This course at National Taipei University delves into fundamental concepts, research issues, and practical applications of Big Data Analysis. Taught by Dr. Min-Yuh Day, the syllabus covers topics such as AI, machine learning, deep learning, and industry practices related to big data analysis. Studen

5 views • 80 slides

The Digital Personal Data Protection Act of 2023 aims to regulate the processing of digital personal data while balancing individuals' right to data protection and lawful data processing. It covers various aspects such as obligations of data fiduciaries, rights of data principals, and the establishm

3 views • 28 slides

NCI Data Collections BARPA & BARRA2 serve as critical enablers of big data science and analytics in Australia, offering a vast research collection of climate, weather, earth systems, environmental, satellite, and geophysics data. These collections include around 8PB of regional climate simulations a

6 views • 22 slides

The wheat research community faces challenges in meeting the increasing demand for wheat production due to a lack of data harmonization and standards. The Wheat Data Interoperability Working Group aims to improve the interoperability of wheat-related data through shared guidelines, tools, and recomm

4 views • 10 slides

The integration of NLP into data pipelines represents a paradigm shift in data engineering, offering companies a powerful tool to reinvent their data workflows and unlock the full potential of their data. By automating data processing tasks, handling diverse data sources, and fostering a data-driven

9 views • 2 slides

The integration of NLP into data pipelines represents a paradigm shift in data engineering, offering companies a powerful tool to reinvent their data workflows and unlock the full potential of their data. By automating data processing tasks, handling diverse data sources, and fostering a data-driven

7 views • 2 slides

Explore the practical steps involved in analyzing single-cell RNA sequencing (scRNA-seq) data with Seurat in this informative session. Learn how to install R and essential packages, download example datasets, understand raw data files, load data into R-Studio, and access data within a Seurat object.

3 views • 19 slides

In today's data-driven world, organizations rely on robust data engineering pipelines to collect, process, and analyze vast amounts of data efficiently. At the heart of these pipelines lies data wrangling, a critical process that involves cleaning, transforming, and preparing raw data for analysis.

2 views • 2 slides

Data wrangling tool like Ask On Data plays a pivotal role in reshaping IT strategies by elevating data quality, streamlining data preparation, facilitating data integration, empowering citizen data scientists, and driving innovation and agility. As businesses continue to harness the power of data to

2 views • 2 slides

In current data world, businesses thrive on their ability to harness and interpret vast amounts of data. This data, however, often comes in raw, unstructured forms, riddled with inconsistencies and errors. To transform this chaotic data into meaningful insights, organizations need robust data wrangl

0 views • 2 slides

Organizations generate and gather enormous amounts of data from diverse sources in today's data-driven environment. This raw data, often unstructured and messy, holds immense potential for driving insights and informed decision-making. However, transforming this raw data into a usable format is a ch

0 views • 2 slides

The importance of a robust data wrangling tool like Ask On Data cannot be overstated in today's data-centric landscape. By streamlining the data preparation process, enhancing productivity, ensuring data quality, and fostering collaboration, Ask On Data empowers organizations to unlock the full pote

0 views • 2 slides

Data migration tools are indispensable for organizations looking to transform their big data into actionable insights. Ask On Data exemplifies how these tools can streamline the migration process, ensuring data integrity, scalability, and security. By leveraging Ask On Data, organizations can achiev

0 views • 2 slides

Data wrangling is the cornerstone of effective business intelligence. Without clean, accurate, and well-organized data, the insights derived from analysis can be misleading or incomplete. Ask On Data provides a comprehensive solution to the challenges of data wrangling, empowering businesses to tran

0 views • 2 slides

In today's data-driven world, the ability to seamlessly migrate and manage data is essential for businesses striving to stay competitive and agile. Data migration, the process of transferring data from one system to another, can often be a daunting task fraught with challenges such as data loss, com

1 views • 2 slides

Graph-based data science offers a powerful approach to analyzing data by leveraging graph structures. This involves using graph representation, analysis algorithms, ML/AI techniques, kernels, embeddings, and neural networks. Real-world examples show the utility of data graphs in various domains like

3 views • 37 slides

Delve into the world of data science with Grade IX Version 1.0! This educational material covers essential topics such as the definition of data, distinguishing data from information, the DIKW model, and how data influences various aspects of our lives. Discover the concept of data footprints, data

1 views • 31 slides

Exploratory Data Analysis (EDA) is a crucial approach for analyzing data by utilizing various techniques to extract insights, identify anomalies, and visualize trends. By leveraging EDA using tools like Pandas, researchers can improve their understanding of data variables, detect errors, and explore

1 views • 14 slides

The purpose of data analysis is to organize, structure, and derive meaning from research data. Qualitative analysis involves insight, creativity, and hard work. Researchers play a crucial role as instruments for data analysis, exploring and reflecting on interview discussions. Steps include transcri

1 views • 27 slides

Data Governance and Data Analytics play crucial roles in transforming data into knowledge and insights for generating positive impacts on various operational systems. They help bring together disparate datasets to glean valuable insights and wisdom to drive informed decision-making. Managing data ma

