This course is designed to provide you with a comprehensive understanding of how to effectively explore and preprocess data using Python and SQL. Through hands-on exercises and practical examples, you will learn various techniques to clean, transform, and analyze data, enabling you to derive valuable insights and make informed decisions.

• Gain a solid understanding of the Exploratory Data Analysis (EDA) process and its significance in data analysis.
• Learn how to load, manipulate, and clean data using Python libraries such as Pandas and NumPy.
• Explore data visually using Matplotlib and Seaborn to uncover patterns, trends, and outliers.
• Understand data preprocessing techniques like handling missing values, dealing with categorical data, and feature scaling.
• Apply advanced techniques like dimensionality reduction and feature engineering to enhance data quality and model performance.

• Basic knowledge of Python programming language.
• Familiarity with fundamental concepts of data analysis and statistics.
• Understanding of SQL fundamentals would be beneficial but not mandatory.

Introduction to EDA and Data Preprocessing

• Importance of EDA in data analysis
• Overview of data preprocessing techniques

Data Loading and Cleaning with Python

• Introduction to Pandas and NumPy libraries
• Loading and inspecting data
• Handling missing values and outliers
• Data cleansing techniques

Exploratory Data Analysis (EDA)

• Data visualization using Matplotlib and Seaborn
• Descriptive statistics and summary metrics
• Identifying patterns and relationships in data

 Preprocessing Categorical and Numerical Data

• Dealing with categorical data using encoding techniques
• Feature scaling and normalization
• Binning and discretization

Introduction to SQL

• Introduction to SQL and relational databases
• Executing SQL queries for data manipulation