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Hi I 19f left school when I was 14 recently dropped from community college because my financial aide wouldn't go through. My credit is bad I, have no co-signers so no loans. I only have a part time job, so basically can't afford college. Recently I got into coding, basically I just watch tutorials on YouTube and decided I really wanted to learn. Spent some time reading on different sites and came up with a study guide to learn what I may have learned had I stayed in school. I know the list is but I plan to spend three years on it, I know others have learned how to code in months but I don't think that would be me. I would love some feedback please and thank you. (Please excuse any typos it's only a rough copy)

Study Guide!!!

(Spend 1 week on each individual subject, 8 hours a day, 30 minutes of studying, 5 minute break repeat)

(Full course completion should take 2 years and 6 months)

(Once finished, spend next 6 months freelancing and building a porfolio on github)

Actual studying:

-Watch a video on each individual subject (possibly 2hrs.)

-Go through course work on codeacademy (and various other sites) on each individual subject (2hrs.)

-Read a book on each individual subject (3hrs.)

-Write out questions on paper on each individual subject as you go along (Review questions at a later date)

-Make flashcards on each individual subject (Review at a later date)

-Go through Google course on various subjects (1hr.)

Daily*

(HTML/CSS):

1.Semantic HTML. 2.Be able to explain the CSS Box Model. 3.Benefits of CSS preprocessors 4.CSS Media Queries to target different devices and write responsive CSS. 5.Bootstrap

(JavaScript): 6.Understand how to work with the DOM. Also know what JSON is and how to manipulate it. 7.Important language features such as functional composition, prototypal inheritance, closures, event delegation, scope, higher-order functions. 8.Asynchronous control flow, promises, and callbacks. 9.Learn how to properly structure your code and modularize parts of it, things like webpack, browserify, or build tools like gulp. 10.Know how to use at least one popular framework 11.jQuery code 12.Some knowledge on testing frameworks and why they’re important 13.Learn about some important new ES6 features

(Back-End Language): 14.Node.js 15.Ruby 16.Python 17.Java 18.PHP

(Databases & Web Storage): 19.Understand the benefits of relational data, e.g. SQL. 20.Learn about NoSQL databases, e.g. MongoDB. 21.Understand which would be better in certain situations. 22.Know how to connect a database with your chosen back-end language (e.g. Node.js + MongoDB). 23.Understand the benefits of in-memory data stores like Redis or memcached. 24.Web storage to store sessions, cookies, and cached data in the browser. 25.Scaling databases, ACID, and ORM (all optional).

(HTTP & REST): 26.What is REST and why is it important in regards to the HTTP protocol and web applications. 27.Best practices for designing a RESTful API. POST/GET requests. 28.Learning how to use Chrome DevTools can be extremely helpful. 29.What are SSL Certificates. 30.HTTP/2 & SPDY (optional). 31.WebSockets, Web Workers, and Service Workers (all optional).

(Web Application Architecture): 32.learn how to architect your web applications efficiently: 33.Learn about common platforms as a service, e.g. Heroku and AWS. 34.Performance optimization for applications and modern browsers. 35.Learn what a web application architecture should include. 36.Designing Web Applications by Microsoft. 37.MVC. 38.Most importantly, try to work on projects with people, look at codebases of popular projects on GitHub, and learn as much as you can from senior developers.

(Git): 39.update parts of the code 40.make fixes 41.change other people’s code without breaking things 42.Learn the concept behind Git and play around with it yourself. 43.learn some common git commands you’ll likely use

(Basic Algorithms & Data Structures): 44.Improve your Algorithms & Data Structure Skills Article 45.Study hash tables and try to understand them on a deeper level. 46.Understand how trees and graphs can be beneficial as data structures. 47.Understand the basics of Big-O analysis 48.Know when to use an object vs an array and understand the trade-offs. 49.Learn why caching is so important when working with a large amount of data. Also learn the pros and cons of in-memory vs disk storage. 50.Learn the difference between queues and stacks. 51.Learn how to visualize and conceptionalize code

(What every programmer should know): 52.Desktop Scripting 53.Server-side scripting 54.Web frameworks 55.Web APIs 56.Command Line Scripting 57.Add-ons