0 views • 8 slides

Data governance in Grade XII Data Science Version 1.0 covers aspects like data quality, security, architecture, integration, and storage. Ethical guidelines emphasize integrity, honesty, and accountability in handling data. Data privacy ensures control over personal information collection and sharin

7 views • 44 slides

Explore the intricacies of analyzing 10X Single-Cell RNA-Seq data, from how the technology works to using tools like CellRanger, Loupe Cell Browser, and Seurat in R. Learn about the process of generating barcode counts, mapping, filtering, quality control, and quantitation of libraries. Dive into di

0 views • 34 slides

TrueSight IT Data Analytics Architecture provides a comprehensive framework for collecting, indexing, and analyzing data from target hosts. Components like Console Server, Configuration Database, Collection Agents, and more work together to ensure efficient data processing and storage. The architect

0 views • 12 slides

The Data Quality Monitor (DQM) plays a crucial role in ensuring the effective data taking period in the EEE Run Coordination Meeting for schools. It involves monitoring key quantities using specific tools, analyzing data, and checking for correct behavior. The acquisition of data is essential for st

0 views • 33 slides

Data preparation, also known as data pre-processing, is a crucial step in the data mining process. It involves transforming raw data into a clean, structured format that is optimal for analysis. Proper data preparation ensures that the data is accurate, complete, and free of errors, allowing mining

1 views • 37 slides

Sentiment analysis, also known as opinion mining, is the process of analyzing text to determine if it expresses a positive, negative, or neutral sentiment. It has applications in analyzing movie reviews, product feedback, public opinion, and political sentiments. By extracting and analyzing sentimen

0 views • 34 slides

Explore the impact and role of data utilization in organizations through the investigation of data collection methods, data quality, decision-making processes, reliability of collection methods, factors affecting data quality, and privacy considerations. Two scenarios are presented: data collection

1 views • 24 slides

Development of guidelines for publishing statistical data as linked open data, merging statistics and geospatial information, with a primary focus on preparing a background for LOD implementation in official statistics. The project aims to identify data sources, harmonize statistical units, transfor

1 views • 31 slides

Epistemic Network Analysis (ENA) is a method for studying relationships between elements in coded data with various applications. It involves analyzing nodes as occurrences of codes and links as co-occurrences of codes. Using examples like analyzing quitting behavior in a game, ENA can compare group

0 views • 30 slides

Explore the journey of data from collection to preservation in a group setting. Post-its are arranged to represent the different stages like Analyzing Data, Preserving Data, Processing Data, and more. Snippets cover tasks such as Collecting data, Migrating data, Managing and storing data, and more,

0 views • 4 slides

Exploring new data involves asking general questions about variables, data format, types of variables, and whether it's sample or population data. Steps include identifying data variables, calculating statistics like mean and standard deviation, and analyzing summaries to gain insights into informat

0 views • 29 slides

INDEPTH Network emphasizes the importance of iSHARE2 & CiB to enhance data sharing and management among member centers. iSHARE2 aims to streamline data provision in a standardized manner, while CiB provides a comprehensive data management solution. The objectives of iSHARE2 include facilitating data

0 views • 17 slides

Big data involves analyzing and extracting information from large and complex datasets that traditional software cannot handle. AI algorithms play a crucial role in processing big data to find patterns that humans may overlook. Ethical considerations arise in defining what is "interesting" in the da

0 views • 25 slides

Data collection is essential for analyzing business performance and making informed decisions. Various data collection methods like primary and secondary data collection help evaluate outcomes and draw conclusions. Primary data includes raw information obtained firsthand, while quantitative methods

0 views • 15 slides

Business Intelligence (BI) involves utilizing techniques and tools to convert raw data into valuable insights for strategic decision-making. By analyzing unstructured data, BI helps identify new opportunities and enhance business strategies for competitive advantage and long-term success. Components

0 views • 7 slides

Anne and Kate, two new students at the school, are analyzing their classmates' data to understand them better. Anne calculates averages of social media accounts, phone usage, and weekly spending, while Kate uses a stem-and-leaf diagram to analyze the number of people classmates talk to daily. Differ

0 views • 5 slides

This content highlights the importance of data governance in early childhood data systems, focusing on Part C and Part B 619 data systems. It discusses the findings from the DaSy Center needs assessment, covering topics such as data governance, data quality, and procedures for ensuring accurate and

0 views • 23 slides

Learn about the roles of Data Protection Officers (DPOs), the Data Protection Act (DPA) of 2004, key elements of the act, definitions of personal data, examples of personal data categories, and sensitive personal data classifications. Explore how the DPO enforces privacy rights and safeguards person

0 views • 33 slides

This module delves into various types of data, the sensitivity of different data types, data access, legal aspects, and data classification. Explore aggregate data, microdata, methods of data collection, identifiable, pseudonymised, and anonymised data. Learn to differentiate between individual heal

0 views • 13 slides

Evolution of data ecosystem, importance of data ethics for data scientists, and understanding data governance framework are crucial aspects covered in this content. Examples of data breaches highlight the need for ethical data collection practices, while implementing a data governance framework ensu

0 views • 77 slides