What a programmer shouldn't be able to do. What is the job of a software engineer?

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What knowledge is needed to become a good programmer? Are there any skills that distinguish a good programmer from a bad one? If you are preparing to choose a profession and want to figure it out, our article is for you.

To figure out what a good programmer needs to know, we need to define what a “good programmer” is. Stereotypes have rewarded the image of a good programmer with poor eyesight, constant participation in programming and mathematics Olympiads, as well as victories in them. Of course, you can raise the bar as much as you like, but we will try to offer more general principles rather than competitive selection. So let's note the things that are inherent in a non-idealized good programmer.

Fundamental knowledge.

Nowadays it is not necessary to graduate from a specialized university to become a programmer. Enough, and if you really want, you can learn to program on your own. Bypassing technical universities, students do not receive the necessary foundation for development in programming. Of course, deep knowledge of mathematics and physics is not the main criteria when hiring, but it opens up great prospects and horizons for a programmer, know this. Fundamental knowledge, such as algorithms, is essential for anyone who wants to become a good programmer.

Analytical thinking.

A good programmer most often has an analytical mind and type of thinking. This means that he is logical, consistent, understands and can explain his every step. Such people are able to find the most effective and optimal solution to any problem in a short time. By the way, anyone can learn to think analytically, for example, by solving logical problems and playing special games.

The desire to be better, to know more is self-development.

Without constant self-development you cannot become a good programmer. The IT sector is developing tirelessly: new languages, frameworks and other tools are appearing. Regular updating of your knowledge, doubts that you know “how to do it right” will lead you to success in your career and make you a real professional.

Love for programming

Every day, going to a job you don’t like to write tedious code... We think this is everyone’s fear: doing something you don’t like. Listen to yourself to see if programming is truly your passion. Let's do only what we like, because then it will work out better.

So, in general terms, we understand who a “good programmer” is. Let's now figure out what a programmer needs to know.

What knowledge does a programmer need?

English language.

You will need English in order to write code. Also, many companies work with English-speaking clients with whom you will have regular working communication. In addition, many good books on programming are in English. Therefore, we advise you to practice your skills daily, communicate, read and write in English, and listen to English-language podcasts.

Programming languages.

Algorithms and data structures.

In programming, everything is based on algorithms and data structures. If you can break down a large task into shelves, then you can easily find a solution to any problem in the arrangement of these shelves. In fact, a programmer uses algorithmic knowledge every day without even noticing it. Whatever problems he solves, he always uses data structures. And in order to have at least a superficial understanding of what data structures are and how they work, you also need to understand what algorithms are. If you don't know either way, how can you be sure you're making the right decision in using a particular algorithm?

We talked about the basic things a programmer should know. We hope you love writing code and will spend a lot of time becoming a good programmer. If you need help with training, we are waiting for you at the Training CenterISsoft.

My list includes the following languages:

C++. It is in this language that many of the platforms listed below are written (JVM, CLR, Node and others). It will also make it easier for you to understand how to manually perform memory optimization.
C# or Java. No, you don't need to know both at once. Learning one of these object-oriented languages is very similar to learning another.
HTML. I don't consider it one of the programming languages, but others do, so...
CSS. Essential for any type of web development.
JavaScript. Its a more modern and nicer version, not the crap we used 20 years ago. Note that the language itself has not changed much from that time until now. We, namely the programmers who use it, are the ones who have really changed.
SQL. Needed to work with relational databases.
Lisp. Yes, Lisp. Or, to be more precise, “one of the embodiments of this language.” Clojure, Lisp, Scheme, Nu or something similar will do. You need to know what homoiconic language is and understand how, with the right tools, the boundaries between code and data disappear.
Smalltalk. Yeah. Any GUI framework built on proprietary hardware (Windows, macOS, OS/2, X/Windows, Android, iOS, etc.) uses the same basic principles that were formulated and implemented in Smalltalk back in the 60s. s years.
Ruby or Python. You need to know at least one dynamic programming language. Javascript is also suitable here, but only if you first learn the language itself, without any DOM models that can significantly distort the initial picture (React, Angular, VueJS - they all contain these same “DOM models” that we are talking about .) By the way, once you understand Lisp, it will become much easier for you to understand JavaScript.
AspectJ. Learning about aspect-oriented programming will completely change the way you look at objects, and that's a wonderful thing.
Haskell, ML, Ocaml or Miranda. Spend some time on functional languages that don't have objects.
Bash or zsh or other shell script. Because not every problem or task in programming should be solved using a full-fledged application.
F#, Scala, Clojure or any other object/functional hybrid programming language. Because once you've got the basics down and mastered the basic set that the first 11 programming languages on this list include, you'll be ready for something that won't be easy to learn.
Swift and/or Kotlin. Both are types of object-oriented languages and have some built-in functionality. After studying them, you will be ready to work on mobile applications.
x86 or ARM assembler. Knowing how to read assembly language will allow you to debug code without using the source code.