(Important topics of number theory every programmer should know): 58.Number theory [Number Theory]: Modular arithmetic, Fermat’s theorem, Chinese remainder theorem(CRT), Euclidian method for GCD, Logarithmic Exponentiation, Sieve of Eratosthenes, Euler’s totient function. 59.Graph algorithms [graph theory]: Breadth first search(BFS), Depth first search(DFS), Strongly connected components(SCC), Dijkstra, Floyd-Warshall, Minimum spanning tree(MST), Topological sort. 60.boolean logic, boolean algebra 61.What does O(n) mean? 62.The compound interest formula 63.matrix/linear algebra, numerical analysis 64.The statistics behind A/B testing 65.numerical approximation of a derivative, integral, differential equation 66.Read "numerical recipes in C"

(For programmers in Artificial Intelligence and Machine Learning area): 67.knowledge of Matrix (mathematics) calculations 68.Numerical analysis is a must 69.Basic understanding of Differential equation and Integral equation 70.High School Algebra 71.Information Systems and Databases: Set Theory

(Gaming): 72.Basic principles of Nim game, Grundy numbers, Sprague-Grundy theorem. 73.Vector Analysis/Mathmatics 74.Discrete Mathematics 75.Solid Geometry 76.Linear Algebra 77.Calculus (Read the use of Newton's Method of Approximation in Quake)

(Textual Analysis): 78.Discrete Mathematics 79.Recurrence Relations 80.GraphTheory

(Business Intelligence and Statistical Analysis): 81.Statistics 82.Set Theory

(Optimization): 83.Graph Theory 84.Linear Algebra 85.Operations Research topics

(Precise Computation): 86.Calculus and other advanced fields. 87.Arithmetic. 88.Basics of data representation - number bases, two's complement, floating point, etc. 89.Graph theory, because almost all modern programs have some kind of graph as their main data structure. 90.Induction, because its vital to be able to reason about recursion and this is how you do it. 91.Finite state machines. 92.Computability and intractability - the proof of the halting theorem and that SAT is NP-complete and what these imply.

(Other): 93.Greedy [goal programming, simplex algorithm, multi-criteria decision analysis]: Standard problems such as Activity selection. 94.Search techniques [lineavector algebra[2]]: Binary search, Ternary search and Meet in the middle. 95.Data structures (Basic) [graph theory]: Stacks, Queues, Trees and Heaps. 96.Data structures (Advanced)[more graph theory]: Trie, Segment trees, Fenwick tree or Binary indexed tree(BIT), Disjoint data structures. 97.Strings [set theory]: Knuth Morris Pratt(KMP), Z algorithm, Suffix arrays/Suffix trees. These are bit advanced algorithms. 98.Computational geometry [computational geometry]: Graham-Scan for convex hull, Line sweep. 99.Basic formal type theory. No need to get carried away, but familiary with the Hindley-Milner type system covariance and contravariance helps to understand even languages like C++ and Java whose type systems are much less formal 100.Basic graph theory 101.Recursive reasoning 102.You have to know about probabilistic and statistical methods to suggest the best possible correction and to even understand popular algorithms like the Viterbi or Baum Welch algorithms, and also for natural language processing and machine learning applications 103.Dynamic programming [self-similarity, finite subdivision rules, nonlinear equations]: Standard dynamic programming problems such as Rod Cutting, Knapsack, Matrix chain multiplication etc. 104.Lamba Calculus 105.Propositional Logic

(Software engineering): 106.Object oriented analysis & design 107.Software quality factors 108.Data structures & algorithms 109.Big-O notation: Big-O notation indicates the performance of an algorithm/code section. Understanding it is very important for comparing performances. 110.UML notation 111.Software processes and metrics 112.Design patterns 113.Operating systems basics 114.Computer organization basics 115.Network basics 116.Requirement analysis 117.Software testing 118.Dependency management 119.Continuous integration 120.ORM (Object relational mapping) 121.DI (Dependency Injection) 122.Version controlling systems 123.Internationalization (i18n) 124.Architectural patterns 125.Writing clean code

submitted by Yourconnect_ to learnprogramming [link] [comments]
Study Guide!!!

(Spend 1 week on each individual subject, 8 hours a day, 30 minutes of studying, 5 minute break repeat)

(Full course completion should take 2 years and 6 months)

(Once finished, spend next 6 months freelancing and building a porfolio on github)

Actual studying:

-Watch a video on each individual subject (possibly 2hrs.)

