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The 'Complete Python Machine Learning & Data Science Fundamentals' course covers the foundational concepts of machine learning, data science, and Python programming. It includes hands-on exercises, data visualization, algorithm evaluation techniques, feature selection, and performance improvement using ensembles and parameter tuning. Learning Outcomes: Understand the fundamental concepts and types of machine learning, data science, and Python programming. Learn to prepare the system and environment for data analysis and machine learning tasks. Master the basics of Python, NumPy, Matplotlib, and Pandas for data manipulation and visualization. Gain insights into dataset summary statistics, data visualization techniques, and data preprocessing. Explore feature selection methods and evaluation metrics for classification and regression algorithms. Compare and select the best machine learning model using pipelines and ensembles. Learn to export, save, load machine learning models, and finalize the chosen models for real-time predictions. Why buy this Complete Python Machine Learning & Data Science Fundamentals? 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 Complete Python Machine Learning & Data Science Fundamentals 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 Complete Python Machine Learning & Data Science Fundamentals 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 Complete Python Machine Learning & Data Science Fundamentals does not require you to have any prior qualifications or experience. You can just enrol and start learning.This Complete Python Machine Learning & Data Science Fundamentals 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 Complete Python Machine Learning & Data Science Fundamentals is a great way for you to gain multiple skills from the comfort of your home. Course Curriculum Course Overview & Table of Contents Course Overview & Table of Contents 00:09:00 Introduction to Machine Learning - Part 1 - Concepts , Definitions and Types Introduction to Machine Learning - Part 1 - Concepts , Definitions and Types 00:05:00 Introduction to Machine Learning - Part 2 - Classifications and Applications Introduction to Machine Learning - Part 2 - Classifications and Applications 00:06:00 System and Environment preparation - Part 1 System and Environment preparation - Part 1 00:08:00 System and Environment preparation - Part 2 System and Environment preparation - Part 2 00:06:00 Learn Basics of python - Assignment Learn Basics of python - Assignment 1 00:10:00 Learn Basics of python - Assignment Learn Basics of python - Assignment 2 00:09:00 Learn Basics of python - Functions Learn Basics of python - Functions 00:04:00 Learn Basics of python - Data Structures Learn Basics of python - Data Structures 00:12:00 Learn Basics of NumPy - NumPy Array Learn Basics of NumPy - NumPy Array 00:06:00 Learn Basics of NumPy - NumPy Data Learn Basics of NumPy - NumPy Data 00:08:00 Learn Basics of NumPy - NumPy Arithmetic Learn Basics of NumPy - NumPy Arithmetic 00:04:00 Learn Basics of Matplotlib Learn Basics of Matplotlib 00:07:00 Learn Basics of Pandas - Part 1 Learn Basics of Pandas - Part 1 00:06:00 Learn Basics of Pandas - Part 2 Learn Basics of Pandas - Part 2 00:07:00 Understanding the CSV data file Understanding the CSV data file 00:09:00 Load and Read CSV data file using Python Standard Library Understanding the CSV data file 00:09:00 Load and Read CSV data file using NumPy Load and Read CSV data file using Python Standard Library 00:09:00 Load and Read CSV data file using Pandas Load and Read CSV data file using Pandas 00:05:00 Dataset Summary - Peek, Dimensions and Data Types Dataset Summary - Peek, Dimensions and Data Types 00:09:00 Dataset Summary - Class Distribution and Data Summary Dataset Summary - Class Distribution and Data Summary 00:09:00 Dataset Summary - Explaining Correlation Dataset Summary - Explaining Correlation 00:11:00 Dataset Summary - Explaining Skewness - Gaussian and Normal Curve Dataset Summary - Explaining Skewness - Gaussian and Normal Curve 00:07:00 Dataset Visualization - Using Histograms Dataset Visualization - Using Histograms 00:07:00 Dataset Visualization - Using Density Plots Dataset Visualization - Using Density