And yes, I really know all the languages listed above. And I even teach many of them.

And no, you don't need to know them all to become a reasonably competent programmer. If you just want to become a good developer, you need to know one of the main languages (HTML/CSS/Javascript) + a language for backend programming (most often this is C#, Java, Python, Ruby or NodeJS-Javascript) + SQL if you You are using a relational database management system (RDBMS). This will be quite enough.

However, learning all other languages is exactly what will allow you to move from an “ordinary” to a “leading” programmer, and then achieve the maximum level of knowledge and skills.

Nishan Pantha, Computer Engineering Specialist, Language DeveloperPython

I adhere to the so-called language agnosticism, so if someone, under penalty of death, forced me to compile such a list, it would look like this:

1.Python. Because it is extremely convenient and easy to prototype, and also due to the existence of a huge number of open source frameworks. In addition, it would be worth mentioning the widely developed community.

2. C. Because with its help you can clearly understand how everything works. Great for simple tasks.

3. C++. Also for performing simple tasks. Suitable for using object-oriented paradigms. In addition, it can be used for a number of other tasks.

4. Bash. At first glance, working with it may cause confusion and even some difficulties. However, once you master it, you will carry out any automation processes with its help.

5 . Scala. Used as a hybrid of a functional and imperative programming language. You can create amazing things with it.

6. Javascript. The most powerful language for web development. Personally, I like “pure” JS (it’s worth considering that web development is definitely not my thing).

7 . Java. To perform tasks in object-oriented programming. And for using Spring framework.

8 . Haskell. Every programmer should know functional programming paradigms.

9 . PHP. If you need to do anything related to web development. In my opinion, this language is definitely not a symbol of convenience and simplicity.

10. R. For statistical analysis. Personally, I haven’t even tried to work with it, since I’m more than okay with Python itself.

11 . HTML. As I said, I don't usually do web development. However, sometimes at work I am required to show what was done on the backend. In this case, knowledge of HTML is a must.

12 . Markdown. One of the most commonly used markup languages. As a rule, I use it for some kind of notes, writing a README file and much more.

13. VimL / vimscript. I used to write scripts using VimL to create custom plugins and configurations for vim. This is one of those scripting languages that not many people dare to work with.

14.CSS. Using CSS every now and then won't hurt your HTML at all.

15. Matlab/ Octave. I've used it to perform complex calculations before. However, now I do it all in Python thanks to NumPy.

Esteban Fargas, I develop applications and take part in programming competitions

Java: Oracle probably paid your university quite a bit to have you study this particular language as an introduction to object-oriented programming. It will also be useful if you want to participate in programming competitions.
C/ C++: In principle, very similar to the above Java. By the way, I still perceive these two languages as one.
C#: Not a bad language, resulting from a mixture of the two above-mentioned languages.
HTML: It is the basis of the entire network. However, it is still not ideal.
CSS: Allows various things to look beautiful on the Internet. Also far from ideal.
JavaScript: Allows you to build logical circuits for the Internet.
Python: It's also a good idea to have a different programming language and set of paradigms to write the backend for the application. In addition, it is currently the generally accepted language for machine learning technologies.
Ruby: Another great language for creating a backend. It won't take much time to study it.
Golang: It combines all the nice features found in various C languages. It's great. Large-scale projects can be completed using its coupled model.
Scala: Also allows you to create really great professional software. Getting to know the functional paradigm can be extremely interesting and full of exciting challenges.
Haskell: Studying the functional paradigm at the maximum level.
Lisp: The same as the above Haskell, only in an even more unusual version based on an unimaginably bizarre syntax.
Bash: Is this even a language or not? One way or another, it would be nice to master it as one of the convenient developer tools.
SQL: Used to work with databases.
PHP: A language that was used to create backends in the last century.

This text appeared as a response to the standard reaction of “real programmers” in response to my words that I am a 1C programmer. “Ah-ah, 1c-nick, what kind of programmer are you, a script writer at best. You dig into your accounting and for some reason consider yourself programmers. Do a real programmer know how much everything you should know?”

When I ask with interest what exactly a REAL PROGRAMMER should know, I usually get in response some specific problems of the particular programmer with whom I am talking at that moment. Moreover, they are presented almost as the true essence of all programming as such. Well, something like - “If you don’t know how to use semaphores in Delphi, you’re not a programmer.” Or - “if you don’t know how hash tables work, you’re not a programmer.” I got tired of this, I scoured the Internet and decided to collect in one post everything that a REAL PROGRAMMER should know, according to the REAL PROGRAMMERS themselves. List broken down by knowledge section under the cut

