cluster architecture

Responsible for the installation and maintenance of software.

Overseeing the management of databases, ensuring their efficiency and security.

Data science is not a standalone course but a comprehensive major integrating various disciplines. It encompasses:

- Data Mining: Uncovering actionable insights from data.

- Big Data: Handling and analyzing massive datasets.

- Machine Learning: Utilizing algorithms to enable systems to learn and make predictions.

Understanding and manipulating matrices and vectors, fundamental to handling high-dimensional data.

Techniques for optimizing models and algorithms to improve efficiency and performance in handling large datasets.

A method for solving complex problems by breaking them down into simpler, overlapping subproblems, often used in optimization.

Utilizing hashing techniques, such as Locality-Sensitive Hashing (LSH) and Bloom filters, for efficient data storage and retrieval.

Concepts related to handling data streams and concurrent processing.

Data that is small enough for human inference and accumulated slowly.

Data generated in huge volumes and could be structured, semi-structured, or unstructured.

Data Collection

Data Collection

Data Modeling

Data Processing

Data Visualization

The process of collecting data as a result of a business problem.

Creating a data model to make sense of the collected data and establish relationships.

Using tools like Apache Spark to process and analyze the modeled data.

Presenting data in a graphical format to derive meaningful insights.

An interconnected network of smart devices that collect, analyze, and act upon data.

Execution of algorithms on a single CPU, with direct data access from memory

The ability of a system to handle an increasing amount of data, including storage scaling and computational scaling.

there are six tool in Big Data

• Analytics an Visualization

• Business Intelligence

• Cloud providers

• NoSQL

• Programing tools

• data technology

Different types of data, such as high-dimensional data, graph data, infinite data, and labeled data.

Finding human-interpretable patterns that describe the data.

Using variables to predict unknown or future values of other variables.

Dealing with machine failures and redundancy in storage infrastructure.

A file system that enables clients to access file storage from multiple hosts through a computer network.

In the realm of data mining, various tasks contribute to the extraction of meaningful insights from vast datasets. For this course, our focus is on installing existing software and conducting analyses without delving deeply into database intricacies.

Data mining involves the extraction of substantial, Actionable Data

Conclusive Meaning: The data leads to meaningful conclusions.

Relevance: The insights extracted are pertinent to the objectives at hand.
Consider actionable data as information that goes beyond raw figures. It leads to conclusions that hold significance. An example could be extracting insights that not only provide statistical information but also contribute to informed decision-making.