Cyril Mialik’s Post

To take “Operations Research” course has been on my to-do list since I was a graduate student. Even though my thesis was done using dynamic and non-linear programming and I solved a practical problem using integer programming in my current company, I still want to learn this field formally. However, I don’t have a chance to accomplish it due to time constraints. Now, thanks for the Coursera and NTU. I can take the course online in my spare time. The course is separated into three courses(the link is my summary article): (1) Models and Applications https://lnkd.in/gxQCaacJ (2) Optimization Algorithms https://lnkd.in/g6vZi4WY (3) Theory https://lnkd.in/ga3dGvvm Overall, the course is well-organized and neat. If you just want to take a look at what this field is, the first course is very very enough. In the second course, you can learn how to implement it in the programming language(python). And I would say you can skip course three without any impact when you solve practical problems because it has many theories and a lot of math proofs.

[ A CHEMICAL ENGINEERING STUDENT’S DIP INTO COMPUTER SCIENCE ]   For the last 2-3 months, I have been unofficially taking Harvard’s CS50 “Introduction to the Intellectual Enterprises of Computer Science and the Art of Programming” course. This course includes topics regarding computational thinking, programming syntax, and computer science more generally. This course has been one I have wanted to study ever since the second semester after gaining an interest in programming. Even though I have previously studied Python programming for numerical methods during my university courses, studying the fundamentals of programming through the lens of computer science has been quite an insightful experience.   During my time taking the course, I have realized that learning programming is not at all different from studying chemical engineering. Programming languages are simply tools with which to solve problems. This, I feel, is analogous to studying mass and energy balances in chemical engineering. Both tools (programming languages and chemical engineering balances) are relatively simple concepts to understand and learn; however, the challenge (and fun!) comes with utilizing them to solve a variety of problems. A few topics that I learned and enjoyed during this course were: • The C and Python programming languages • Basic programming syntax, such as variables, loops, and conditionals • Basic computer science principles, such as computer memory and algorithmic problem solving • The basics of SQL for relational database management • HTML, CSS, and JavaScript for static web development and Flask for dynamic web development   I would definitely recommend this course to any non-computer science students who are interested in learning about computer science more generally in comparison to courses focusing on a specific programming language. Information regarding the course can be accessed in the following link: https://lnkd.in/gkCCqy3H “What ultimately matters is not so much where you end up relative to others but where you end up relative to yourself when you began.” - David J. Malan, CS50 Course Instructor

Coding Fundamentals: The Critical Role of Algorithms in Computing Algorithms are the backbone of computing, guiding the computer through detailed steps to solve problems or perform tasks. They are essential for everything from the simple task of checking whether an email is in your inbox, to complex operations like rendering graphics in video games. What is an Algorithm? An algorithm is a set of instructions designed to perform a specific task. Each algorithm is a methodical step-by-step process, where each step follows the others in a logical sequence. This systematic approach makes algorithms crucial in problem-solving within computer science. Why Are Algorithms Important? 1. Efficiency: Algorithms optimize tasks to use less memory and execute faster, which is crucial for software performance and handling large amounts of data. 2. Problem-solving: Algorithms provide a clear pathway to analyze and solve complex problems, breaking them down into simpler, manageable tasks. 3. Automation: They enable automation of tasks, reducing human error and increasing consistency and reliability in processes. Basic Types of Algorithms in Computer Science - Sorting Algorithms: These arrange data in a specific order, making it easier to search, analyze, or display. Common sorting algorithms include QuickSort, MergeSort, and Bubble Sort. Sorting is fundamental in database searches, organizational tasks, and data analysis frameworks. - Searching Algorithms: These help in finding specific data within a dataset. Linear search and binary search are prominent examples, where binary search offers a faster way to find an item by repeatedly dividing the search interval in half. - Graph Algorithms: Useful in networking, mapping, and routing, these include algorithms like Dijkstra’s algorithm for finding the shortest path, and depth-first search (DFS) and breadth-first search (BFS) for traversing graphs. Practical Applications of Algorithms - Internet Search Engines: Google uses complex algorithms to fetch the information you search for quickly and efficiently. - Social Media Feeds: Platforms like Facebook and Instagram use algorithms to determine which posts appear in your feed, based on relevancy and your interaction patterns. - E-commerce Recommendations: Sites like Amazon use algorithms to suggest products, enhancing user experience and increasing sales. The study of algorithms combines creativity and analysis, encouraging you to think about how problems can be broken down and systematically solved. This is why learning about algorithms is so valuable in computer science—it not only improves your programming skills but also enhances your problem-solving abilities. P.S. Which sorting algorithm have you found most intriguing or useful in your studies or projects? 🔄 Repost this post