MATHEMATICS

Numerical methods, dichotomy/Newton's method, inter- and extrapolation, splines, Gauss/Jacobi/Seidel method, QR and LU decomposition, SVD, least squares method, Runge-Kutta methods, Adams method, Newton-Cotes formulas, Ritz method, Bubnov method -Galerkin, finite difference/element method, FFT/STFT, convergence and stability, l-bfgs and other quasi-Newton methods, adagrad, PARAFAC, cassowary, interior point methods, variational methods for Bayesian inference, nesterov, automatic differentiation, alternating least squares, what every computer scientist should know about floating point arithmetics by Goldberg, Nocedal & Wright/Boyd & Vandenberghe

Algorithms, Knuth-Graham-Patashnik/Zorich/Winberg, Spivak/Dummit-Foote, mathematical analysis, linear algebra, complex analysis, functional analysis, differential geometry, number theory, differential equations/integral equations/calculus of variations/optimal control, generating functions , series, combinatorics, probability theory/mathematical statistics/random processes/queuing theory, Markov chains, integral transforms (Fourier, Laplace, Wavelet), NZQRCHOS, mathematical packages (Mathematica, Maple), category theory

Information theory, compression, Huffman, RLE, BWT, LZ, error correction codes, lossy compression (images, audio, video), information entropy, Shannon's formula, Kolmogorov complexity, maximum entropy problem, kullback-leibler divergence, elias/shannon- Elias encoding

Discrete mathematics, K2, Post's theorem, circuits, finite state machines (DFA and NDKA), Kalashnikov assault rifle, cellular automata

Cryptography, Schneier/Yashchenko, Kerkhoffs principle, symmetric (DES, AES), asymmetric (RSA), PRNG quality, Diffie-Hellman algorithm, elliptic curves, hashing (MD5, SHA, CRCn), DHT, cryptographic strength, crypto-attacks (grandmaster attack) , WEP/WPA/WPA2 and attacks on them, digital signature and certificates, PKI, HTTPS/SSL, zero-knowledge proof, threshold scheme, murmurhash/cityhash, DKIM

Quantum computing, Shor's algorithm, quantum cryptography

GENERAL BASICS OF PROGRAMMING

Multithreading, dining philosophers, deadlock/livelock/race condition/starvation, atomicity, processor lock instructions, memory model/barrier/ordering, CAS or LL/SC, wait/lock/obstruction-free, ABA problem, writing lock-free containers, spin-lock, TLS/per-thread data, Amdahl's law, OpenMP, MPI, map-reduce, critical section/mutex/semaphore/condition variable, WaitForSingleObject/WaitForMultipleObjec ts, green thread/coroutine, pthreads, future/deferred/promise, actor model, parameter server, RDD (as seen in sparks), downpour SGD, wait-free, stackful vs stackless

Computability, Turing machine, Markov normal algorithms, Post machine, Diophantine equations of Matiyasevich, Church lambda functions, partially recursive Kleene functions, Sheinfinkel combinatorial programming, Brainfuck, equivalence of Turing bogs, stopping and self-applicability problem, countability of a set of computable functions, RAM machine, Tarski algorithm, SAT/SMT solvers, theory of formal systems, interactive proofs, Lewin-Cook theorem, 3SAT, PSPACE = NPSPACE,

Algorithms and combinatorial optimization, Cormen/Skiena/Sedgwick/Knuth/Aho-Hopcroft-Ulman/Papadimitriou/Shriver-Goldberg/Prep Arata-Shamos/e-maxx.ru, data structures, algorithms, complexity, Landau symbolism, Acra's theorem Buzzy, time-space tradeoff, complexity classes, NP-complete problems, KMP, graphs and trees, flows in networks, Kirchhoff matrix, search trees (especially RB-tree and B-tree), occlusion detection, heap, hash tables and ideal hash, Petri nets, Russian peasant algorithm, Karatsuba method and Winograd-Strassen matrix multiplication, sorting, greedy algorithms and matroids, dynamic programming, linear programming, diff algorithms, randomized algorithms and fuzzy search algorithms, pseudorandom numbers, fuzzy logic, gusfield (suffix tree, string alignment), motif search, scanning line, cache oblivious, funnel sorting, VEB-layout, root optimization, algorithms for dynamic graphs, calculation models (RAM-machine/pointer machine/decision trees, etc.) , algorithms in memory hierarchies/streaming algorithms, time forward processing, range & rank, LSM-trees, buffered a-b-trees, toku trees, persistent structures, succint structures, lossy structures (bloom/bloomier filter, hash-tables with false positives), locality sensitive hashing, space-time tradeoff in hash tables, scheduling strategies

Machine learning, Tibshirani/Bishop, approaches to AI modeling, retraining/cross-validation, Bayesian networks, neural networks, Kohonen networks, Restricted Boltzmann machine, gradient descent/hill climbing, stochastic optimization (Monte Carlo method, annealing method, genetic algorithms, ant algorithms ), SVM, gradient boosting, cluster analysis, principal component method, LSH, reinforcement learning, MDP, information retrieval/data mining/natural language processing, computer vision, Szeliski, OpenCV, image processing, OCR, Sobel filters, Haar cascade, Viola-Jones framework, SURF, introduction to the psychophysiology of vision, IPython/pandas/scikit-learn, (ME)HMM, CRF, label bias problem, stacked NN, LeToR, factorization machines, autoencoders, RNN/CNN, separate tasks are better instead of NLP (language modeling, co-reference detection, text chunking, POS-tagging, probabilistic parsing, statistical machine translation, misspell correction, question answering, NER, collocation detection, text summarization, speech recognition, fact extraction, sentiment analysis), efficient softmax calculation , feature engineering/selection, quality estimation, Manning/Jurafsky/McCallum/Koehn, latent topics (LDA, Chinese restaurant, pLSI), parallel coordinates, vowpal wabbit, NLTK, structured learning, EM algorithm, contrastive divergence, optimal brain surgery, belief propagation, semi-supervised learning, inductive vs transductive learning, kernel trick, discriminative/generative pairs (as seen by Ng & Jordan), sequence to sequence learning, bagging, social graph analysis, recommender systems/collaborative filtering, multimodal learning

GENERAL PRINCIPLES OF WRITING PROGRAMS

Architecture and code style, McConnell/Fowler/Leblanc/Gamma/Alexandre Rescu-Sutter/Butch, defensive programming, patterns, SOLID/GRASP/KISS DRY SPOT/YAGNI, UML, OOP (Smalltalk), OOD/OOA, code metrics, uncle Bob

Development methodologies, Waterfall/RUP/Agile/Scrum/Kanban/XP, TDD/BDD, CASE

Development tools, IDE, IntelliSense, debuggers (VS/Olly/WinDbg/kdb/gdb) and tracers (strace/ltrace), DWARF debug information format, disassemblers and decompilers (IDA/HexRays/Reflector), version control systems (SVN, GIT), merge/branch/trunk, file and branch naming systems, continuous integration, ant, code coverage, static analysis (lint, cppcheck), dynamic analysis (valgrind, fuzzing), software verification and validation (Frama-C, RAISE ( RSL), Coq), profiling, bug trackers, code documentation, build systems (CMake), package managers (NuGet)

Frameworks, Qt, moc and meta-information, slot-signal concept, Summerfield-Blanchette/Schlee, PoCo, industrial libraries: GMP, i18n, lapack, fftw, pcre

GUI design and information presentation, Raskin/Tufty, usability, design and typography basics, Fitts' law, layout basics, LaTeX, data visualization algorithms (as seen in d3), subpixel rendering

Testing, unit tests, functional, load, integration testing, UI testing, mocks/stubs/spies, fixture, smells and test patterns (Osherove/Meszaros)

PROGRAMMING LANGUAGES

General understanding of programming languages, grammars, Chomsky hierarchy, Myhill-Nerowd theorem, pumping lemma and Ogden's lemma, Kleene algebra, NDKA → DKA, algorithmically undecidable problems in formal languages, Dragonbook, Friedl, regexps and their complexity, PCRE, BNF, Boost.Spirit + Karma + Qi/Ragel, LL, LR/SLR/LALR/GLR, PEG/packrat, yacc/bison/flex/antlr, static code analysis, compilation/decompilation/obfuscation/deobfuscation, Clang/LLVM/XMLVM /Emscripten, GCCXML, OpenC++, building virtual machines, JiT/AoT/GC, DSL/DSEL, on-stack replacement, type checking/type inference algorithms, CYK parser, advanced compiler design and implementation by Muchnick

Assembly language, Zubkov/Hyde/Drepper/Kaspersky/Fog/Abrash, x86, FPU/MMX/SSEn/AVX, AT&T and Intel syntax, masm32, macros, stack, heap/heap managers, calling conventions, hex codes, machine data representation, IEEE754, little/big endian, SIMD, hardware exceptions, interrupts, virtual memory, reversing, stack and heap disruption, return oriented programming, alphanumeric shellcode, L1/L2/RAM/page fault and their timing, ARM assembly language

C++, standard, Comeau, 1TBS, Stroustrap/D&E/Josattis/Vanderwood, Dewhurst/Meyers/Sutter, RAII/copy-and-swap/exception-safety, rule of five, Alexandrescu/Abrahams-Gurtovoy, type erasure, CRTP, NVI, SFINAE, Koenig lookup, Duff"s device, Boost, Sick-Lumsdain/Karlsson, TR on C++ performance, Stepanov test, forwarding problem/move semantics, SPECS, GotW, Meyer"s singleton, cppgm

C++ compilers, features of the implementation of the standard, implementation restrictions, intrinsics, differences between standard libraries (containers, rand), ABI, implementation of virtual functions, virtual inheritance, exceptions, RTTI, switch, pointers to functions and methods; optimizations, copy elision (RVO, NRVO), sizeof on various platforms, compiler and environment definitions, __declspec, compiler switches, empty-base optimization, static and dynamic linking, mangling, distributed compilation, precompiled header, single compilation unit, (strict) aliasing/restrict, inline/_forceinline, volatile, fast calculation of mathematical functions using bithacks, linkers & loaders by Levine

Application programming, C#/F#, Schildt/Troelsen/Richter, generics, yield, linq/plinq, reflection, AST, WCF, WinForms/WPF/Silverlight, AOP, logging frameworks, .NET assembly, Scala, Horstmann/Odersky, pattern matching , macros/quasi-quotes

Functional programming, Haskell/Ocaml/Scheme/Alice or Oz, SICP/TaPL/YAHT/Purely Functional Data Structures/Harrison-Field, HOF (map/fold/filter), Hindley-Milner type system, monads, typeclasses, ADT, dependent types, laziness/energy, logic programming (Prolog or Mercury), competitive programming (Erlang or Oz)

Web Programming and Scripting Languages, Flanagan/Zend PHP5 Certification Course + Study Guide, Apache/nginx, CGI/FastCGI, PHP/Zend Framework/ReactPHP/Zend Engine/Doctrine or Propel/CodeIgniter or Symphony or Yii, Python/Django/Twisted , Ruby/RoR, ASP.NET MV*, JavaScript/jQuery/React/Google Closure/ExtJS/node.js, OOP in JavaScript, HTML5, CSS3/table and block layout, RSS, canvas/WebGL, Ajax/WebSockets, questions security (XSS, SQL injection, CSRF), highload, C10k problem, SWIG, CDN, shadow DOM, browser quirks, real time bidding/trading, anomaly detection, single page apps architecture, web crawler device, web/social graph random walk , asm.js and compilation in js, v8/spidermonkey internals, PaaS/IaaS, SPDY

DATABASE

Databases/Distributed Systems, Gruber/Date, ANSI SQL, T-SQL, ODBC, MySQL/PostgreSQL/MS SQL/BDB/SQLite/Sphinx, Stored Procedures, Triggers, Codd/A Algebra, Tutorial D, Normal Forms, Optimization and query execution, index data structures, transactions and ACID, Brewer's CAP theorem, graph DB, document store, wide column store, key-value storage, distributed systems theory, CRDT, net split problem, consensus protocols, sharding/replication theory, ORM (C++ ODB), ERD, OLAP, semantic web, triplestore, RDF/Turtle, SPARQL, OWL, Semanticscience Integrated Ontology, reasoner, DBpedia, big table/hbase vs. dynamodb/cassandra/riak, 2/3PC, chubby/zoo keeper, leader election (paxos/raft), hdfs/gfs/glusterfs, deduplication problem, causality detection (vector clock/stamps), R/W quorum, load balancing, device search engine indexes, event sourcing, CRDT, protocol design and principles of communication, from the point of view of evolution, extensibility, reliability, design of program interfaces (APIs)

OPERATING SYSTEMS

Operating systems, Silberschatz/Richter/Solomon-Russinovich/R obachevsky/Vakhalia/Stevens/Tanenbaum/Lov e/Linux Kernel Internals, memory manager, heap manager and its device (LAL/LFH/slab), device manager, process manager, context switch, real and protected mode, executables (PE/ELF/Mach), kernel objects, debug mechanisms (strace/ptrace/dtrace/pydbg, Debug API) and minidumps, bash, network stack and high-performance servers, netgraph, CR0, IPC , window subsystem, security system: ACE/ACL and access rights, virtualization technologies, RTOS (QNX), driver programming, IRQL, IRP, file systems, BigTable, NDIS/miniport/FS drivers/filter driver, Mm-, Io-, Ldr functions, DKOM and rootkits, GDT/IDT/SDT, Windows/Linux/BSD kernels, POSIX, TRIM

Formats, XML/XSLT/XPath/XMLStarlet/DOM/SAX, RTF/ODF, JSON/BSON/bencode, YAML, JPEG/PNG/WebP, AVI/MPEG/RIFF/WAV/MP3/OGG/WebM, SVG, Unicode, single-byte encodings/UTF-8/UTF-16/UCS-2/UTF-32, length problems and comparison of Unicode strings, base64, markdown

Component-based models, Rogerson/Tavares, COM/OLE/ActiveX/COM+/DCOM RPC, ATL, apartments, monikers, MIDL, XPCOM, CORBA, TAO, D-Bus

Network, Stevens, OSI model/Internet model, Ethernet, TCP/IP, TCP window, Nagle algorithm, sockets, Protocol buffers/Thrift/Avro/ASN.1, AMQP, ICMP, routing/BGP/OSPF, ARP, Mitnik attack, syn flood, HTTP/FTP, P2P/DHT, DHCP, SMB/NBNS, IRC/XMPP, POP3/SMTP/ESMTP/IMAP, DNS, WiFi/WiMax/GSM/CDMA/EDGE/Bluetooth/GPS, ACE, Wireshark

