Data Processing

The Data Processing category focuses on turning raw data into clean, usable input for analysis and machine learning. You’ll learn how to read, clean, transform, and prepare data using Python, with an emphasis on practical techniques used in real projects.

This category builds on Python fundamentals and introduces the essential skills needed to handle datasets from files, sensors, APIs, and experiments.

What You’ll Learn

In this category, you’ll learn how to work confidently with real-world data and prepare it for further analysis or modeling.

By the end of Data Processing, you’ll be able to:

  • Read and write common data formats such as CSV and JSON
  • Clean and transform raw data using Python
  • Work with numerical data efficiently
  • Handle missing, noisy, or inconsistent data
  • Prepare datasets for analysis and machine learning

Learning Path

Working with Files and Data Formats

Learn how to load, inspect, and save data using common file formats used in data workflows.

  • Reading and Writing Text Files
  • Working with CSV Files
  • Parsing JSON Data
  • Handling Binary Data
  • Understanding File Paths and Encoding

Numerical Data with NumPy

Learn how to work efficiently with numerical data using NumPy arrays and operations.

  • Introduction to NumPy Arrays
  • Array Shapes and Indexing
  • Vectorized Operations
  • Basic Statistics with NumPy
  • Working with Multidimensional Data

Data Cleaning and Transformation

Learn how to prepare raw data by fixing common issues and transforming it into a usable format.

  • Handling Missing Values
  • Data Type Conversion
  • Filtering and Sorting Data
  • Normalization and Scaling
  • Removing Noise and Outliers

Time-Series and Sensor Data

Learn techniques for working with sequential data commonly found in logs and sensor readings.

  • Understanding Time-Series Data
  • Sampling and Resampling
  • Windowing and Segmentation
  • Basic Signal Processing Concepts
  • Working with Sensor Data Streams

Feature Preparation

Learn how to transform raw data into meaningful inputs for machine learning models.

  • What Are Features and Why They Matter
  • Feature Extraction Basics
  • Feature Selection Concepts
  • Encoding Categorical Data
  • Preparing Data for Model Training

Who This Category Is For

This category is ideal if you:

  • Know basic Python and want to work with real data
  • Need to clean and prepare datasets for analysis or ML
  • Want practical data-handling skills, not just theory
  • Are preparing to move into data science or machine learning

Begin with the first lesson and work through each section in order. Each lesson focuses on practical techniques you can apply immediately to real datasets.