Practical Data Analytics & Business Intelligence with: MySQL SQL MicrosoftSQL PostgreSQL Excel Power BI Tableau
What you will learn
☑ Perform Data Analysis with SQL
☑ Perform Data Analysis with MySQL
☑ Perform Data Analysis with Microsoft SQL
☑ Perform Data Analysis with PostgreSQL
☑ Perform Data Analysis with Excel
☑ Perform Data Analysis with Power BI
☑ Perform Data Analysis with Tableau
☑ Connect to data sources
☑ Clean and transform data
☑ Create data visualization
☑ Query databases
Description
Data analysis is a process of inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information,informing conclusions, and supporting decision-making. Data analysis has multiple facets and approaches, encompassing diverse techniques under a variety of names, and is used in different business, science, and social science domains.In today’s business world, data analysis plays a role in making decisions more scientific and helping businesses operate more effectively.
Business intelligence (BI) combines business analytics, data mining, data visualization, data tools and infrastructure, and best practices to help organisations to make more data-driven decisions. In practice, you know you’ve got modern business intelligence when you have a comprehensive view of your organisation’s data and use that data to drive change,eliminate inefficiencies and quickly adapt to market or supply changes.
SQL is a standard language for accessing and manipulating databases.
What is SQL?
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SQL stands for Structured Query Language
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SQL lets you access and manipulate databases
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SQL became a standard of the American National Standards Institute (ANSI) in 1986, and of the International Organization for Standardization (ISO) in 1987
What Can SQL do?
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SQL can execute queries against a database
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SQL can retrieve data from a database
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SQL can insert records in a database
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SQL can update records in a database
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SQL can delete records from a database
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SQL can create new databases
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SQL can create new tables in a database
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SQL can create stored procedures in a database
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SQL can create views in a database
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SQL can set permissions on tables, procedures, and views
Microsoft SQL Server is a relational database management system developed by Microsoft.
Transact-SQL (T-SQL) is Microsoft’s proprietary extension to the SQL (Structured Query Language) used to interact with relational databases
MySQL is a DBMS, or database management system. It is developed, supported and distributed by Oracle, but since it is open-source it is freely available to anyone under the GPL. MySQL databases are relational, meaning that the data is split up between tables. MySQL is very fast and lightweight so you can run it alongside your other applications on a desktop or laptop. It can also be scaled up to take advantage of all the CPU power and memory available on a dedicated machine.
PostgreSQL is a powerful, open source object-relational database system that uses and extends the SQL language combined with many features that safely store and scale the most complicated data workloads.
Microsoft Excel is one of the top tools for data analysis and the built-in pivot tables are arguably the most popular analytic tool.
Power BI is Microsoft’s interactive data visualization and analytics tool for business intelligence (BI).
Tableau is a powerful and fastest data visualization tool used in the Business Intelligence Industry. It helps in simplifying raw data in a very easily understandable format.
In this course we will be performing data analysis and business intelligence using MySQL, SQL, Microsoft SQL ,PostgreSQL , Excel, Power BI and Tableau.
English
Language
Content
Introduction
Course Introduction
What is Data Analysis
What is Business Intelligence
What is Tableau
What is MySQL
What is Microsoft SQL Server
What is PostgreSQL
What is Power BI
What is Power BI Desktop
What is Excel Power Pivot
What is Excel Power Query
What is SQL
What is T-SQL
Database Concepts
Data Analysis with SQL and MySQL
MySQL Installation (Windows)
MySQL Installation(Mac)
MySQL Workbench
Installing MySQL Workbench (Mac)
SQL Statement and query
Analyzing data with INNER JOIN
Analyzing data with LEFT JOIN
Analyzing data with RIGHT JOIN
Analyzing data with SELF JOIN
Analyzing data with Sub queries
Analyzing data with nested sub query
Analyzing data with derived tables
Analyzing data with Between Operator
Analyzing data with IN Operator
Analyzing data with LIKE Operator
Analyzing data with UNION Operator
Analyzing data with AVG Aggregate Function
Analyzing data with COUNT Aggregate Function
Analyzing data with SUM Aggregate Function
Analyzing data with MIN Aggregate Function
Analyzing data with MAX Aggregate Function
Data Analysis with T-SQL and MS SQL Server
Download SQL Server
Install SQL Server
Install SSMS
Connect SSMS to SQL Server
Install Sample Database
Analyzing data with Rank Functions
Analyzing data with NTILE Functions
Analyzing data with DENSE_Rank Functions
Analyzing data with ROW_NUMBER Functions
Analyzing data with LEAD Functions
Analyzing data with LAG Functions
Analyzing data with LAST VALUE Functions
Analyzing data with FIRST_VALUE Functions
Analyzing data with PERCENT_RANK Functions
Analyzing data with CUME_DIST Functions
Analyzing data with PERCENTILE_DISC Functions
Analyzing data with PERCENTILE_CONT Functions
Analyzing data with SET Operators
Analysing data with EXCEPT Operator
Analyzing data with INTERSECT SET Operators
Analyzing data with UNION SET Operators
Analyzing data with comparisons
Analyzing data with equality filters
Analyzing data with logical comparisons
Analyzing data with string comparison
Analyzing data with NULL values
Data Analysis with SQL and PostgreSQL
Installing PostgreSQL (Windows)
Installing PostgreSQL (Macs)
Connecting to PostgreSQL
Installing PgAdmin4 on Macs
Load sample database
Analyzing data with UNION Operator
Analyzing data with UNION ALL Operator
Analyzing data with INTERSECT Operator
Analyzing data with EXCEPT Operator
Analyzing data with IN Operator
Analyzing data with AND Operator
Analyzing data with OR Operator
Analyzing data with AND |OR Operator
Analyzing data with LIKE Operator
Analyzing data with INNER JOINS
Analyzing data with LEFT JOINS
Analyzing data with FULL OUTER JOIN
Analyzing data with CROSS JOIN
Analyzing data with NATURAL JOIN
Data Analysis & Visualization with Excel
Office 365 setup (Optional )
Activating Office 365
Login to office 365
Enable Power Pivot
Connect to data source
Prepare query
Cleansing data
Enhance query
Create data model
Import data | build relationships
Create lookups with DAX
Analyse data with Pivot Tables
Analyse data with Pivot Charts
Refresh Source Data
Update queries
Create reports
Data Analysis & Visualization with Power BI
Installing Power BI Desktop
Connect to web based data
Clean and transform data: part 1
Clean and transform data: part 2
Combine data sources
Creating Visuals : Part 1
Creating Visuals : Part 2
Publish reports to power bi service
Connecting to MS SQL Database with Power BI
Connecting to PostgreSQL Database with Power BI : Part1
Connecting to PostgreSQL Database with Power BI: Part 2
Data Analysis & Visualization with Tableau
Tableau Desktop Setup
Tableau Public Desktop
Tableau Online Setup
Tableau Data Sources
Tableau File Types
Connecting to data sources
Join related data sources
Join data sources with inconsistent field
Data Cleaning
Exploring Tableau Interface
Reorder fields in visualization
Change Summary
Split text into multiple columns
Presenting data using stories