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Free Certificate
Introduction to Database Design with MySQL
Learn how to structure, create, and manage databases using MySQL Workbench. This database design with MySQL free course covers ER models, normalization, SQL queries, and relational schema concepts.
8 hours of learning
Database Design
Data Analysis
Data Warehousing
For enquiries call:
18002102020
-6bbe1ab373b64ec08ad9ca4301fd4dd8.webp&w=3840&q=75)
Key Highlights Of This Database Design With MySQL Course
What You Will Learn
Understanding upGrad Coding Console
The program includes coding problems to boost your problem-solving skills. It's crucial to engage with these for the best upGrad experience. Start with the coding console tutorial.
Topics Covered
- Understanding Primary Actions
Gain a foundational understanding of the core operations necessary to begin solving problems on the coding console. This includes navigating the interface, accessing problem statements, writing code, running sample tests, and submitting final solutions. Mastering these actions sets the stage for a productive hands-on learning experience. - Statuses & Important Pointers
Each code submission is accompanied by system-generated statuses such as “Passed,” “Failed,” or “Compilation Error.” Understanding these messages is critical to iterative debugging and optimization. This section highlights how to interpret feedback, recognize logical gaps, and apply constructive changes to improve overall code quality. - Problem Stub
Every problem on the console comes with a predefined code template, known as the problem stub. This stub includes the function signature and sometimes helper code. Learning to read and build upon the stub correctly helps in maintaining structure, meeting expected inputs/outputs, and aligning with system test cases. - Execution Time Limit
Each problem has a set time constraint for code execution. Writing efficient, optimized code is vital to stay within this execution window. This section will teach you how to identify time complexity issues, choose suitable algorithms, and avoid unnecessary computational overhead that may lead to timeouts. - Load Language Stub
The console supports multiple programming languages. Choosing the correct language and loading its respective stub ensures that you’re working within the appropriate syntax and environment. This section guides you in selecting your preferred language and initializing your workspace for a smooth coding experience. - Demystifying Test Cases
Test cases are used to verify the accuracy and robustness of your solution. This section explains the role of sample and hidden test cases, how to design your code to handle edge conditions, and how to use test outputs for effective debugging and final validation of your logic.
Database Design
Using Python or Excel for data analysis can risk inconsistencies with multiple files. Databases and Database Management Systems (DBMS) offer efficient data access and analysis. Learn more about DBMS in this session.
Topics Covered
- Data Warehouse
A data warehouse is a centralized repository designed for large-scale data storage and complex querying. It integrates data from multiple sources, enabling structured reporting and business intelligence. This topic explores how warehouses support historical analysis, pattern recognition, and enterprise-level decision-making through consolidated, structured datasets. - Star Schema
The star schema is a commonly used data warehouse architecture that simplifies complex queries. It organizes data into fact and dimension tables, optimizing performance for analytical processing. This section covers the schema’s structure, advantages in query speed, and its role in simplifying business reporting. - OLAP (Online Analytical Processing)
OLAP systems support multidimensional analysis of data, commonly through “data cubes” that enable slicing, dicing, and drilling of metrics. Learn how OLAP enhances strategic decision-making by enabling fast aggregation, trend analysis, and performance comparisons across various business dimensions. - OLTP (Online Transaction Processing)
OLTP systems are designed to handle high-volume, real-time transactional workloads, such as banking, retail, or reservation systems. This section discusses their architecture, consistency models, and how they support day-to-day business operations through fast insert, update, and delete functionalities. - SETL (Source, Extract, Transform, Load)
SETL refers to the process of sourcing data from different platforms, transforming it for analysis, and loading it into a central repository like a data warehouse. Learn how this pipeline ensures data quality, consistency, and reliability across systems, setting the stage for effective analytics. - Constraints
Constraints are rules applied to database tables to ensure data integrity, consistency, and validity. This section details types such as primary keys, foreign keys, not null, unique, and check constraints—highlighting how they safeguard database structure and prevent invalid or duplicate entries. - ERD (Entity Relationship Diagram)
An ERD visually represents the structure of a database by mapping entities, relationships, and attributes. Learn to design ERDs that capture business logic clearly, helping translate real-world scenarios into well-structured database models that form the foundation of robust DBMS implementations.
