Brilliant is a tight-knit team of scientists, educators, engineers, storytellers, and illustrators who are redesigning education at scale

Brilliant.org

brilliantorg

Scientific American Supplement, No. 799, April 25, 1891 by Various, is part of the HackerNoon Books Series. You can jump to any chapter in this book . MARBLE AND MOSAIC. here MARBLE AND MOSAIC. By T.R. SPENCE. I do not propose to enter into any historical details as to the first and subsequent application of mosaics. In a general sense we understand mosaic as a combination of various more or less imperishable materials—fixed together by cement or other adhesive substances—and laid over walls, floors, etc., with a view to permanent decorative effect. The substance of the tesseræ is of many kinds, namely, glass, cheap and precious marbles, hard stone, and burnt clay, these mentioned being mainly in use for architectural purposes. For decorative schemes we collect as many gradations of color as are obtainable in such durable materials in their natural or manufactured state, and thus form a color palette which we regard in the same sense as a painter would his pigments. Of course, the first proceeding is to prepare a design on a small scale, which shall embrace your notions of color only. Then follows a full-sized cartoon, which I need hardly add shall embrace your best efforts in drawing. A tracing is made of the latter and transferred to sheets of cardboard. This cardboard is cut to the size of certain sections of your design, and, for convenience, should not be more than, say, 20 in. square. Of course, it will not always be square, but will bear the same relation to your complete cartoon as a map of the counties would to that of all England. Now, working from the small design (of color), the tesseræ are cut to the forms required, laid face downward, and glued on to the cardboard sections containing your enlarged cartoon. When the design is all worked out on these sections they are ready for fixing on walls or floor by laying them home on a float of cement. When the cement sets, the cardboard sticking to the face is washed off, and the joints of tesseræ flushed over with cement and cleaned off, leaving all joints filled up level. There are other processes used for the same end. The technical processes need not occupy our attention at present. There is one process that may appeal to you, and that is executing the work by floating on a limited expanse of cement, and sticking on the tesseræ at once. It has the advantage of enabling the artist or architect to see the effect of his efforts under the fixed conditions of light and height. in situ I shall confine myself to vitreous or glass mosaic, which for durability, extended scales of primary colors and their numerous semi-transparent gradations is unequaled by any substance yet used for wall or floor decoration. I am surprised, having all these fine qualities, it is not more used by architects. If you require proofs of its triumphs, go to St. Mark's, of Venice, and stand under its mellow golden roof. There you will find its domes and vaulted aisles, nave and transepts entirely overlaid with gold mosaic, into which ground is worked—in the deepest and richest colors and their gradations that contemporary manufacturers could produce—subjects selected from the creation down to the life of Christ, in addition containing a complete alphabet of early Christian symbolism. The roof surfaces being one succession of over-arching curves become receptive of innumerable waves of light and broad unities of soft shadows, giving the whole an incomparable quality of tone and low juicy color. Never use your gold but on curved or undulating surfaces. Flat planes of gold only give the effect of a monotonous metallic yellow, and can never be beautiful, owing to the absence of the variations that come with waves of shadow. By letting out the reins of imagination we might feel that in this a tenth century Giorgione has given off the mental impressions of all the golden autumn of his life. His material gave him an advantage over his great followers of the fifteenth and sixteenth centuries, insomuch that glass has a living and glowing quality of light not existing in the somewhat clouded purity of oil or fresco. In St. Mark's we have an example of the superb treatment in deepest and most Titianesque scales applied to curved forms, but to find a similarly complete example of the use of lighter tones and on flat surfaces, we must turn to Ravenna. I can give you no adequate description of the wall mosaics of Ravenna. In the sense of delicate color they remind me of some of the subtile harmonies of many of the finest works of the modern French school—of the Impressionists and others who combine that quality with a true instinct for design. In standing before them you feel that the Dagnan Bouverets, the Mersons, the Cazins, the Puvis de Chavannes, etc., of the fifth century have had a hand in the conception and realization of the beautiful compositions to be found on the nave walls of the two churches of St. Appollinare Nuovo and St. Appollinare in Classe. Here all the scales are of delicate degrees of light tones, supreme in their beauty, completeness, and, most important to us, their true decorative instinct. In the Baptistery we find what I may term a third essay in color, by weaving in rich, dark, and glowing colors on figures and bold sinuous forms of ornament in such a skillful and judicious manner that the whole dome seems to be alive with harmonies, although they are mostly primaries. As you know, rules for the disposition of color are futile, yet some details that struck me as eminently satisfactory may interest you. In all cases the tesseræ are of small dimensions, about a quarter of an inch square. The stucco joints are large and open, surfaces far from level, but undulating considerably. The tesseræ stick up in parts, brilliant edges showing. Absence of flatness gives play to the light. The gray of the stucco joints brings the whole composition together, serving as cool grays in a picture to give tender unity. Gold, apart from backgrounds and large surfaces, is used very cleverly in small pieces in borders of garments, and more especially in thin outlines to make out the drawing and certain flowing forms of ornament. Brilliant pieces of glass actually moulded at the kiln into forms of jewels add brilliancy to crowns, borders, etc. These stick boldly out from the surface. I noticed in the Baptistery below the springing of the dome a frieze about 2 ft. 6 in. deep, having the ground entirely in black, through which was woven in thin gold lines a delicate foliated design. This, in conjunction with the upper surfaces in dark, rich color, had a most delightful effect. We, as students, can learn most from the Ravenna examples, for great are the needs of light and silvery color in this country, where gray and gloomy days far outnumber those in which the sun gives liberally of his light. I may say, in passing, as our subject is really a matter of decoration, that our nineteenth century efforts in this direction are all of a somewhat gloomy tendency. We fill our rooms with imitations of somber Spanish leather, stain and paint our woodwork in leathery and muddy tones, to arrive at what is now a sort of decorator's god. Quaintness is the name of that god. Many are the sins for which he has to answer. Had we not better worship a deity called beauty, whose place is a little higher up Parnassus? Why should we not in our endeavors attempt in some measure to transfix the brilliant harmonies that follow the sun in his liberal and gracious course? This muddy quaintness is certainly pleasant for brief periods, when lamps are low and fire light gilds and deepens its parts. Turn the sunlight on these so-called triumphs of the modern decorator's art, and then you feel the lack of many a phase of color that might have been borrowed from the thousand and one examples that in nature he vivifies and makes brilliant. Referring again to the Ravenna mosaics, I can only add that at the present day an extended palette of colored glass is available. The technical difficulties are not great, and there is no question as to the fine qualities of design and color that are to be obtained in this material. The great point in this, as in all other schemes of decoration, is the art, the mental quality of conception, and the sense of color and fitness. If we hold the precious heritage of an artist's mind—that divine and rare something which gives form, color, and completeness to a story, a dream or a vision—then very little difficulty follows in making vitreous mosaic a valued servant in the realization of a fine creation. It is the function of architects to design suitable spaces for color decoration, so bound in by dignified mouldings and other details of his constructive art, in such a manner that the addition of decorative color shall in no way mar the scheme of his complete work, but shall (under these well ordered distributions) have set on them the seal and crown of color which is inseparable from a perfect piece of architecture. In such spaces he may dream his dreams, tell his stories, and stamp on them for centuries his subtilest and divinest thoughts. May I not urge that to such spaces must be given the best that is in you? for once placed so shall they remain unchanged through generations, time being powerless to add any mellow garment of tone or softening quality whatever. I mistook the title of the subject in thinking that it was mosaic only, and at the last moment found it was marble and mosaic. However, the same dominant principles shall underlie the treatment of marble. It is a question of the finer instincts for form and color. In recent years the demand for choice decorative materials has been the means of opening out many marble quarries all over the world. Transit being easy, a large scale of varieties is available. One fine addition is the Mexican onyx. My feeling is that the most beautiful marbles are those where the soft and sinuous veins melt and die into the general body, comparatively sharp markings dying right away at the edges into innumerable gradations. Marbles having strong and hardly marked veins present great difficulties in distribution. If they are near, they offend you with their coarseness; and, placed at a distance, the hard vein lines have very little decorative value. I should say use these in narrow slips, with very little moulded profile or as parts of intazzio. Mouldings should be specially designed for different marbles. I should say mainly on the principle of sudden contrasts; that is, large members with very little curve bound with members very small in detail, thus obtaining sharp lines, having little surface to be influenced or distorted by the veined markings, and serving to sharpen up and give form to the broader members (which show the color qualities of the marble), much as you sharpen up an ink drawing by underlining. These small members serve the architect's purpose for the expression of vertical and horizontal lines, and where decisive and cutting shadows are required in the composition of his work. If delicate carving forms part of your design, I should say statuary is the best, as you have no veins to distort your detail. I need hardly add that economy should be studied in using precious marbles, without injuring the durability of the work. Contours may be built up in thin sections. Intazzio is a beautiful form of treating marble on an inexpensive ground. Gem-like effects may be obtained by inlaying with smaller pieces, following such ornamental forms as your inventive brains shall dictate. Perhaps the pockets of your clients will be the chief dictator. Heraldic emblazonings, inlaid in marble, are highly effective. The conditions of the heraldry necessitate the use of many varieties, but in such small quantities that on a large simple field they are rarely out of harmony. In addition they map out a large and interesting variety that will save the worry of creation of designs coming entirely from your own brain, and you know the worry of an architect's life makes him hail with pleasure at times a rest from the strain of creation. This heraldic work may be seen to perfection in the chapel of the tombs of the Medici at Florence. At the Pitti Palace are some tables which you may know where marble intazzio can no further go. Alabaster does not appeal to me, it is somewhat sugary in results. If you are fortunate enough to have a sculptor who is a sort of nineteenth century Donatello, let him work his will on statuary or such restful marble. The celebrated monument in the church of S. Giovanni Paulo, at Venice, which Ruskin says is the finest monument in the world, if my recollection serves me correctly, is in white marble, and its beauty comes entirely from the sculptor's art. Such monuments give you much better than any words of mine ample suggestions for marble treatment. I may quote such names as Nicolo Pisano and Verocchio. Photos of some of their work I have brought. Note Pisano's beautiful white altar at Bologna, and Mina de Fiesole's work in Florence. They all show the sculptor as supreme. Why should not we encourage individual young sculptors more? Give them portions of your work in which they can put all the fervor and enthusiasm of young manhood. Their powers may not be ripe, but they possess a verve and intensity that may have forever fled when in later years the imagination is less enthusiastic and the pulses slower. I am sure there are many young sculptors now wanting commissions who have been trained at the academy, and better still, in the best French schools. I maintain that the contemporary French school of sculpture is in its line equal to any school of sculpture that has ever existed, not excepting that of Phidias or that of the Italian Renaissance of the fifteenth and sixteenth centuries. I believe history will confirm this. Why not give these men an opportunity, and help on the movement to found a truly English school of sculpture, rather than give all such work to trading firms of carvers, who will do you any number of superficial feet, properly priced and scheduled, and in the bills of quantities, of any style you please, from prehistoric to Victorian Gothic? Of course, this is our British way of founding a great school. There is one method of treatment that appeals to me very strongly, and that is the application of colored metals to marble, more especially bronze and copper. I may quote as a successful example near the Wellington Memorial at St. Paul's. Another suggestion—although it is not used in combination with marble, but it nevertheless suggests what might be done in the way of bronze panels—that is, the Fawcett Memorial, by Gilbert, in the west chapel at Westminster Abbey. A paper recently read before the Architectural Association, London.— . From the Architect About HackerNoon Book Series: We bring you the most important technical, scientific, and insightful public domain books. This book is part of the public domain. Various (2004). Scientific American Supplement, No. 799, April 25, 1891. Urbana, Illinois: Project Gutenberg. Retrieved https://www.gutenberg.org/cache/epub/11649/pg11649-images.html This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at , located at . www.gutenberg.org https://www.gutenberg.org/policy/license.html

