Data Handling and Preprocessing is a practical, hands-on training program designed to help participants develop the essential skills needed to collect, clean, transform, organize, and prepare data for analysis and machine learning. Since the quality of data directly affects the reliability of analytical results and predictive models, effective data preparation is a critical step in any data-driven project.
Duration 2 days – 14 hrs
Overview
This course introduces learners to the fundamental skills of working with datasets, cleaning and transforming data, and performing exploratory data analysis (EDA). Participants will gain hands-on experience in preparing raw data for AI and machine learning models, ensuring high-quality input for better outcomes.
Learning Objectives
- Understand the structure and types of datasets.
- Apply techniques for data cleaning and transformation.
- Visualize data to uncover patterns and insights.
- Perform basic exploratory data analysis (EDA) to inform decision-making.
- Prepare datasets for machine learning model training.
Audience
- Basic knowledge of Python programming (variables, data types, loops).
- Completion of a basic Python course (recommended but not required).
Prerequisites
- Basic algebra (addition, multiplication, simple equations).
- No advanced mathematics or programming knowledge required.
Course Content
Day 1: Understanding and Preparing Data
- Introduction to datasets: structure, formats (CSV, JSON, Excel)
- Loading and inspecting datasets using Python (Pandas)
- Data cleaning fundamentals:
- Handling missing values
- Removing duplicates
- Data type conversions
- Data transformation basics:
- Normalization and standardization
- Encoding categorical variables
Day 2: Visualization and Exploratory Data Analysis (EDA)
- Introduction to data visualization (Matplotlib, Seaborn)
- Creating basic charts: histograms, bar plots, scatter plots
- Identifying patterns, correlations, and outliers
- Introduction to summary statistics (mean, median, mode, variance)
- Basic EDA workflow:
- Formulating questions
- Visual storytelling with data
- Preparing datasets for machine learning
Final Hands-On Activity:
- Mini project: Clean, transform, and perform EDA on a sample real-world dataset.
