Introduction to Big Data Course Guidelines

Big data is the massive amount of data generated and collected from various sources, including social media, online transactions, sensor data, etc.

The data deluge refers to the rapid increase in the volume, velocity, and variety of data generated by people, devices, and processes.

The consequences of the data deluge include the challenges of storing, processing, and analyzing vast amounts of information, leading to the need for big data analytics.

Big data refers to the point where the cost of storage becomes cheaper than the cost of discarding data, making it economically feasible to retain large volumes of data.

The class assessment is based on a final exam, an individual assignment, and a group project with presentation. Plagiarism and cheating are strictly prohibited.

To achieve a passing grade, students need to earn points through their performance in the final exam, assignment, and project. Communication with the instructor is crucial for addressing issues and informing them of potential deadlines.

The class requires an 80% attendance rate for students to be eligible to sit for the final exam. This rule ensures students engage actively and benefit fully from the course.

Class behavior requires students to maintain a quiet environment, allowing for focused learning. However, asking questions and engaging with the material is encouraged.

The point where the sheer volume, speed, and diverse types of data become overwhelming for an organization's systems to handle effectively for timely, accurate decision making.

The massive amount of data generated and collected, often exceeding the capacity of traditional processing and storage systems.

The rate at which data is generated and collected, requiring real-time processing and analysis.

The diverse formats and types of data, including structured, unstructured, and semi-structured data.

The changing nature of data over time, including variations in data values, formats, and standards.

The complexity of relationships, structures, and patterns within data, making it challenging to analyze and interpret.

The process of extracting meaningful insights from data, often involving statistical analysis, data mining, and visualization.

The use of data to understand customer behavior, improve business processes, and make better decisions.

SAS is a widely used software suite for business intelligence, known for its comprehensive range of tools for data management, advanced analytics, and reporting. It's a popular choice across various industries due to its powerful capabilities and industry-specific solutions.

R is a programming language specifically designed for statistical computing and graphics. It's favored by data scientists and statisticians for its wide range of statistical methods, graphical capabilities, and extensibility.

Hadoop is a popular open-source framework for handling and processing massive datasets, known for its scalability, handling huge amounts of data across multiple machines. It's a cornerstone of big data technologies.

Python is a versatile and widely used programming language known for its readability and ease of use. It's often used in web development, data science, machine learning, and various other applications.

Tableau is a visual analytics tool that allows users to create interactive dashboards and visualizations from data, providing insights for business decision-making. It's known for its user-friendly interface and ability to create compelling visuals.

Data Science, Advanced Analytics, and Software Engineering all come together to analyze and interpret large and diverse data sets.

Focuses on identifying the likelihood of future outcomes based on historical data. This allows for predicting future events.

Helps to understand what happened and why it happened by investigating key relationships and patterns within data.

Provides information about the optimal decisions to make based on predicted future scenarios. It suggests the best course of action.

A term that encompasses various fields and techniques for extracting meaningful patterns and knowledge from data.

A branch of computer science that focuses on enabling machines to learn from data. It involves algorithms that allow machines to improve their performance based on experience.

A collection of technologies and methodologies used to understand and interpret data, enabling the extraction of insights and trends.

A type of data analysis that goes beyond simply describing past events, predicting the likelihood of future outcomes.

A broader field that encompasses machine learning and other technologies. It aims to create intelligent machines that can perform human-like tasks.

A specialized field within AI that uses neural networks to learn from complex data. It powers advanced AI applications such as image recognition and natural language processing.

The ability of computers to understand and process information from images and videos. It enables tasks like object recognition and scene analysis.

The branch of AI focused on the interaction between humans and computers through natural language. It enables machines to understand and respond to human language.

The process of finding the best possible solution to a problem under given constraints. It aims to optimize resource usage and maximize outputs.

The rapid increase in the volume, velocity, and variety of data generated and collected from diverse sources, such as social media, online transactions, and sensor data.