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Data Decoder: Making Sense of the Numbers

< Level: Intermediate >

Data Literacy

Welcome to “Data Decoder: Making Sense of the Numbers”. This enlightening course is designed to transform you into a proficient data whisperer! As an intermediate course, we assume a basic understanding of statistics and mathematics. We’ll delve into the art and science of transforming raw, lofty numbers into actionable insights that can greatly shape our AI-driven world.

Chapter Breakdown

  • Chapter 1 – Introduction to Data Literacy: Get introduced to the importance and principles of data literacy in today’s world.
  • Chapter 2 – Types of Data: Understand different types of data: structured & unstructured, numerical, categorical, ordinal, and more.
  • Chapter 3 – Data Collection: Learn about data collection methodologies, reliability, and bias.
  • Chapter 4 – Data Cleaning: Master ways to check data quality, clean, and preprocess data before analysis.
  • Chapter 5 – Data Visualization Basics: Understand the power of visual communication through charts, graphs, and data dashboards.
  • Chapter 6 -Using Excel for Data Analysis: Familiarize yourself with Excel functions and formulas needed for basic data manipulation.
  • Chapter 7 – Introduction to SQL: Get an introduction to SQL, and understand how it’s used to query databases and retrieve data.
  • Chapter 8 – Statistical Analysis Basics: Learn key statistical concepts necessary for data analysis, including mean, median, mode and standard deviation.
  • Chapter 9 – Correlations and Regression: Understand how variables relate with each other and study regression analysis.
  • Chapter 10 – Probability Concepts: Refresh your knowledge of key probability concepts used in data analysis.
  • Chapter 11 – Hypothesis Testing and Confidence Intervals: Learn to determine significance of data and its conformity to hypothesis using tests and confidence intervals.
  • Chapter 12 – Introduction to R: Get acquainted with R, a major tool for data manipulation, analysis, and visualization.
  • Chapter 13 – Introduction to Python for Data Science: Learn basics of Python with frameworks like pandas, numpy and matplotlib for data science.
  • Chapter 14 – Real-life Applications of Data Literacy: Discover how data literacy skills apply to real-world scenarios across different sectors, from healthcare to economics.
  • Upon completing this course, you’ll have the tools to make informed, data-backed decisions. You’ll be the human behind the data, the one who interprets, understands, and puts it to good use. So let’s dive in and begin decoding!

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