-Go through course work on codeacademy (and various other sites) on each individual subject (2hrs.)

-Read a book on each individual subject (3hrs.)

-Write out questions on paper on each individual subject as you go along (Review questions at a later date)

-Make flashcards on each individual subject (Review at a later date)

-Go through Google course on various subjects (1hr.)

Daily*

(HTML/CSS):

1.Semantic HTML. 2.Be able to explain the CSS Box Model. 3.Benefits of CSS preprocessors 4.CSS Media Queries to target different devices and write responsive CSS. 5.Bootstrap

(JavaScript): 6.Understand how to work with the DOM. Also know what JSON is and how to manipulate it. 7.Important language features such as functional composition, prototypal inheritance, closures, event delegation, scope, higher-order functions. 8.Asynchronous control flow, promises, and callbacks. 9.Learn how to properly structure your code and modularize parts of it, things like webpack, browserify, or build tools like gulp. 10.Know how to use at least one popular framework 11.jQuery code 12.Some knowledge on testing frameworks and why they’re important 13.Learn about some important new ES6 features

(Back-End Language): 14.Node.js 15.Ruby 16.Python 17.Java 18.PHP

(Databases & Web Storage): 19.Understand the benefits of relational data, e.g. SQL. 20.Learn about NoSQL databases, e.g. MongoDB. 21.Understand which would be better in certain situations. 22.Know how to connect a database with your chosen back-end language (e.g. Node.js + MongoDB). 23.Understand the benefits of in-memory data stores like Redis or memcached. 24.Web storage to store sessions, cookies, and cached data in the browser. 25.Scaling databases, ACID, and ORM (all optional).

(HTTP & REST): 26.What is REST and why is it important in regards to the HTTP protocol and web applications. 27.Best practices for designing a RESTful API. POST/GET requests. 28.Learning how to use Chrome DevTools can be extremely helpful. 29.What are SSL Certificates. 30.HTTP/2 & SPDY (optional). 31.WebSockets, Web Workers, and Service Workers (all optional).

(Web Application Architecture): 32.learn how to architect your web applications efficiently: 33.Learn about common platforms as a service, e.g. Heroku and AWS. 34.Performance optimization for applications and modern browsers. 35.Learn what a web application architecture should include. 36.Designing Web Applications by Microsoft. 37.MVC. 38.Most importantly, try to work on projects with people, look at codebases of popular projects on GitHub, and learn as much as you can from senior developers.

(Git): 39.update parts of the code 40.make fixes 41.change other people’s code without breaking things 42.Learn the concept behind Git and play around with it yourself. 43.learn some common git commands you’ll likely use

(Basic Algorithms & Data Structures): 44.Improve your Algorithms & Data Structure Skills Article 45.Study hash tables and try to understand them on a deeper level. 46.Understand how trees and graphs can be beneficial as data structures. 47.Understand the basics of Big-O analysis 48.Know when to use an object vs an array and understand the trade-offs. 49.Learn why caching is so important when working with a large amount of data. Also learn the pros and cons of in-memory vs disk storage. 50.Learn the difference between queues and stacks. 51.Learn how to visualize and conceptionalize code

(What every programmer should know): 52.Desktop Scripting 53.Server-side scripting 54.Web frameworks 55.Web APIs 56.Command Line Scripting 57.Add-ons

(Important topics of number theory every programmer should know): 58.Number theory [Number Theory]: Modular arithmetic, Fermat’s theorem, Chinese remainder theorem(CRT), Euclidian method for GCD, Logarithmic Exponentiation, Sieve of Eratosthenes, Euler’s totient function. 59.Graph algorithms [graph theory]: Breadth first search(BFS), Depth first search(DFS), Strongly connected components(SCC), Dijkstra, Floyd-Warshall, Minimum spanning tree(MST), Topological sort. 60.boolean logic, boolean algebra 61.What does O(n) mean? 62.The compound interest formula 63.matrix/linear algebra, numerical analysis 64.The statistics behind A/B testing 65.numerical approximation of a derivative, integral, differential equation 66.Read "numerical recipes in C"

(For programmers in Artificial Intelligence and Machine Learning area): 67.knowledge of Matrix (mathematics) calculations 68.Numerical analysis is a must 69.Basic understanding of Differential equation and Integral equation 70.High School Algebra 71.Information Systems and Databases: Set Theory

