Relational Data Model
Data Relationships
Data Models
Data Architecture
The Evolution of Big Data Technologies
Data Modeling
Creating Machine Learning Pipelines
Understanding Feature Engineering
Data Models 
Data models are essential frameworks used in business analytics and statistical analysis to structure, organize, and manage data
...types include: Conceptual Data Model Logical Data Model Physical Data Model Dimensional Data Model
Relational Data Model 1
...
Data Relationships Data relationships refer to the connections and associations between different data elements within a dataset
...Data Relationship
Models Several models are used to represent data relationships, including: Model Description Entity-Relationship Model (ER Model) A visual representation of entities (data points) and
...Relational Model A model based on the theory of relations, where data is organized into tables that can be linked through relationships
...
Data Models Data models are essential frameworks that define how data is structured, stored, and manipulated within a business environment
...These models differ significantly from traditional
relational models and include: Document-based models (e
...
Data Architecture Data architecture refers to the structural design of an organization's data assets
...It encompasses the
models, policies, rules, and standards that govern the collection, storage, integration, and usage of data within an organization
...Common storage solutions include:
Relational Databases: Structured data storage using tables (e
...
The Evolution of Big Data Technologies Big
Data refers to the vast volumes of structured and unstructured data generated every second in our digital world
...Traditional
relational database management systems (RDBMS) were primarily designed to manage structured data
...Codd proposed the relational
model, leading to the creation of SQL (Structured Query Language)
...
Data Modeling Data modeling is a crucial aspect of business analytics and big data management that involves creating a conceptual representation of data structures, relationships, and constraints
...Business intelligence, reporting NoSQL Data Modeling Techniques tailored for non-
relational databases, focusing on data storage and retrieval
...
Creating Machine Learning Pipelines Machine learning pipelines are a series of
data processing steps that transform raw data into a format suitable for training machine learning
models
...include: Data Source Description Databases Structured data stored in
relational databases
...
Understanding Feature Engineering It plays a significant role in the overall success of machine learning
models, as the quality of the features can greatly influence the accuracy and effectiveness of predictions
...is Feature Engineering? Feature engineering refers to the process of using domain knowledge to extract features from raw
data ...Featuretools A Python library for automated feature engineering, allowing users to create features from
relational datasets
...
Data Science Data Science is an interdisciplinary field that utilizes scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data
...Model Building Developing predictive models using machine learning algorithms
...SQL Language for managing and querying
relational databases
...
Data Preparation for Machine Learning Projects Data preparation is a critical step in the machine learning workflow that involves transforming raw data into a clean and usable format
...Effective data preparation can significantly enhance the performance of machine learning
models, making it an essential component of business analytics and machine learning projects
...Source Description Databases Structured data stored in
relational databases
...
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