MARBLE AND MOSAIC

Ex-Google TechLead shares must-know resume tips to land your job! Visit for unlimited access to Brilliant's Courses and Daily Challenges.  Get 20% off premium. http://brilliant.org/TechLead/ Join me in my new coding interview training program: http://techinterviewpro.com/ The brownies I'm eating: https://amzn.to/2XPq5IQ Videos of 100+ programming interview problems explained: https://coderpro.com/ Sign up for my FREE daily coding interview practice: http://dailyinterviewpro.com/ Learn how I built a $1,000,000+ business on YouTube: http://youtubebackstage.com/ Get 2 FREE stocks on WeBull (valued up to $1,400): https://act.webull.com/k/S4oOH2yGOtHk... My computer/camera gear: http://amazon.com/shop/techlead The TechLead "ultra-thin" wallet https://amzn.to/2WNhqEP The TechLead custom coffee-maker: https://amzn.to/3dxwrzr Join my Discord: https://discord.gg/pFUBUtE 🎉 Party up: http://instagram.com/techleadhd/ http://twitter.com/techleadhd https://www.linkedin.com/in/patrickshyu/ Disclosure: Some links are affiliate links to products. I may receive a small commission for purchases made through these links. #techlead : Unfortunately it was not possible to generate the usual transcript on this video as is done with all of TechLead's other YouTube video submissions. We apologise for any inconvenience caused. Please note

How to Take Your Resume from Pure Garbage to Landing Your Dream Job: Tips for Software Engineers

