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Unit VI - Data Visualisation and Hadoop

Overview

  • Objective: [Briefly summarize the goals of this unit]
  • Instructor: [Instructor's name]
  • Weeks Covered: [Specify the weeks this unit spans]

Resources

  • Textbook Chapters: [List relevant chapters and pages]
  • Lectures:
    • Lecture 1 - Topic
    • Lecture 2 - Topic
  • Videos:
    • [Link to instructional video 1]
    • [Link to instructional video 2]
  • Additional Readings:
  • Tools & Software: [List any tools or software relevant to this unit]

Syllabus Topics

  • Introduction to Data VisualizationIntroduction to Data VisualizationData visualization is a critical component of data science, transforming raw data into visual formats that are easy to understand and interpret. This process helps uncover patterns, trends, and insights that might be missed in traditional data analysis. Below, I will delve into the concept of data visualization, its importance, and foundational principles. Introduction to Data Visualization Definition and Purpose: Data visualization is the graphical representation of information and data. By,
  • Types of data visualizationTypes of data visualizationAs an expert in Data Science, leveraging insights from "Data Science & Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data" by Wiley (2015) and Chirag Shah's "A Hands-On Introduction to Data Science" (Cambridge University, 2020), I can provide a detailed explanation of the types of data visualization. Types of Data Visualization Data visualization is the graphical representation of information and data. By using visual elements like charts, graphs, and maps, data visual,
  • Data Visualization TechniquesData Visualization TechniquesData visualization techniques are methods and tools used to represent data graphically, making it easier to understand and interpret large and complex datasets. Effective data visualization can reveal hidden patterns, trends, and insights that may not be immediately apparent from raw data. Here, I will explain various data visualization techniques in detail, incorporating insights from Data Science & Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data (Wiley, 2015) and A,
  • Tools used in Data VisualizationTools used in Data VisualizationData visualization tools are essential for transforming raw data into graphical representations that facilitate better understanding, analysis, and communication. As someone familiar with "Data Science & Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data" by Wiley and "A Hands-On Introduction to Data Science" by Chirag Shah, let's delve into the tools used in data visualization, categorizing them based on their capabilities and features. Categories of Data Visualization,
  • Challenges to Big data visualizationChallenges to Big data visualizationBig data visualization presents several challenges due to the complexity, scale, and nature of the data involved. Here are the primary challenges: 1. Volume Challenge: The sheer volume of data generated in big data environments is enormous, often running into terabytes or petabytes. Explanation: * Traditional visualization tools and techniques may not be capable of handling such large datasets efficiently. * Rendering large datasets can be time-consuming and computationally intensive, leadin,
  • Visualizing Big Data, Analytical techniques used in Big data visualizationVisualizing Big Data, Analytical techniques used in Big data visualizationVisualizing Big Data Visualizing Big Data involves representing large-scale datasets in a visual format that can be easily interpreted to identify patterns, trends, and insights. Due to the sheer volume, velocity, and variety of big data, traditional visualization techniques often fall short. Therefore, specialized methods and tools are needed to effectively visualize big data. Key Considerations in Big Data Visualization 1. Scalability: * Visualization tools must handle the high volume o,
  • Hadoop ecosystem, Map Reduce, Pig, HiveHadoop ecosystem, Map Reduce, Pig, HiveThe Hadoop ecosystem is a robust framework designed to store, process, and analyze vast amounts of data in a distributed computing environment. It consists of several key components, each serving a unique purpose within the ecosystem. Three of the most significant components are MapReduce, Pig, and Hive. Below, I provide a detailed explanation of each of these components. Hadoop Ecosystem Overview The Hadoop ecosystem includes a collection of open-source software utilities that facilitate the,.
  • Data Visualization using Python Line plot, Scatter plot, Histogram, Density plot, Box- plotData Visualization using Python Line plot, Scatter plot, Histogram, Density plot, Box- plotData Visualization Using Python: Line Plot, Scatter Plot, Histogram, Density Plot, Box Plot Data visualization is a crucial component of data analysis and presentation, providing insights that are often difficult to glean from raw data. Python, with its rich ecosystem of libraries, offers powerful tools for creating a wide range of visualizations. Below, we will delve into the specifics of line plots, scatter plots, histograms, density plots, and box plots, exploring their purposes, implementat.

Previous Year Questions (PYQs)

  • PYQs - (Data Visualisation and Hadoop)PYQs - (Data Visualisation and Hadoop)1. What is data visualization? What are the different methods of data visualization explain in detail. 1. Explain in the detail the Hadoop Ecosystem with suitable diagram. 1. Describe the Data visualization tool "Tableau". Explain its applications in brief. 1. With a suitable example explain and draw a Box plot and explain its usages. 1. With a suitable example explain Histogram and explain its usages.

Lecture Notes

  • Week 1: [Topic Name]

    • Summary: [Brief summary of the lecture content]
    • Key Concepts: [List key concepts discussed]
    • Important Diagrams/Models:
      • Diagram Description
    • Lecture Slides: Slides for Lecture X
    • External Resources:

  • Week 2: [Topic Name]

    • [Repeat format as needed for each week]

Case Studies

  • Case Study 1: Link to Case Study or Summary
    • Relevance: [Explain how it relates to the unit topics]
  • Case Study 2: Link to Case Study or Summary

Exercises and Assignments

  • Assignment 1 - Topic
  • Exercise Set 1

Active Recall Questions

  • ARQ Set 1: Link to Active Recall Questions
    • Q1: [Question]
    • Q2: [Question]

Mind Maps

  • Mind Map 1: [Link or embedded image]
    • Purpose: [Describe what this mind map helps to recall or understand]

Keywords and Flashcards

  • Flashcard Set 1: Link to Flashcards
    • Term 1: [Definition]
    • Term 2: [Definition]

Summary

  • Key Takeaways: [List major points learned in this unit]
  • Next Steps: [Suggestions for further study or related units]

Review Checklist

  • Revisit lecture notes
  • Practice exercises
  • Review flashcards
  • Engage with case studies
  • Test understanding with Active Recall Questions
  • Update mind map as needed