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

Learn essentials of Data Engineering with Python, Data Manipulation, Machine Learning, ETL, SSIS , Web Scraping.

What you'll 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

Requirements

Basic Computer Literacy: Familiarity with basic computer operations, such as installing software, navigating file systems, and using a web browser.

Interest in Data Engineering: A strong interest in data engineering, data analysis, and machine learning.

Computer: A laptop or desktop with internet access.

Description

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!

Overview

Section 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

Section 2: Python Environment Setup

Lecture 6 Python Installation on Windows

Lecture 7 What are virtual environments

Lecture 8 Creating and activating a virtual environment on Windows

Lecture 9 Python Installation on macOS

Lecture 10 Creating and activating a virtual environment on macOS

Lecture 11 What is Jupyter Notebook

Lecture 12 Install Text Editor

Lecture 13 Installing Pandas and Jupyter Notebook in the Virtual Environment

Lecture 14 Starting Jupyter Notebook

Lecture 15 Exploring Jupyter Notebook Server Dashboard Interface

Lecture 16 Creating a new Notebook

Lecture 17 Exploring Jupyter Notebook Source and Folder Files

Lecture 18 Exploring the Notebook Interface

Section 3: Python Programming Fundamentals

Lecture 19 Python Expressions

Lecture 20 Python Statements

Lecture 21 Python Code Comments

Lecture 22 Python Data Types

Lecture 23 Casting Data Types

Lecture 24 Python Variables

Lecture 25 Python List

Lecture 26 Python Tuple

Lecture 27 Python Dictionaries

Lecture 28 Python Operators

Lecture 29 Python Conditional Statements

Lecture 30 Python Loops

Lecture 31 Python Functions

Section 4: Data Manipulation and visualization with Python

Lecture 32 Overview of Pandas

Lecture 33 Creating a Pandas Series from a List

Lecture 34 Creating a Pandas Series from a List with Custom Index

Lecture 35 Creating a pandas series from a Python Dictionary

Lecture 36 Accessing Data in a Series using the index by label

Lecture 37 Accessing Data in a Series By position

Lecture 38 Slicing a Series by Label

Lecture 39 Creating a DataFrame from a dictionary of lists

Lecture 40 Creating a DataFrame From a list of dictionaries

Lecture 41 Accessing data in a DataFrame

Lecture 42 Download Dataset

Lecture 43 Loading Dataset into a DataFrame

Lecture 44 Inspecting the data

Lecture 45 Data Cleaning

Lecture 46 Data transformation and analysis

Lecture 47 Visualizing data

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

Lecture 48 What is Machine Learning?

Lecture 49 Installing and importing libraries

Lecture 50 Introduction to Data Preprocessing

Lecture 51 What is a Dataset

Lecture 52 Downloading dataset

Lecture 53 Exploring the Dataset

Lecture 54 Handle missing values and drop unnecessary columns.

Lecture 55 Encode categorical variables.

Lecture 56 What is Feature Engineering

Lecture 57 Create new features.

Lecture 58 Dropping unnecessary columns

Lecture 59 Visualize survival rate by gender

Lecture 60 Visualize survival rate by class

Lecture 61 Visualize numerical features

Lecture 62 Visualize the distribution of Age

Lecture 63 Visualize number of passengers in each passenger class

Lecture 64 Visualize number of passengers that survived

Lecture 65 Visualize the correlation matrix of numerical variables

Lecture 66 Visualize the distribution of Fare.

Lecture 67 Data Preparation and Training Model

Lecture 68 What is a Model

Lecture 69 Define features and target variable.

Lecture 70 Split data into training and testing sets.

Lecture 71 Standardize features.

Lecture 72 Train logistic regression model.

Lecture 73 Making Predictions

Lecture 74 Evaluate the model using accuracy, confusion matrix, and classification report.

Lecture 75 Visualize the confusion matrix.

Lecture 76 Saving the Model

Lecture 77 Loading the model

Lecture 78 Improving Understanding of the model's prediction

Lecture 79 Building a decision tree

Lecture 80 Building a random forest

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

Lecture 81 What is SSIS

Lecture 82 What is an SSIS Package

Lecture 83 What is ETL

Lecture 84 What is SQL Server

Lecture 85 Download SQL Server

Lecture 86 Install SQL Server

Lecture 87 Install SQL Server Management Studio ( SSMS)

Lecture 88 Connect SSMS to SQL Server

Lecture 89 Download Sample Databases

Lecture 90 Restore Sample Databases

Lecture 91 Installing Visual Studio

Lecture 92 Starting Visual Studio

Lecture 93 Installing SQL Server Data Tools(SSDT) Templates Extensions

Lecture 94 Create a new Integration Services project

Lecture 95 Exploring the data

Lecture 96 Add and configure a Flat File connection manager

Lecture 97 Add and configure an OLE DB connection manager

Lecture 98 Add a Data Flow task to the package

Lecture 99 Add and configure the flat file source

Lecture 100 Add and configure the lookup transformations

Lecture 101 Add and configure the OLE DB destination

Lecture 102 Annotate and format the package

Lecture 103 Test the package

Section 7: Data Extraction using Web Scraping

Lecture 104 What is Web Scraping

Lecture 105 Tools for web scraping

Lecture 106 Create and activate a new virtual environment

Lecture 107 What we will scrape

Lecture 108 Inspecting Elements

Lecture 109 Overview of Web Scraping with BeautifulSoup

Lecture 110 Installing BeautifulSoup

Lecture 111 Installing Scrapy

Lecture 112 Building a web scraping script - part 1

Lecture 113 Building a web scraping script - part 2

Lecture 114 Prototyping the script : part 1

Lecture 115 Prototyping the script : part 2

Lecture 116 Prototyping the script : part 3

Lecture 117 Prototyping the script : part 4

Lecture 118 Prototyping the script : part 5

Lecture 119 Testing |Running |Saving Scraped data to file

Lecture 120 Overview of Web Scraping with Scrapy

Lecture 121 Creating a Scrapy project

Lecture 122 Components of a Scrapy Project

Lecture 123 Scrapy Architecture

Lecture 124 Creating a Spider : part 1

Lecture 125 Creating a Spider : part 2

Lecture 126 Scraping data with scrapy shell : Part 1

Lecture 127 Scraping data with scrapy shell : Part 2

Lecture 128 Running the spider and saving scraped data

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