) Written By Esco Obong (@ escobyte on Twitter , Senior Software Engineer @ Uber , Founder of Algorythm study group on Facebook and Black Software Engineers Career Support Group on LinkedIn . This was me in 2015 ☠️. A startup I had joined as “founding employee” after we raised a $500k seed round from a prototype was shut down six months later and we were looking for new roles. I secured an interview with through a referral from one of the startup founders (also GIPHY but I wasn’t interested for some reason. Since then they sold to Facebook 🤦) Codecademy On the phone with Codecademy they said “don’t worry we don’t ask crazy algorithms questions or anything like that”. I took that to mean I didn’t need to study algorithms at all 🤣. During the on-site interview, I got two rounds of algorithms questions that were extremely basic in hindsight. I remember one of them was asking how to traverse from point A to point B in a grid. I had no clue how to do that so I was just doing random shit. I landed on an infinite loop… I actually wrote on the board: while “ while (true) {…” In the loop, the point changed direction on each iteration depending on if it hit a wall and would eventually manually break the loop if the target was found. The interviewer must’ve been like WTFFF, but he kept his cool and kept entertaining my different ideas. while That fiasco opened my eyes to the types of questions I needed to be able to answer. Two and a half months later I passed phone screens at Google, Uber, Shutterstock and Rent the Runway. I did the on-site interviews for Shutterstock, Rent the runway, and Uber and passed all of them. Google rescheduled my interview to be two weeks later than what I told them in the last minute. By that time I already had three offers and was pretty excited about Uber so I pulled the trigger and joined Uber. I went from 0 → 100 in just a few months and I didn’t do anything special aside from studying consistently. That’s why I strongly believe any engineer can get good at these DS & Algo questions and get into F.A.A.N.G. or similar high paying roles. At first, I felt I wasn’t really qualified because I had to study so hard to pass my interviews. In 2019, after having run my own consulting firm and startup for almost 2 years, I decided to go back into full-time work and found myself in the same position as I was in 2015. This time, I had a larger network of friends at Uber and other top companies like Google and Facebook that gave me insight into the real deal. They had to study just as hard. Even the ones that didn’t drop out of college like I did. all The ones who stayed and completed their Algorithms’ courses and had gotten their Computer Science degrees, they all still needed to study hard. I discarded this notion of the mythical engineer who can on a whim pass a tech interview and started to appreciate the reality of the situation, that tech interviews are like the SAT’s they give in school. It doesn’t matter that you spent four years learning all of the content in high school, you still need to prep if you want to ace the test. Just like with the SATs all of your past work and grades don’t contribute to the score. Your success depends entirely on how well you perform on the test. Once I realized the truth, that everybody needs to study, it was enough motivation for me to put in the hard work since I realized that’s what my competition was doing. Through that motivation, I formed a process for studying that helped me pass every tech screen and on-site interview I did in 2019. I, a college dropout, passed technical screens for Stripe, Coinbase and Triplebyte. I passed screens and on-site interviews for Google, Amazon, Uber (again), Reddit, Squarespace and Braze. A 100% pass rate wasn’t expected and isn’t likely to continue to happen, but I believe focusing on the fundamentals helps approach that goal. Google sends you swag when you pass the interview and match with a team It wasn’t a smooth transition. When I first started preparing for interviews, it took me over 2 hours to solve what would now be called a “leetcode easy” and at the time it felt impossible! I truly believed that I just wasn’t naturally smart enough and would have to study even harder to get to the same level where I could solve the problem at the same speed as a Google engineer. ALMOST I got one thing right, you do need to practice. What I didn’t realize is that this level of difficulty and frustration in the beginning is to be expected by everyone just starting out, those Google engineers. including I’m still the same person. The only difference between now and then was practice, practice, practice. Most of the time it’s not enough to just provide a working solution to problems during the interview. You have to do it within a certain time limit and the code has to run at a certain level performance described by time & space complexity. A common question people have is “how do I study”? It’s hard to know where to start, what to do next, if you’re making progress and even harder to stay on track. To solve a lot of these issues when I was studying, I created a Trello board with all the topics I wanted to cover. The board helped me focus on the most important topics I should be studying and manage my time to keep progress consistent. I made a post about the Trello board on LinkedIn and it went viral. A Trello PM contacted me about creating an official Trello template for it which can be found here: . Interview Study Tracker The tracker provides a template for how to study. It requires identifying a list of topics and searching for two or more resources that can teach you about the topic. Then assigning yourself 2–3 practice questions to solidify your understanding. It can be from any source, the most important thing is the content itself. I found most of my content with simple google searches using the topics as the keyword. Learning the same thing from different perspectives helps you understand it better. For example, I would read an article on about binary search trees and also read chapters in Cracking the Coding Interview about them. Then I would do 2–3 practice questions or enough to feel comfortable before moving on. geeksforgeeks.org My copy of “Cracking The Coding Interview 6th Edition” signed by Gayle Laakmann McDowell List of Study Topics Below is a list of nearly all of the data-structures and algorithms that you can come across during a tech interview along with a very brief description and some resources to get started building your study plan. The goal is to provide a starting point for you to build your plan and not to explain in detail what each topic is. Part of your plan is finding resources to learn more about each topic from different perspectives. If you haven’t heard of some data-structures in the list or haven’t learned much about them before, they might seem complex but as you progress, you’ll soon experience the same feelings you had before and after you understood Arrays. Think about how complex the concept of an Array is to a non-programmer and how basic it is to even the most junior developers. These data-structures are just the basic building blocks that help you create algorithms… they actually make things simpler once you take the time to understand them. Just as an Array handles a lot of the work required to maintain an ordered list of elements, these data-structures have functions and containers for efficiently working with data. Basic Data-Structures These are considered basic. Everyone should know how they work, when to use them, and the reasoning behind their tradeoffs: how to implement them Array Set Hashmap LinkedList Stack Queue Tree Graph If this list seems long, don’t worry. A lot of these data-structures are closely related and even build upon each other so you’ll be able to learn them all quicker than you might think. Hashmaps build upon Arrays. Stacks build upon LinkedLists or Arrays. Queues build upon LinkedLists. Trees share concepts of “nodes” and “links” from LinkedList. Graphs also share the same concepts. They also have a lot of similarities to trees. All trees are technically graphs too so you can apply a lot of the same techniques to them. Advanced Data-Structures After mastering the basic data-structures, knowing these more advanced ones will give you a higher chance of success. Some come up very often during interviews like Heaps and Binary Search Trees. LRU Caches and Tries come up less frequently but are becoming more common. Disjoint Sets and Skip Lists rarely come up, but even if not explicitly asked about them, they can be powerful tools to help you come up with quick and performant solutions. Heap (a.k.a Priority Queue) LRU Cache Binary Search Tree (AVL, Redblack) Disjoint Set Trie Skip List As we go