Plots 00:06:00 Dataset Visualization - Box and Whisker Plots Dataset Visualization - Box and Whisker Plots 00:05:00 Multivariate Dataset Visualization - Correlation Plots Multivariate Dataset Visualization - Correlation Plots 00:08:00 Multivariate Dataset Visualization - Scatter Plots Multivariate Dataset Visualization - Scatter Plots 00:05:00 Data Preparation (Pre-Processing) - Introduction Data Preparation (Pre-Processing) - Introduction 00:09:00 Data Preparation - Re-scaling Data - Part 1 Data Preparation - Re-scaling Data - Part 1 00:09:00 Data Preparation - Re-scaling Data - Part 2 Data Preparation - Re-scaling Data - Part 2 00:09:00 Data Preparation - Standardizing Data - Part 1 Data Preparation - Standardizing Data - Part 1 00:07:00 Data Preparation - Standardizing Data - Part 2 Data Preparation - Standardizing Data - Part 2 00:04:00 Data Preparation - Normalizing Data Data Preparation - Normalizing Data 00:08:00 Data Preparation - Binarizing Data Data Preparation - Binarizing Data 00:06:00 Feature Selection - Introduction Feature Selection - Introduction 00:07:00 Feature Selection - Uni-variate Part 1 - Chi-Squared Test Feature Selection - Uni-variate Part 1 - Chi-Squared Test 00:09:00 Feature Selection - Uni-variate Part 2 - Chi-Squared Test Feature Selection - Uni-variate Part 2 - Chi-Squared Test 00:10:00 Feature Selection - Recursive Feature Elimination Feature Selection - Recursive Feature Elimination 00:11:00 Feature Selection - Principal Component Analysis (PCA) Feature Selection - Principal Component Analysis (PCA) 00:09:00 Feature Selection - Feature Importance Feature Selection - Feature Importance 00:07:00 Refresher Session - The Mechanism of Re-sampling, Training and Testing Refresher Session - The Mechanism of Re-sampling, Training and Testing 00:12:00 Algorithm Evaluation Techniques - Introduction Algorithm Evaluation Techniques - Introduction 00:07:00 Algorithm Evaluation Techniques - Train and Test Set Algorithm Evaluation Techniques - Train and Test Set 00:11:00 Algorithm Evaluation Techniques - K-Fold Cross Validation Algorithm Evaluation Techniques - K-Fold Cross Validation 00:09:00 Algorithm Evaluation Techniques - Leave One Out Cross Validation Algorithm Evaluation Techniques - Leave One Out Cross Validation 00:05:00 Algorithm Evaluation Techniques - Repeated Random Test-Train Splits Algorithm Evaluation Techniques - Repeated Random Test-Train Splits 00:07:00 Algorithm Evaluation Metrics - Introduction Algorithm Evaluation Metrics - Introduction 00:09:00 Algorithm Evaluation Metrics - Classification Accuracy Algorithm Evaluation Metrics - Classification Accuracy 00:08:00 Algorithm Evaluation Metrics - Log Loss Algorithm Evaluation Metrics - Log Loss 00:03:00 Algorithm Evaluation Metrics - Area Under ROC Curve Algorithm Evaluation Metrics - Area Under ROC Curve 00:06:00 Algorithm Evaluation Metrics - Confusion Matrix Algorithm Evaluation Metrics - Confusion Matrix 00:10:00 Algorithm Evaluation Metrics - Classification Report Algorithm Evaluation Metrics - Classification Report 00:04:00 Algorithm Evaluation Metrics - Mean Absolute Error - Dataset Introduction Algorithm Evaluation Metrics - Mean Absolute Error - Dataset Introduction 00:06:00 Algorithm Evaluation Metrics - Mean Absolute Error Algorithm Evaluation Metrics - Mean Absolute Error 00:07:00 Algorithm Evaluation Metrics - Mean Square Error Algorithm Evaluation Metrics - Mean Square Error 00:03:00 Algorithm Evaluation Metrics - R Squared Algorithm Evaluation Metrics - R Squared 00:04:00 Classification Algorithm Spot Check - Logistic Regression Classification Algorithm Spot Check - Logistic Regression 00:12:00 Classification Algorithm Spot Check - Linear Discriminant Analysis Classification Algorithm Spot Check - Linear Discriminant Analysis 00:04:00 Classification Algorithm Spot Check - K-Nearest Neighbors Classification Algorithm Spot Check - K-Nearest Neighbors 00:05:00 Classification Algorithm Spot Check - Naive Bayes Classification Algorithm Spot Check - Naive Bayes 00:04:00 Classification Algorithm Spot Check - CART Classification Algorithm Spot Check - CART 00:04:00 Classification Algorithm Spot Check - Support Vector Machines Classification Algorithm Spot Check - Support Vector Machines 00:05:00 Regression Algorithm Spot Check - Linear Regression Regression Algorithm Spot Check - Linear Regression 00:08:00 Regression Algorithm Spot Check - Ridge Regression Regression Algorithm Spot Check - Ridge Regression 00:03:00 Regression Algorithm Spot Check - Lasso Linear Regression Regression Algorithm Spot Check - Lasso Linear Regression 00:03:00 Regression Algorithm Spot Check - Elastic Net Regression Regression Algorithm Spot Check - Elastic Net Regression 00:02:00 Regression Algorithm Spot Check - K-Nearest Neighbors Regression Algorithm Spot Check - K-Nearest Neighbors 00:06:00 Regression Algorithm Spot Check - CART Regression Algorithm Spot Check - CART 00:04:00 Regression Algorithm Spot Check - Support Vector Machines (SVM) Regression Algorithm Spot Check - Support Vector Machines (SVM) 00:04:00 Compare Algorithms - Part 1 : Choosing the best Machine Learning Model Compare Algorithms - Part 1 : Choosing the best Machine Learning Model 00:09:00 Compare Algorithms - Part 2 : Choosing the best Machine Learning Model Compare Algorithms - Part 2 : Choosing the best Machine Learning Model 00:05:00 Pipelines : Data Preparation and Data Modelling Pipelines : Data Preparation and Data Modelling 00:11:00 Pipelines : Feature Selection and Data Modelling Pipelines : Feature Selection and Data Modelling 00:10:00 Performance Improvement: Ensembles - Voting Performance Improvement: Ensembles - Voting 00:07:00 Performance Improvement: Ensembles - Bagging Performance Improvement: Ensembles - Bagging 00:08:00 Performance Improvement: Ensembles - Boosting Performance Improvement: Ensembles - Boosting 00:05:00 Performance Improvement: Parameter Tuning using Grid Search Performance Improvement: Parameter Tuning using Grid Search 00:08:00 Performance Improvement: Parameter Tuning using Random Search Performance Improvement: Parameter Tuning using Random Search 00:06:00 Export, Save and Load Machine Learning Models : Pickle Export, Save and Load Machine Learning Models : Pickle 00:10:00 Export, Save and Load Machine Learning Models : Joblib Export, Save and Load Machine Learning Models : Joblib 00:06:00 Finalizing a Model - Introduction and Steps Finalizing a Model - Introduction and Steps 00:07:00 Finalizing a Classification Model - The Pima Indian Diabetes Dataset Finalizing a Classification Model - The Pima Indian Diabetes Dataset 00:07:00 Quick Session: Imbalanced Data Set - Issue Overview and Steps Quick Session: Imbalanced Data Set - Issue Overview and Steps 00:09:00 Iris Dataset : Finalizing Multi-Class Dataset Iris Dataset : Finalizing Multi-Class Dataset 00:09:00 Finalizing a Regression Model - The Boston Housing Price Dataset Finalizing a Regression Model - The Boston Housing Price Dataset 00:08:00 Real-time Predictions: Using the Pima Indian Diabetes Classification Model Real-time Predictions: Using the Pima Indian Diabetes Classification Model 00:07:00 Real-time Predictions: Using Iris Flowers Multi-Class Classification Dataset Real-time Predictions: Using Iris Flowers Multi-Class Classification Dataset 00:03:00 Real-time Predictions: Using the Boston Housing Regression Model Real-time Predictions: Using the Boston Housing Regression Model 00:08:00 Resources Resources - Python Machine Learning & Data Science Fundamentals 00:00:00

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Discover the power of data science and machine learning with Python! Learn essential techniques, algorithms, and tools to analyze data, build predictive models, and unlock insights. Dive into hands-on projects, from data manipulation to advanced machine learning applications. Elevate your skills and unleash the potential of Python for data-driven decision-making.

Learn how to combine the three most important tools in data science: Python, SQL, and Tableau

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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

Kickstart your journey into deep learning and gain a strong understanding of deep neural networks through practical exercises. Develop your intuition and learn the fundamentals of artificial neural networks, activation functions, and loss functions. Gain practical experience with Python and TensorFlow 2.x, and apply your skills to build powerful deep learning models.

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