Byte-Sized ML Series: Data Exploration and Analysis
- Created By raju2006
- Last Updated December 12th, 2023
- Overview
- Prerequisites
- Audience
- Audience
- Curriculum
Description:
This brief session will provide exposure to both data exploration and data analysis and their contributions to effective machine learning. We will discuss how to clean and prepare the data for analysis i.e., data cleansing and data pre-processing and how exploring the data i.e., EDA(exploratory data analysis)can draw your attention to what’s important. We will also examine how data exploration and data analysis can extract valuable insights that often enhance data understanding and the machine learning process.
Course Code/Duration:
BDT175 / 90 minutes.
Learning Objectives:
You will learn:
- Approaches to cleaning and preparing data for analysis
- How data exploration can help identify what’s important
- The effectiveness of data visualization
- How data analysis provides a deeper understanding of the data
- The contributions of data exploration and analysis to improved machine learning models
Training material provided:
Yes (Digital format)
- No prior technical knowledge.
- This session is for learners who would like to become familiar with the machine learning process.
- This session is for learners who would like to become familiar with the machine learning process.
Course Outline:
Introduction to Data Exploration and Analysis
- Overview of the role of data exploration and analysis in machine learning.
- Understanding the significance of data cleansing and pre-processing.
- Approaches to Data Cleaning
- Techniques for cleaning and preparing data for analysis.
- Importance of data quality in machine learning.
- Exploratory Data Analysis (EDA)
Introduction to EDA and its role in drawing attention to crucial aspects of the data.
- The effectiveness of data visualization in the exploration process.
- Insights from Data Analysis
How data analysis provides a deeper understanding of the data.
- Extracting valuable insights to enhance data understanding.
- Contributions to Machine Learning
Understanding the direct contributions of data exploration and analysis to improved machine learning models.
- Real-world examples showcasing the impact on the machine learning process.
