Search results for: PANEL DATA ANALYSIS

• Qualitative data analysis methods

  e-Learning Courses

  • M. Starnawska

  This is the continuation of Qualitatative Data Analysis Methods course provided online

• Data Analysis 2023/24

  e-Learning Courses

  • K. Flisikowski

  Data Analysisdr inż. Karol Flisikowski, prof. PG - winter semester 2023/24

• Deep neural networks for data analysis

  e-Learning Courses

  • K. Draszawka

  The aim of the course is to familiarize students with the methods of deep learning for advanced data analysis. Typical areas of application of these types of methods include: image classification, speech recognition and natural language understanding. Celem przedmiotu jest zapoznanie studentów z metodami głębokiego uczenia maszynowego na potrzeby zaawansowanej analizy danych. Do typowych obszarów zastosowań tego typu metod należą:...

• Data analysis - team project 2024

  e-Learning Courses

  • O. Aydin

• Qualitative Data Analysis Methods -Summer 2022

  e-Learning Courses

  • M. McPhillips

• [ITiT] Technologies of Spatial Data Analysis and Processing

  e-Learning Courses

  • M. Kulawiak
  • E. Lubecka

  {mlang pl} Dyscyplina:  Informatyka Techniczna i Telekomunikacja Zajęcia obowiązkowe dla doktorantów II roku Prowadzący:  dr hab. inż. Marcin Kulawiak Liczba godzin: 30 h Forma zajęć: wykład/seminarium {mlang} {mlang en} Discipline: Technical Informatics and Telecommunications Obligatory course for 2nd year PhD students Academic teacher:  dr hab. inż. Marcin Kulawiak Total hours of training: 30 teaching hours Course...

• Scientific methods of computer data analysis and presentation

  e-Learning Courses

  • T. E. Berezowski

• Qualitative Data Analysis Methods -Summer 22/23

  e-Learning Courses

  • M. McPhillips

• Deep neural networks for data analysis 24/25

  e-Learning Courses

  • J. Cychnerski
  • K. Draszawka

  This course covers introduction to supervised machine learning, construction of basic artificial deep neural networks (DNNs) and basic training algorithms, as well as the overview of popular DNNs architectures (convolutional networks, recurrent networks, transformers). The course introduces students to popular regularization techniques for deep models. Besides theory, large part of the course is the project in which students apply...

• Deep neural networks for data analysis 27/28

  e-Learning Courses

  • K. Draszawka

• Deep neural networks for data analysis 25/26

  e-Learning Courses

  • K. Draszawka

• Deep neural networks for data analysis 26/27

  e-Learning Courses

  • K. Draszawka

• [AEiE, ITiT] Scientific methods of computer data analysis and presentation - 2022/2023

  e-Learning Courses

  • T. E. Berezowski

  {mlang pl} Dyscyplina: AEiE, ITiT Zajęcia obowiązkowe dla doktorantów II roku Prowadzący:  Tomasz Berezowski Liczba godzin: 30 Forma zajęć: wykład/projekt/seminarium {mlang} {mlang en} Discipline: AEiE, ITiT Obligatory course for 2nd-year PhD students Academic teacher: Tomasz Berezowski Total hours of training: 30 teaching hours Course type: Lecture/project/seminar {mlang}      

• [AEEiTK, ITiT] Scientific methods of computer data analysis and presentation - 2023/2024

  e-Learning Courses

  • T. E. Berezowski

  {mlang pl} Dyscyplina: AEiE, ITiT Zajęcia obowiązkowe dla doktorantów II roku Prowadzący:  Tomasz Berezowski Liczba godzin: 30 Forma zajęć: wykład/projekt/seminarium {mlang} {mlang en} Discipline: AEiE, ITiT Obligatory course for 2nd-year PhD students Academic teacher: Tomasz Berezowski Total hours of training: 30 teaching hours Course type: Lecture/project/seminar {mlang}      

• [AEEiTK, ITiT] Scientific methods of computer data analysis and presentation 2024/2025

  e-Learning Courses

  • T. E. Berezowski

  {mlang pl} Dyscyplina: AEiE, ITiT Zajęcia obowiązkowe dla doktorantów II roku Prowadzący:  Tomasz Berezowski Liczba godzin: 30 Forma zajęć: wykład/projekt/seminarium {mlang} {mlang en} Discipline: AEiE, ITiT Obligatory course for 2nd-year PhD students Academic teacher: Tomasz Berezowski Total hours of training: 30 teaching hours Course type: Lecture/project/seminar {mlang}      

• Analiza danych typu Big Data 2022/23

  e-Learning Courses

  • P. Weichbroth
  • W. Waloszek

  The aim of the course is to familiarize students with the methods of storing and analysis of big data. Practical tools for these tasks are presented.

