This comprehensive 20-session course covers essential concepts, tools, and best practices for Java full-stack web development, with each session lasting 2 hours. The course will guide you through both front-end and back-end development using Java, popular frameworks, and various web technologies. You will also work on a hands-on demo project that simulates real-world industry scenarios, enabling you to apply the skills learned throughout the course.
About Course
This comprehensive 20-session course is designed to provide you with the skills and knowledge required to excel as a Java full-stack web developer. Each session lasts 2 hours, and the course covers key concepts, tools, and best practices for both front-end and back-end development using Java, popular frameworks, and various web technologies. The curriculum includes a hands-on industry demo project that simulates real-world scenarios, enabling you to apply the skills learned throughout the course.
In the last three sessions, you will work on a hands-on demo project that simulates real-world industry scenarios. The project aims to provide practical experience in designing, implementing, and deploying a full-stack Java web application using the skills and knowledge acquired throughout the course. The project will help you gain a deep understanding of Java full-stack web development and equip you with the skills needed to tackle real-world industry projects.
No, prior experience in data science is not required. The course starts with the fundamentals of data science, making it accessible to beginners. However, some basic knowledge of programming, statistics, or data analysis would be helpful.
This course is suitable for aspiring data scientists, professionals seeking a career change, business analysts, software developers, and IT professionals who want to enhance their skills in data science.
The hands-on demo project is designed to give you practical experience in working with real-world data, from collecting and preprocessing the data to building, evaluating, and deploying machine learning models. The project helps you apply the concepts and techniques you have learned throughout the course, ensuring you gain the skills needed to work on real-world industry projects.
While there are no strict prerequisites, some basic knowledge of programming, statistics, or data analysis is recommended to get the most out of the course.
This course covers a wide range of data science topics, which may be helpful in preparing for various data science certifications. However, the course is not specifically designed to prepare you for a particular certification exam. If you’re interested in pursuing a certification, it’s a good idea to identify the specific exam you’d like to take and review its requirements, then use this course as a foundation and supplement it with additional study materials and practice exams as needed.
What Will You Learn?
- Java programming basics, including syntax, data types, variables, control structures, loops, methods, exception handling, and file I/O.
- Object-oriented programming with Java, covering classes, objects, inheritance, interfaces, abstract classes, polymorphism, encapsulation, and access modifiers.
- Java Collection framework, including lists, sets, maps, and generics.
- JDBC and database interaction, including CRUD operations, handling SQL exceptions, and connection pooling.
- Java Servlets and JSP, including their lifecycle, request/response handling, and JSP tags.
- Spring Framework and its modules, such as Spring Core, Spring MVC, Spring Boot, Spring Data, and Spring Security.
- Hibernate ORM, including mapping Java objects to database tables, configuration, and best practices.
Audience
This course is suitable for aspiring web developers, software developers, and IT professionals who want to enhance their skills in Java full-stack web development.
Course Content:
Understanding the role of a data scientist
Key concepts in data science
Overview of the data science process
Python programming basics
Data manipulation with pandas
Data visualization with matplotlib and seaborn