HARDWARE

Hardware, Horowitz-Hill/Titze-Schenk, semiconductor electronics/spintronics/photonics, transistor, trigger, circuit design, microcode, processor technology, logic synthesis, static timing analysis, FPGA, Verilog/VHDL/SystemC, SISAL, Arduino, devices memory (ROM → EEPROM, RAM, SSD, HDD, DVD), RISC/CISC, Flynn's taxonomy (ID), Princeton and Harvard approach, processor architectures, x86 architectures, VID/PID

Processors, pipelining, hyper-threading, Tomasulo algorithm, speculative execution, static/dynamic branch prediction, prefetching, multiple associative cache, cache line/cache miss, clock cycles, protection rings, memory in multiprocessor systems (SMP/NUMA), timing memory, intel optimization manuals, performance counters
___________________________________

Well, is it impressive? I wonder if there is at least ONE PERSON in the world who really knows all this?

Programmer is a specialist who develops algorithms and computer programs based on special mathematical models. The profession is promising and in great demand all over the world (median value). You can become a programmer at any age. The profession is suitable for men and women with interests in programming, mathematics, languages, as well as good analytical skills and developed logic (can you pass the test to see if you can become a programmer). There are also colleges that teach programming, but you can learn it on your own; as a rule, programmers with skills are valued higher. Exist . The profession has its own. The profession is suitable for those who are interested in computer science (see choosing a profession based on interest in school subjects).

Varieties

In programming, not only practical skills are given first place, but also the ideas of a specialist. Programmers can be divided into three categories depending on their specialization:

Application programmers They are mainly engaged in the development of applied software - games, accounting programs, editors, instant messengers, etc. Their area of work also includes the creation of software for video and audio surveillance systems, access control systems, fire extinguishing or fire alarm systems, etc. Their responsibilities also include adapting existing programs to the needs of a particular organization or user.
System programmers develop operating systems, work with networks, and write interfaces to various distributed databases. Specialists in this category are among the rarest and highest paid. Their task is to develop software systems (services) that, in turn, control the computing system (which includes the processor, communications and peripheral devices). The list of tasks also includes ensuring the functioning and operation of the created systems (device drivers, boot loaders, etc.).
Web programmers also work with networks, but, in most cases, with global ones - the Internet. They write the software components of websites, create dynamic web pages, web interfaces for working with databases.

Features of the profession

Based on the analysis of mathematical models and algorithms for solving scientific, technical and production problems, the programmer develops programs for performing computational work. Draws up a computational scheme for problem solving methods, translates solution algorithms into formalized machine language. Determines the information entered into the machine, its volume, methods for monitoring operations performed by the machine, the form and content of source documents and calculation results. Develops layouts and schemes for input, processing, storage and output of information, conducts desk checks of programs.

Defines a set of data that provides a solution to the maximum number of conditions included in a given program. Conducts debugging of developed programs, determines the possibility of using ready-made programs developed by other organizations. Develops and implements programming automation methods, standard and standard programs, programming programs, translators, input algorithmic languages.

Performs work on the unification and typification of computing processes, participates in the creation of catalogs and cards of standard programs, in the development of forms of documents subject to machine processing, in design work to expand the scope of application of computer technology.

Pros and cons of the profession

Pros:

high profit payment;
relatively high demand for specialists;
sometimes you can get a job without having a higher education;
is primarily a creative profession.

Minuses:

you often have to explain the same thing a lot, since what is clear and obvious to the programmer is not always clear and obvious to the user;
work in emergency mode (sometimes) in a stressful situation;
the profession leaves a specific imprint on the character, which not everyone around him likes.

Place of work

IT companies and web studios;
research centers;
organizations that include in their structure a staff unit or departments of programmers.

Important qualities

Programming is a rapidly evolving field, so a programmer must be able to quickly adapt to the current state of technology and constantly learn new technologies. Therefore, the ability to self-learn is one of the main skills that a programmer must have. Otherwise, in a few years his value as a specialist will be noticeably lower.

Proficiency in English at the level of reading technical documentation is another mandatory requirement for representatives of this profession. For such specialists, the ability to work in a team, on large projects, with collective development tools, and with large financial systems (budgetary, banking, management accounting) is very important. For applicants for the position of lead programmer, project and team management skills, independence, initiative, as well as the ability to bear personal responsibility for the assigned task are desirable.

Programmer training

STEP Computer Academy - IT education as it should be. Since 1999, they are designers and systems engineers who cannot be replaced by artificial intelligence. To do this, in addition to deep specialized knowledge, they are taught to understand tasks, think in ready-made projects and work in a team. And they do everything to ensure that STEP Academy graduates are hired immediately after defending their diplomas.