into more advanced data-structures you’ll see they are usually made using the basic ones as building blocks. Getting a good understanding of the basic data-structures will make learning these advanced ones much easier. You’ll think of them in terms of the smaller structures you already know rather than big new complicated structures. Heaps, also known as Priority Queues are a type of tree that behave like a queue. Binary Search Trees (BSTs) are another type of tree that make it faster to search for nodes. AVL and Redblack are two popular specific types of balanced BSTs. An LRU Cache is a memory efficient cache built with a Hashmap and LinkedList. A Trie is another type of tree that makes prefix/substring searches fast. Disjoint Sets are a special type of set that separate its members into non-overlapping subsets, useful for the Union Find algorithm. A Skip List is an optimized version of a LinkedList that reduces the time it takes to find certain nodes. Basic Searching/Traversal Algorithms All data structures are meant to hold information. Some structures need specials ways to access this information efficiently that are more involved than simply accessing an array index or hashmap key. Data-structures that hold collections of data such as Trees and Graphs use a few basic algorithms to access the information within. Breadth First Search (BFS) Depth First Search (DFS) Binary Search Advanced Searching/Traversal Algorithms There is a ton of research in this field, but for the purposes of a tech interview, the most advanced searching algorithms you’ll likely come across in order of frequency are: Quick Select Dijkstra Bellman-Ford A-star Sorting Algorithms Sorting is a common tool used to increase the performance of a solution. There are many sorting algorithms, but the most popular for interviews are listed below. Know how to implement all of these without looking anything up. Quick Sort Merge Sort Topological Sort Counting Sort Important Topics Recursion is an extremely important topic that doesn’t come up as much in day to day software engineering as it does during interviews. Bit manipulation may seem scary at first, but once you take time to understand the binary number format and operations you’ll realize how simple it is. Recursion Greedy Algorithms Dynamic Programming Bit Manipulation (AND, NOT, OR, XOR) Common Patterns These patterns can be used to solve many similar algorithms questions Backtracking Two Pointers Sliding Window Divide & Conquer Reservoir sampling Math based problems Permutations / Combinations Power Set Other Common Problems String to Integer Integer to String Adding huge numbers (that can't fit in memory) Addition/Subtraction/Multiplication/Division without using operators Practice Self Practice Studying means nothing without practice. Every time you learn a new topic from your study plan, you should put it to use. That’ll help you remember it longer and also give a deeper understanding. Do a few practice questions for every topic you finish. Sites like leetcode.com let you search for practice questions using tags like “trees” or “graphs”. Practice should happen in two phases. The first is when you learn the topic for the first time. During this phase you should focus on really understanding whats involved and why your solution works. The second phase is after you’re comfortable with the concepts. At this point you should time yourself and aim for faster and faster solution times. During an interview you’ll be expected to solve a problem within 15–45 minutes depending on the difficulty of the question. When you start out, it’ll likely take hours for you to do the first one. Over the first few weeks that time should start to come down. After a month or two of consistent practice (not just time passing), you should start being able to solve a decent amount of problems on time. Sometimes an entire interview question will only involve implementing a single data-structure such as an LRU Cache or a Trie. Other times it’ll only involve implementing a single operation on a data-structure such as the delete operation on a Binary Search Tree. Practice implementing all of the data-structures mentioned earlier in this article until you don’t need to look anything up and you will have a very easy time with these questions during the real interview. Focus on understanding why its implemented the way it is rather than trying to remember the exact code. Implement from understanding of what needs to happen next and not from memory. You probably won’t be asked to implement an Array or balanced BSTs like AVL or Redblack trees but you should know how they work. Assisted Practice (Mock Interviews) Practicing topics and questions alone isn’t enough. During the interview you’ll be interacting with a real person who is judging your skills in real-time. The situation is more stressful than when you study at home. To get around the awkwardness and anxiety of a real interview, you should do many practice interviews beforehand. This will help you be comfortable so that you can focus on the interview question rather than being distracted by non-technical things. These types of practice interviews are called Mock Interviews. You can have a friend help you or find a service online that’ll match you with an interviewer. Knowing when you’re ready You’ll never feel “ready” no matter how long you study. Theres always more topics to dive deeper into or weak areas that could use more practice. There is definitely an element of luck in the whole process. It’s very possible that you get a question on a topic you haven’t prepared for at all or are really weak on. It’s also possible that you get all questions on topics you’re really strong in since two months ago and didn’t need to spend that extra time studying to pass. You can’t prepare for every possible outcome, but your chances of success get higher than everyone else’s with more practice. Keeping track of your progress and getting feedback is very important in knowing when you’re truly ready to perform on the real interview without relying on just emotion. Keep track of your completion times when you do practice questions and aim for at most 40 minutes to complete most medium level questions and 1 hour to complete hard level questions on sites like leetcode.com or hackerrank.com. Realize that you must pass every edge case for it to count on those sites but luckily this isn’t the case for real interviews! Most interview code is evaluated on a small set of edge cases and small errors get passed or considered to be irrelevant all the time. The point of evaluation isn’t to be so strict as to expect perfection, but they need to see enough to be convinced. Do at least three mock interviews before you do the real one. Only move forward when you can consistently perform well during practice. If you follow the above, knowing when you’re ready is easy. You might still feel you aren’t but you’ll know for sure because you’re hitting your completion times and have passed many mock interviews. Being ready means you’ve proved undoubtedly through practice that you can pass interviews that ask the same types of difficult questions under the same time limits. This article only focuses on data-structures and algorithms questions. System Design and Behavioral questions also play a large role in the hire decision. I’ll leave those topics for another article. Resources Books Cracking the Coding Interview 6th Edition Elements of Programming Interviews JavaScript Algorithms Websites www.geeksforgeeks.org www.educative. www.brilliant.org www.algoexpert.io Bootcamps www.interviewkickstart.com www.outco.io Practice Self leetcode.com www.hackerrank.com Assisted www.interviewbit.com www.pramp.com All of the topics you need to know along with resources explaining them are freely available online. Google searching any of the topics mentioned in this article, even the obscure ones like “two pointers” will yield a wealth of information about them. You don’t need a special college education, lucky genes or a long track record of success to get a job at a top tech company. The only thing you need is to dedicate time to consistent study and practice. This article was inspired by a few related questions asked in the Facebook study group for black software engineer s At the time of writing, we have 1,500+ black software engineers studying and helping each other with data-structures and algorithms. If you are an underrepresented person of color in tech who needs help studying or is in a position to help others study, please join us! Otherwise please share the group with someone who has the time or needs the help. Also published at https://escobyte.medium.com/how-to-study-for-data-structures-and-algorithms-interviews-at-faang-65043e00b5df

