Database Systems: All Concepts of Graduate Level for CS Graduates

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Excel, SQL, and Python: Essential Tools for Data Analysts As a data analyst, mastering Excel, SQL, and Python is crucial for effectively extracting, manipulating, and analyzing data. This comprehensive course is designed to equip you with the practical skills needed to excel in these essential tools. Key areas of focus include: Excel: Learn advanced Excel techniques, including data manipulation, pivot tables, and data visualization. SQL: Gain proficiency in SQL for querying databases, extracting relevant data, and performing data analysis. Python: Discover the power of Python for data analysis, including data cleaning, manipulation, and visualization using popular libraries like Pandas, NumPy, and Matplotlib. Through hands-on exercises and real-world examples, you'll develop the confidence and expertise to tackle complex data analysis challenges and drive informed decision-making.

Module 1: SQL Overview Outlining SQL as the cornerstone of database activity Applying the ANSI/ISO standards Describing the fundamental building blocks: tables, columns, primary keys, and foreign keys Module 2: Building the Database Schema - Creating tables and columns Building tables with CREATE TABLE Modifying table structure with ALTER TABLE Adding columns to an existing table Removing tables with DROP TABLE - Protecting data integrity with constraints Guaranteeing uniqueness with primary key constraints Enforcing integrity with foreign key constraints Imposing business rules with check constraints - Improving performance with indexes Expediting data retrieval with indexes Recommending guidelines for index creation Module 3: Manipulating Data - Modifying table contents Adding table rows with INSERT Changing row content with UPDATE Removing rows with DELETE - Applying transactions Controlling transactions with COMMIT and ROLLBACK Deploying BEGIN TRANSACTION in SQL Server Module 4: Working with the SELECT Statement - Writing Single Table queries Retrieving data with SELECT Specifying column expressions Sorting the result with ORDER BY Handling NULL values in expressions - Restricting rows with the WHERE filter Testing for equality or inequality Applying wildcard characters Avoiding NULL value pitfalls Module 5: Querying Multiple Tables - Applying the ANSI/ISO standard join syntax Matching related rows with INNER JOIN Including nonmatched rows with OUTER JOIN Creating a Cartesian product with CROSS JOIN - Combining results with set operators Stacking results with UNION Identifying matching rows with INTERSECT Utilizing EXCEPT to find nonmatching rows Module 6: Employing Functions in Data Retrieval - Processing data with row functions Conditional formatting with the CASE expression Utilizing the CASE expression to simulate IF tests Dealing with NULL values - Performing analysis with aggregate functions Summarizing data using SUM, AVG and COUNT Finding the highest/lowest values with MAX and MIN Defining the summary level with GROUP BY Applying filter conditions with HAVING Module 7: Constructing Nested Queries - Applying subqueries in filter conditions Creating correlated vs. noncorrelated subqueries Testing the existence of rows - Including subqueries in expressions Placing subqueries in the column list Creating complex expressions containing subqueries Handling subqueries that return no rows Module 8: Developing In-Line and Stored Views - Breaking down complex problems Selecting data from a query result set Using subqueries in the FROM clause - Creating views in a database Building reusable code Creating updateable vs. non-updateable views

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