• Analiza danych typu Big Data 2023/24 KOPIA

  e-Learning Courses

  • W. Waloszek

  The aim of the course is to familiarize students with the methods of storing and analysis of big data. Practical tools for these tasks are presented.

• Smart city and data management foundations

  e-Learning Courses

  • M. Krzyżanowski
  • W. Drapiński
  • J. Sudakowska
  • A. Guzik
  • K. Dytrych
  • A. Modrzejewska
  • A. Orłowski

  This e-learning course, specifically designed for individuals aiming to establish startups, those proficient in technology yet unsure of how to leverage and market it, and students aspiring to further their studies in data analysis, transportation, IT management, or data engineering. This course serves as an entry point for roles such as Smart City Specialist and Chief Data Officer in municipal settings. Participants will gain...

• Głębokie Sieci Neuronowe Do Analizy Danych

  e-Learning Courses

  • T. M. Boiński
  • K. Draszawka

  {mlang pl}Kurs przeznaczony jest dla studentów kierunku Inżynieria Danych.{mlang} {mlang en}Course for Data Analysis students.{mlang}

• European Economic Development 2023

  e-Learning Courses

  • A. Parteka
  • R. Ślosarski

  Main aim of the subject is to transfer knowledge on theoretical and empirical aspects of economic development in Europe.  Key topics: •concepts of economic development, alternative measures of development •factors of economic growth and development (‘determinants of growth’) •history of economic development in Europe •cross-country and cross-regional comparisons •sources: where to find information and data needed for empirical...

• Team project I (2020/21)

  e-Learning Courses

  • A. Dąbrowski
  • M. Chodnicki

  Within this team project, students will be designing a space mission. Each student will choose a subsystem: mission analysis, propulsion, communication, structure, mechanisms, power, thermal, onboard data handling. Together, by means of concurrent engineering a primary system definition will be completed.

• Advanced methods in biosensing: fundamentals and applications

  e-Learning Courses

  • R. Bogdanowicz
  • J. Ryl

  This course provides instruction in the basic science and engineering conceptsrequired to understand the design and application of biosensors. Different biosensorsystems are explored, ranging from electrochemical devices, through to optical orthermal systems. Instruction is also given in the general principles of sampling andanalysis, statistical presentation and manipulation of data. The primary focus will bethe physics of biomolecule...

• Magnetism from fundamentals to spintronics 22/23

  e-Learning Courses

  • L. Piotrowski

  1. Basic magnetic quantities2. Magnetism of atoms and molecules, atoms in external magnetic fields3. Solid state magnetism, types of magnetic materials (dia-, para-, and ferromagnetism)4. Ferromagnetism and domain structures5. Magnetism of small particles, single domain particles (StonerWohlfarth model), thin films6. Experimental techniques of magnetic properties and magnetisation state determination. Domain structurevisualisation...

• TEAM RESEARCH PROJECT I & II_2024/25

  e-Learning Courses

  • A. Orchowska

  The aim of a team research project is to conduct a process in which Students will verify the research hypothesis set by the Client. For this purpose, the project may require the creation of a product, e.g. an application, a device and conducting appropriate research, analysis of results, etc. In the event that the University/Client shares confidential information (including data), the Students will be required to sign an undertaking...

• Financial Analysis exercises - New

  e-Learning Courses

  • P. Figura

  To obtain theoretical and practical experience of financial and economic analysis.   Methods of economic analysis:  Elements of production and human resources analysis, tangible assets analysis, inventory analysis, which would enable to understand the business case, to measure them and find suitable decision. To obtain theoretical and practical experience of financial analysis. 

• Financial Analysis exercises - New (copy)

  e-Learning Courses

  • K. Kubiszewska

  To obtain theoretical and practical experience of financial and economic analysis.   Methods of economic analysis:  Elements of production and human resources analysis, tangible assets analysis, inventory analysis, which would enable to understand the business case, to measure them and find suitable decision. To obtain theoretical and practical experience of financial analysis. 

• Decisions Analysis 2023/2024

  e-Learning Courses

  • N. Rizun

  This is content to the subject: "Decisions Analysis" to be used as a template for futher courses.

• Data Warehouses 2023/2024

  e-Learning Courses

  • A. Nabożny
  • G. Gołaszewski
  • T. Zawadzka

  The aim of the course is to familiarize students with the development process of data warehouses for BI systems. The course is prepared for students of Data Engineering.

• Decisions Analysis STAC 22/23

  e-Learning Courses

  • G. Musiatowicz-Podbiał

  This is content to the subject: "Decisions Analysis" 22/23

• Data Mining 2022/2023

  e-Learning Courses

  • W. Waloszek

  This is a web page for our course in Data Mining.

• Data Mining 2021/2022

  e-Learning Courses

  • W. Waloszek

  This is a web page for our course in Data Mining.

• Data Structures (Doctoral Studies)

  e-Learning Courses

  • K. Goczyła

  The course covers basic data structures and computer algorithms used in information tehcnology applications. 

