• 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

Welcome to this course. which is designed to equip you with the essential skills and knowledge needed to excel in the rapidly evolving field of data engineering. Whether you are a beginner or an experienced professional looking to broaden your skill set, this course offers a detailed, hands-on approach to mastering data engineering.

Course Overview:

Data engineering is the backbone of modern data science and analytics, providing the foundation for collecting, processing, and analyzing large datasets. This course starts with the basics and gradually progresses to more complex topics, ensuring a solid understanding of each concept before moving on to the next.

Section 1: Overview of Data Engineering We begin with an introduction to data engineering, covering its role within the data ecosystem. You will learn about key concepts, terminology, and the typical workflow of a data engineer, from data collection to analysis. This section sets the stage for the more technical aspects to come.

Section 2: Python Environment Setup Python is a fundamental tool for data engineers. In this section, you will learn how to set up your Python environment on both Windows and macOS, including the creation and activation of virtual environments. We will also cover essential tools like Jupyter Notebook and popular text editors, preparing you for efficient Python programming and data manipulation.

Section 3: Python Programming Fundamentals With your environment set up, we dive into Python programming. Starting with basic expressions and statements, you will progress to more complex topics such as data types, variables, lists, tuples, dictionaries, control flow statements, and functions. This section ensures you have a strong foundation in Python, which is crucial for data engineering tasks.

Section 4: Data Manipulation and Visualization with Python Learn to harness the power of Pandas for data manipulation. You will explore how to create and manage Series and DataFrames, load and inspect datasets, clean and transform data, and visualize data using various techniques. By the end of this section, you will be adept at preparing and analyzing data for insights.

Section 5: Machine Learning Essentials This section introduces you to the basics of machine learning. You will learn about data preprocessing, handling missing values, encoding categorical variables, and feature engineering. We will guide you through training and evaluating machine learning models, making predictions, and visualizing results. You will also learn to save and load models for future use.

Section 6: Creating and Running ETL Packages with SSIS and SQL Server Explore the world of Extract, Transform, Load (ETL) processes using SQL Server Integration Services (SSIS). You will learn to create and manage ETL packages, handle data from various sources, and automate data workflows. This section provides practical skills for managing large-scale data integration tasks.

Section 7: Data Extraction Using Web Scraping Finally, we cover web scraping techniques using BeautifulSoup and Scrapy. You will learn to extract data from websites, write and test web scraping scripts, and save scraped data for analysis. This section equips you with the skills to gather data from the web, a valuable asset for any data engineer.

Intended Learners:

This course is ideal for aspiring data engineers, data analysts, software developers, students, tech enthusiasts, and professionals transitioning into data engineering roles. No prior experience is required, making it accessible to beginners.

Why Enroll?

By enrolling in this course, you will gain practical, hands-on experience with the tools and techniques used by data engineers. You will learn to build robust data pipelines, manipulate and analyze data, and create and deploy machine learning models. Our step-by-step approach ensures you can confidently apply these skills in real-world scenarios, making you a valuable asset in the data-driven industry.

Join us on this journey to master data engineering and unlock the power of data!

이 강의의 대상:

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