Reading Data
In this course, you'll learn how to read data from various sources, like surveys, sensors, and machines, and how to use Python tools to clean, aggregate, and visualize it. You'll also explore the value of data for different use cases and industries.
What you'll learn
Data is everywhere, but how can you make sense of it? In this course, Reading Data, you’ll gain the fundamental ability to identify and read data across a variety of sources, performing the first steps for clean and efficient data analysis.
First, you’ll explore the types of real-world data and the value they bring to different use cases, enabling you to determine what kind of data can best fit your use case.
Next, you’ll discover common data sources used in different industries (e.g.: surveys, sensor data, machine-generated data), their strengths and weaknesses, and their implementation using pandas with Python.
Then, you’ll implement data aggregations with sqlite3 in Python, allowing you to group and generate summary statistics and trends across multiple data points - a key aspect of data analysis.
Finally, you’ll learn how to understand and create impactful visualizations out of data to communicate insights from it.
When you’re finished with this course, you’ll have the skills and knowledge of reading data needed to mine different types of data from a variety of sources and maximize its value through aggregations and visualization, regardless of your use case.
Table of contents
- Applying Aggregations for Data Analysis 5m
- Basic Aggregations 1m
- Demo: Basic Aggregations - Part 1 7m
- Demo: Basic Aggregations - Part 2 6m
- SQL Commands for Data Analysis 2m
- Demo: SQL Commands for Data Analysis - Part 1 4m
- Demo: SQL Commands for Data Analysis - Part 2 6m
- Visualize Aggregations 0m
- Demo: Visualize Aggregations 6m
- Summary 1m