Embark on a comprehensive journey into the world of MySQL with a focus on its applications in Data Science and Analytics. This course is structured to take you from the fundamentals to advanced topics in MySQL Server. Covering SQL basics, data manipulation and definition, control and analytic functions, and database management, you'll acquire the essential skills for harnessing MySQL's power in data-driven decision-making. Learning Outcomes: Establish a strong foundation in SQL and MySQL. Set up and configure SQL Server for efficient data handling. Master SQL's Data Manipulation, Definition, and Control Language. Create and optimize SQL queries for data analysis. Perform advanced data analytics using SQL. Understand the power of GROUP BY and JOIN statements. Implement data constraints and views for data integrity and security. Develop proficiency in stored procedures, data import/export, and database backup/restore. Why buy this Learn MySQL from Scratch for Data Science and Analytics? Unlimited access to the course for forever Digital Certificate, Transcript, student ID all included in the price Absolutely no hidden fees Directly receive CPD accredited qualifications after course completion Receive one to one assistance on every weekday from professionals Immediately receive the PDF certificate after passing Receive the original copies of your certificate and transcript on the next working day Easily learn the skills and knowledge from the comfort of your home Certification After studying the course materials of the Learn MySQL from Scratch for Data Science and Analytics you will be able to take the MCQ test that will assess your knowledge. After successfully passing the test you will be able to claim the pdf certificate for £5.99. Original Hard Copy certificates need to be ordered at an additional cost of £9.60. Who is this course for? This Learn MySQL from Scratch for Data Science and Analytics course is ideal for Aspiring Data Scientists and Analysts. Database Administrators and Developers. Students and professionals seeking to enter the field of Data Science. Anyone looking to enhance their SQL and MySQL skills for data-related roles. Prerequisites This Learn MySQL from Scratch for Data Science and Analytics was made by professionals and it is compatible with all PC's, Mac's, tablets and smartphones. You will be able to access the course from anywhere at any time as long as you have a good enough internet connection. Career path Data Analyst: £25,000 - £50,000 per year. Database Administrator: £30,000 - £60,000 per year. SQL Developer: £30,000 - £60,000 per year. Data Scientist: £40,000 - £80,000 per year. Business Intelligence Analyst: £35,000 - £65,000 per year. Course Curriculum Learn MySQL from Scratch for Data Science and Analytics Section 01: Getting Started Introduction 00:02:00 How to get course requirements 00:01:00 Getting started on Windows, Linux or Mac 00:01:00 How to ask great questions 00:01:00 FAQ's 00:01:00 What is Source Code? 00:09:00 Section 02: SQL Server setting up Section Introduction 00:01:00 MySQL Server Installation 00:14:00 Connect MySQL Server Instance 00:06:00 MySQL Workbench overview 00:11:00 Download and Restore Sample Database 00:08:00 Section 03: SQL Database basics Section Introduction 00:01:00 Overview of Databases 00:09:00 Creating Database 00:07:00 SQL Data Types 00:04:00 Column Data Types on Workbench 00:07:00 Creating Table 00:04:00 Overview of Primary and Foreign Key 00:03:00 Primary Key 00:06:00 Foreign Key 00:12:00 Creating Temporary tables 00:12:00 EER - Enhanced Entity Relationship Diagrams 00:04:00 Section 04: SQL DML (Data Manipulation Language) Section Introduction 00:01:00 Insert statement 00:07:00 Update statement 00:06:00 Delete statement 00:03:00 Section 05: SQL DDL (Data Definition Language) Section Introduction 00:01:00 CREATE table statement 00:08:00 DROP statement 00:03:00 ALTER statement 00:05:00 TRUNCATE statement 00:03:00 COMMENT in query 00:02:00 RENAME table 00:03:00 Section 06: SQL DCL (Data Control Language) Create Database user 00:03:00 GRANT permissions 00:06:00 REVOKE permissions 00:04:00 Section 07: SQL Statement Basic Section Introduction 00:01:00 SQL Statement basic 00:03:00 SELECT Statement 00:03:00 SELECT DISTINCT 00:02:00 SELECT with column headings 00:01:00 Column AS statement 00:02:00 DASHBOARD Analytics 00:06:00 Section 08: Filtering Data rows SELECT WHERE Clause - theory 00:03:00 SELECT WHERE Clause - practical 00:06:00 Section 09: Aggregate functions for Data Analysis Sum() 00:06:00 Min()-Max() 00:03:00 Section 10: SQL Data Analyticstatements Order By statement 00:05:00 SELECT TOP 3 records 00:02:00 BETWEEN command 00:06:00 IN operator 00:03:00 Search Data usingLIKE cards 00:05:00 Section 11: SQL Group by statement Section Introduction 00:01:00 Group