How to Prepare Yourself For Data Structures and Algorithms Interviews at FAANG

In this article, I am trying to implement and test the efficiency of a cuckoo filter over a bloom filter. (Read previous post on Chord DHT , implementing a distributed hash table in Golang) Introduction Probabilistic data structures are very useful, especially when processing large data sets. Most of the times, whilst working on the data side of things, one would want to do a simple “is the item not present” or “is the item already present” query whilst processing the real time data. Say you want to answer queries in real time, like number of unique ips, most frequent ips, if an ad has already been served to a user, using probabilistic data structures provide a space efficient way to answer these queries. The typical approach to such queries would be to use either a HashMap or a HashTable, or store it is some external cache (like redis), but the problem is with large datasets, these simple data structures can’t fit into memory. This is where probabilistic data structures come into play because of their space and time advantages. Example Use cases Google Bigtable, Apache HBase and Apache Cassandra, and Postgresql use Bloom filters to reduce the disk lookups for non-existent rows or columns. Avoiding costly disk lookups considerably increases the performance of a database query operation. uses Bloom filters to check if an article has already been recommended to an user Medium Ethereum uses Bloom filters for quickly finding logs on the Ethereum blockchain The web browser used to use a Bloom filter to identify malicious URLs. Any URL was first checked against a local Bloom filter, and only if the Bloom filter returned a positive result was a full check of the URL performed (and the user warned, if that too returned a positive result) Google Chrome What’s in a “Cuckoo”? We’ve used bloom filters in many places for answering such queries on the data platform. Recently I came across this paper on which caught my interest. The title itself says, so I decided to check it out. Cuckoo filter “Cuckoo Filter: Practically Better Than Bloom”, Cuckoo filters improve upon the design of the bloom filter by offering deletion, limited counting, and a bounded false positive probability, while still maintaining a similar space complexity. They use cuckoo hashing to resolve collisions and are essentially a compact cuckoo hash table. Cuckoo and bloom filters are both useful for set membership testing when the size of the original data is large. They both only use 7 bits per entry. They are also useful when an expensive operation can be avoided prior to execution by a set membership test. For example, before querying a database, a set membership test can be done to see if the desired object is even in the database. Algorithm Parameters of the Filter:
1. Two Hash Functions: h1 and h2
2. An array B with n buckets. The i-th bucket will be called B[i]

Input: L, a list of elements  to be inserted into the cuckoo filter.

