Advanced HTML and CSS: Creating Responsive and Interactive Websites
Welcome to “Advanced HTML and CSS: Creating Responsive and Interactive …
What you'll learn
Dive deeper into HTML and CSS (Cascading Style Sheets) to create more advanced and dynamic web pages.
Learn advanced HTML techniques for structuring content, including semantic elements and HTML5 features.
Study CSS properties and selectors to gain more control over the layout and styling of web pages.
Explore CSS Flexbox and CSS Grid to create responsive and flexible page layouts.
Understand CSS animations and transitions to add interactivity and visual effects to your web pages.
Learn about media queries and responsive design principles to make your websites adapt to different screen sizes and devices.
HTML5 and Beyond: Exploring Modern Web Development Techniques
Welcome to “HTML5 and Beyond Exploring Modern Web Development Techniques”! …
What you'll learn
Delve into the latest features and capabilities of HTML5 and how they enhance web development.
Learn about new HTML5 elements, such as <video>, <audio>, , and , for multimedia and graphics.
Explore HTML5 APIs, such as Geolocation, Web Storage, and Web Workers, for advanced web applications.
Study responsive web design principles and media queries to create websites that adapt to different devices and screen sizes.
Understand how to use CSS3 to add styling and animations to your web pages.
Explore JavaScript frameworks and libraries, such as React or Vue.js, to build interactive and dynamic web applications.
Foundations of Data Science: Principles and Techniques
The course “Foundations of Data Science: Principles and Techniques” provides …
What you'll learn
Learn the fundamental concepts and principles of data science, including data manipulation, analysis, and visualization.
Study various data types and formats, such as structured, unstructured, and semi-structured data.
Explore data cleaning, preprocessing, and transformation techniques to ensure data quality.
Understand statistical methods and techniques for analyzing data and extracting insights.
Gain knowledge of data visualization tools and techniques to effectively communicate findings.
Machine Learning and Predictive Modelling for Data Science
The course “Machine Learning and Predictive Modelling for Data Science” …
What you'll learn
Explore the principles and techniques of machine learning and its applications in data science.
Study the different types of machine learning algorithms, such as supervised, unsupervised, and semi-supervised learning.
Learn about predictive modelling and how to use machine learning algorithms to make predictions based on data.
Understand data preprocessing and feature engineering techniques to prepare data for machine learning models.
Study model evaluation and performance metrics to assess the accuracy and effectiveness of predictive models.
Gain knowledge of popular machine learning libraries and frameworks, such as scikit-learn or TensorFlow.
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Advanced Data Analytics: Methods and Applications
The course “Advanced Data Analytics: Methods and Applications” is designed …
What you'll learn
Understanding the principles and challenges of big data analytics and data preprocessing techniques.
Study of advanced data mining algorithms, such as decision trees, clustering, and association rule mining.
Techniques for predictive modeling and machine learning for making data-driven decisions.
Exploration of natural language processing (NLP) and sentiment analysis for text data.
Application of data visualization techniques and tools for effective data communication.
Practice in using data analytics tools and programming languages like Python and R for data analysis.
Fundamentals of Database Design: Principles and Techniques
The course “Fundamentals of Database Design: Principles and Techniques” provides …
What you'll learn
Understanding the basic concepts of databases, data models, and relational database management systems (RDBMS).
Study of entity-relationship (ER) modeling and how to represent real-world entities and their relationships in a database.
Techniques for designing tables, attributes, and primary keys for data organization and integrity.
Exploration of normalization techniques to eliminate data redundancy and ensure data consistency.
Application of data constraints, foreign keys, and indexes for data integrity and efficient querying.
Practice in designing database schemas for specific application requirements and use cases.
Advanced Database Development: Optimizing Performance and Scalability
The course “Advanced Database Development: Optimizing Performance and Scalability” is …
What you'll learn
Study of advanced indexing techniques, query optimization, and caching strategies to enhance database query performance.
Techniques for database partitioning, sharding, and replication to improve scalability and handle large amounts of data.
Exploration of database normalization and denormalization for efficient data storage and retrieval.
Application of advanced database features, such as stored procedures, triggers, and materialized views, for improved functionality and performance.
Practice in designing and implementing high-performance and scalable database solutions for real-world applications.
Data Modelling and Database Management: Designing Efficient Data Structures
The course “Data Modelling and Database Management: Designing Efficient Data …
What you'll learn
Understanding the basics of data modelling techniques, such as entity-relationship diagrams (ERD) and UML diagrams.
Study of different data modelling approaches, including conceptual, logical, and physical data models.
Techniques for translating data models into database schemas and designing tables, keys, and relationships.
Exploration of database management systems (DBMS) and their role in efficiently managing data.
Application of indexing, partitioning, and clustering for optimizing database performance.
Practice in implementing data modelling concepts in popular database systems like MySQL, Oracle, or SQL Server.
