I preferred the exercise and learning about the nooks and crannies of Python
Connor Brierley-Green - Natural Resources Canada
Szkolenia Python
Python Programming Language courses
Opinie uczestników
Plany Szkoleń Python
| Kod | Nazwa | Czas trwania | Charakterystyka kursu |
|---|---|---|---|
| pythonprog | Programowanie w języku Python | 28 godz. | This course is designed for those wishing to learn the Python programming language. The emphasis is on the Python language, the core libraries, as well as on the selection of the best and most useful libraries developed by the Python community. Python drives businesses and is used by scientists all over the world – it is one of the most popular programming languages. The course can be delivered using Python 2.7.x or 3.x, with practical exercises making use of the full power of both versions of the language. This course can be delivered on any operating system (all flavours of UNIX, including Linux and Mac OS X, as well as Microsoft Windows). The practical exercises constitute about 70% of the course time, and around 30% are demonstrations and presentations. Discussions and questions can be asked throughout the course. Note: the training can be tailored to specific needs upon prior request ahead of the proposed course date. Introduction to Python Programming Writing and running Python Programs Outputting to the screen Inputting from the keyboard Data types and int(), float() and str() Arithmetic operations Exercise Program Structures Indentation Conditional statements Looping statements Exercise Sequences Strings Lists Tuples Dictionaries Command line parameters Exercise Functions What are functions Parameters and return values Predefined functions Recursion Exercise Modules Modules Importing modules Unit testing modules Packages Exercise Error Handling Exceptions Exception types try except try except else try finally Raining exceptions Exercise File Handling Types of file File handling principles Opening files Reading files Writing files Exercise String Manipulation String manipulation String manipulation functions Regular expressions Exercise Database Access in Python MySQL Python database access principles Selecting data Inserting data Deleting data Exercise CGI HTML CSS CGI Python CGI Exercise |
| ooprog | Programowanie obiektowe | 21 godz. | Szkolenie skierowane jest dla osób chcących zapoznać się z możliwościami programowania obiektowego oraz realizacją paradygmatu w wybranym języku: C#, Java, Groovy, Scala lub PHP. Potrzeba programowania obiektowego Próba odzwierciedlenia realnego świata w programowaniu Pocztątki i ewolucja programowania obiektowego Programowanie obiektowe w aspekcie reguł KISS i DRY Klasy i obiekty Idee i byty w filozofii Platona Rola klas Realizacja klas za pomoc obiektów Klasy, właściwości i metody statyczne Konwencje nazewnictwa i kodowania Nazewnictwo klas Nazewnictwo właściwości i metod Nazewnictwo pakietów i folderów Pozostałe konwencje Struktura klasy Właściwości/pola jako opis stanu obiektu Akcesory Metody jako realizacja funkcjonalności Modelowanie i graficzna reprezentacja klas Analiza wymagań Modelowanie struktury klas i relacji Opisywanie obiektów biznesowych Diagramy EER Diagram klas UML Paradygmaty programowania obiektowego Pojęcie paradygmatu Hermetyzacja Abstrakcja Dziedziczenie Polimorfizm Projektowanie i realizacja warstwy abstrakcji Klasy abstrakcyjne Interfejsy Cechy -Traits Klazy zagnieżdżone Klasy generyczne Tworzenie obiektów Sposoby tworzenia obiektów Rola konstruktora Wzorzec Fabryki Zależności między klasami Agregacja Kompozycja Rozdział zależności - Decoupling Wstrzykiwanie zależności, kontenery DIC Wzorzec Mediatora Organizacja kodu Projektowanie kodu wielokrotnego użycia Struktura folderów Przestrzenie nazw, pakiety, moduły Programowanie obiektowe a wydajność Rezerwacja pamięci dla obiektów Garbage Collector Jawne usuwanie obiektów, destruktory Praca z referencjami Mechanizm refleksji Obszar zastosować refleksji Pozyskiwanie informacji o klasach i obiektach Znaczenie refleksji w tworzeniu dokumentacji i testowaniu oprogramowania Obsługa błędów Możliwe modele obsługi błędów Obiektowy model obsługi błędów Rola wyjątków i klasa Exception, rzucanie i przechwytywanie wyjtków Blok try-catch-final, zagnieżdżanie bloku Antywzorce programowania obiektowego Nadmierna odpowiedzialność klas, wzorzec Delegacji Silne zależności Singleton i potencjalne problemy Anemic Domain Model Pozostałe antywzorce |
| wppyth | Wzorce projektowe w Python | 14 godz. | Grupa docelowa: Technical Team Leader, Software Developer Cel szkolenia: Celem szkolenia jest nabycie umiejętności projektowania zawansowanych struktur programistycznych / projektowych w języku Python. Podstawy teoretyczne wzorców projektowych Historia wzorców projektowych Podział wzorców projektowych Wzorce (teoria i ćwiczenia) - Creational Design Patterns Abstract Factory Builder Factory Method Object Pool Prototype Singleton Wzorce (teoria i ćwiczenia) - Structural Design Patterns Adapter Bridge Composite Decorator Facade Flyweight Private Class Data Proxy Wzorce (teoria i ćwiczenia) - Behavioral Design Patterns Chain of responsibility Command Interpreter Iterator Mediator Memento Null Object Observer State Strategy Template method Visitor Wzorce złożone MVC (Model - View - Controller) MVP (Model - View - Presenter) MVVM (Model -View -View Model) Symulacja Projektowania Architektury - Hands On Labs Opracowanie Architektury Sytemu w grupach na bazie podanego Business Case |
| mlfunpython | Machine Learning Fundamentals with Python | 14 godz. | The aim of this course is to provide a basic proficiency in applying Machine Learning methods in practice. Through the use of the Python programming language and its various libraries, and based on a multitude of practical examples this course teaches how to use the most important building blocks of Machine Learning, how to make data modeling decisions, interpret the outputs of the algorithms and validate the results. Our goal is to give you the skills to understand and use the most fundamental tools from the Machine Learning toolbox confidently and avoid the common pitfalls of Data Sciences applications. Introduction to Applied Machine Learning Statistical learning vs. Machine learning Iteration and evaluation Bias-Variance trade-off Machine Learning with Python Choice of libraries Add-on tools Regression Linear regression Generalizations and Nonlinearity Exercises Classification Bayesian refresher Naive Bayes Logistic regression K-Nearest neighbors Exercises Cross-validation and Resampling Cross-validation approaches Bootstrap Exercises Unsupervised Learning K-means clustering Examples Challenges of unsupervised learning and beyond K-means |
| python_nltk | Natural Language Processing with Python | 28 godz. | This course introduces linguists or programmers to NLP in Python. During this course we will mostly use nltk.org (Natural Language Tool Kit), but also we will use other libraries relevant and useful for NLP. At the moment we can conduct this course in Python 2.x or Python 3.x. Examples are in English or Mandarin (普通话). Other languages can be also made available if agreed before booking.Overview of Python packages related to NLP Introduction to NLP (examples in Python of course) Simple Text Manipulation Searching Text Counting Words Splitting Texts into Words Lexical dispersion Processing complex structures Representing text in Lists Indexing Lists Collocations Bigrams Frequency Distributions Conditionals with Words Comparing Words (startswith, endswith, islower, isalpha, etc...) Natural Language Understanding Word Sense Disambiguation Pronoun Resolution Machine translations (statistical, rule based, literal, etc...) Exercises NLP in Python in examples Accessing Text Corpora and Lexical Resources Common sources for corpora Conditional Frequency Distributions Counting Words by Genre Creating own corpus Pronouncing Dictionary Shoebox and Toolbox Lexicons Senses and Synonyms Hierarchies Lexical Relations: Meronyms, Holonyms Semantic Similarity Processing Raw Text Priting struncating extracting parts of string accessing individual charaters searching, replacing, spliting, joining, indexing, etc... using regular expressions detecting word patterns stemming tokenization normalization of text Word Segmentation (especially in Chinese) Categorizing and Tagging Words Tagged Corpora Tagged Tokens Part-of-Speech Tagset Python Dictionaries Words to Propertieis mapping Automatic Tagging Determining the Category of a Word (Morphological, Syntactic, Semantic) Text Classification (Machine Learning) Supervised Classification Sentence Segmentation Cross Validation Decision Trees Extracting Information from Text Chunking Chinking Tags vs Trees Analyzing Sentence Structure Context Free Grammar Parsers Building Feature Based Grammars Grammatical Features Processing Feature Structures Analyzing the Meaning of Sentences Semantics and Logic Propositional Logic First-Order Logic Discourse Semantics Managing Linguistic Data Data Formats (Lexicon vs Text) Metadata |
| mlfsas | Machine Learning Fundamentals with Scala and Apache Spark | 14 godz. | The aim of this course is to provide a basic proficiency in applying Machine Learning methods in practice. Through the use of the Scala programming language and its various libraries, and based on a multitude of practical examples this course teaches how to use the most important building blocks of Machine Learning, how to make data modeling decisions, interpret the outputs of the algorithms and validate the results. Our goal is to give you the skills to understand and use the most fundamental tools from the Machine Learning toolbox confidently and avoid the common pitfalls of Data Sciences applications. Introduction to Applied Machine Learning Statistical learning vs. Machine learning Iteration and evaluation Bias-Variance trade-off Machine Learning with Python Choice of libraries Add-on tools Regression Linear regression Generalizations and Nonlinearity Exercises Classification Bayesian refresher Naive Bayes Logistic regression K-Nearest neighbors Exercises Cross-validation and Resampling Cross-validation approaches Bootstrap Exercises Unsupervised Learning K-means clustering Examples Challenges of unsupervised learning and beyond K-means |
| flask | Web application development with Flask | 14 godz. | This practical course is addressed to Python developers that want to create and maintain their first web applications. It is also addressed to people who are already familiar with other web frameworks such as Django or Web2py, and want to learn how using a microframework (i.e. a framework which glues together third-party libraries instead of providing a self-contained universal solution) changes the process. A significant part of the course is devoted not to Flask itself (it's tiny), but to third-party libraries and tools often used in Flask projects. Why web frameworks are needed Overview of available Python web frameworks Installation of Flask Routing requests to view functions Serving static files Rendering templates with Jinja2 Loops and conditionals Template inheritance Macros in templates Flat pages with Flask-Flatpages HTML5 Boilerplate as a starting point Producing JSON Issuing redirects Application context and Request context Dealing with file uploads with Flask-Uploads Structuring a complex application: how to avoid circular imports Structuring a complex application: Blueprints Commonly used ORMs: SQLAlchemy and Peewee Database migrations Form validation with WTForms and Flask-WTF Sending email with Flask-Mail User session management with Flask-Login and Flask-User The admin interface created by Flask-Admin Internationalization with Flask-BabelEx Preprocessing of frontend files with Flask-lesscss and Flask-Assets Deploying Flask applications into production |
| BigData_ | A practical introduction to Data Analysis and Big Data | 28 godz. | Participants who complete this training will gain a practical, real-world understanding of Big Data and its related technologies, methodologies and tools. Participants will have the opportunity to put this knowledge into practice through hands-on exercises. Group interaction and instructor feedback make up an important component of the class. The course starts with an introduction to elemental concepts of Big Data, then progresses into the programming languages and methodologies used to perform Data Analysis. Finally, we discuss the tools and infrastructure that enable Big Data storage, Distributed Processing, and Scalability. Audience Developers / programmers IT consultants Format of the course Part lecture, part discussion, heavy hands-on practice and implementation, occasional quizing to measure progress. Introduction to Data Analysis and Big Data What makes Big Data "big"? Velocity, Volume, Variety, Veracity (VVVV) Limits to traditional Data Processing Distributed Processing Statistical Analysis Types of Machine Learning Analysis Data Visualization Languages used for Data Analysis R language (crash course) Why R for Data Analysis? Data manipulation, calculation and graphical display Python (crash course) Why Python for Data Analysis? Manipulating, processing, cleaning, and crunching data Approaches to Data Analysis Statistical Analysis Time Series analysis Forecasting with Correlation and Regression models Inferential Statistics (estimating) Descriptive Statistics in Big Data sets (e.g. calculating mean) Machine Learning Supervised vs unsupervised learning Classification and clustering Estimating cost of specific methods Filtering Natural Language Processing Processing text Understaing meaning of the text Automatic text generation Sentiment/Topic Analysis Computer Vision Acquiring, processing, analyzing, and understanding images Reconstructing, interpreting and understanding 3D scenes Using image data to make decisions Big Data infrastructure Data Storage Relational databases (SQL) MySQL Postgres Oracle Non-relational databases (NoSQL) Cassandra MongoDB Neo4js Understanding the nuances Hierarchical databases Object-oriented databases Document-oriented databases Graph-oriented databases Other Distributed Processing Hadoop HDFS as a distributed filesystem MapReduce for distributed processing Spark All-in-one in-memory cluster computing framework for large-scale data processing Structured streaming Spark SQL Machine Learning libraries: MLlib Graph processing with GraphX Search Engines ElasticSearch Solr Scalability Public cloud AWS, Google, Aliyun, etc. Private cloud OpenStack, Cloud Foundry, etc. Auto-scalability Choosing right solution for the problem The future of Big Data Closing remarks |
| seleniumpython | Selenium with Python for test automation | 14 godz. | Selenium is an open source library for automating web application testing across multiple browsers. Selenium interacts with a browser as people do: by clicking links, filling out forms and validating text. It is the most popular tool for web application test automation. Selenium is built on the WebDriver framework and has excellent bindings for numerous scripting languages, including Python. In this training participants combine the power of Python with Selenium to automate the testing of a sample web application. By combining theory with practice in a live lab environment, participants will gain the knowledge and practice needed to automate their own web testing projects using Python and Selenium. Audience Testers and Developers Format of the course Part lecture, part discussion, heavy hands-on practice Introduction to Selenium with Python Python vs Java for writing test scripts Installation and setup Selecting a Python IDE or editor Overview of Selenium architecture Selenium IDE Selenium WebDriver Selenium Grid Python scripting essentials for test automation Working with Selenium Webdriver The anatomy of a web application Locating page elements through Page Objects Creating a unit test Accessing a database Developing a test framework Running test suites against multiple browsers Working with SeleniumGrid Troubleshooting Closing remarks |
| pythonautomation | Python: Automate the boring stuff | 14 godz. | This instructor-led training is based on the popular book, "Automate the Boring Stuff with Python", by Al Sweigart. It is aimed at beginners and covers essential Python programming concepts through practical, hands-on exercises and discussions. The focus is on learning to write code to dramatically increase office productivity. By the end of this training, participants will know how to program in Python and apply this new skill for: Automating tasks by writing simple Python programs. Writing programs that can do text pattern recognition with "regular expressions". Programmatically generating and updating Excel spreadsheets. Parsing PDFs and Word documents. Crawling web sites and pulling information from online sources. Writing programs that send out email notifications. Use Python's debugging tools to quickly resolve bugs. Programmatically controlling the mouse and keyboard to click and type for you. Audience Non-programmers wishing to learn programming with Python Professionals and company teams wishing to optimize their office productivity Managers wishing to automate tedious processes and workflows Format of the course Part lecture, part discussion, exercises and heavy hands-on practice Introduction to Python Controlling the flow of your program Working with lists Working with the dictionary data type Manipulating strings Pattern matching with regular expressions Reading, writing and managing files Debugging your code Pulling information from the internet (web scraping) Working with Excel, Word, and PDF Documents Working with CSV and JSON Keeping time Scheduling tasks Launching programs Sending emails and other messages Manipulating images GUI Automation Closing remarks |
| kivy | Kivy: Building Android Apps with Python | 7 godz. | Kivy is an open-source cross-platform graphical user interface library written in Python, which allows multi-touch application development for a wide selection of devices. In this instructor-led, live training participants will learn how to install and deploy Kivy on different platforms, customize and manipulate widgets, schedule, trigger and respond to events, modify graphics with multi-touching, resize the screen, package apps for Android, and more. By the end of this training, participants will be able to Relate the Python code and the Kivy language Have a solid understanding of how Kivy works and makes use of its most important elements such as, widgets, events, properties, graphics, etc. Seamlessly develop and deploy Android apps based on different business and design requirements Audience Programmers or developers with Python knowledge who want to develop multi-touch Android apps using the Kivy framework Android developers with Python knowledge Format of the course Part lecture, part discussion, exercises and heavy hands-on practice To request a customized course outline for this training, please contact us. |
| pythonmultipurpose | Advanced Python | 28 godz. | In this instructor-led training, participants will learn advanced Python programming techniques, including how to apply this versatile language to solve problems in areas such as distributed applications, finance, data analysis and visualization, UI programming and maintenance scripting. Audience Developers Format of the course Part lecture, part discussion, exercises and heavy hands-on practice Notes If you wish to add, remove or customize any section or topic within this course, please contact us to arrange. Introduction Python versatility: from data analysis to web crawling Python data structures and operations Integers and floats Strings and bytes Tuples and lists Dictionaries and ordered dictionaries Sets and frozen sets Data frame (pandas) Conversions Object-oriented programming with Python Inheritance Polymorphism Static classes Static functions Decorators Other Data Analysis with pandas Data cleaning Using vectorized data in pandas Data wrangling Sorting and filtering data Aggregate operations Analyzing time series Data visualization Plotting diagrams with matplotlib Using matplotlib from within pandas Creating quality diagrams Visualizing data in Jupyter notebooks Other visualization libraries in Python Vectorizing Data in Numpy Creating Numpy arrays Common operations on matrices Using ufuncs Views and broadcasting on Numpy arrays Optimizing performance by avoiding loops Optimizing performance with cProfile Processing Big Data with Python Building and supporting distributed applications with Python Data storage: Working with SQL and NoSQL databases Distributed processing with Hadoop and Spark Scaling your applications Python for finance Packages, libraries and APIs for financial processing Zipline PyAlgoTrade Pybacktest quantlib Python APIs Extending Python (and vice versa) with other languages C# Java C++ Perl Others Python multi-threaded programming Modules Synchronizing Prioritizing UI programming with Python Framework options for building GUIs in Python Tkinter Pyqt Python for maintenance scripting Raising and catching exceptions correctly Organizing code into modules and packages Understanding symbol tables and accessing them in code Picking a testing framework and applying TDD in Python Python for the web Packages for web processing Web crawling Parsing HTML and XML Filling web forms automatically Closing remarks |
| progbio | Programming for Biologists | 28 godz. | This is a practical course, which shows why programming is a powerful tool in the context of solving biological problems. During the course participants will be taught the Python programming language, a language widely considered both powerful as well as easy to use. This course might be considered as a demonstration how bioinformatics improves biologists lives. The course is designed and aimed for people without computer science background who want to learn programming. This course is suited for: Researchers dealing with biological data. Scientists who would like to learn how to automate everyday tasks and analyse data. Managers who want to learn how programming improves workflows and conducting projects. By the end of the course, participants will be able to write short programs, which will allow them to manipulate, analyse and deal with biological data and present results in a graphical format. Introduction to the Python programming language Why Python? Using Python to deal with biological data Working with the iPython shell Your first programme Writing Python scripts Importing modules Working with protein and RNA/DNA sequences Finding motives Transcription and translation in silico Handling sequence alignments Parsing data in different biological formats Parsing FASTA Data format conversions Running biological analyses BLAST Accessing biological web services Dealing with biological 3D structures using Python Python facilitates statistical analysis Visualizing data Creating bar and scatter plots Calculating an Area Under Curve (AUC) Working with .xls and .csv files Importing data from and exporting to MS Excel / OpenOffice Calc Writing .xls and .csv files Using Python to create an automated data processing pipeline |
| 3627 | Wprowadzenie do programowania | 35 godz. | Celem szkolenia jest przedstawienie podstaw związanych z programowaniem od podstaw składni po powszechne paradygmaty programowania. Szkolenie poparte jest przykładami opartymi o języki programowania takie jak: C, Java, