Overview What You Will Learn Curriculum Instructor

Course Overview

Data mining is a critical field in modern data science, enabling organizations to discover patterns, extract valuable insights, and make data-driven decisions. As businesses increasingly rely on data, understanding how to mine it effectively becomes a vital skill for professionals in analytics, marketing, finance, and beyond. This course provides a practical, hands-on approach to learning data mining, guiding learners through its real-world applications, techniques, and tools that empower smarter business strategies.

This course begins with the basics of data mining, including its importance and application in business contexts. It moves on to the essentials of handling datasets, preprocessing, and transforming data for analysis. Learners explore mining techniques such as market basket analysis and the Apriori algorithm, before diving into clustering using DBSCAN and advanced techniques like the Birch algorithm. The course also includes decision trees, ensemble methods like bagging and boosting, and methods to detect and handle outliers. It concludes with a comprehensive view of the data mining project pipeline, featuring real-world implementation strategies and tools.

This chapter-based course equips learners with practical knowledge of data mining techniques, from foundational concepts to advanced applications, preparing them to solve complex business problems using data.

What You Will Learn

  • Data Mining techniques and practical uses
  • How different industries uses data mining as a tool
  • How we can increase the business as a data scientist using data mining
  • How real-world data flows and pain to clean it and use it
  • Advance clustering and classification techniques with deep understanding of mathematics

Program Curriculum

  • What is Data Mining and Why Use It?
  • Practical Business Use Case in Data Mining
  • $7 Million Cybersecurity Scholarship by EC-Council
  • Chapter 1 Quiz

  • What are Datasets?
  • Understanding Pre-processing
  • Data Reduction and Transformation
  • Chapter 2 Quiz

  • Mining Frequency Patterns
  • Market Basket Analysis
  • Apriori Algorithm
  • Pattern Growth Approach
  • Chapter 3 Quiz

  • What is Clustering and Its Uses?
  • What is DBSCAN?
  • Implementation of DBSCAN
  • Chapter 4 Quiz

  • Understanding Decision Trees
  • Hands-on with Decision Tree
  • Hands-on Bagging and Boosting Technique
  • Chapter 5 Quiz

  • What are Outliers and Their Disadvantages
  • Basic Outlier Detection with Observation
  • Hands-on with Statistical and Proximity Methods
  • Chapter 6 Quiz

  • Understanding Basic Steps for Any Project Implementation
  • Hands-on Encoding and Data Cleaning Techniques
  • Hands-on Data Mining Techniques
  • Chapter 7 Quiz

  • Introduction to Birch Algorithm
  • Deep Dive into Birch Algorithm
  • Hands-on with Birch Algorithm
  • Chapter 8 Quiz
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Instructor

Vivek Chaudhary chaudhary

Vivek Chaudhary currently works as a freelance data scientist and has worked with different product-based & EdTech startups. He has published one of the best-selling books on Amazon, “Data Investigation-EDA the right way”. His areas of expertise are applied statistics, EDA, data cleaning techniques, and feature engineering and process to building statistical models. According to him “Building Assumptions” is the important factor to apply statistical tools in real-time. If you can’t build assumptions, then no matter how much you learn at the end, it will be difficult to apply statistical techniques. He has mentored 200+ professionals to start their journey and helped them understand applied statistics & EDA.

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