Introduction to Data in Data Analysis

Introduction to Data in Data Analysis

Lesson Plan: Introduction to Data for Data Analysis

Lesson Objective

By the end of the session, learners will:

  • Understand what data is and its importance.
  • Learn the types and sources of data.
  • Grasp the basics of data collection, cleaning, and analysis.
  • Be introduced to the role of data in business insights and decision-making.

Lesson Outline

Understanding Data

  1. What is Data?

    • Definition of data: Data refers to raw facts and figures that can be processed to extract meaningful information. It can be structured (organized in tables) or unstructured (like text or images).
    • Data vs. Information: The transformation of data into meaningful insights. Data vs Information
    • Examples of data in everyday life (e.g., weather reports, social media metrics).

  2. Types of Data

  3. Sources of Data

    • Internal vs. External Sources:

      • Internal: Company databases, employee records.
      • External: Market research, social media analytics.

    • Primary vs. Secondary Data:

      • Primary: Surveys, interviews, experiments.
      • Secondary: Articles, government reports, public datasets.

      Reference(s): Sources of Data, What are data sources

    Activity: Brainstorm sources of data learners might encounter in daily life.

Why Data Matters

  1. The Role of Data in Modern Business

    • How data drives decision-making (examples: pricing strategies, market trends).
    • Real-world examples of data-driven companies (e.g., Amazon, Google).

    Reference(s): Advantages of data driven decision making by Harvard Business School

  2. Applications of Data Analysis

    • Understanding customer behavior.
    • Improving operational efficiency.
    • Forecasting and risk management.

  3. Benefits of Data-Driven Decision-Making

    • Accuracy and precision in insights.
    • Competitive advantage in the market.
    • Cost reduction and profitability.

    Reference(s): Data driven decision making by Asana, A guide to data driven decision making by Tableau,

    Discussion: Learners share examples of how businesses around them use data.

Basics of Data Collection and Cleaning

  1. Data Collection Techniques

    • Surveys and Questionnaires.
    • Observations and Experiments.
    • Web scraping and API integration (brief mention).

    Reference(s): PiggyVest Savings Report 2023, PiggyVest Savings Report 2024, Data Collection by Simplilearn, Data Collection Techniques

  2. Introduction to Data Cleaning

    • Definition: Removing errors, duplicates, and inconsistencies.
    • Importance of clean data for accurate analysis.
    • Examples of errors: Typos, missing values, outliers.

    Reference(s): Data Cleansing by Wikipedia, What is data cleansing by Career Foundry, Data Cleaning by Career Foundry

    Activity: Display a messy dataset and ask learners to identify issues.

Data in Action

  1. Exploratory Data Analysis (EDA)

    • What is EDA? Gaining insights from raw data.
    • Overview of basic techniques:
      • Descriptive statistics: Mean, median, mode.
      • Visualizations: Bar charts, line graphs, scatter plots.
    • Walkthrough of a simple dataset in Excel or Google Sheets.

    Reference(s): Exploratory data analysis by IBM, Exploratory data analysis by Career Foundry

  2. Case Study: Data-Driven Insights

    • Present a real-life business scenario:
      • Example: Open Discussion on ways DDI can be applied in students’ current workplace, UNN ICT
    • Activity: Learners practice drawing insights from a simplified dataset.

  3. Future Steps in Data Analysis

    • Overview of advanced concepts:
      • Predictive analytics.
      • Machine learning.
    • Importance of continuous learning and practice.

    Reference(s): Predictive analysis by Harvard Business School, Predictive analysis by Investopedia, Predictive analysis by IBM, 5

Assessment

  • Questions throughout the session to gauge understanding.
  • Small group activity where learners summarize the session’s key points.
  • Optional: Provide a simple dataset for learners to analyze after class.

Homework

  • Research and note down 3 examples of how data is used in businesses or industries learners are interested in.
  • Explore and clean a small dataset using Excel or Google Sheets.