Algorithm:
While L is not empty:
    Let x be the first item in the list L. Remove x from the list.
    If B[h1(x)] is empty:
        place x in B[h1(x)]
    Else, If B[h2(x) is empty]:
        place x in B[h2(x)]
    Else:
        Let y be the element in B[h2(x)].
        Prepend y to L
        place x in B[h2(x)] Implementation The implementation seems pretty straightforward, so I decided to have a go at it and compare how space/time efficient it is compared to a bloom filter. The Cuckoo filter consists of a that stores the ‘fingerprints’ of items inserted. The fingerprint of an item is a bit string derived from the hash of that item. A cuckoo hash table consists of an array of buckets where an item to be inserted is mapped to two possible buckets based on two hash functions. Each bucket can be configured to store a variable number of fingerprints. Typically, a Cuckoo filter is identified by its fingerprint and bucket size. For example, a (2,4) Cuckoo filter stores 2 bit length fingerprints and each bucket in the Cuckoo hash table can store up to 4 fingerprints. Cuckoo hash table Insertion Algorithm: f = fingerprint(x);
i1 = hash(x);
i2 = i1 ⊕ hash(f);

if bucket[i1] or bucket[i2] has an empty entry then
   add f to that bucket;
   return Done;

// must relocate existing items;
i = randomly pick i1 or i2;
for n = 0; n < MaxNumKicks; n++ do
   randomly select an entry e from bucket[i];
   swap f and the fingerprint stored in entry e;
   i = i ⊕ hash(f);
   if bucket[i] has an empty entry then
      add f to bucket[i];
      return Done;

// Hashtable is considered full;
return Failure; Code: <a href="https://medium.com/media/fb67f33fdc8334fa7f9ec0af7bb85fce/href">https://medium.com/media/fb67f33fdc8334fa7f9ec0af7bb85fce/href</a> Search Algorithm: f = fingerprint(x);
i1 = hash(x);
i2 = i1 ⊕ hash(f);

if bucket[i1] or bucket[i2] has f then
    return True;

return False; Code: <a href="https://medium.com/media/90d845abd04c0e11f41bbe113395645f/href">https://medium.com/media/90d845abd04c0e11f41bbe113395645f/href</a> Delete Algorithm: f = fingerprint(x);
i1 = hash(x);
i2 = i1 ⊕ hash(f);

if bucket[i1] or bucket[i2] has f then
   remove a copy of f from this bucket;
   return True;

return False; Code: <a href="https://medium.com/media/31a0be9921f81e3f26d1eb425c147a3d/href">https://medium.com/media/31a0be9921f81e3f26d1eb425c147a3d/href</a> Performance Test I’ve used Will Fitzgerald library for the test on . The FPP (False Positive Probability) ration taken for cuckoo filter is 0.001 Bloom filter <a href="https://medium.com/media/eb93ffc65a9a4878e55c27b47d12597d/href">https://medium.com/media/eb93ffc65a9a4878e55c27b47d12597d/href</a> Space Complexity With regards to the cuckoo and bloom filters, they perform differently at different false positive probabilities. When the false positive probability of the filter is less than or equal to 3%, the cuckoo filter has fewer bits per entry. When it is higher, the bloom filter has fewer bits per entry. Time Complexity In cuckoo hashing, inserting an element seems like much worse than O(1) in the worst case because there could be many instances during collision, where we have to remove a value in order to make room for the current value. Plus, if there is a cycle then the entire table must be rehashed. Doing a time analysis of both the filters yields the following results: <a href="https://medium.com/media/8991ffe2b9d120643326957ae8d09a50/href">https://medium.com/media/8991ffe2b9d120643326957ae8d09a50/href</a> Throughout this experiment (keeping in mind my code may not be fully optimized), Bloom filters seem to perform exceptionally well in space complexity, occupying less amount of space for large number of items. Cuckoo filter seems to perform better at insertion of large number of items, but a little slower in lookup(search times) due to it’s implementation. Inference I wouldn’t really take a side on which filter to recommend, I think they both have their own use cases. Bloom filters do not support deletions because hashing is lossy and irreversible. Though counting bloom filters solve that problem, Cuckoo filters are useful in the case where you would require deletions. Of course Cuckoo filters give an error when the filter is full, and that has it’s own advantages, whereas in a Bloom filter, there is no control over capacity, it just rehashes over the existing bit array. Code arriqaaq/cuckoo References https://brilliant.org/wiki/cuckoo-filter/ https://www.cs.cmu.edu/~dga/papers/cuckoo-conext2014.pdf https://en.wikipedia.org/wiki/Cuckoo_hashing https://blog.fastforwardlabs.com/2016/11/23/probabilistic-data-structure-showdown-cuckoo.html P.S If you find anything wrong with the tests/implementation, please feel free to leave your suggestion/comments.

