Data Engineering : Python,Machine Learning,ETL,Web Scraping

Data Engineering : Python,Machine Learning,ETL,Web Scraping, available at $54.99, with 128 lectures, and has 10 subscribers.

You will learn about Understand the Role of Data Engineering: Grasp the significance and responsibilities of data engineering within the broader data ecosystem. Learn Key Data Engineering Concepts: Familiarize with essential terminology and concepts in data engineering. Set Up a Python Environment: Successfully install Python and create virtual environments on both Windows and macOS. Utilize Jupyter Notebook: Install, set up, and navigate Jupyter Notebook for interactive data analysis and visualization. Develop Python Programming Skills: Understand and apply Python programming fundamentals, including expressions, statements, and data types. Manipulate Data Structures in Python: Efficiently use Python lists, tuples, and dictionaries. Perform Data Manipulation with Pandas: Use Pandas to create, manipulate, and analyze data in Series and DataFrames. Load and Inspect Datasets: Import datasets into Pandas DataFrames and perform initial data inspection. Clean and Transform Data: Apply data cleaning and transformation techniques to prepare data for analysis. Visualize Data with Python: Create various types of visualizations to explore and present data insights. Understand Machine Learning Basics: Gain a foundational understanding of machine learning concepts and workflows. Preprocess Data for Machine Learning: Perform data preprocessing tasks including handling missing values, encoding categorical variables, and feature engineerin Train and Evaluate Machine Learning Models: Train machine learning models, make predictions, and evaluate their performance using appropriate metrics. Work with Logistic Regression Models: Train, evaluate, and interpret logistic regression models. Visualize Model Evaluation Metrics: Create visualizations to interpret confusion matrices and other evaluation metrics. Save and Load Machine Learning Models: Save trained models and load them for future use and deployment. Build Decision Trees and Random Forests: Understand and implement decision trees and random forest algorithms. Create and Run ETL Packages with SSIS: Learn to create and execute ETL packages using SQL Server Integration Services (SSIS). Extract Data Using Web Scraping: Use BeautifulSoup and Scrapy to extract data from websites. Develop Web Scraping Scripts: Write and test scripts to automate web scraping tasks. Build Comprehensive Data Engineering Solutions: Integrate skills and knowledge to build robust data engineering pipelines that include data collection, processi This course is ideal for individuals who are Aspiring Data Engineers or Data Analysts and Scientists or Software Developers or Students and Recent Graduates or Tech Enthusiasts and Hobbyists or Professionals Transitioning Careers or Entrepreneurs and Business Analysts It is particularly useful for Aspiring Data Engineers or Data Analysts and Scientists or Software Developers or Students and Recent Graduates or Tech Enthusiasts and Hobbyists or Professionals Transitioning Careers or Entrepreneurs and Business Analysts.

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Summary

Title: Data Engineering : Python,Machine Learning,ETL,Web Scraping

Price: $54.99

Number of Lectures: 128

Number of Published Lectures: 128

Number of Curriculum Items: 128

Number of Published Curriculum Objects: 128

Original Price: $79.99

Quality Status: approved

Status: Live

What You Will Learn

• Understand the Role of Data Engineering: Grasp the significance and responsibilities of data engineering within the broader data ecosystem.
• Learn Key Data Engineering Concepts: Familiarize with essential terminology and concepts in data engineering.
• Set Up a Python Environment: Successfully install Python and create virtual environments on both Windows and macOS.
• Utilize Jupyter Notebook: Install, set up, and navigate Jupyter Notebook for interactive data analysis and visualization.
• Develop Python Programming Skills: Understand and apply Python programming fundamentals, including expressions, statements, and data types.
• Manipulate Data Structures in Python: Efficiently use Python lists, tuples, and dictionaries.
• Perform Data Manipulation with Pandas: Use Pandas to create, manipulate, and analyze data in Series and DataFrames.
• Load and Inspect Datasets: Import datasets into Pandas DataFrames and perform initial data inspection.
• Clean and Transform Data: Apply data cleaning and transformation techniques to prepare data for analysis.
• Visualize Data with Python: Create various types of visualizations to explore and present data insights.
• Understand Machine Learning Basics: Gain a foundational understanding of machine learning concepts and workflows.
• Preprocess Data for Machine Learning: Perform data preprocessing tasks including handling missing values, encoding categorical variables, and feature engineerin
• Train and Evaluate Machine Learning Models: Train machine learning models, make predictions, and evaluate their performance using appropriate metrics.
• Work with Logistic Regression Models: Train, evaluate, and interpret logistic regression models.
• Visualize Model Evaluation Metrics: Create visualizations to interpret confusion matrices and other evaluation metrics.
• Save and Load Machine Learning Models: Save trained models and load them for future use and deployment.
• Build Decision Trees and Random Forests: Understand and implement decision trees and random forest algorithms.
• Create and Run ETL Packages with SSIS: Learn to create and execute ETL packages using SQL Server Integration Services (SSIS).
• Extract Data Using Web Scraping: Use BeautifulSoup and Scrapy to extract data from websites.
• Develop Web Scraping Scripts: Write and test scripts to automate web scraping tasks.
• Build Comprehensive Data Engineering Solutions: Integrate skills and knowledge to build robust data engineering pipelines that include data collection, processi

Who Should Attend

• Aspiring Data Engineers
• Data Analysts and Scientists
• Software Developers
• Students and Recent Graduates
• Tech Enthusiasts and Hobbyists
• Professionals Transitioning Careers
• Entrepreneurs and Business Analysts

Target Audiences

• Aspiring Data Engineers
• Data Analysts and Scientists
• Software Developers
• Students and Recent Graduates
• Tech Enthusiasts and Hobbyists
• Professionals Transitioning Careers
• Entrepreneurs and Business Analysts