Coding Fundamentals: The Critical Role of Algorithms in Computing Algorithms are the backbone of computing, guiding the computer through detailed steps to solve problems or perform tasks. They are essential for everything from the simple task of checking whether an email is in your inbox, to complex operations like rendering graphics in video games. What is an Algorithm? An algorithm is a set of instructions designed to perform a specific task. Each algorithm is a methodical step-by-step process, where each step follows the others in a logical sequence. This systematic approach makes algorithms crucial in problem-solving within computer science. Why Are Algorithms Important? 1. Efficiency: Algorithms optimize tasks to use less memory and execute faster, which is crucial for software performance and handling large amounts of data. 2. Problem-solving: Algorithms provide a clear pathway to analyze and solve complex problems, breaking them down into simpler, manageable tasks. 3. Automation: They enable automation of tasks, reducing human error and increasing consistency and reliability in processes. Basic Types of Algorithms in Computer Science - Sorting Algorithms: These arrange data in a specific order, making it easier to search, analyze, or display. Common sorting algorithms include QuickSort, MergeSort, and Bubble Sort. Sorting is fundamental in database searches, organizational tasks, and data analysis frameworks. - Searching Algorithms: These help in finding specific data within a dataset. Linear search and binary search are prominent examples, where binary search offers a faster way to find an item by repeatedly dividing the search interval in half. - Graph Algorithms: Useful in networking, mapping, and routing, these include algorithms like Dijkstra’s algorithm for finding the shortest path, and depth-first search (DFS) and breadth-first search (BFS) for traversing graphs. Practical Applications of Algorithms - Internet Search Engines: Google uses complex algorithms to fetch the information you search for quickly and efficiently. - Social Media Feeds: Platforms like Facebook and Instagram use algorithms to determine which posts appear in your feed, based on relevancy and your interaction patterns. - E-commerce Recommendations: Sites like Amazon use algorithms to suggest products, enhancing user experience and increasing sales. The study of algorithms combines creativity and analysis, encouraging you to think about how problems can be broken down and systematically solved. This is why learning about algorithms is so valuable in computer science—it not only improves your programming skills but also enhances your problem-solving abilities. P.S. Which sorting algorithm have you found most intriguing or useful in your studies or projects? 🔄 Repost this post

Breaking Down Binary: The Language of Computers. One of the first challenges faced by beginner Computer Science Engineering students is understanding binary - the fundamental language of computers. Let's unravel this mystery. Binary code is a system of representing text or computer processor instructions using the binary number system, which uses only two binary digits, 0 and 1. It forms the basis of all computer systems. Here's the essence of binary: 1. Binary Digits (Bits): In binary code, each digit is referred to as a bit. A bit is the smallest unit of data in computing, representing a state of either 0 or 1. 2. Combining Bits: Bits are combined into groups, typically 8 bits (a byte), to represent more complex data. For example, the binary code for the letter 'A' is 01000001. 3. Processing: Computers use binary to process data. When you execute a program or even click your mouse, you're sending a series of binary codes to your computer’s processor. Understanding binary is essential for grasping how computers work at the most fundamental level. It's the core language that drives all the operations, whether it's simple word processing or complex programming. For beginners, delving into binary can seem intimidating, but it's a stepping stone to becoming proficient in computer science. It's not just about numbers; it's about understanding the language that powers the digital world. P.S. Have you tried converting a simple word into binary? What was it? → Join 6000+ subscribers to learn Python for Free here: https://lnkd.in/dpqiNJ6H 👉 10-Day Coding Course: Start Today www.codingdsa.com 🔄 Repost this post

Learning about CS with CS50 by Harvard University | Day- 1 Today, I started learning about CS from the CS50 course. Here's what I learned 1. What I learned about computer programming : Computer programming is about taking input from something --> using that input to make something --> carrying the final result (Output). 2. Binary system : Computers use a special way to showcase different things like numbers, letters & emojis using a special combination of numeric system called bits which consists of zeroes and ones. 3. ASCII & Unicode Using zeroes and ones is too confusing, therefore CS engineers created a special system called ASCII to make it easier to write letters and numbers using a set of numbers representing one letter/number each (an example of that would be the letter 'A' represented by number 65). However, Unicode is a more extended version of ASCII, including many more characters like emojis. As a result, it enables a better text representation. 4. Representation of Data: Binary data can help in representing colors in the code of RGB. Using this our devices can show us the picture we are trying to see. As our device screens contain little dots called pixels and each pixel contains its own color. hence, it's able to show us different colors, text, and emojis 5. Algorithm : The algorithm is a very useful tool for finding something, as there is too much web content to find your specific result from. Therefore, The Algorithm finds the search-specific results showing us what we need and want. Today's session has greatly improved my computer science foundation, and I'm excited to continue with CS50.

