What you'll get
  • 2+ Hours
  • 1 Courses
  • Course Completion Certificates
  • Self-paced Courses
  • Technical Support
  • Case Studies

Synopsis

  • Basic Python skills for analyzing and visualizing data
  • How to import libraries and preprocess raw data
  • Data visualization techniques: pie charts, histograms, violin plots, and more
  • Advanced visualizations like heatmaps for correlation and basic predictive modeling
  • Applying clustering algorithms for dataset segmentation
  • Interpreting data to extract meaningful insights for decision-making
  • Implementing clustering methods and PCA in Python
  • Understanding the K-Means algorithm and its limitations
  • Explaining the Expectation–Maximization (EM) algorithm

Content

Courses No. of Hours Certificates Details
Project on Cluster Analysis: Customer Shopping Analysis1h 8m✔View Curriculum

Description

This course offers an easy and practical introduction to data analysis and visualization using Python. You will learn how to preprocess raw data, create a variety of visualizations, explore correlations, and apply basic clustering techniques to uncover meaningful insights from real-world datasets.

We begin by installing essential Python libraries such as Pandas, NumPy, Matplotlib, and Seaborn, then proceed to hands-on data-cleaning lessons on handling missing values, normalizing data, and encoding categories. You will then explore multiple visualization methods, including pie charts, histograms, violin plots, pair plots, and distribution plots, to understand both categorical and numerical data.

The course also covers heatmaps and correlation analysis to help you identify relationships between variables. In the modeling section, you will learn clustering techniques such as K-Means and apply them to practical scenarios like customer segmentation and shopping behavior analysis.

Through short lessons and hands-on exercises, you will learn to use Python for real-world data analysis confidently. By the end of the course, you will have a strong foundation in data preprocessing, visualization, clustering, and insight extraction, equipping you to make data-driven decisions in any domain.

 

Requirements

  • A foundational understanding of linear algebra, including vectors, matrices, matrix operations, determinants, and linear spaces

  • Basic knowledge of probability and statistics, such as mean, covariance, and normal distributions

  • Familiarity with Python 3 programming and running simple scripts.

Target Audience

  • Scientists, engineers, programmers, and data enthusiasts

  • Students and professionals entering machine learning or data science

  • Analysts and researchers applying ML to real-world data

  • Developers expanding their AI and modeling skills.