CUCKOO vs BLOOM filter, from a Gopher’s perspective

6 Skills Junior Developer Should Prioritize

ELI5: Plasma Cash

The vast majority of algorithms of interest operate on data. Therefore, there are particular ways of organizing data that play a critical role in the design and analysis of algorithms. From that, we can say that data structures are simply ways of organizing data. They are either . Arrays and linked lists are examples of linear data structures. On the other hand, graphs and trees are forms of non-linear data structures. linear or non-linear For example, one common data structure is the or , which is an ordered sequence of values. Here’s a list of numbers: 0, 1, 1, 2, 3, 5, 8, 13. The concept of the list isn’t particular to one language, and it’s also used outside of programming in everyday life — wish lists, shopping lists, and so on. list array Algorithms are recipes for logical execution of the problem. They are not the same as data structures. Algorithms are usually “better” if they work faster or more efficiently (using less time, memory, or both). But here in this article, it’s all about looking into non-linear data structures: graphs Diving into graphs A graph is a system in which there are potentially multiple ways to get from an arbitrary point, A, to another arbitrary point, B. A graph is normally defined as a pair of sets (V,E). V is a set of arbitrary objects called vertices or nodes, and E is a set of pairs of vertices, which we call edges or (more rarely) arcs. In an undirected graph, the edges are unordered pairs, or just sets of two vertices. I usually write instead of {u,v} to denote the undirected edge between u and v. u v In a directed graph, the edges are ordered pairs of vertices. I will use instead of (u,v) to denote the directed edge from u to v and vice versa for all edges in this article. u → v Graphs can also be undirected or directed, cyclic or acyclic (mostly directed), or weighted. Traversing a graph To visit each node or vertex which is a connected component, tree-based algorithms are used. You can do this easily by iterating through all the vertices of the graph, performing the algorithm on each vertex that is still unvisited when examined. Two algorithms are generally used for the traversal of a graph: Depth first search (DFS) and Breadth first search (BFS). Depth first search (DFS) algorithm Visualizing DFS traversal Depth-first Search (DFS) is an for searching a or data structure. The algorithm starts at the root (top) node of a tree and goes as far as it can down a given branch (path), and then backtracks until it finds an unexplored path, and then explores it. The algorithm does this until the entire graph has been explored. algorithm graph tree Many problems in computer science can be thought of in terms of graphs. For example, analyzing networks, mapping routes, scheduling, and finding spanning trees are graph problems. To analyze these problems, graph search algorithms like depth-first search are useful. - Source The simplest pseudo-code would be: Depth-first search is a common way that many people naturally use when solving problems like mazes. First, we select a path in the maze (for the sake of this example, let’s choose a path according to some rule we lay out ahead of time) and we follow it until we hit a dead end or reach the end of the maze. If a given path doesn’t work, we backtrack and take an alternative path from a past junction, and try that path. To turn this into a graph traversal algorithm, we basically replace “child” with “neighbor”. But to prevent infinite loops, we only want to visit each vertex once. Just like in BFS, we can use marks to keep track of the vertices that have already been visited, so we don’t visit them again. Also, just like in BFS, we can use this search to build a spanning tree with certain useful properties. dfs(vertex v){visit(v);for each neighbor w of vif w is unvisited{dfs(w);add edge vw to tree T}} Here’s the Python implementation using recursion: This was a basic overview of how DFS works. If you want to dive deeper, there is some great stuff available all over the internet and also on Medium. Breadth first search (BFS) algorithm It starts at the tree root (or some arbitrary node of a graph, sometimes referred to as a ‘search key’), and explores all of the neighbor nodes at the present depth prior to moving on to the nodes at the next depth level. The only catch here is, unlike trees, graphs may contain cycles, so we may come to the same node again. To avoid processing a node more than once, we use a boolean visited array. For simplicity, it is assumed that all vertices are reachable from the starting vertex. What we do in a BFS is a simple step-by-step process: Start from a vertex . Let this vertex be at what is called…. “Level 0”. S Find all the other vertices that are immediately accessible from this starting vertex , that is they are only a single edge away (the adjacent vertices). S Mark these adjacent vertices to be at “Level 1”. You might be coming back to the same vertex due to a loop or a ring in the graph. If this happens, your BFS will take time. So, you will go only to those vertices who do not have their “Level” set to some value. ∞ Mark which is the parent vertex of the current vertex you’re at, i.e., the vertex from which you accessed the current vertex. Do this for all the vertices at Level 1. Now, find all those vertices that are a single edge away from all the vertices which are at “Level 1”. These new set of vertices will be at “Level 2”. Repeat this process until you run out of graph.\ See this — https://www.programiz.com/dsa/graph-bfs Python Implementation Using Queue Directed and Undirected graphs An is a graph in which edges have no orientation. The edge ( , ) is identical to the edge ( , ). That is, they are not ordered pairs, but unordered pairs — i.e., sets of two vertices { , } (or 2-multisets in the case of ). The maximum number of edges in an undirected graph without a loop is ( − 1)/2. undirected graph x y y x x y loops n n For any edge and in an undirected graph, we call u a neighbor of v and vice versa. The degree of a node is its number of neighbors. In directed graphs, we have two kinds of neighbors. For any directed edge , we call u a predecessor of v and v a successor of u. u v u — >v Cyclic and Acyclic graphs Among the many properties of graphs, two are important for a great number of applications : connectivity and acyclicity. Both are based on the notion of a path. A cyclic graph is a graph containing at least one . graph cycle Also remember that cyclic graphs cannot be a form of tree because tree’s nodes are only visited once via DFS or BFS(traversal methods). An acyclic graph is a graph without cycles (a cycle is a complete circuit). When following the graph from node to node, you will never visit the same node twice. What is Directed Acyclic Graph? A directed acyclic graph is an acyclic graph that has a direction as well as a lack of cycles. A tree is a formation of Directed acyclic graph Source — http://www.statisticshowto.com/directed-acyclic-graph/ The parts of the above graph are: = the set for the the Vertices. Integer = {1,2,3,4,5,6,7}. Vertices set = {(1,2), (1,3), (2,4), (2,5), (3,6), (4,7), (5,7), (6,7)}. Edge set A directed acyclic graph has a . This means that the nodes are ordered so that the starting node has a lower value than the ending node. A DAG has a unique topological ordering if it has a directed path containing all the nodes; in this case the ordering is the same as the order in which the nodes appear in the path. topological ordering In , DAGs are also called . When a DAG is used to detect a deadlock, it illustrates that a resources has to another process to continue. computer science wait-for-graphs wait for How can we detect cycles in a graph? As it turns out, the reason that the depth-first search algorithm is particularly good at detecting cycles is because of the fact that it is efficient at finding backward edges. We’ll look in to DFS algorithm later in this article Adjacency matrices and adjacency list representation Consider this graph as example for understanding adjacency lists and adjacency matrices Carrying out graph algorithms using the representation of graphs by lists of edges, or by adjacency lists, can be cumbersome if there are many edges in the graph. To simplify computation, graphs can be represented using matrices. Two types of matrices commonly used to represent graphs will be presented here. One is based on the adjacency of vertices, and the other is based on incidence of vertices and edges. Given an adjacency matrix, we can decide in Θ(1) time whether two vertices are connected by an edge just by looking in the appropriate slot in the matrix. We can also list all the neighbors of a vertex in Θ(V) time by scanning the corresponding row (or column). Understanding Adjacency matrix from above given image… Let’s understand how adjacency matrix is constructed from above given image. For simplicity I have discussed this only for vertex “a” . Same applies to all vertex. Due to page limit, I have not included analysis for and As we can conclude from image that there is an edge from a → d and a → e respectively. a → d a → e. And what about adjacency list? The adjacency list data structure should immediately remind you of hash tables with chaining;the two data structures are identical. An adjacency list is an array of linked lists, one list per vertex. Each linked list stores the neighbors of the corresponding vertex. For example for **a** vertex there are edges leading to neighbors as **b,d and e** . So its respective linked list contains vertex that are connected via edge. Reminder → For undirected graphs, each edge(u,v) is stored twice, once in u’s neighbor list and once in v’s neighbor list; for directed graphs, each edge is stored only once. Weighted Graphs Weighted graph. Source — https://cs.stackexchange.com A weighted graph (or weighted digraph) is a graph (or di-graph) with numbers assigned to its edges. These numbers are called weights or costs. An interest in such graphs is motivated by numerous real-world applications, such as finding the shortest path between two points in a transportation or communication network or the traveling salesman problem . Examples of graphs in real life Google Maps — it’s just one big graph! Where Edges represent streets and vertices represent crossings. Graph theory underlies the Internet. It’s very heavily used in networking code (building routing tables, etc), but it comes up all kinds of places like building an internet search engine, or a social media platform. For diving deep into trees, this article is one of the preferable I chose — https://medium.freecodecamp.org/all-you-need-to-know-about-tree-data-structures-bceacb85490c Facts that matters the most: All trees are graphs. Not all graphs are trees. A tree is a kind of graph, Only if it’s connected. For instance, the graph consisting of the vertices A and B and no edges is not a tree, although it is an acyclic graph. A single vertex is also considered a tree (no cycles, vacuously connected). So two unconnected vertices makes a forest of two trees. A tree is a special kind of graph where there are never multiple paths, that there is always only one way to get from A to B, for all possible combinations of A and B. Shot — https://dribbble.com/shots/3152667-Astronaut-Glove Resources: _Depth-first Search (DFS) is an algorithm for searching a graph or tree data structure. The algorithm starts at the root…_brilliant.org Depth-First Search (DFS) | Brilliant Math & Science Wiki _A Computer Science portal for geeks. It contains well written, well thought and well explained computer science and…_www.geeksforgeeks.org GeeksforGeeks | A computer science portal for geeks _We’ve partnered with Dartmouth college professors Tom Cormen and Devin Balkcom to teach introductory computer science…_www.khanacademy.org Algorithms | Computer science | Computing | Khan Academy _Detailed tutorial on Linear Search to improve your understanding of Algorithms. Also try practice problems to test &…_www.hackerearth.com Linear Search Tutorials & Notes | Algorithms | HackerEarth http://www.cs.uiuc.edu/~jeffe/teaching/algorithms

