Data Analysis & Business Intelligence: SQL MySQL Power BI
Data Analysis & Business Intelligence: SQL MySQL Power BI, available at $59.99, has an average rating of 4.05, with 122 lectures, based on 273 reviews, and has 19528 subscribers.
You will learn about 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 This course is ideal for individuals who are Beginner Data Analyst or Beginner Business Intelligence Analyst or Beginner Data Scientist or Beginner Data Engineer It is particularly useful for Beginner Data Analyst or Beginner Business Intelligence Analyst or Beginner Data Scientist or Beginner Data Engineer.
Enroll now: Data Analysis & Business Intelligence: SQL MySQL Power BI
Summary
Title: Data Analysis & Business Intelligence: SQL MySQL Power BI
Price: $59.99
Average Rating: 4.05
Number of Lectures: 122
Number of Published Lectures: 122
Number of Curriculum Items: 122
Number of Published Curriculum Objects: 122
Original Price: $84.99
Quality Status: approved
Status: Live
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
Who Should Attend
- Beginner Data Analyst
- Beginner Business Intelligence Analyst
- Beginner Data Scientist
- Beginner Data Engineer
Target Audiences
- Beginner Data Analyst
- Beginner Business Intelligence Analyst
- Beginner Data Scientist
- Beginner Data Engineer
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.
Course Curriculum
Chapter 1: Introduction
Lecture 1: Course Introduction
Lecture 2: What is Data Analysis
Lecture 3: What is Business Intelligence
Lecture 4: What is Tableau
Lecture 5: What is MySQL
Lecture 6: What is Microsoft SQL Server
Lecture 7: What is PostgreSQL
Lecture 8: What is Power BI
Lecture 9: What is Power BI Desktop
Lecture 10: What is Excel Power Pivot
Lecture 11: What is Excel Power Query
Lecture 12: What is SQL
Lecture 13: What is T-SQL
Lecture 14: Database Concepts
Chapter 2: Data Analysis with SQL and MySQL
Lecture 1: MySQL Download Link
Lecture 2: MySQL Installation (Windows)
Lecture 3: MySQL Installation(Mac)
Lecture 4: MySQL Workbench
Lecture 5: Installing MySQL Workbench (Mac)
Lecture 6: SQL Statement and query
Lecture 7: Analyzing data with INNER JOIN
Lecture 8: Analyzing data with LEFT JOIN
Lecture 9: Analyzing data with RIGHT JOIN
Lecture 10: Analyzing data with SELF JOIN
Lecture 11: Analyzing data with Sub queries
Lecture 12: Analyzing data with nested sub query
Lecture 13: Analyzing data with derived tables
Lecture 14: Analyzing data with Between Operator
Lecture 15: Analyzing data with IN Operator
Lecture 16: Analyzing data with LIKE Operator
Lecture 17: Analyzing data with UNION Operator
Lecture 18: Analyzing data with AVG Aggregate Function
Lecture 19: Analyzing data with COUNT Aggregate Function
Lecture 20: Analyzing data with SUM Aggregate Function
Lecture 21: Analyzing data with MIN Aggregate Function
Lecture 22: Analyzing data with MAX Aggregate Function
Chapter 3: Data Analysis with T-SQL and MS SQL Server
Lecture 1: Download SQL Server
Lecture 2: Install SQL Server
Lecture 3: Install SSMS
Lecture 4: Connect SSMS to SQL Server
Lecture 5: Install Sample Database
Lecture 6: Analyzing data with Rank Functions
Lecture 7: Analyzing data with NTILE Functions
Lecture 8: Analyzing data with DENSE_Rank Functions
Lecture 9: Analyzing data with ROW_NUMBER Functions
Lecture 10: Analyzing data with LEAD Functions
Lecture 11: Analyzing data with LAG Functions
Lecture 12: Analyzing data with LAST VALUE Functions
Lecture 13: Analyzing data with FIRST_VALUE Functions
Lecture 14: Analyzing data with PERCENT_RANK Functions
Lecture 15: Analyzing data with CUME_DIST Functions
Lecture 16: Analyzing data with PERCENTILE_DISC Functions
Lecture 17: Analyzing data with PERCENTILE_CONT Functions
Lecture 18: Analyzing data with SET Operators
Lecture 19: Analysing data with EXCEPT Operator
Lecture 20: Analyzing data with INTERSECT SET Operators
Lecture 21: Analyzing data with UNION SET Operators
Lecture 22: Analyzing data with comparisons
Lecture 23: Analyzing data with equality filters
Lecture 24: Analyzing data with logical comparisons
Lecture 25: Analyzing data with string comparison
Lecture 26: Analyzing data with NULL values
Chapter 4: Data Analysis with SQL and PostgreSQL
Lecture 1: Installing PostgreSQL (Windows)
Lecture 2: Installing PostgreSQL (Macs)
Lecture 3: Connecting to PostgreSQL
Lecture 4: Installing PgAdmin4 on Macs
Lecture 5: Load sample database
Lecture 6: Analyzing data with UNION Operator
Lecture 7: Analyzing data with UNION ALL Operator
Lecture 8: Analyzing data with INTERSECT Operator
Lecture 9: Analyzing data with EXCEPT Operator
Lecture 10: Analyzing data with IN Operator
Lecture 11: Analyzing data with AND Operator
Lecture 12: Analyzing data with OR Operator
Lecture 13: Analyzing data with AND |OR Operator
Lecture 14: Analyzing data with LIKE Operator
Lecture 15: Analyzing data with INNER JOINS
Lecture 16: Analyzing data with LEFT JOINS
Lecture 17: Analyzing data with FULL OUTER JOIN
Lecture 18: Analyzing data with CROSS JOIN
Lecture 19: Analyzing data with NATURAL JOIN
Chapter 5: Data Analysis & Visualization with Excel
Lecture 1: Office 365 setup (Optional )
Lecture 2: Activating Office 365
Lecture 3: Login to office 365
Lecture 4: Enable Power Pivot
Lecture 5: Connect to data source
Lecture 6: Prepare query
Lecture 7: Cleansing data
Lecture 8: Enhance query
Lecture 9: Create data model
Lecture 10: Import data | build relationships
Lecture 11: Create lookups with DAX
Lecture 12: Analyse data with Pivot Tables
Lecture 13: Analyse data with Pivot Charts
Lecture 14: Refresh Source Data
Instructors
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Bluelime Learning Solutions
Making Learning Simple
Rating Distribution
- 1 stars: 8 votes
- 2 stars: 6 votes
- 3 stars: 48 votes
- 4 stars: 82 votes
- 5 stars: 129 votes
Frequently Asked Questions
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Can I take my courses with me wherever I go?
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