In this course, you can obtain a programmer profession remotely in 1-3 months. Diploma of professional retraining established by the state. Training in a completely distance learning format. The largest educational institution of additional professional education. education in Russia.

Classes are taught by practicing Web developers with specialized education and 5 years of experience in their specialty. . 10 years in the education market, 4 million users. All courses can be purchased with interest-free installments for a year. You can also get an IT degree at Geek University and pay for your studies a year after it starts.

In 115 hours you can learn how to create websites and online stores, and earn 120 thousand rubles a month from this. After completing the training, guaranteed employment. Advantages: access to the course forever, 3 specializations at the end, flexible and open schedule of classes, work with a personal mentor, employment is guaranteed with documents.

Universities

Salary

Programmer is one of the most popular and highly paid professions in Russia. Even the least advanced specialist can find a job in accordance with his level of knowledge, and then gradually learn and gain experience. The intern's salary is about $1000. A full-time programmer in a mid-level company (not IT) earns up to $1500-1800, a little more in an organization associated with mass software development. The lead programmer's salary is $2500-3000. The next step is the head of the IT department. The required knowledge is supplemented by mandatory work experience, foreign language proficiency, personnel management skills, etc., and earnings can reach $4,000. A good programmer can become the manager of a large software development project, and here the income level reaches $5,000 or more.

Salary as of 02/05/2020

Russia 30000—150000 ₽

Moscow 70000—200000 ₽

Career steps and prospects

A good start to your career can be joining a group of programmers when developing a project. Large projects often attract the attention of Western companies, which “outbid” Russian programmers. For example, once a group of our young scientists developed the Elbrus processor for the Ministry of Defense, but in the end they were all bought up by Intel Corporation, and now our scientists and programmers work abroad, and the Elbrus project itself was slowly closed. The problem of “brain drain” in this profession is one of the most acute.

A programmer can make a career up to the head of a group of programmers (team leader), IT director of an enterprise, IT project manager, etc. During the course of work, a programmer can move within his specialty, improving professionally.

Famous and great programmers

Knut Donald Erwin
Matsumoto Yukihiro
Tanenbaum Andrew
Raymond Eric Stephen
Fowler Martin
Hopper Grace
Stallman Richard Matthew
Kay Alan
Meyer Sid
Stroustrup Björn

The emergence of programming as an occupation and, especially, as a professional activity is difficult to date unambiguously.

Often considered the first programmable device, the jacquard loom was built in 1804 by Joseph Marie Jacquard, which revolutionized the weaving industry by providing the ability to program patterns on fabrics using punched cards.

The first programmable computing device, the Analytical Engine, was designed by Charles Babbage (but was unable to build it). On July 19, 1843, Countess Ada Augusta Lovelace, daughter of the great English poet George Byron, is believed to have written the first program in human history for the Analytical Engine. This program solved the Bernoulli equation, which expresses the law of conservation of energy of a moving fluid.

In her first and only scientific work, Ada Lovelace examined a large number of issues. A number of general principles expressed by her (the principle of saving working memory cells, the connection of recurrent formulas with cyclic computation processes) have retained their fundamental importance for modern programming. Babbage's materials and Lovelace's comments outlined concepts such as subroutine and subroutine library, instruction modification, and index register, which began to be used only in the 1950s.

However, none of the programs written by Ada Lovelace were ever launched.

Ada Augusta, Countess of Lovelace, is generally considered an honorary first programmer (although, of course, writing a single program cannot be considered an occupation or professional activity by modern standards). History has preserved her name in the name of the universal programming language “Ada”.

The first working programmable computer (1941), the first programs for it, and also (with certain reservations) the first high-level programming language Plankalküll were created by the German engineer Konrad Zuse.

History has not preserved the names of the people who first began to professionally carry out the work of programming itself (in isolation from setting up computer equipment), since at first programming was viewed as a secondary setup operation.

Murphy's Laws for Programmers

1. Nothing works as planned.

2. Nothing is programmed the way it should work.

3. A good programmer is characterized by the ability to prove why a task cannot be completed when he is simply too lazy to complete it.

4. It takes three times less time to solve a problem than to discuss all the pros and cons of its solution.

5. The promised delivery date is the carefully calculated project completion date plus six months.

6. The programmer always knows the sequence of actions by which a user can hang his program, but he never fixes this problem, hoping that no one will ever think of executing this sequence.

7. Real programmers love Windows - all mistakes made due to their own stupidity can be blamed on Microsoft.

8. Consequence - 99% of the problems blamed on Microsoft are a consequence of the stupidity of the programmers themselves.

9. In a fit of anger, for some reason everyone is hitting the innocent monitor instead of the system unit.

10. In the event of a hunger strike, a real programmer will be able to eat food picked out from under the keyboard buttons for another month.