Folks -- if you've put up with my ramblings for even a little while, you know my passion around helping people learn algorithms. I teach algorithms courses when I can, I write books about algorithms (Algorithmic Thinking)... and when someone else writes a great algorithms book, well, I want people to know about it. Shout out to my colleague Aditya Bhargava for writing Grokking Algorithms 2/e: https://lnkd.in/ekQCfPEN I'm grateful to have been asked to write the foreword for this book. Most of it is below and outlines why I'm excited about this book. We need more algorithms people. We need more diversity. INTRUDE! foreword More people than ever need to learn how to program. Sure, some people literally program for their jobs (software engineers or web developers, for example). But many other jobs, not historically requiring programming, have a programming component now or will in the future. Programming also helps people understand the technological world in which they live. Unfortunately, the benefits of programming are not equally distributed. In North American computer science (CS) programs, for example, we have a very low participation of women and some ethnic/racial groups. It’s critical that we be able to expand programming and CS to a more diverse group. The solution will involve making progress on a number of fronts, including overcoming bias, training more teachers, and offering more diversified learning experiences. We need to help more people “get in.” I’m excited about Bhargava’s book because it offers a new way to get into algorithms, which is a key component of effective programming. Some people will tell you that there’s only one way to learn algorithms: find a dense mathematical book about algorithms, read it, and, like, understand everything. But that privileges the types of people who can learn that way, who have time to learn that way, and who need to learn that way in the first place. It also assumes that we know why someone wants to learn algorithms, which, let’s face it, is not a fair assumption to make. To be clear, some of my favorite CS books are exactly those kinds of mathematically oriented algorithms books. Those books work for me. They work for a lot of CS professors. But maybe that’s the problem: it’s too easy to assume that the way we learn is the same way that others learn. What we need are all kinds of learning resources about all kinds of CS topics, each designed for a particular audience. Bhargava’s book is intentionally designed for people who want a nonmathematical introduction to algorithms. What impresses me most here is not what Bhargava chose to include but what he chose not to include. You can’t include everything in a book like this—that would be overwhelming and is not the point. Bhargava’s teaching expertise enables him to wring a lot of teaching out of not a lot of pages. ... #LearnWithManning

I am thrilled to announce that I have successfully completed CS50: Introduction to Computer Science, a comprehensive and intellectually stimulating course offered by Harvard University. This course has provided a strong foundation in key computer science concepts, including: 1. Algorithms: Efficiency, Time Complexity (Big O), Sorting, Searching, implemented primarily in C to grasp low-level memory management and optimization. 2. Data Structures: Arrays, Linked Lists, Hash Tables, Stacks, Queues, Trees, and Tries, explored through programming in C, and later in Python for more high-level manipulation. 3. Memory Management: Mastered pointers, dynamic memory allocation, and buffer overflows while coding in C, gaining insights into how memory works under the hood. 4. Recursion: Tackled recursive solutions to complex problems, using both C and Python, which helped develop a deeper understanding of problem-solving techniques. 5. Encapsulation and Abstraction: Leveraged the power of clean and modular code while working with functions and data structures in C and Python. 6. Web Development: Built interactive web pages using HTML, CSS, and JavaScript, and later applied Flask, a Python web framework, to create dynamic web applications. 7. Security: Studied cryptography, authentication, and security vulnerabilities, and used tools like SQL and web technologies to understand security in web applications. A heartfelt thank you to Professor David J. Malan, Carter Zenke, and the entire CS50 team for designing such an engaging and transformative learning experience. This journey has not only deepened my understanding of core computer science principles but also strengthened my programming skills across multiple languages and tools. I'm excited to apply these skills in future projects and challenges!