by - theory 00:04:00 Data Analytics with Group By 00:04:00 HAVING statement 00:03:00 Section 12: JOINS Overview of Joins 00:02:00 What are Joins 00:02:00 Inner join 00:07:00 Left outer join 00:02:00 Right outer join 00:02:00 Union 00:03:00 CERTESIAN Product or Cross Join 00:03:00 Query Exercise 00:01:00 Solution for Query Exercise 00:01:00 Section 13: SQL Constraints Section introduction 00:01:00 Check constraint 00:09:00 NOT NULL constraint 00:03:00 UNIQUE constraint 00:06:00 Section 14: Views Creating Views 00:03:00 Data Analytic Views from multiple tables 00:03:00 Section 15: Advanced SQL Functions Section Introduction 00:01:00 Timestamp 00:03:00 Extract from timestamp 00:03:00 Mathematical scalar functions 00:03:00 String functions3 00:07:00 Advanced functions 00:04:00 Sub Queries 00:03:00 SELECT with calculations 00:05:00 Section 16: SQL Stored procedures Create stored procedure 00:06:00 Stored procedure with parameter 00:03:00 Drop Procedure 00:01:00 Section 17: Import & Export data Section Introduction 00:01:00 Import .csv file 00:04:00 Export Data to .csv file 00:02:00 Section 18: Backup and Restore Database Section Introduction 00:01:00 Creating Database backup 00:02:00 Restoring Database backup 00:02:00
This comprehensive course, 'SQL for Data Science, Data Analytics, and Data Visualization,' covers essential SQL concepts and tools for working with data. Participants will learn to manipulate, analyze, and visualize data using SQL Server, Azure Data Studio, and other relevant tools. The course also delves into advanced SQL commands, stored procedures, and data import/export, making it ideal for aspiring data professionals. Learning Outcomes: Set up and configure SQL Server and SQL Azure Data Studio for data analysis. Master SQL statements for data manipulation, data structure, and user management. Utilize SQL queries, joins, and aggregate functions for efficient data analysis. Understand SQL constraints, views, and advanced commands for in-depth data exploration. Create and implement SQL stored procedures to automate tasks. Leverage Azure Data Studio for data visualization and perform data analysis with SQL. Why buy this SQL for Data Science, Data Analytics and Data Visualization? Unlimited access to the course for forever Digital Certificate, Transcript, student ID all included in the price Absolutely no hidden fees Directly receive CPD accredited qualifications after course completion Receive one to one assistance on every weekday from professionals Immediately receive the PDF certificate after passing Receive the original copies of your certificate and transcript on the next working day Easily learn the skills and knowledge from the comfort of your home Certification After studying the course materials of the SQL for Data Science, Data Analytics and Data Visualization there will be a written assignment test which you can take either during or at the end of the course. After successfully passing the test you will be able to claim the pdf certificate for £5.99. Original Hard Copy certificates need to be ordered at an additional cost of £9.60. Who is this course for? This SQL for Data Science, Data Analytics and Data Visualization course is ideal for Students Recent graduates Job Seekers Anyone interested in this topic People already working in the relevant fields and want to polish their knowledge and skill. Prerequisites This SQL for Data Science, Data Analytics and Data Visualization does not require you to have any prior qualifications or experience. You can just enrol and start learning.This SQL for Data Science, Data Analytics and Data Visualization was made by professionals and it is compatible with all PC's, Mac's, tablets and smartphones. You will be able to access the course from anywhere at any time as long as you have a good enough internet connection. Career path As this course comes with multiple courses included as bonus, you will be able to pursue multiple occupations. This SQL for Data Science, Data Analytics and Data Visualization is a great way for you to gain multiple skills from the comfort of your home. Course Curriculum Section 01: Getting Started Introduction 00:03:00 How to get course requirements 00:03:00 Getting started on Windows, Linux or Docker 00:01:00 How to ask great questions 00:01:00 FAQ's 00:01:00 Section 02: SQL Server setting up Section Introduction 00:02:00 Microsoft SQL Server Installation 00:19:00 SQL Server Management Studio (SSMS) Installation 00:08:00 How to connect MS SQL (Windows Authentication) 00:04:00 How to connect MS SQL (SQL Server Authentication) 00:03:00 Download and Restore Sample Database 00:07:00 Section 03: SQL Azure Data Studio What is Azure Data Studio 00:06:00 Azure Data Studio Installation steps 00:07:00 Azure Data Studio to Connect SQL Server 00:09:00 