Database Creation in MySQL Workbench
This session explores how hospitals use databases to manage patient info and make real-time decisions. Learn data retrieval with SQL in RDBMS, focusing on MySQL and MySQL Workbench.
Topics Covered
- DDL Statements
Data Definition Language (DDL) statements are used to define and manage database structures. Commands such asCREATE,ALTER, andDROPallow you to build and modify tables, schemas, and indexes. This topic explains how DDL helps lay the foundation of a relational database before any data is inserted. - DML Statements
Data Manipulation Language (DML) statements enable users to insert, update, delete, and retrieve data from tables. Using commands likeINSERT,UPDATE,DELETE, andSELECT, learners will gain hands-on experience in modifying datasets and conducting queries that return meaningful insights from relational data. - Modifying Columns
This section covers how to alter existing table columns in a safe and efficient manner. You’ll learn how to rename columns, change data types, add constraints, or modify defaults—all while ensuring data integrity is maintained. This is crucial for evolving database schemas in dynamic business environments.
Querying in MySQL
It's time to master SQL's core, focusing on query statements essential for data extraction, analysis, and informed business decisions. Develop the analytical skills needed to navigate and utilize SQL effectively.
Topics Covered
- SQL Query Basics: SELECT, FROM
Understand how to retrieve data from one or more tables using theSELECTandFROMclauses. This section introduces the building blocks of SQL querying—where you learn to specify the columns and tables you want to pull data from, forming the basis for every query. - Understand WHERE, GROUP BY, HAVING
Learn how to apply filters (WHERE), group data based on common fields (GROUP BY), and apply conditions to grouped records (HAVING). These commands are essential for narrowing down data sets and generating aggregated results like totals, averages, and conditional counts. - Master ORDER BY and LIMIT Statements
Discover how to organize query results usingORDER BY, whether ascending or descending, and how to control data volume withLIMIT. These commands improve query efficiency and usability, especially when analyzing large datasets or creating paginated outputs for reports. - Use of Operators in Queries
Explore how logical (AND,OR,NOT) and comparison operators (=,>,<,BETWEEN, etc.) refine query results. This section helps learners structure precise conditions that drive accurate outputs and help answer specific business queries. - Functions for Complex Data Needs
Dive into built-in SQL functions likeCOUNT(),SUM(),AVG(),MIN(),MAX(), and text/date functions. These tools allow for powerful data aggregation, transformation, and formatting, giving analysts deeper insights into patterns and anomalies within structured data.
Joins and Set Operations
Dive into SQL: Use joins to query multiple tables simultaneously and master set operations to seamlessly merge results with 'union'. Unlock powerful data handling in one session!
Topics Covered
- Union
TheUNIONoperator allows you to merge results from two or moreSELECTqueries into a single dataset, eliminating duplicates by default. It’s useful for consolidating data from similar tables or sources while ensuring consistency and readability in reporting and dashboards. - Intersect
INTERSECTreturns only the rows that appear in both result sets of two queries. This operation is especially valuable when identifying commonalities between datasets—such as customers in multiple programs or products listed across different regions. - Minus
TheMINUSoperator (orEXCEPTin some systems) identifies records in the first query that do not appear in the second. This is essential for comparative analysis—helping uncover gaps, mismatches, or exclusions in datasets when assessing change or filtering out noise. - Inner Join
INNER JOINreturns only those records that have matching values in both joined tables. It’s one of the most commonly used joins and is ideal for focusing strictly on intersecting data points—such as matched transactions, shared attributes, or linked entities. - Left Join
ALEFT JOINretrieves all records from the left table and only matching records from the right table. This is helpful when you want to preserve all entries from one dataset while bringing in related data wherever available from another table. - Right Join
RIGHT JOINis the inverse of aLEFT JOIN. It pulls all records from the right table and matched records from the left. This join is useful when the primary reference table is on the right side, and the left-side data is supplementary or optional.
Database Design With MySQL Course Certification
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