11. A real programmer has already replaced at least three beer-drenched keyboards.

12. Anyone who has problems setting up the encoding is automatically considered Neanderthals.

13. Amateurish conversations about computers cause severe nausea, even vomiting. The question of how to change the “wallpaper” in Windows makes you want to cut the throat of the questioner.

14. For most people who need your help, the reason for the error in the program is purely genetic.

15. HTML, HTTP, FTP, SMTP, TCP/IP, RTFM, etc. These are words, not abbreviations.

16. The phrase “mouse-norushka” does not make any sense.

17. The most mystical problems, widely inflated and advertised, in the end turn out to be your stupidest mistakes.

18. Corollary - if your program performs mystical actions, then you did something incredibly stupid.

19. The worst feeling for a programmer is when ten people are standing around you and everyone is trying to find the cause of a problem in your program, and you already understand what the problem is, but you’re afraid to say it because it’s something blatantly stupid...

20. The solution to all life's problems is on the Internet. You just need to be able to search well.

21. A conflict of logical instructions in life causes a fatal error in the work of the programmer’s brain - a rise in temperature and severe dizziness, including vomiting or loss of consciousness, are possible.

22. Programmers despise those who despise programmers more than those who despise programmers despise programmers who despise those who despise them.

23. If you understood the previous one, then you are a programmer.

There are 10 types of people in this world - those who understand the binary number system and those who do not understand it.

Video: you're a programmer

The specifics of knowledge and skills, of course, are largely determined by the area in which a person works. Some write applications for tablet devices, others develop website engines, others develop toys, etc. But nevertheless, there are some skills that are useful to any programmer.

Be on first terms with the computer

This is the simplest and most basic skill inherent in every programmer. It provides for a keen interest in computer technology, which, in turn, gives rise to certain questions for the future specialist: for starters, how to install Windows? How to find and hack a fashion toy? What is BIOS, hosting, system kernel, OOP? - and away we go...

Then your relatives, friends, girlfriend, girlfriends of girls start tugging at you, they say, install the program, clean the system from viruses, rearrange Windows, etc. At first, you even like it: it’s nice to feel competent in one area or another. But then a logical question arises: why not start making money on it? Of course, there is some reluctance to take money from friends, but selling your knowledge and enthusiasm to some serious company is a very real idea. You choose this or that programming language, start learning and again feel like a complete noob..

What is all this for? And to the fact that the world of information technology is a world of toys that you should want to play with. It all begins with interest in these “toys”. If he is not there, there is no programmer.

English

You can't do without English. To learn a programming language, you will have to dig through a lot of literature, video tutorials and documentation. Almost all (99%) primary sources are written in English. If you don’t know it, then the range of materials to study will narrow significantly. Accordingly, the speed of learning a programming language will decrease significantly.

In addition, you need to know English in order to understand the syntax of the language, as well as to successfully pass an interview.

Conversational level and listening comprehension of English are additional bonuses. The phrase “know English” means knowing the so-called “technical English”. That is, you need to understand specialized articles and documentation, and this is far from the same as “live” communication with foreigners.

Mathematics

Mathematics is not as straightforward a question as learning English. It would be more accurate to say that logical and abstract thinking should be at a fairly high level. Mathematics at a university or school develops the mentioned skills, so mathematics education is a big plus. But nevertheless, there are many programmers who do not have a mathematical education. There are even those who do not have higher education at all. This suggests that logic and abstract thinking can be developed not only with mathematical constructions.

There are, of course, areas of programming where you need to know specific formulas, but, firstly, this is not the case everywhere, and secondly, you can get acquainted with them along the way and specialized mathematical programs can help with this. Physics and mathematics are especially useful if you want to create high-quality computer games.

Sociability

Sociability is one of the key properties of a good programmer. The fact is that most serious projects are written in a team - this is both more convenient and faster. If you want to work for a more or less serious “office”, you need to be able to communicate, negotiate, discuss and agree with people. You will also need to be able to get along with your superiors, who will sometimes understand programming better than you, and sometimes will not understand anything about this matter at all.

There is a certain myth that says that recluse and isolation are an integral part of a programmer’s nature. For the most part, programmers are very interesting and sociable people. And hermitism is a certain image inherent in all geniuses, regardless of profession, be it a programmer, linguist, doctor, etc.

flexible mind

Intelligence itself (the ability to operate with the necessary knowledge) is not enough; it needs to be flexible. A flexible mind involves the ability to learn (replenish knowledge, replace outdated information with new). There are people who seem smart, but are very conservative. They have good logical thinking, speak clearly and consistently, but, alas, they cannot learn anything new. But there is the opposite - the head is very inquisitive, it is interested in everything, everything is needed, but the head is a mess, that is, it cannot connect all the accumulated information into a coherent position, or structure it.

There is always a learning curve in programming. The most important and difficult test of a beginner’s abilities will be learning the first programming language. It will be easier later, but you need to remember that you will never be able to completely relax during professional activities.