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.

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

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

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!

Course Curriculum

Chapter 1: Overview of Data Engineering

Lecture 1: Introduction

Lecture 2: Understanding the role of data engineering in the data ecosystem

Lecture 3: Key concepts and terminology

Lecture 4: Data Engineering Workflow: From data collection to data analysis

Lecture 5: Overview of data engineering processes and pipelines

Chapter 2: Python Environment Setup

Lecture 1: Python Installation on Windows

Lecture 2: What are virtual environments

Lecture 3: Creating and activating a virtual environment on Windows

Lecture 4: Python Installation on macOS

Lecture 5: Creating and activating a virtual environment on macOS

Lecture 6: What is Jupyter Notebook

Lecture 7: Install Text Editor

Lecture 8: Installing Pandas and Jupyter Notebook in the Virtual Environment

Lecture 9: Starting Jupyter Notebook

Lecture 10: Exploring Jupyter Notebook Server Dashboard Interface

Lecture 11: Creating a new Notebook

Lecture 12: Exploring Jupyter Notebook Source and Folder Files

Lecture 13: Exploring the Notebook Interface

Chapter 3: Python Programming Fundamentals

Lecture 1: Python Expressions

Lecture 2: Python Statements

Lecture 3: Python Code Comments

Lecture 4: Python Data Types

Lecture 5: Casting Data Types

Lecture 6: Python Variables

Lecture 7: Python List

Lecture 8: Python Tuple

Lecture 9: Python Dictionaries

Lecture 10: Python Operators

Lecture 11: Python Conditional Statements

Lecture 12: Python Loops

Lecture 13: Python Functions

Chapter 4: Data Manipulation and visualization with Python

Lecture 1: Overview of Pandas

Lecture 2: Creating a Pandas Series from a List

Lecture 3: Creating a Pandas Series from a List with Custom Index

Lecture 4: Creating a pandas series from a Python Dictionary

Lecture 5: Accessing Data in a Series using the index by label

Lecture 6: Accessing Data in a Series By position

Lecture 7: Slicing a Series by Label

Lecture 8: Creating a DataFrame from a dictionary of lists

Lecture 9: Creating a DataFrame From a list of dictionaries

Lecture 10: Accessing data in a DataFrame

Lecture 11: Download Dataset

Lecture 12: Loading Dataset into a DataFrame

Lecture 13: Inspecting the data

Lecture 14: Data Cleaning

Lecture 15: Data transformation and analysis

Lecture 16: Visualizing data

Chapter 5: Machine Learning Essentials: Build and Train a Machine Learning Model

Lecture 1: What is Machine Learning?

Lecture 2: Installing and importing libraries

Lecture 3: Introduction to Data Preprocessing

Lecture 4: What is a Dataset

Lecture 5: Downloading dataset

Lecture 6: Exploring the Dataset

Lecture 7: Handle missing values and drop unnecessary columns.

Lecture 8: Encode categorical variables.

Lecture 9: What is Feature Engineering

Lecture 10: Create new features.

Lecture 11: Dropping unnecessary columns

Lecture 12: Visualize survival rate by gender

Lecture 13: Visualize survival rate by class

Lecture 14: Visualize numerical features

Lecture 15: Visualize the distribution of Age

Lecture 16: Visualize number of passengers in each passenger class

Lecture 17: Visualize number of passengers that survived

Lecture 18: Visualize the correlation matrix of numerical variables

Lecture 19: Visualize the distribution of Fare.

Lecture 20: Data Preparation and Training Model

Lecture 21: What is a Model

Lecture 22: Define features and target variable.

Lecture 23: Split data into training and testing sets.

Lecture 24: Standardize features.

Lecture 25: Train logistic regression model.

Lecture 26: Making Predictions

Lecture 27: Evaluate the model using accuracy, confusion matrix, and classification report.

Lecture 28: Visualize the confusion matrix.

Lecture 29: Saving the Model

Lecture 30: Loading the model

Lecture 31: Improving Understanding of the models prediction

Lecture 32: Building a decision tree

Lecture 33: Building a random forest

Chapter 6: How to Create and run ETL Packages with SSIS,SQL Server,SSDT

Lecture 1: What is SSIS

Lecture 2: What is an SSIS Package

Lecture 3: What is ETL

Lecture 4: What is SQL Server

Lecture 5: Download SQL Server

Lecture 6: Install SQL Server

Lecture 7: Install SQL Server Management Studio ( SSMS)

Lecture 8: Connect SSMS to SQL Server

Lecture 9: Download Sample Databases

Lecture 10: Restore Sample Databases

Lecture 11: Installing Visual Studio

Lecture 12: Starting Visual Studio

Lecture 13: Installing SQL Server Data Tools(SSDT) Templates Extensions

Lecture 14: Create a new Integration Services project

Instructors

Bluelime Learning Solutions
Making Learning Simple

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