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  3. Algorithmic Solutions: Design, Problem Solving, Reporting

Algorithmic Solutions: Design, Problem Solving, Reporting

This course is a comprehensive course designed to introduce learners to the fundamental concepts of algorithm design, advanced problem-solving techniques, and effective reporting of results.

Karlis Zars
Karlis Zars
Data Science | core | 1 hour |   Published: Aug 2024
In partnership with:  Coursera

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Overview

1KSTUDENTS*
98.9%RECOMMEND*

This course includes:

  • 1 hour of on-demand video  
  • Certificate of completion  
  • Direct access/chat with the instructor 
  • 100% self-paced online 

“Algorithmic Solutions: Design, Problem Solving, Reporting” is a comprehensive course designed to introduce learners to the fundamental concepts of algorithm design, advanced problem-solving techniques, and effective reporting of results. This course blends theoretical lessons with practical examples to equip participants with the skills necessary to approach complex problems, develop optimized algorithms, and communicate their solutions clearly.  I recall a pivotal project where our team's initial approach to a complex scheduling algorithm led to inefficiencies that nearly jeopardized the project's deadline. This experience taught us the critical importance of robust algorithm design and adaptive problem-solving. It's a lesson in the necessity of not just solving problems, but solving them right—the first time.  For instance, participants will learn how to implement and analyze the Bubble Sort algorithm to understand sorting techniques, and use constraint satisfaction techniques to solve Sudoku puzzles and scheduling problems. These examples ensure that learners can directly apply theoretical knowledge to real-world scenarios, enhancing both their problem-solving abilities and practical skills.  By the end of this course, learners will be able to articulate the core principles of algorithm design, apply diverse algorithmic techniques to solve complex problems, and effectively communicate their findings. Whether you are new to programming or looking to deepen your existing problem-solving skills, this course will provide you with comprehensive insights into the art and science of algorithm design. 

Skills You Will Gain

Algorithm Design
Complexity Analysis
Constraint Satisfaction
Data Visualization
Linear Programming
Problem-Solving

Learning Outcomes (At the end of this program you will be able to)

  • Show understanding about the fundamentals of algorithm design and complexity analysis.
  • Apply different algorithmic techniques to solve a variety of problems.
  • Develop skills to effectively communicate algorithmic solutions through reports and presentations.

Prerequisites

Participants should have a basic grasp of programming fundamentals, including variables, loops, conditionals, and basic data structures like arrays and lists. Additionally, a fundamental understanding of mathematics is required, particularly in algebra, discrete mathematics (sets, functions, and logic), and basic combinatorics. This foundational knowledge ensures learners can effectively engage with advanced algorithmic concepts and methodologies covered throughout the course.

Who Should Attend

This course caters to aspiring software engineers, computer science students, IT professionals, and data analysts eager to deepen their understanding of algorithmic design and problem-solving. Participants will explore advanced techniques vital for optimizing software performance and enhancing computational efficiency. Whether you’re looking to advance your career or solidify your academic foundation, this course equips you with essential skills to navigate complex programming challenges and excel in diverse IT roles.

Curriculum

Instructors

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*Where courses have been offered multiple times, the “# Students” includes all students who have enrolled. The “%Recommended” shown is also based on this data.
1Module 1: Introduction to Algorithm Design
2Module 2: Optimization and Search Techniques
3Module 3: Reporting and Presenting Solutions

Segment 08: Crafting Detailed Reports

Segment 09: Visualization of Complex Data

Segment 10: Presenting to Non-Technical Audiences

Segment 11: Congratulations and Continuous Learning Journey

Segment 05: Linear Programming

Segment 06: Constraint Satisfaction Problems

Segment 07: Search Algorithms

Karlis Zars

Karlis Zars

Karlis is a dedicated professional committed to shaping the next generation of software developers. With over seven years of experience, he serves as a JAVA Trainer at Software Development Academy, where he mentors aspiring developers and equips them with the skills needed to thrive in the fast-paced tech industry.

Beyond the classroom, Karlis is deeply involved in cutting-edge research as a PhD student in Computer Science at the University of Latvia. His research focuses on artificial intelligence and machine learning, exploring innovative applications that drive industry advancements.

Karlis began his journey in technology with hands-on experience as a Robotic Process Automation Developer at Rimi Baltic Group, where he designed and implemented automated solutions that transformed business processes. His previous roles as a Developer at ELVA and a Junior Software Developer at D8 Corporation allowed him to hone his expertise in Java, JavaScript, Linux, and Flutter, contributing to his well-rounded understanding of software development.

Throughout his career, Karlis has remained committed to delivering high-quality, user-friendly solutions that address complex challenges. His experience as a System Administrator at Lauku Avīze AS and a Software Tester at Lattelecom helped cultivate a diverse skill set and a keen eye for detail.

Karlis’ academic journey at Latvijas Universitāte, where he is pursuing a Doctor of Philosophy in Computer Science, has further enriched his knowledge and reinforced his commitment to excellence in both academia and industry.

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Segment 01: Introduction to the Course & Meet Your Instructor

Segment 02: Principles of Algorithm Design

Segment 03: Analyzing Algorithm Complexity

Segment 04: Examples of Basic Algorithms