Create a Database 00:07:00 Create a Table 00:09:00 Insert Data rows 00:07:00 View the Data returned by Query 00:03:00 Section 04: SQL Database basic SSMS Section Introduction 00:01:00 Overview of Databases8 00:11:00 Creating Database 00:05:00 SQL Data Types 00:03:00 Column Data Types on SSMS 00:04:00 Creating Table 00:09:00 Overview of Primary Key and Foreign Key 00:04:00 Primary Key 00:04:00 Foreign Key 00:07:00 Creating Temporary tables 00:06:00 Section 05: SQL Statements for DATA Section Introduction 00:01:00 Insert statement 00:08:00 Update statement 00:05:00 Delete statement 00:03:00 Section 06: SQL Data Structure statements Section Introduction 00:01:00 CREATE table statement 00:03:00 DROP statement 00:02:00 ALTER statement 00:05:00 TRUNCATE statement 00:04:00 COMMENT in query 00:01:00 RENAME 00:02:00 Section 07: SQL User Management Create Database user 00:04:00 GRANT permissions 00:06:00 REVOKE permissions 00:04:00 Section 08: SQL Statement Basic Section Introduction 00:01:00 SQL Statement basic 00:03:00 SELECT Statement 00:07:00 SELECT DISTINCT 00:03:00 SELECT with column headings 00:03:00 Column AS statement 00:02:00 Section 09: Filtering Data rows SELECT WHERE Clause - theory 00:04:00 SELECT WHERE Clause - practical 00:07:00 Section 10: Aggregate functions Sum() 00:08:00 Min()-Max() 00:06:00 Section 11: SQL Query statements Order By statement 00:05:00 SELECT TOP clause in SQL 00:04:00 BETWEEN command 00:08:00 IN operator 00:04:00 Wildcard Characters and LIKE 00:05:00 Section 12: SQL Group by statement Section Introduction 00:01:00 Group by - theory8 00:03:00 Group by - practical 00:05:00 HAVING statement 00:04:00 Section 13: JOINS for Multiple table Data Analysis Overview of Joins 00:02:00 What are Joins 00:02:00 Inner join 00:08:00 Left outer join 00:03:00 Right outer join 00:02:00 Full outer join 00:01:00 Union 00:03:00 Cartesian Product with the Cross Join 00:03:00 Query Exercise 00:01:00 Solution for Query Exercise 00:01:00 Section 14: SQL Constraints Section introduction 00:01:00 Check constraint 00:07:00 NOT NULL constraint 00:08:00 UNIQUE constraint 00:05:00 Section 15: Views Creating Views 00:04:00 Reporting with multiple tables 00:03:00 Section 16: Advanced SQL commands Section Introduction 00:01:00 Timestamp 00:04:00 Extract from timestamp 00:03:00 Mathematical scalar functions 00:04:00 String functions 00:05:00 Sub Query 00:03:00 SELECT with calculations 00:06:00 Section 17: SQL Stored procedures Create stored procedure 00:05:00 Stored procedure with parameter 00:04:00 Section 18: Azure Data Studio Visualization Installing SandDance Extension 00:03:00 Visualization Charts 00:05:00 Multiple Table Data Charts 00:06:00 Section 19: Azure Studio SQL for Data Analysis Type Decision for Data Analysis 00:13:00 Data Analysis with Case Statement and String Text 00:09:00 Section 20: Import & Export data Section Introduction 00:01:00 Import Flat File 00:05:00 Import .csv or excel file 00:03:00 Export Data to Excel or any format 00:06:00 Section 21: Backup and Restore Database Section Introduction 00:01:00 Creating Database backup 00:04:00 Restoring Database backup 00:04:00
A comprehensive package for beginners to learn how to automate social media applications such as WhatsApp, YouTube, and Facebook from scratch using Python. The course will help you understand the impact of social media automation in real-world applications and provide a unique hands-on experience in developing different real-time exciting projects.
This is an introductory course on machine learning. The course covers a wide range of topics, from handling a dataset to model delivery. Some prior training in Python programming and basic calculus knowledge will help you get the best out of this course.
In this course, you will learn the fundamentals of data visualization in Python using the well-known Matplotlib and Seaborn data science libraries and perform exploratory data analysis (EDA) by visualizing a data set using a variety of charts.
All about containers, Docker, and Kubernetes for Python engineers.
The underlying patterns in your data hold vital insights; unearth them with cutting-edge clustering and classification techniques in R
Data scraping is used to get the data available on different websites and APIs. This also involves automating the web flows to extract the data from different web pages. Data Scraping and Data Mining with Python is a well-designed course for beginners to develop a solid groundwork for the skills necessary.
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The goal of this course is to use Python machine learning to create algorithms that you can use in the real world. You'll start with the basics of machine learning. You'll learn how to create, train, and optimize models and use these models in real-world applications.