0. Подготовительные проекты для машинного обучения

Mastering Data Analysis: Controlled & Uncontrolled Learning

This course offers a comprehensive collection of materials focused on both controlled and uncontrolled learning methodologies.

Important Note: This course incorporates updated content from two previously published courses on my Udemy profile. If you've taken those courses, please note that this is a consolidated and refined version designed for a more cohesive and streamlined learning experience – eliminating the need to navigate between separate courses.

Practical Projects & Real-World Applications

This course emphasizes hands-on learning through four practical projects, applying data analysis techniques to real-world scenarios.

Project 1: A/B Testing Web Traffic

Dive into the world of digital marketing with this project. You'll analyze company web traffic data to determine the impact of different website versions on conversion rates. You will learn to:

• Clean and explore web data effectively using Pandas.
• Apply statistical tests like the t-test and hypothesis testing.
• Visualize results and make data-driven decisions.
• Prepare a report with actionable insights to optimize website performance.

Project 2: Analyzing Banking Data for Machine Learning

This practical case study focuses on analyzing a financial dataset containing customer information from a bank. You'll prepare the data for machine learning applications, identifying patterns and insights. Key skills you'll develop include:

• Data cleaning and transformation using Pandas and NumPy.
• Visual analysis with Seaborn to uncover trends and correlations.
• Applying descriptive statistics to understand customer behavior.
• Data preparation for subsequent model training.

Project 3: Insurance Data Analysis

Work with a real-world insurance company dataset to identify key factors influencing policy costs and customer risk. You will gain expertise in:

• Cleaning and transforming customer and claim data.
• Applying Exploratory Data Analysis (EDA) to identify anomalies and trends.
• Using statistics and visualization to support informed decision-making.
• Preparing data for building risk assessment models.

Project 4: Analyzing Malware Data

Explore malware data to identify cyberattack patterns and prevent vulnerabilities. This project will focus on:

• Cleaning and structuring malware data.
• Data visualization to highlight important characteristics.
• Applying preprocessing techniques to enhance data quality.