Coding Fundamentals: The Critical Role of Algorithms in Computing Algorithms are the backbone of computing, guiding the computer through detailed steps to solve problems or perform tasks. They are essential for everything from the simple task of checking whether an email is in your inbox, to complex operations like rendering graphics in video games. What is an Algorithm? An algorithm is a set of instructions designed to perform a specific task. Each algorithm is a methodical step-by-step process, where each step follows the others in a logical sequence. This systematic approach makes algorithms crucial in problem-solving within computer science. Why Are Algorithms Important? 1. Efficiency: Algorithms optimize tasks to use less memory and execute faster, which is crucial for software performance and handling large amounts of data. 2. Problem-solving: Algorithms provide a clear pathway to analyze and solve complex problems, breaking them down into simpler, manageable tasks. 3. Automation: They enable automation of tasks, reducing human error and increasing consistency and reliability in processes. Basic Types of Algorithms in Computer Science - Sorting Algorithms: These arrange data in a specific order, making it easier to search, analyze, or display. Common sorting algorithms include QuickSort, MergeSort, and Bubble Sort. Sorting is fundamental in database searches, organizational tasks, and data analysis frameworks. - Searching Algorithms: These help in finding specific data within a dataset. Linear search and binary search are prominent examples, where binary search offers a faster way to find an item by repeatedly dividing the search interval in half. - Graph Algorithms: Useful in networking, mapping, and routing, these include algorithms like Dijkstra’s algorithm for finding the shortest path, and depth-first search (DFS) and breadth-first search (BFS) for traversing graphs. Practical Applications of Algorithms - Internet Search Engines: Google uses complex algorithms to fetch the information you search for quickly and efficiently. - Social Media Feeds: Platforms like Facebook and Instagram use algorithms to determine which posts appear in your feed, based on relevancy and your interaction patterns. - E-commerce Recommendations: Sites like Amazon use algorithms to suggest products, enhancing user experience and increasing sales. The study of algorithms combines creativity and analysis, encouraging you to think about how problems can be broken down and systematically solved. This is why learning about algorithms is so valuable in computer science—it not only improves your programming skills but also enhances your problem-solving abilities. P.S. Which sorting algorithm have you found most intriguing or useful in your studies or projects? 🔄 Repost this post

Coding Fundamentals: The Critical Role of Algorithms in Computing Algorithms are the backbone of computing, guiding the computer through detailed steps to solve problems or perform tasks. They are essential for everything from the simple task of checking whether an email is in your inbox, to complex operations like rendering graphics in video games. What is an Algorithm? An algorithm is a set of instructions designed to perform a specific task. Each algorithm is a methodical step-by-step process, where each step follows the others in a logical sequence. This systematic approach makes algorithms crucial in problem-solving within computer science. Why Are Algorithms Important? 1. Efficiency: Algorithms optimize tasks to use less memory and execute faster, which is crucial for software performance and handling large amounts of data. 2. Problem-solving: Algorithms provide a clear pathway to analyze and solve complex problems, breaking them down into simpler, manageable tasks. 3. Automation: They enable automation of tasks, reducing human error and increasing consistency and reliability in processes. Basic Types of Algorithms in Computer Science - Sorting Algorithms: These arrange data in a specific order, making it easier to search, analyze, or display. Common sorting algorithms include QuickSort, MergeSort, and Bubble Sort. Sorting is fundamental in database searches, organizational tasks, and data analysis frameworks. - Searching Algorithms: These help in finding specific data within a dataset. Linear search and binary search are prominent examples, where binary search offers a faster way to find an item by repeatedly dividing the search interval in half. - Graph Algorithms: Useful in networking, mapping, and routing, these include algorithms like Dijkstra’s algorithm for finding the shortest path, and depth-first search (DFS) and breadth-first search (BFS) for traversing graphs. Practical Applications of Algorithms - Internet Search Engines: Google uses complex algorithms to fetch the information you search for quickly and efficiently. - Social Media Feeds: Platforms like Facebook and Instagram use algorithms to determine which posts appear in your feed, based on relevancy and your interaction patterns. - E-commerce Recommendations: Sites like Amazon use algorithms to suggest products, enhancing user experience and increasing sales. The study of algorithms combines creativity and analysis, encouraging you to think about how problems can be broken down and systematically solved. This is why learning about algorithms is so valuable in computer science—it not only improves your programming skills but also enhances your problem-solving abilities. P.S. Which sorting algorithm have you found most intriguing or useful in your studies or projects? 🔄 Repost this post