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Real-Time Backend

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We created this program for those of you, who want to learn how to work with large amounts of information in real-time.

During the first semester, we focus on the essential fundamentals, such as basic concepts of math and programming, which are the same for all programs.

In the Math & Statistic course, students will learn about discrete mathematics and the basics of probability theory. Algorithms & Data Structures covers the basic algorithms on graphs, data structures, and recurrence relations. Also, during the first semester, students learn how to process and analyze data, and build simple models based on this data in the Data Science course. In the Advanced Programming Languages course, students will learn about programming languages and their fundamental differences.

During the second semester, in the Operating Systems course, students will learn about various operating systems and their features. Networks & Clouds course goes over the basics of networking and security. The Systems Architecture course will cover the features of system architecture for different operating systems.

Real-Time Backend is the major course of this program, where students will be offered a real-life project example to work with a large amount of data.

Semester 1

Math & Statistics

  • Number theory

  • Sets, functions, and sequences

  • Logic

  • Induction

  • Sequences

  • Combinatorics

  • Relations

  • Recurrence relations

  • Graphs (basics)

  • Counting techniques

  • Graphs and Numbers

  • Growth of Functions

  • The probability of an Event

  • Conditional probability

  • Discrete Random Variables

  • Continuous probability

  • Sampling Theory

  • Statistics

Algorithms and Data Structures

  • Graphs (basics)

  • BFS and DFS

  • Dynamic Programming

  • LIS, LCS and other 2D problems

  • Heaps

  • Dijkstra, Floyd, Bellman-Ford

  • DFS applications

  • Knapsack problem

  • Dynamic programming over subsets

  • Amortized Time Complexity (Queue via Stack)

  • Disjoint Set Union

  • Dynamic programming on a tree

  • Minimum Spanning Trees

  • Matching

  • Segment Trees

  • Binary Search Trees

  • LCA

  • What is NP and how is it useful?

  • Strings

Data Science

  • Basic tools for data analysis

  • Simple data collection and analysis

  • Data Types in Statistics

  • Visualization

  • Stat in Data Science

  • Probability and its applications

  • Estimation, Confidence Intervals

  • Hypothesis testing

  • Correlation and Dependence

  • Regression

  • Generalized linear model

  • Feature engineering (Linear Regression Deep Dive)

  • Decision Trees

  • ANOVA

Advanced Programming Languages

  • Grammars, Regular Expressions, Backus-Naur Form

  • Computation Models

  • Functional Programming

  • Clojure Programming Language (Basics, Concurrency, Actors, Refs, Atoms, Macros)

  • Compiling and linking

  • Monads

  • Custom hierarchies and multimethods in Clojure

  • Logical Programming

  • Prolog

  • LLVM (basics, garbage collection)

  • Constraint Programming

  • MiniZinc

  • Guiding the Search in Constraint Programming

  • Data Oriented Programming

  • Data storage and querying in XML ecosystem

Semester 2

Networks & Clouds

  • OSI, analogues, comparison

  • Data layers of OSI, REST/RPC/SOAP/GraphQL, solutions for this levels

  • SSH, Telnet, working remotely, solving OverTheWire: Bandit

  • Media layers

  • Clouds

  • Containerization

  • IoT, smart home, zigbee/bluetooth, wifi, making smart home ourselves

Systems Architecture

  • C++ - syntax, OOP basics, UB, numbers

  • RUST - syntax, programming paradigms, safe/unsafe, comparison with C++

  • Memory allocation, caching algorithms

  • Memory (its components), cache-friendly algorithms

  • Assembler (Instructions, RISC, CISC, x86, Intel, asm basics)

  • Syscalls

  • Processes, Scheduling

  • Threads, mutexes, condvars, atomics

  • Verilog

  • System limits - rlimit, cgoups, Linux namespaces, seccomp

Real-Time Backend (Architecture)

  • CAP. MapReduce

  • Storage. GFS

  • RPC. Models. Fault tolerance

  • Physical & logical time, clocks, ordering of events

  • Broadcast protocols

  • Consensus and transactions in distributed systems

  • Election

  • Consensus

  • FLP theorem

  • Raft algorithm

  • State machine replication

  • Distributed transactions

  • Atomic commit protocols

  • 2-phase commit

  • Distributed File System (DFS)

  • Industrial Systems Design & System Design. Principles and main concepts. Technology overview

  • TinyUrl/Pastebin Design Design of industrial systems

  • Netflix/Youtube architecture

  • Development of a group project together with a team from the Real-Time Frontend specialty

Operating Systems

  • UNIX Filesystem basics

  • Processes and signals

  • Permissions, ownership, setuid executables

  • Basics of Bash scripting language

  • System calls

  • Usage of system calls in C language

  • Fork/exec technique

  • Signal handing in C

  • Virtual memory

  • chroot, Linux namespaces and containers

  • Sockets

  • Operating system internals

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