# Complex Logic

Back to Programs ## Graduates of this special program will have general expertise in various areas.

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 Product Strategy, Vision, Marketing, and Innovation course, students will learn how to build their own project, create financial forecasts, and develop the project for the entire product. Data Science (part 2) will give an insight into machine learning, where students will try some of the most popular concepts. Networks & Clouds offers fundamentals of networks, storage, and security. In the System Architecture course, students will get familiar with the main concepts of systems architecture.

## Semester 1

#### Math & Statistics

• Number theory

• Sets, functions, and sequences

• Generalized Mobius function and asymptotics

• Relations

• Recurrence relations

• Counting techniques

• Logic and techniques of proof

• Trees and graphs

• Partitioning numbers into terms

• Algorithms

• Mathematical theory of sampling

• Normal populations and distributions

• Chi-square, t, and F distributions

• Hypothesis testing

• Estimation

• Confidence intervals

• Sequential Analysis

• Correlation, regression

• Analysis of variance

• Limit theorems

• Chromatic numbers of graphs and Kneser graph

#### Algorithms and Data Structures

• Simple data structures

• Sorting

• Binary and ternary search

• Divide and conquer method

• Recursion

• Hash functions and hash tables

• Graphs

• Dynamic programming

• Greedy algorithms

• Tree data structures

• Balanced trees

• Heap, priority queue, and more

• String algorithms

• Segment tree, Fenwick tree, SQRT-decomposition, Cartesian tree, and more

• Iterating over all subsets for som sets

• Optimizations

• Enumeration of all combinations, permutations, other combinations.

#### Data Science

• Basic tools for data analysis

• Simple data collection and analysis

• Probability and expected values

• Variability, distribution, and asymptotics

• Intervals, testing, and Pvalues

• Power, bootstrapping, and permutation tests

• Logistic regression & Poisson regression

• Prediction, errors, cross-validation

• Caret package

• Predicting with trees, random forests, and model-based predictions

• Regularized regression and combining predictions

• Exploratory data analysis & modeling

• Prediction model

• Creative exploration

• Least-squares, linear regression, multivariable regression, residuals, and diagnostics

• Language design principles

• Programming languages

• Syntax description

• Semantic description

• Lexical and syntax analysis

• Variables

• Data types and assignment statements

• Expressions and assignments

• Parallel and concurrent programming

• Functional programming

• Logic programming

## Semester 2

#### Networks & Clouds

• IoT (Business and Products, Architecture and Technologies, Networks)

• Wi-Fi and Bluetooth

• Cloud technology

• Key security concepts and security tools

• Authentication and access control

• Windows/Linux/MacOS operating system security basics

• Virtualization

• Compliance frameworks and industry standards

• Cryptography and compliance Pitfalls

• TCP/IP Framework, IP addressing and the OSI Model

• Security (injection vulnerability, penetration testing, incident response, digital forensics, threat intelligence, thread hunting

• Application security and testing

• Phishing scams and more

#### Systems Architecture

• System calls and interrupt handling

• The concept of process, stream, and thread

• Stream synchronization

• Classification of types of memory

• Pointers

• Process memory

• Disk storage device

• File systems

• Basic elements of the operating system

• Architecture: scheduler, memory manager, IPC

#### Data Science

• Practical application of the methods studied in the first half of the course

• Solving practical problems on regression and classification

• Familiarity with Tensorflow and keras using examples of classical tasks

• Building neural networks and more

#### Product Strategy, Vision, Marketing, and Innovation

• Description of the project or product selected by the student or teacher

• Building a business plan for the project and presentation of the project

• Financial evaluation of the project, Risk assessment, and forecasts

• Implementation of the project, search for sponsors and further promotion of the project