DATA INSPIRED

[ADP] #1. Data Understanding

Subject 1 ‘Data Understanding’ covers the fundamental concepts and characteristics of data, emphasizing the importance of data exploration and big data analysis. Understanding data types, characteristics, collection, cleaning, visualization techniques, and recognizing the significance of data quality, technology, and skilled personnel in big data analysis are crucial. This knowledge helps in deriving business insights and supporting strategic decision-making.

Chapter 1: Concepts and Characteristics of Data

1. Definition and Types of Data

  • Structured Data: Data stored in fixed fields (e.g., database tables).
  • Unstructured Data: Data that is not organized in a pre-defined manner (e.g., text, images, videos).
  • Semi-structured Data: Data that has some organizational properties but is not fully structured (e.g., XML, JSON).

2. Characteristics of Data

  • Volume: The amount of data.
  • Velocity: The speed at which data is generated and processed.
  • Variety: The diversity in data forms and sources.
  • Veracity: The reliability and accuracy of data.

3. Value and Importance of Data

  • Data provides business insights, supports decision-making, and enhances efficiency.

Chapter 2: Data Exploration

1. Purpose and Importance of Data Exploration

  • Understanding the basic characteristics and patterns of data.
  • Identifying outliers, patterns, and relationships through data exploration.

2. Data Collection and Preparation

  • Collecting data from various sources (e.g., internal systems, external sources).
  • Data Cleaning: Handling missing values, removing duplicates, treating outliers.
  • Data Transformation: Normalization, discretization, feature extraction.

3. Data Visualization

  • Visually representing data characteristics and patterns for better understanding.
  • Key techniques: Histograms, box plots, scatter plots, pie charts, etc.

Chapter 3: Big Data Analysis

1. Overview of Big Data Analysis

  • Analyzing large data sets to extract meaningful insights.
  • Key stages: Data collection, cleaning, storage, processing, analysis, visualization, and interpretation.

2. Strategic Insights from Big Data Analysis

  • The Big Data Hype and Skepticism: Expectations and criticisms of big data.
  • Causes and Diagnosis of Big Data Skepticism: Data quality issues, limitations of analysis techniques, cost and time, lack of skilled personnel.
  • Big Data Analysis: ‘Big’ is Not the Key: Emphasizing data quality over quantity.
  • The Pitfall of Analysis Without Strategic Insights: The risk of analysis not linked to business goals.

3. Three Elements of Big Data Utilization

  • Data: Various sources and types, data quality management.
  • Technology: Data storage, processing, analysis technologies (e.g., cloud computing, machine learning).
  • People: The need for skilled data scientists, engineers, analysts.

답글 남기기

이메일 주소는 공개되지 않습니다. 필수 필드는 *로 표시됩니다