• Data Mining Path

  e-Learning Courses

  • G. Gołaszewski
  • T. Zawadzka
  • A. Karpus
  • M. Wróbel
  • A. Przybyłek
  • W. Waloszek
  • A. Landowska

  Within this path, various issues regarding data mining and their practical application in various systems are discussed.   4 semestr specjalności ISI i ZAD

• Data Warehouses DE 2022/2023

  e-Learning Courses

  • A. Nabożny
  • G. Gołaszewski
  • T. Zawadzka

  The aim of the course is to familiarize students with the development process of data warehouses for BI systems. The course is prepared for students of Data Engineering.

• Data Warehouses DE 2023/2024

  e-Learning Courses

  • G. Gołaszewski
  • T. Zawadzka

  The aim of the course is to familiarize students with the development process of data warehouses for BI systems. The course is prepared for students of Data Engineering.

• Algorithms and Data Structures 2022

  e-Learning Courses

  • R. Ostrowski
  • K. Manuszewski
  • T. Pikies
  • K. Wereszko
  • A. Jastrzębski
  • M. Jurkiewicz
  • T. Goluch

  WETI, DS, Algorithms and Data Structures

• Data Warehouses 2022/2023

  e-Learning Courses

  • A. Nabożny
  • G. Gołaszewski
  • T. Zawadzka

  The aim of the course is to familiarize students with the development process of data warehouses for BI systems. The course is prepared for students of Informatics - 5th semester.

• Technical physics (Data Engineering)

  e-Learning Courses

  • S. Bielski

  Field of study: Data Engineering; Subject name: Technical physics; Lecture notes and other course materials.

• Macroeconomic analysis

  e-Learning Courses

  • M. Olczyk

• Market analysis

  e-Learning Courses

  • A. Kozłowska

  Kurs przeznaczony dla studentów studiów stacjonarnych Inżynierii Danych, I stopnia, semestr 7 (zimowy) w roku akademickim 2021/2022.

• Financial Analysis

  e-Learning Courses

  • K. Kubiszewska

• Decision analysis

  e-Learning Courses

  • B. Woliński

• Databases (Data Engineering) - 2022

  e-Learning Courses

  • A. Nabożny
  • G. Gołaszewski
  • M. Wróbel
  • A. Landowska
  • K. Goczyła

  The course comprises basic and selected advanced issues of modern relational databases, including modelling, implementing and querying databases. It also refers to such important topics as normalization and transactional processing. The course is a necessary prerequisite to the Data Warehouses course. 

• Databases (Data Engineering) - 2023

  e-Learning Courses

  • G. Gołaszewski
  • A. Karpus
  • M. Pykała
  • A. Landowska
  • K. Goczyła

  The course comprises basic and selected advanced issues of modern relational databases, including modelling, implementing and querying databases. It also refers to such important topics as normalization and transactional processing. The course is a necessary prerequisite to the Data Warehouses course. 

• Databases (Data Engineering) - 2024

  e-Learning Courses

  • G. Gołaszewski
  • A. Karpus
  • A. Landowska
  • K. Goczyła

  The course comprises basic and selected advanced issues of modern relational databases, including modelling, implementing and querying databases. It also refers to such important topics as normalization and transactional processing. The course is a necessary prerequisite to the Data Warehouses course. 

• Data Quality Assurance 2024

  e-Learning Courses

  • P. Weichbroth

  Data quality assurance / Zapewnianie jakości danych 2023 Prowadzący: Paweł Weichbroth Wykład: odbywają się zdalnie. Projekt w drugiej połowie semestru Kod dostępu do przedmiotu w eNauczanie będzie przekazany podczas pierwszego wykładu

• Data quality assurance - 2023

  e-Learning Courses

  • P. Weichbroth
  • A. Wardziński

  Data quality assurance / Zapewnianie jakości danych 2023 Prowadzący: Andrzej Wardziński Wykład: od 13 marca, poniedziałki 13:15 sala NE Audytorium 2 Projekt w drugiej połowie semestru Kod dostępu do przedmiotu w eNauczanie będzie przekazany podczas pierwszego wykładu

• Data Mining (for Data Engineering) - Nowy

  e-Learning Courses

  • T. Zawadzka
  • A. Karpus
  • W. Waloszek
  • K. Goczyła

• Decision analysis (l/lab) winter 2020/21

  e-Learning Courses

  • B. Woliński

  Decision analysis (l/lab)_winter 2020/21

• METHODS OF QUANTITATIVE ANALYSIS (TEAM PROJECT) (2022/2023)

  e-Learning Courses

  • P. Paradowski

  METHODS OF QUANTITATIVE ANALYSIS (TEAM PROJECT) - Economic Analytics, Lecture and Lab, Winter Semester 2022/2022. Teacher: dr Piotr Paradowski