An intro to Data Structures: Graphs and its traversal algorithms

Una introducción a las estructuras de datos: gráficos y sus algoritmos transversales

My Intensive Program to Level Up and Become an better Front-End Engineer I have been coding professionally as a full-stack developer for more than 3 years now and I particularly enjoy working on the front end. I am a bootcamp grad and self-taught developer, and while I learn a lot on the job on a daily basis, I have always had this urge to take the time to fully grasp the deep fundamentals of and , and to become a stronger developer. JavaScript programming Since I made an intensive plan for myself to level up and ace technical coding interviews, I decided to share it with you. Sometimes you just need to see the plan unfold to get started, so I’ll be happy if it can be of use to anyone. I needed a program that would really , that I could follow , and . get me ready for code interviews while working a full-time job not very expensive It was important to me that the program be intensive: I find that it’s easier to build and keep momentum in short bursts of massive action than to dabble for too long. I am more of a sprinter, but of course if you are of the marathonian type, you can easily spread this program over a longer period of time to fit your learning style. I also purposefully chose several overlapping resources for each course: learning from a variety of medias (text, video) and being exposed to different ways of presenting a concept helps grab the content a lot better, not to mention the demonstrated benefits of repetition. I have spent a lot of time researching, reading, looking for the best resources I could to build my own tailored curriculum (thanks for the inspiration , and in this ), and this is the result: combined. (It can cost much less if you choose alternative free resources of your choice of course). FrontEndMasters JavaScriptisSexy Nick Ciubotariu article a 4-month (very) intensive front-end engineering training, with a selection of excellent resources that cost less than 500€ Note that you should be comfortable with front-end development basics to hop on; Git, HTML, CSS, the DOM and JavaScript indispensable concepts (scope, this, closures, etc) are not part of this learning path. Tons of free good resources can help you quickly learn the beginner parts of the front end before taking on this program. THE PROGRAM (120 days) I am planning to work a minimum of 3 hours a day, with every day at the beginning of each session, that I will push on Github. one mandatory coding exercise You can find the Google Sheet with the planning + resources here. RESOURCES Here is the list of the resources I will use along the program: Books (21.32 €) Beginning JavaScript (5th Edition) (37.62€) Cracking the Coding Interview (6th Edition) (Free) Eloquent JavaScript (16.62 €) High Performance JavaScript (19.99€) JavaScript: the Definitive Guide (6th Edition) (Free) Learning JavaScript Design Patterns (21.34€) Professional JavaScript for Web Developers (3rd Edition) Free Learning Apps You will have to create an account on the majority of them to access the content Brilliant Codecademy CodeSchool FreeCodeCamp JavaScript is Sexy ReactTraining Udacity Viking Code School Prep Work Subscription-Based Learning Apps (33€ / month, you can cancel anytime) Front-End Masters (25€ / month, you can cancel anytime) PluralSight Udemy Courses Wait for their frequent big sales where each course is 10$ Accelerated ES6 JavaScript training Advanced JavaScript (Free) JavaScript Essentials JavaScript: Understanding the Weird Parts Learning Data Structures in JavaScript from Scratch Modern React with Redux The Complete JavaScript Course The Web Developer BootCamp WebPack 2: the Complete Developer Guide Free Exercise / Coding Interview Apps Exercism Hacker Rank JS 30 LeetCode DETAILED PLANNING (120 days) Exercises (1 per day for 120 days): ( ) JS 30 30 days ( ) Hacker Rank 30 30 days ( ) Hacker Rank — Cracking the Coding Interview 20 days Book ( ) (Free alternative: or ) Cracking the Coding Interview 40 days exercism.io leetcode.com I. Mastering JavaScript (30 days) Objects & Prototypal Inheritance PluralSight: JavaScript Objects and Prototypes Book : Chapter 6 “Objects” JavaScript, The Definitive Guide : javascriptissexy.com JavaScript Objects in Detail j : avascriptissexy.com JavaScript Prototype in Plain Language Book : Chapter 6 “Object-Oriented Programming”, Section “Understanding Objects” Professional JavaScript FreeCodeCamp: Section “Basic JavaScript”, 9 Challenges ( , , , , , , , , ) Build JavaScript Objects Accessing Objects Properties with the Dot Operator Accessing Objects Properties with Bracket Notation Accessing Objects Properties with Variables Updating Object Properties Using Objects for Lookups Testing Objects for Properties Manipulating Complex Objects Accessing Nested Objects JSON, XML & APIs Book : Chapter 18 “XML in Javascript” and Chapter 20 “JSON” Professional JavaScript Udemy: : Section 25 “Working with APIs”, Lecture 237 “JSON and XML” The Web Developer BootCamp Udemy: : Section 4 “Object and Functions”, Lecture 33 “JSON and Object Literals” JavaScript: Understanding the Weird Parts Udemy: , Section 6 “Networking” Advanced JavaScript Pluralsight: Hands-on JavaScript Project: JSON Regular Expressions Book : Chapter 10 “Pattern Matching with Regular Expressions” JavaScript, The Definitive Guide The Coding Train: Series Introducing Regular Expressions FreeCodeCamp: Section “Basic JavaScript”, 4 Challenges ( s, , , ) Sift through Text with Regular Expression Find Numbers with Regular Expressions Find Whitespace with Regular Expressions Invert Regular Expression Matches with JavaScript Advanced DOM Manipulation Udemy: , Section 4 “DOM Manipulation” JavaScript Essentials Udemy: , Section 14 “Advanced DOM Manipulation” The Web Developer BootCamp : Chapters 1 to 10 DOM Enlightenment JavaScript Events in depth Book : Chapter 13 “Events” Professional JavaScript Book : Chapter 17 “Handling Events” JavaScript, The Definitive Guide Book : Chapter 22 “Advanced Techniques”, Section “Custom Events” Professional JavaScript Philip Roberts: “What the heck is the Event Loop Anyway?” PluralSight: , Sections 2-3 Advanced Techniques in JavaScript and jQuery : Chapter 11 DOM Enlightenment jQuery Book : Chapter 19 “The jQuery Library” JavaScript, The Definitive Guide FreeCodeCamp: Section “jQuery” CodeSchool: Try jQuery PluralSight: , Section 4 Advanced Techniques in JavaScript and jQuery Strict Mode Book : Appendix B (“Strict Mode”) Professional JavaScript Udemy : , Section 7 “Odds and Ends”, Lessons 67 and 68 JavaScript: Understanding the weird parts Udemy: , Section 2 “Basics”, Lesson 3 Advanced JavaScript Handling Errors Book : Chapter 14 “The Window Object”, Section 14.6 “Error Handling” JavaScript, The Definitive Guide Book : Chapter 17 “Error handling and Debugging” Professional JavaScript Book : Chapter 8 “Bugs and Error Handling” Eloquent JavaScript Ajax Book : Chapter 14 “Ajax” Beginning JavaScript Book : Chapter 21 “Ajax and Comet” Professional JavaScript Udacity: Introduction to Ajax FreeCodeCamp: JSON APIs and Ajax Front-End Data Storage Book : Chapter 13 “Data Storage” Beginning JavaScript Book : Chapter 23 “Offline Applications and Client-Side Storage ” Professional JavaScript Book : Chapter 20 “Client-Side Storage” JavaScript, The Definitive Guide Dive into HTML5: Chapter 7 “The past, present and future of local storage for web applications” ECMAScript6 Udemy: Accelerated ES6 JavaScript training PluralSight: Rapid ES6 Training Udemy: , Section 7 “Get Ready for the Future: ES6 / ES2015” The Complete JavaScript Course Object-Oriented Programming in JS Udacity: Object-Oriented JavaScript Book : Chapter 5 “JavaScript: An Object-Oriented Language” Beginning JavaScript Book : Chapter 6 “Object-Oriented Programming” Professional JavaScript Book : Chapter 9 “Classes and Modules” JavaScript, The Definitive Guide j : avascriptissexy.com OOP: what you need to know Udemy: , Section 5 “Object-Oriented JavaScript and Prototypal inheritance” JavaScript: Understanding the weird parts FreeCodeCamp: Section “Object-Oriented and Functional Programming”, 5 challenges ( , , , , ) Declare JavaScript Objects as Variables Construct JavaScript Objects with Functions Make Instances of Objects with a Constructor Function Make Unique Objects by Passing Parameters to our Constructor Make Object Properties Private Final Project FreeCodeCamp: & projects Tic-Tac-Toe Simon Game II. Software Engineering via JavaScript (37 days) Software engineering & CS basics : Chapter “Basic Prep Work”, Section 2 “Software Engineering Basics” Viking Code School Prep Work FrontEndMasters: Workshop “Four Semesters of Computer Science in Six Hours” Brilliant.org: Complete Course Computer Science FreeCodeCamp: “Computer Basics” Blog Articles: Computer Science in JavaScript Simple Programmer: Approaching problems, breaking it down FreeCodeCamp: “Big O Notation” CodeCademy: Big O Algorithm and Data Structures Udemy: Learning Data Structures in JavaScript from Scratch FrontEndMasters: Algorithms and Data Structures in JavaScript Free Code Camp: Algorithm Scripting (Beginner, Intermediate and Advanced) JS Design Patterns Book Learning JavaScript Design Patterns PluralSight: Practical Design Patterns in JavaScript PluralSight: JavaScript Design Patterns Functional Programming FunFunFunction: Series Functional Programming in JavaScript Functional Light JS Final Project Build a Vanilla JavaScript Web App III. Front-end Development Tools & Architecture (23 days) JS templating (Handlebars.js) j : avascriptissexy.com Handelbar.js Tutorial PluralSight: JavaScript Templating with Handlebars Tooling & Automation PluralSight: JavaScript Build Automation With Gulp.js JS Module loaders / bundlers (Webpack) Udemy: WebPack 2: the Complete Developer Guide PluralSight: Building a JavaScript Development Environment Principles of a Single Page Application Front-End JavaScript Single Page Application Architecture Nicholas Zakas: Scalable JavaScript Application Architecture Making a Single Page App Without a Framework Architecting Front-End Web Applications Book : Eloquent JavaScript Chapter 10 “Modules” Udemy: , Sections 8 and 9 JavaScript: Understanding the Weird Parts Udemy: , Section 6 “The Budget App project” The Complete JavaScript Course Testing for Front-End Developers PluralSight: Front-End First: Testing and Prototyping JavaScript Apps Udacity: JavaScript Testing High Performing Code in JS Book High Performance JavaScript: Build Faster Web Application Interfaces IV. React & Redux (16 days) TylerMcGinnis: React Fundamentals Udemy: Modern React with Redux PluralSight: Building Applications with React and Redux in ES6 V. Go further (14 days) Progressive Web Apps A Beginner’s Guide to Progressive Web Apps Udacity: Intro to Progressive Web Apps PluralSight: Getting Started with Progressive Web Apps Site Performance Optimization Udacity: Browser Rendering Optimization Udacity: WebSite Performance Optimization SASS PluralSight: A Better CSS: LESS and SASS SEO Fundamentals PluralSight: SEO Fundamentals Get the job PluralSight: Preparing for a Job Interview : Chapter “Getting Hired Phase 1” Viking Code School Prep Work PluralSight: Designing a Killer Job Search Strategy PluralSight: Get Involved! SOME TIPS While learning, be sure to you will encounter, and to practice as much as possible. write and reproduce most of the code Push as much code as you can on Github: having says a lot about your grit and passion for code. It will reflect very well when you get to the phase of the real interviews. long streaks of public commits for extended periods of time When you really don’t understand a key concept: . It will force you to seek out external resources and to try and understand from various explanations. Write the article as if you were explaining the concept to someone who is a true beginner and doesn’t have background on the topic. This helped me a lot grasp many parts of JavaScript and is known to be amongst the best methods to understand and retain difficult concepts. take 1 or 2 days to write an in-depth article about it Don’t be a perfectionist: . Spending 10 days on the quirks of Regular Expressions will not be very useful if you don’t intend to use them in the coming weeks. Understand what they are and move on: when the necessity to use them comes you will have the opportunity to go deeper and learn all the details about them. be clever about what is most suited to your needs Try to reach to solutions by yourself but don’t get stuck: when you can’t move forward. get help on StackOverflow or in the FreeCodeCamp community group If you want to join me in this program and share your best resources for learning, use the hashtag on Twitter: let’s encourage each other and code together! #120DaysBetterDev

Stories, News & Mentions about BRILLIANTORG

Brilliantorg’s Stories on HackerNoon