Key Outcomes
What is a Database Management System (DBMS)?
A database management system is a software tool used to create and manage one or more databases, offering an easy way to create a database, update tables, retrieve information, and enhance data. A DBMS is where data is accessed, modified and locked to prevent conflicts.
A database management system also provides tools to administer the database schema – which dictates the structure of the database itself. In many cases, the database management system will be seen only by the database developer, because the developer will provide a different front-end for the customer. This front-end could be considered, by the most technical definition, to be a database management system in its own right — however, it is more likely to go by another name, such as a customer relationship management (CRM) tool.
RDBMS vs. (the traditional) DBMS
The DBMS is the predecessor to the relational database or an RDBMS. RDBMS are far more popular than DBMS for several reasons, including:
ACID (atomicity, consistency, isolation, durability) properties – Sometimes, there can be inconsistencies with a DBMS. This is because the traditional DBMS doesn’t follow ACID properties:
Atomicity – ensures that either the entire transaction takes place or not.
Consistency – Ensures the correctness of a database.
Isolation – Ensures that several operations can co-occur without causing any inconsistency in the database.
Durability – Ensures that once a transaction is complete, the updates and modifications made to the database exists even if there is a system failure.
Data access – Only a single data element can be accessed at a single time for a classic DBMS. Several data elements are accessible at the same time in a relational database management system.
Data fetching – A relational database management system offers rapid data fetching due to its relational approach.
Data redundancy – Classical databases often suffer from data redundancy, which is merely having the same data repeated at different places. Keys and indexes eliminate data redundancy in relational databases, thereby reducing resource waste.
Normalization – Normalization is used for organizing data in a database. It helps in reducing the data redundancy present in a database. A traditional DBMS doesn’t support normalization, while an RDBMS does.
Relationships amongst data – There are no relationships between data in a classic database management system. On the other hand, data is stored in the form of tables in an RDBMS that is related to one another with foreign keys.
Database structure – A classical DBMS stores data in either a hierarchical or navigational form. An RDBMS follows a tabular structure approach, rows and columns, for storing data.
Distributed databases – Management of data with different levels of transparency and increased availability are two of the many advantages of using distributed databases. An RDBMS supports distributed databases while a classical DBMS doesn’t.
Hardware and software requirement – A classical DBMS requires low software and hardware specifications than a typical relational database management system.
Integrity constraints – Integrity constraints, such as referential integrity for handling data integrity, ensure accuracy and consistency in a database. A DBMS doesn’t support integrity constraints. These aren’t imposed at the file level. Contrary to this, integrity constraints are supported by an RDBMS at the schema level.
The number of users – While a DBMS supports only a single user, RDBMSs offer support for more than one user.
Data security – Security is an essential aspect of the internet today. However, no data security measures are available for a traditional DBMS. As a result, it is prone to malicious data modifications, data theft, and so on. On the contrary, modern RDBMS feature multiple-level security, ensuring that data can only be accessed by those intended.
What are the benefits of NoSQL databases?
NoSQL databases offer many benefits over relational databases. NoSQL databases have flexible data models, scale horizontally, have incredibly fast queries, and are easy for developers to work with.
Flexible data models
NoSQL databases typically have very flexible schemas. A flexible schema allows you to easily make changes to your database as requirements change. You can iterate quickly and continuously integrate new application features to provide value to your users faster.
Horizontal scaling
Most SQL databases require you to scale-up vertically (migrate to a larger, more expensive server) when you exceed the capacity requirements of your current server. Conversely, most NoSQL databases allow you to scale-out horizontally, meaning you can add cheaper commodity servers whenever you need to.
Fast queries
Queries in NoSQL databases can be faster than SQL databases. Why? Data in SQL databases is typically normalized, so queries for a single object or entity require you to join data from multiple tables. As your tables grow in size, the joins can become expensive. However, data in NoSQL databases is typically stored in a way that is optimized for queries. The rule of thumb when you use MongoDB is data that is accessed together should be stored together. Queries typically do not require joins, so the queries are very fast.
Easy for developers
Some NoSQL databases like MongoDB map their data structures to those of popular programming languages. This mapping allows developers to store their data in the same way that they use it in their application code. While it may seem like a trivial advantage, this mapping can allow developers to write less code, leading to faster development time and fewer bugs.