(Gaming): 72.Basic principles of Nim game, Grundy numbers, Sprague-Grundy theorem. 73.Vector Analysis/Mathmatics 74.Discrete Mathematics 75.Solid Geometry 76.Linear Algebra 77.Calculus (Read the use of Newton's Method of Approximation in Quake)

(Textual Analysis): 78.Discrete Mathematics 79.Recurrence Relations 80.GraphTheory

(Business Intelligence and Statistical Analysis): 81.Statistics 82.Set Theory

(Optimization): 83.Graph Theory 84.Linear Algebra 85.Operations Research topics

(Precise Computation): 86.Calculus and other advanced fields. 87.Arithmetic. 88.Basics of data representation - number bases, two's complement, floating point, etc. 89.Graph theory, because almost all modern programs have some kind of graph as their main data structure. 90.Induction, because its vital to be able to reason about recursion and this is how you do it. 91.Finite state machines. 92.Computability and intractability - the proof of the halting theorem and that SAT is NP-complete and what these imply.

(Other): 93.Greedy [goal programming, simplex algorithm, multi-criteria decision analysis]: Standard problems such as Activity selection. 94.Search techniques [lineavector algebra[2]]: Binary search, Ternary search and Meet in the middle. 95.Data structures (Basic) [graph theory]: Stacks, Queues, Trees and Heaps. 96.Data structures (Advanced)[more graph theory]: Trie, Segment trees, Fenwick tree or Binary indexed tree(BIT), Disjoint data structures. 97.Strings [set theory]: Knuth Morris Pratt(KMP), Z algorithm, Suffix arrays/Suffix trees. These are bit advanced algorithms. 98.Computational geometry [computational geometry]: Graham-Scan for convex hull, Line sweep. 99.Basic formal type theory. No need to get carried away, but familiary with the Hindley-Milner type system covariance and contravariance helps to understand even languages like C++ and Java whose type systems are much less formal 100.Basic graph theory 101.Recursive reasoning 102.You have to know about probabilistic and statistical methods to suggest the best possible correction and to even understand popular algorithms like the Viterbi or Baum Welch algorithms, and also for natural language processing and machine learning applications 103.Dynamic programming [self-similarity, finite subdivision rules, nonlinear equations]: Standard dynamic programming problems such as Rod Cutting, Knapsack, Matrix chain multiplication etc. 104.Lamba Calculus 105.Propositional Logic

(Software engineering): 106.Object oriented analysis & design 107.Software quality factors 108.Data structures & algorithms 109.Big-O notation: Big-O notation indicates the performance of an algorithm/code section. Understanding it is very important for comparing performances. 110.UML notation 111.Software processes and metrics 112.Design patterns 113.Operating systems basics 114.Computer organization basics 115.Network basics 116.Requirement analysis 117.Software testing 118.Dependency management 119.Continuous integration 120.ORM (Object relational mapping) 121.DI (Dependency Injection) 122.Version controlling systems 123.Internationalization (i18n) 124.Architectural patterns 125.Writing clean code

build trading strategies, plot graphs, and; perform backtesting on data. For all these functions, here are a few most widely used libraries: NumPy – NumPy or NumericalPy, is mostly used to perform numerical computing on arrays of data. The array is an element which contains a group of elements and we can perform different operations on it Submit by Ketang (29/01/2012) Victor Sperandeo. Very simple method trendline Sperandeo was named “Changing trends in the one-two-three” (1-2-3 change of trend). The process of constructing a trend line by the method of “ Change of trend in the one-two-three “: A downward sloping trendline connects consistently lowers the resistance, or in other words - price highs. Indicator exits are not that indicator to us as binary traders unless indicator are trading options with an Early Out feature. In options, indicator exit signals may even provide additional entry points for short term entries strategy some cases. The system relies on 2 exponential moving averages, a 5 and 10 bar, as well as stochastic and RSI. Candlestick charts are available on ThinkForex trading platforms for all assets individuals can trade on the platforms. Below is a sample of a candlestick chart derived from the ThinkForex web trading platform: This chart shows price on the right (vertical) axis, and time on the bottom (horizontal) axis. There is no role for pips in binary option trading. Trader has only two things to pick from either put or call. This decision has to be right to make any profits. The underlying asset is not bought. Binary option trading is just about trading on the predictions. Trader need not physically own the asset for trading binary options.

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