282 Data Structure courses

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Description Data Science Diploma Introducing the Data Science Diploma, an online course tailored for those eager to step into the dynamic world of data science. This comprehensive programme ensures participants grasp the essence of contemporary data science techniques, tools, and theories. At the core of this Data Science Diploma is the module titled Foundations of Data Science. It sets the groundwork by instilling fundamental principles, thereby preparing learners to navigate the expansive sea of data efficiently and effectively. As one progresses, the intricate elements of Data Engineering and Big Data come into play, elucidating how vast amounts of data are managed, stored, and processed. An essential aspect of data science lies in understanding uncertainty and making informed decisions. To this end, Probability and Statistics in Data Science offers learners the tools to decipher patterns, predict trends, and make data-driven decisions. Following closely, Clustering and Classification Techniques provide a deep understanding of how to categorise data into specific groups based on inherent characteristics, paving the way for more precise analysis. But what's data science without the necessary mathematical prowess? The Advanced Mathematical Modeling module hones this skill, enabling learners to craft intricate models that can simulate real-world scenarios. Such models act as the backbone of various data analyses and offer a detailed understanding of the underlying processes. The saying, 'A picture is worth a thousand words,' holds especially true in data science. With the Data Visualisation Principles and Design module, learners are equipped with the knowledge to translate complex data into visually compelling stories. This understanding is further solidified with the Web-Based Data Visualisation Tools, offering hands-on experience in using cutting-edge tools to portray data visually. The course recognises the growing demand for intuitive dashboards that provide real-time insights. The Dashboard Design and Mapping module aids participants in creating interactive and user-friendly dashboards, ensuring stakeholders get a clear and concise view of the data. Yet, as one manoeuvres through these diverse modules, a foundational understanding of computing becomes paramount. Hence, Computing for Data Science takes centre stage, familiarising learners with the computational aspects of data analysis, from algorithms to data structures. Concluding the Data Science Diploma is the module on Domain-Specific Data Science Applications. This segment offers a glimpse into how data science principles are applied across different sectors, from healthcare to finance. It accentuates the versatility of data science, proving its indispensable nature in today's digitised world. To sum up, this online Data Science Diploma ensures a holistic understanding of data science. By intertwining theory with practical application, it equips learners with the skill set required to thrive in the data-driven industries of tomorrow. So, if the realm of data beckons you, this diploma is your gateway to excellence. What you will learn 1:Foundations of Data Science 2:Data Engineering and Big Data 3:Probability and Statistics in Data Science 4:Clustering and Classification Techniques 5:Advanced Mathematical Modeling 6:Data Visualisation Principles and Design 7:Web-Based Data Visualisation Tools 8:Dashboard Design and Mapping 9:Computing for Data Science 10:Domain-Specific Data Science Applications Course Outcomes After completing the course, you will receive a diploma certificate and an academic transcript from Elearn college. Assessment Each unit concludes with a multiple-choice examination. This exercise will help you recall the major aspects covered in the unit and help you ensure that you have not missed anything important in the unit. The results are readily available, which will help you see your mistakes and look at the topic once again. If the result is satisfactory, it is a green light for you to proceed to the next chapter. Accreditation Elearn College is a registered Ed-tech company under the UK Register of Learning( Ref No:10062668). After completing a course, you will be able to download the certificate and the transcript of the course from the website. For the learners who require a hard copy of the certificate and transcript, we will post it for them for an additional charge.

Learn BDD with SpecFlow (based on Gherkin) and Selenium WebDriver with C# alongwith an Intro to UnitTesting and TDD with TestStack.White

Computer Science GCSE Syllabus The GCSE Computer Science Tutor Syllabus is designed to provide tutors in England with a comprehensive framework for teaching the GCSE Computer Science curriculum effectively. This syllabus aims to equip tutors with the necessary knowledge and skills to support students in their understanding and application of core computer science concepts.  Module 1: Introduction to Computer Science - Overview of computer science and its relevance in today's world - Understanding the components of a computer system - Introduction to algorithms and problem-solving techniques - Exploration of programming languages and their uses Module 2: Computer Hardware - Understanding the main components of a computer system, including CPU, memory, and storage devices - Exploring input and output devices and their functionalities - Understanding the role of operating systems and software in computer systems Module 3: Software Development - Introduction to programming concepts and languages (e.g., Python or Java) - Understanding variables, data types, and operators - Building algorithms and logical reasoning skills - Introduction to flowcharts and pseudocode - Implementation of simple programs and debugging techniques Module 4: Data Representation - Understanding binary, hexadecimal, and denary number systems - Representation of text, images, and sound using binary - Introduction to data compression and encryption techniques Module 5: Computer Networks - Understanding the basics of computer networks, including LAN, WAN, and the Internet - Introduction to network topologies, protocols, and security - Exploring the impact of digital communication on society Module 6: Cybersecurity and Ethical Issues - Understanding the importance of cybersecurity and data protection - Introduction to common threats and vulnerabilities - Exploring ethical issues related to computer science, such as privacy and intellectual property rights Module 7: Algorithms and Programming Techniques - Advanced programming concepts, including conditionals, loops, and functions - Introduction to sorting and searching algorithms - Exploring data structures, such as arrays and lists Module 8: System Architecture - Understanding the structure and function of a CPU - Introduction to memory hierarchy and cache - Exploring the Von Neumann architecture and its limitations Module 9: Computational Thinking and Problem Solving - Advanced problem-solving techniques using computational thinking - Introduction to algorithms for complex problems - Exploring algorithmic efficiency and optimization techniques Module 10: Exam Preparation and Revision - Reviewing key concepts covered throughout the syllabus - Practicing past exam questions and providing guidance on exam techniques - Supporting students with exam preparation strategies Please note that the duration and depth of each module can vary depending on the level of expertise required and the specific needs of the learners. Additionally, it's important to adapt the curriculum to the learners' proficiency levels, whether they are A Level/GCSE students or adult learners with different experience levels.

Parsing or syntactic analysis is one of the first stages in designing and implementing a compiler. Implementing a full manual parser from scratch allows understanding and seeing this process from the inside, demystifying internal structures, and turning building parsers into an interesting engineering task.

This course is a quick starter for anyone looking to delve into optical character recognition, image recognition, object detection, and object recognition using Python without having to deal with all the complexities and mathematics associated with a typical deep learning process.

In this course, we will process massive streams of real-time data using Spark Streaming and create Spark applications using the Scala programming language (v2.12). We will also get our hands-on with some real live Twitter data, simulated streams of Apache access logs, and even data used to train machine learning models.

This is a comprehensive and practical Apache Spark course. In this course, you will learn and master the art of framing data analysis problems as Spark problems through 20+ hands-on examples, and then scale them up to run on cloud computing services. Explore Spark 3, IntelliJ, Structured Streaming, and a stronger focus on the DataSet API.