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Unit III - Data Analytics Life Cycle

Overview

  • Objective: DS-U3-ObjectiveDS-U3-ObjectiveThe objective of Unit III - Data Analytics Life Cycle is to provide a comprehensive understanding of the end-to-end process involved in performing data analytics projects. This unit aims to equip students with the knowledge and skills necessary to effectively manage and execute data analytics projects from inception to deployment. By covering each phase in detail, this unit ensures that students can systematically approach data analytics problems, plan and build models, and translate insights in

Resources

Syllabus Topics

  • Introduction,
  • Data Analytics Life CycleData Analytics Life CyclePasted image 20240512180604.png Overview : The data analytics lifecycle is a structured approach designed for tackling big data problems and data science projects. It consists of six phases, each of which can occur simultaneously and iteratively. This cyclical nature allows for flexibility, as work can return to earlier phases based on new information uncovered during the process. Mnemonic for Memorization We'll use the first letter of each phase to create a memorable sentence: D**iscovery
  • Data Analytical ArchitectureData Analytical Architecture![[Pasted image 20240512124835.png]] Data Analytical Architecture - An Overview Components of the Data Analytical Architecture Data Analytical Architecture Explained Data Loading and Warehouse Management: * Data loaded into the data warehouse must be well-understood, structured, and normalised with appropriate data types. * Centralisation of data provides security and backup facilities. It also allows for significant preprocessing and checkpoints before storing data. * Control over Enterpris
  • Introduction
    • Phase 1 - DiscoveryPhase 1 - DiscoveryOverview: Discovery marks the beginning of the data analytics lifecycle, focusing on understanding the purpose of the data analysis and how to achieve the desired outcomes by the end of the process. This phase involves identifying key objectives and mapping out the data to uncover valuable insights for the business. Key Processes: 1. Learning the Business Domain: * The team immerses themselves in understanding the specific industry or domain in which the business operates. * This involv
    • Phase 2 - Data PreparationPhase 2 - Data PreparationPhase 2: Data Preparation Overview: Data preparation involves collecting, processing, and cleaning data to make it ready for analysis. In this phase, the focus shifts from understanding business requirements to fulfilling data needs. It's like preparing ingredients before cooking a meal – ensuring everything is ready for the recipe. Key Processes: 1. Preparing the Analytic Sandbox: * The team creates an analytic sandbox or workspace, a safe environment for exploring data without affecting
    • Phase 3 - Model PlanningPhase 3 - Model PlanningModel Planning Overview: Model planning involves deciding on the methods, techniques, and workflow to be used in the subsequent model building phase. The team explores the data to understand relationships between variables and selects key variables and suitable models accordingly. Key Activities: 1. Assess Data Structure: * Evaluating the structure of the data helps determine the tools and analytic techniques needed for the next phase. 1. Alignment with Objectives: * Ensuring that the
    • Phase 4 - Model BuildingPhase 4 - Model BuildingPhase 4: Model Building Overview: In the model building phase, the team creates datasets for testing, training, and production purposes. They also construct and execute models based on the groundwork laid in the model planning phase. This is where the magic happens – turning plans into reality. Key Processes: 1. Designing the Model: * Identifying the most appropriate model for the analysis. This might involve considering various modeling techniques such as decision trees, regression, or n
    • Phase 5 - Communication ResultsPhase 5 - Communication ResultsCommunicate Results Overview: The communication of results is a crucial phase where the project team assesses the success or failure of the project and begins engaging with key stakeholders. It involves identifying significant findings, measuring their impact on the business, and presenting a concise narrative to convey the results effectively. Key Processes: 1. Identifying Key Findings: * The team identifies the most important discoveries and insights uncovered during the analysis. *
    • Phase 6 - OperationalisePhase 6 - OperationalisePhase 6: Operationalize Overview: Operationalize is the final phase where the project transitions from a testing environment (sandbox) to a live production environment. Data is monitored to ensure that the model performs as expected, and adjustments are made if necessary. The team also communicates the project's benefits widely and sets up pilot projects for controlled deployment. Key Processes: 1. Moving to Live Environment: * Data is moved from the sandbox to a live environment for real

Previous Year Questions (PYQs)

  • PYQs - (Data Analytics Life Cycle)
    1. Explain Data Analytics life cycle with the help of diagram.Explain Data Analytics life cycle with the help of diagram.Data Analytics Lifecycle: Overview The data analytics lifecycle is a structured approach to conducting data analytics projects from inception to completion. It encompasses various phases that involve understanding the business problem, preparing data, conducting analyses, and delivering actionable insights. This process helps ensure that the data analytics work is thorough, relevant, and aligned with business needs. The lifecycle concept is well articulated in texts like "Data Science & Big Dat
    2. List different phases in data analytics life cycle and explain Model Building phase in detail.List different phases in data analytics life cycle and explain Model Building phase in detail.Data Analytics Lifecycle Phases The Data Analytics Lifecycle is a structured framework that guides the steps needed to transform raw data into actionable insights. The framework can vary slightly depending on the source, but generally includes the following key phases: 1. Problem Definition: This phase involves identifying the business or research question that the data analytics project aims to address. It includes defining the scope of the project, the objectives, and the potential impact.
    3. What are different phases in data analytics life cycle? Explain Operationalize phase in detail.
    4. Explain Model building phase with its challenges.Explain Model building phase with its challenges.The model building phase in data science is a critical step where theoretical data understanding is translated into practical, actionable insights through various statistical models and machine learning algorithms. This phase is detailed extensively in texts such as "Data Science & Big Data Analytics" published by Wiley in 2015, and Chirag Shah's "A Hands-On Introduction to Data Science". Here, I'll provide a detailed explanation of the model building phase, complemented by an understanding of i
  • IMP Concepts and Questions - DS - U3 - DECODEDS - U3 - DECODEBased on the extracted content from the textbook PDF, here are the important questions for Unit 3: Data Analytics and Lifecycle. Unit 3: Data Analytics and Lifecycle Important Questions: 1. Data Analytics Lifecycle: * What are the key stages of the data analytics lifecycle? Describe each stage in detail. * Explain the importance of the data preparation phase in the data analytics lifecycle. 1. Data Collection: * Discuss the various methods of data collection. What are the advantage

