can lead to new marketing strategies or product recommendations.
Deductive: Testing a marketing hypothesis about the effectiveness of a new ad campaign by comparing its performance against a control group.
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Ref: Internet/Google AI
In a data analytics project, you may use both of the approaches. Initially, you may use an inductive approach to understand and explore data. Then you use the deductive method to test a specific hypothesis.
“This formula ensures that both precision and recall must be high for the F1 score to be high. If either one drops significantly, the F1 score will also drop.”
LEAF and CNN:
LEAF: “Leaf: A learnable frontend for audio classification,” ICLR, 2021
“What Is an Autoregressive Integrated Moving Average (ARIMA)?
An autoregressive integrated moving average, or ARIMA, is a statistical analysis model that uses time series data to either better understand the data set or to predict future trends.
A statistical model is autoregressive if it predicts future values based on past values. For example, an ARIMA model might seek to predict a stock’s future prices based on its past performance or forecast a company’s earnings based on past periods.” : Ref: Investopedia
GCN: Graph Convolutional Networks (GCNs): Architectural Insights and Applications
“GCNs are tailored to work with non-Euclidean data, making them suitable for a wide range of applications including social networks, molecular structures, and recommendation systems.“
Prophet is a procedure for forecasting time series data based on an additive model where non-linear trends are fit with yearly, weekly, and daily seasonality, plus holiday effects.
“Single community-based linear models refer to statistical models where a single linear equation is used to predict a response variable based on the characteristics of a single community or group. These models assume a linear relationship between the predictor variables and the outcome within that specific community”
Multiple Community-Based Linear Models
“The term “Multiple Community-Based Linear Models” likely refers to a modeling framework where separate linear models are fitted for different communities (e.g., neighborhoods, schools, cities, regions), rather than combining all data into a single model.” Reference: ChatGPT Also, this may be reference: https://www.stats.ox.ac.uk/~snijders/mlbook.htm
• “A comprehensive, data-driven strategy for problem solving”
Analytics
• “Analytics uses logic, inductive and deductive reasoning, critical thinking, and quantitative methods along with data to examine phenomena and determine its essential features”
• “any solution that supports the identification of meaningful patterns and relationships among data.”
CONCEPTS [1]
Analytics Methods [1]:
[1] Ref: A Book: The Analytics Lifecycle Toolkit A Practical Guide for an Effective Analytics Capability, Wiley
Associations are relationships between classes in a UML Class Diagram i.e. How these classes are associated (in real world). Two types: Aggregation and Composition.
Aggregation and Composition are subsets of association meaning they are specific cases of association. In both aggregation and composition object of one class “owns” object of another class. But there is a subtle difference:
Aggregation implies a relationship where the child can exist independently of the parent. Example: Class (parent) and Student (child). Delete the Class and the Students still exist.
Composition implies a relationship where the child cannot exist independent of the parent. Example: House (parent) and Room (child). Rooms don’t exist separate to a House.
“Generalization is a mechanism for combining similar classes of objects into a single, more general class.”
“Specialization is the reverse process of Generalization means creating new sub-classes from an existing class.”
For OOP Concepts: Polymorphism, Encapsulation, Data Abstraction and Inheritance in Object-Oriented Programming
Needed to format a 1TB Memory Card. 64 KB seemed to be a good Block Size for general use. Windows by default did not give an option to select a smaller block size. Powershell commands could be used or 3rd party software could be used.
Singleton Design Pattern: Limit instantiation of a clas to only one instance
Prototype Design Patterns: Object creation based on Prototype Object Instance; Simpler Object Creation than Factory.
Builder Design Patterns: Object Creation, Complex Object Creation, Keep Complex Object Creation separate from it’s representation.
For Factory: Simple Factory, Factory Method, Abstract Factory
Factory Method: Construction separated from implementation. Objects can be created without defining the exact class of object to be created.
Abstract Factory: One layer above Factory Method. Super Factory. Creates other Factories to create objects.
Builder Design Patterns: Helps to create complex objects. Uses construction methods/processes that can be used to create different representations of Objects. Step by step construction.
