Welcome to the Introduction to Data Analysis course! This page provides an overview of the course content and what you can expect to learn.

Course Outline

  • Module 1: Introduction to Data Analysis

    • What is Data Analysis?
    • Importance of Data Analysis
    • Data Analysis Techniques

  • Module 2: Data Collection

    • Types of Data
    • Data Collection Methods
    • Data Quality and Cleaning

  • Module 3: Data Visualization

    • Introduction to Visualization Tools
    • Common Visualization Techniques
    • Creating Effective Visualizations

  • Module 4: Statistical Analysis

    • Descriptive Statistics
    • Inferential Statistics
    • Hypothesis Testing

  • Module 5: Predictive Modeling

    • Regression Analysis
    • Time Series Analysis
    • Machine Learning Basics

Learning Objectives

  • Understand the basics of data analysis and its importance in decision-making.
  • Learn how to collect, clean, and analyze data effectively.
  • Gain hands-on experience with data visualization and statistical analysis tools.
  • Develop skills in predictive modeling and machine learning.

Course Materials

  • Textbook: "Data Analysis for Business and Economics" by John W. Tukey
  • Software: Python, R, Excel

Prerequisites

  • Basic knowledge of statistics and mathematics
  • Familiarity with programming (Python or R is recommended)

Additional Resources

For further reading and additional practice, we recommend visiting our Data Science Resources page.

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