Lecture Notes

  • DS III - Lecture Notes 1DS III - Lecture Notes 1* Discovery * Data Preparation * Model Planning * Model Building * Communication results * Operationalise
  • DS III - Lecture Notes 2DS III - Lecture Notes 2Analytics of Vidhya What is Data Science? | Lifecycle, Application, Tools & More Campus X Links: GitHub | Data Science Course Python Resources Pandas NumPy Matplotlib

Case Studies

Case Study 1: Global Innovation Social Network and Analysis (GINA).

  • Relevance: [Explain how it relates to the unit topics]

Exercises and Assignments

  • Assignment 3 - Data ScienceAssignment 3 - Data ScienceExplain Data Analytics lifecycle with the help of diagram. To explain the Data Analytics Lifecycle, it's important to outline the sequence of stages through which data passes—from its initial acquisition to the generation of insights. This lifecycle is crucial for transforming raw data into actionable information. Here, I'll describe each phase of the lifecycle and provide a diagram for better visualization. Stages of the Data Analytics Lifecycle 1. Business Case Evaluation: * Objective: U

Active Recall Questions

  • ARQ Set 1: DS-U3-ARQ 1DS-U3-ARQ 1Active recall questions are a powerful method for reinforcing learning and ensuring deep understanding. Here are some active recall questions based on Unit III - Data Analytics Lifecycle to help you prepare for the exam effectively: Unit 3: Data Analytics Lifecycle - Active Recall Questions 1. Data Analytics Lifecycle 1. What are the six key stages of the data analytics lifecycle? 1. Describe the main objectives and activities involved in the Discovery phase. 1. Why is the Model Planning phas

Mind Maps

  • Mind Map 1: DS-U3-MM Data Analytics LifecycleDS-U3-MM Data Analytics Lifecycle* Introduction * Key Stages * Discovery * Data Preparation * Model Planning * Model Building * Communicating Results * Operationalize * Data Collection * Methods * Surveys and Questionnaires * Web Scraping * Sensor Data * Transactional Data * Ensuring Data Quality * Data Cleaning * Techniques * Removing Duplicates * Handling Missing Values * Correcting Inconsistencies * Filtering Outliers * Challenges * Data Transformation *

Keywords and Flashcards

  • Learning Mnemonic: Unit 3 Learning The MnemonicUnit 3 Learning The MnemonicLearning The Mnemonic Certainly, crafting a mnemonic can be an effective strategy for memorizing the order of the six phases in the Data Analytics Lifecycle. Let’s focus on the first letter of each phase to create a memorable sequence: 1. Discovery 1. Data Preparation 1. Model Planning 1. Model Building 1. Communicating Results 1. Operationalization Given the sequence of initials, we have D, D, M, M, C, O. A mnemonic to encapsulate these letters could be: “Double Data, Models Multiply, Commun
  • DS-U3-KT&F-NoteDS-U3-KT&F-NoteKeywords Data Analytics Lifecycle** Discovery** Data Preparation** Model Planning** Model Building** Communicating Results** Operationalize** Data Collection** Data Cleaning** Data Transformation** Exploratory Data Analysis (EDA)** Data Integration** Data Reduction** Data Analysis** Data Interpretation** Data Visualization** Flashcards Flashcard 1 Front: What are the six key stages of the Data Analytics Lifecycle? Back: Discovery, Data Preparation, Model Planning, Model Building, Communicati
  • Flashcard Set 1:

Summary

  • DS-U3-S-NoteDS-U3-S-NoteKey Takeaways from Unit III - Data Analytics Lifecycle 1. Data Analytics Lifecycle Overview: * Phases: * Discovery: Understanding business problems, identifying data sources, and formulating hypotheses. * Data Preparation: Collecting, cleaning, and transforming data to ensure quality and usability. * Model Planning: Conducting exploratory data analysis (EDA), selecting modeling techniques, and planning the modeling approach. * Model Building: Developing and training pred
  • Key Takeaways:
  • Next Steps:
  • Condense Notes: DS-U3-Short SummaryDS-U3-Short SummaryCondensed Notes: Unit III - Data Analytics Lifecycle 1. Data Analytics Lifecycle Overview Phases:** * Discovery: * Understand business problems and objectives. * Identify data sources and formulate initial hypotheses. * Data Preparation: * Collect, clean, and transform data for analysis. * Ensure data quality and consistency. * Model Planning: * Conduct exploratory data analysis (EDA). * Select appropriate modeling techniques and tools. * Model Building: * D

Review Checklist

  • Revisit lecture notes ✅ 2024-05-24
  • Practice exercises ✅ 2024-05-14
  • Review flashcards ✅ 2024-05-14
  • Engage with case studies ✅ 2024-05-14
  • Test understanding with Active Recall Questions ✅ 2024-05-14
  • Update mind map as needed ✅ 2024-05-14