Data Engineering Certification

Become A Data Engineering

What you'll learn
How To Design A Database
How To Install MySQL
Steps To Design A Simple Data base
Understand the difference between Data / Database and DBMS
Fundamentals of cloud computing
DP-203 : Data Engineering on Microsoft Azure Part 1
How to Use Microsoft Excel For Data Analysis
How To Design Dashboards Using Power BI
Introduction To Python & Jupyter Notebook
Numpy: Data science and analysis Using Python 1
Requirements
You should have an internet connection
Use mobile phone or laptop to watch this videos
Description
Data Engineer Technical SkillsTo become a data engineer, you should be very good at SQL, and you should know those  programming languages used for statistical modeling and data analysis. Also you should know, how to design a data warehousing solutions, and how to build data pipelines.Database design:You should know SQL. SQL is the standard programming language for building and managing relational database systems. Data warehousing solutions:ETL tools.:In computing, extract, transform, load (ETL) is the general procedure of copying data from one or more sources into a destination system which represents the data differently from the source(s) or in a different context than the source(s). The ETL process became a popular concept in the 1970s and is often used in data warehousing. Data extraction involves extracting data from homogeneous or heterogeneous sources; data transformation processes data by data cleaning and transforming them into a proper storage format/structure for the purposes of querying and analysis; finally, data loading describes the insertion of data into the final target database such as an operational data store, a data mart, data lake or a data warehouseMachine learning:Machine learning (ML) is the study of computer algorithms that improve automatically through experience and by the use of data.  It is seen as a part of artificial intelligence. Machine learning algorithms build a model based on sample data, known as "training data", in order to make predictions or decisions without being explicitly programmed to do so.  Machine learning algorithms are used in a wide variety of applications, such as in medicine, email filtering, speech recognition, and computer vision, where it is difficult or unfeasible to develop conventional algorithms to perform the needed tasks

Overview

Section 1: Introduction

Lecture 1 Introduction

Section 2: Data & Database Design

Lecture 2 What is Data ?

Lecture 3 What is Database ?

Lecture 4 What is DBMS?

Section 3: How To Design a Data Base

Lecture 5 7 Steps For Designing A Database

Lecture 6 Design A Database With Single Table

Lecture 7 Design Database With Multiple Table Part 1

Lecture 8 Design Database With Multiple Table Part 2

Lecture 9 Design Database With Multiple Table Part 3

Section 4: MySQL & MYSQL Workbench

Lecture 10 Install MySQL

Lecture 11 Use MYSQL Workbench

Section 5: MySQL Basics

Lecture 12 Introduction To MYSQL & SQL

Lecture 13 CREATE DATABASE

Lecture 14 CREATE TABLE

Lecture 15 INSERT DATA

Lecture 16 FETCH DATA FROM TABLE (SELECT Statement)

Lecture 17 Use Basic Where Conditions

Lecture 18 Use Order By To Sort Your Data

Lecture 19 REMOVE DATA

Lecture 20 UPDATE DATA

Lecture 21 Summary Of MySQL Basics

Lecture 22 ALTER TABLE : Rename To In MYSQL

Lecture 23 Alter Table : Add & Remove Columns From A Table

Lecture 24 Remove A Table From Our Database

Section 6: GROUP BY

Lecture 25 Group By

Lecture 26 Demo: Group By

Section 7: Excel Beginners

Lecture 27 Introduction To Excel

Lecture 28 Add Numbers In Excel

Lecture 29 Save Time By Filling Cells Automatically

Lecture 30 Split Data Using Flash Fill in Excel

Section 8: Excel Data Analysis Basics

Lecture 31 Excel Sort with 1 criteria

Lecture 32 Excel Sort with 2 or more criteria

Lecture 33 Excel Sort by color

Lecture 34 Excel Filter Data Based On Single Column

Lecture 35 Excel Filter with 2 or more criteria

Lecture 36 Excel Filter by color

Section 9: Introduction To Python & Jupyter Notebook

Lecture 37 Installing Python And Anaconda

Lecture 38 Opening Jupyter Notebook

Lecture 39 Shortcuts In Jupyter notebook

Section 10: Numpy: Data science and analysis Using Python 1

Lecture 40 Introduction

Lecture 41 ndarray

Lecture 42 Create Numpy ndarray

Lecture 43 Numpy Index (Single Dimensional Array Indexing)

Lecture 44 Numpy Index (Multiple dimensions Part 1)

Lecture 45 Numpy Index (Multiple dimensions Part 2)

Section 11: Mastering Power BI

Lecture 46 Install Power BI

Lecture 47 Introduction To Power BI Project

Lecture 48 Data Source

Lecture 49 Import Data Into Power BI

Lecture 50 Fix The Column Issue Using Power Query / Transform Data

Lecture 51 Data Preparation: Deriving New Column/Data Using Add Columns

Lecture 52 Data Preparation: Basic Data Cleaning Using Power BI

Lecture 53 Power BI Visualization: Add Slicers

Lecture 54 Power BI Visualization: Add Cards

Lecture 55 Power BI Visualization: Create A Power BI Bar and column charts

Lecture 56 Power BI Visualization: Create A Power BI Bar and column charts

Lecture 57 Power BI Visualization : Map-Based Visualization in Power BI

Section 12: Fundamentals of cloud computing

Lecture 58 What is Cloud Computing

Lecture 59 Public Cloud vs Private Cloud vs Hybrid Cloud

Lecture 60 IAAS vs PAAS vs SAAS

Lecture 61 Availability, Scalability Elasticity Agility Fault Tolerance: Important Concepts

Section 13: DP-203 : Data Engineering on Microsoft Azure Part 1

Lecture 62 How To Create Free Azure Account

Lecture 63 What Is Azure Storage Account?

Lecture 64 Demo: Azure Storage Account Creations And Uses

Lecture 65 How To Use Storage Explore From Portal

Lecture 66 How To Use Storage Explore Desktop Version

Lecture 67 Redundancy In Primary And Secondary Region

Lecture 68 Read Access To Data in The Secondary Region

Lecture 69 What is Azure Data Lake V2

Section 14: Data Engineering on Microsoft Azure Part 2

Lecture 70 Create Azure Data Lake Gen 2 And Azure Databricks

Lecture 71 Register an application with Azure AD and create a service principal

Lecture 72 Assign Roles To The Application To Provide The Service Principal Permissions

Lecture 73 Add application secret to the Azure Key Vault

Lecture 74 Create a Secret Scope in Azure Databricks

Lecture 75 Create Containers ( bronze/ Raw, silver / Processed , and gold/Final)

Lecture 76 Create Your First Cluster in Databricks

Lecture 77 Create A Notebook

Lecture 78 Mount Azure Data Lake without Key Vault

Lecture 79 Read CSV file from Data Lake

Lecture 80 Mount Data lake using Azure Key Vault

Section 15: Introduction To ETL Using Alteryx

Lecture 81 Install Alteryx Designer Studio

Lecture 82 Introduction To Alteryx Designer Studio

Lecture 83 Creating Your first Application Using Alteryx ETL

Lecture 84 How To Use Anchors

Lecture 85 Load Data From Excel

Lecture 86 Load Multiple Excel files using a Single Input Tool

Any student,Student who want to lean data engineering concepts