What are the drawbacks of NoSQL databases?
One of the most frequently cited drawbacks of NoSQL databases is that they don’t support ACID (atomicity, consistency, isolation, durability) transactions across multiple documents. With appropriate schema design, single-record atomicity is acceptable for lots of applications. However, there are still many applications that require ACID across multiple records.
To address these use cases, MongoDB added support for multi-document ACID transactions in the 4.0 release and extended them in 4.2 to span sharded clusters.
Since data models in NoSQL databases are typically optimized for queries and not for reducing data duplication, NoSQL databases can be larger than SQL databases. Storage is currently so cheap that most consider this a minor drawback, and some NoSQL databases also support compression to reduce the storage footprint.
Depending on the NoSQL database type you select, you may not be able to achieve all of your use cases in a single database. For example, graph databases are excellent for analyzing relationships in your data but may not provide what you need for everyday retrieval of the data such as range queries. When selecting a NoSQL database, consider what your use cases will be and if a general-purpose database like MongoDB would be a better option.
Skills you’ll gain
Work with databases using SQL
Create queries for tables
Program Modules
Master SQL, Database Management & Design and learn to work with databases like SQL, No SQL, MySQL, Mongo DB + more. With Exercises!
(Training & Certification Path)
A brief outline of the certificate:
• 6 hours per week for a 3 months duration
• Automated assessments and quizzes
• Repeat unlimited times at no additional cost
• Verified and Certified certificate
• 24×7 support to answer your queries and doubts
• Capstone project
• Resume/Interview preparation & Job placement assistance
Modules
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Module 1 : Database System (Review)
Data Models, Schema, and Instances
Three-Schema Architecture and Data Independence
Database Languages and Interfaces
The Database System Environment
Centralized and Client/Server Architectures for DBMS’s
Classification of DBMS’s
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Module 2 : The Relational Data Model and Relational Database Constraints
Relational Model Concepts
Relational Model Constraints and
Relational Database Schema’s
Update Operations, Transactions, and Dealing with Constraint Violations
Data Modeling Using the Entity-Relationship (ER) Model
Overview of Database Design Process
ER Model Concepts
Entities and Attributes
Entity Types, Value Sets, and Key Attributes
Relationships and Relationship Types
Weak Entity Types
Roles and Attributes in Relationship Types
ER Diagram – Notations
Example Database Application (COMPANY)
ER Diagram for COMPANY Database
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Module 3 : Relational Database Design by ER-to-Relational Mapping (Database Design)
Schema Mapping (Logical Database Design)
Step of Database Design
ER-to-Relational Mapping Algorithm
Step 1: Mapping of Regular Entity Types
Step 2: Mapping of Weak Entity Types
Step 3: Mapping of Binary 1:1 Relation Types
Step 4: Mapping of Binary 1: N Relationship Types.
Step 5: Mapping of Binary M: N Relationship Types.
Step 6: Mapping of Multi valued attributes.
Step 7: Mapping of N-array Relationship Types.
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Module 4 : Functional Dependencies and Normalization for Relational Databases
Informal Design Guidelines for Relation Schema’s
Formal Design Guidelines for Relation Schema’s
Functional Dependencies
Normal Forms Based on Primary Keys
General Definitions of Second and Third Normal Forms
Boyce-Codd Normal Form
Multivalued Dependency and Fourth Normal Form
Join Dependencies and Fifth Normal Form
Introduction to Transaction Processing Concepts and Theory
Introduction to Transaction Processing
Transaction and System Concepts
Desirable Properties of Transactions
Characterizing Schedules based on Recoverability
Characterizing Schedules based on Serializ-ability
Transaction Support in SQL
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Module 5 : Concurrency Control Techniques
Purpose of Concurrency Control (CC)
Two-Phase Locking CC Protocols (2PL)
Other Types of CC Protocols
Timestamp Ordering (TO)
Multi version TO and Multi version 2 PL
Optimistic (validation-based) CC
Multiple Granularity Locking
Index Locking
Other Concurrency Control Issues
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Module 6 : Database Recovery Techniques
Purpose of Database Recovery
Types of Failure
The Log File
Data Cache and Buffers
Data Updating
Roll-back (Undo) and Roll-Forward (Redo)
Checkpoints
Recovery Techniques
Example: ARIES Recovery Scheme
Recovery in Multidata base Transactions
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Module 7 : SQL and No SQL Databases (SQL Deep Dive)
MySQL
Elastic Search
Mongo Db
Oracle
Redis Cache Memory
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Applied Learning Project
All courses in the bootcamp contain multiple hands-on labs and assignments to help you gain practical experience and skills with a variety of data sets. Build your data science portfolio from the artifacts you produce throughout this program. Course-culminating projects include:
Extracting and graphing financial data with the Pandas data analysis Python library
Generating visualizations and conducting statistical tests to provide insight on housing trends using census data
Using SQL to query census, crime, and demographic data sets to identify causes that impact enrollment, safety, health, and environment ratings in schools
*Please Note: All projects and class assignments are based upon instructors’ discretion and the faculty can modify or change the projects anytime.
Program Experience
Office Hours with Learning Facilitators
Coding Exercises in Each Module
Dedicated Program Support Team
Bonus Content on Advanced Topics
Certification & Digital Badge
Who Should Attend?
This online program is designed for anyone who is interested in acquiring programming skills in Python & Machine Learning. No prior programming knowledge is required.
Certificate
Upon successful completion of the program, participants will receive a digital certificate of completion from American Institute for IT Professionals. This is a training program, and it is not eligible for academic credit.
Why Python?
Python is a good starting point for first-time coders. It uses simple, natural language syntax, almost like spoken English. It is powerful and it is versatile, favored by such diverse industry giants as Netflix, PayPal, NASA, Disney, and Dropbox. Python is used by 87% of data scientists.
User-Friendly Syntax: As an interpreted language, Python has simpler, more concise syntax than Java. Python’s simple, concise syntax makes it easy to write algorithms with just a few lines of code.
Open-Source Libraries: Pre-written code is readily available, with algorithms at your disposal, so you do not have to start every project from scratch. You can benefit from highly specific libraries – physics, web development, gaming, machine learning – by simply importing algorithms and applying them to your own data. It is plug and play at its best, with new functionalities being added all the time.
Community Exchanges: Python’s popularity means it has great community support, with almost 8 million Python developers across the world to help you debug or resolve a programming challenge.
Compatibility: Python is a cross-platform language and can be integrated easily with Windows and other platforms.
Adaptability: Almost every field is adopting Python and needs both generalists and specialists who know how to use it. Fields as varied as gaming, web development, healthcare, and fintech prefer Python over other programming languages, making it the must-learn language for STEM professionals and data scientists.
Why Machine Learning?
Machine learning algorithms are transforming systems, experiences, processes, and entire industries. It’s no wonder that business leaders see these data-driven technologies as fundamental for the future—and that practitioners fluent in both fields are in high demand.
At American Institute for IT Professionals, we are fascinated by their world-changing potential, and we’ve created the training program that will come Master’s degree level content, to help students understand the fundamentals of AI and machine learning and how to apply them to solve complex, real-world problems.
Why SQL?
Our SQL course content is the perfect place to start if you are new to the world of database programming or database administration. In addition to learning basic SQL commands and syntax – such as how to insert, update, and delete information from tables – users will be able to practice those same commands through our free online SQL interpreter. Enter SQL commands to receive immediate results and experiment to better understand how database concepts such as table drops and table selects work.
The SQL tutorial and online interpreter currently supports a subset of ANSI SQL. The basics of commands such as select, insert, update, delete, and drop will be covered in this gentle introduction to Structured Query Language (SQL).
If you are already familiar with the basics of SQL, you can still use this database course as a refresher and practice common SQL commands and statements.
Why Statistics?
Statistics is a core component of data analytics and machine learning. It helps you analyze and visualize data to find unseen patterns. If you are interested in machine learning and want to grow your career in it, then learning statistics along with programming should be the first step. In this article, you will learn all the concepts in statistics for machine learning.
Statistics is a branch of mathematics that deals with collecting, analyzing, interpreting, and visualizing empirical data. Descriptive statistics and inferential statistics are the two major areas of statistics. Descriptive statistics are for describing the properties of sample and population data (what has happened). Inferential statistics use those properties to test hypotheses, reach conclusions, and make predictions (what can you expect).
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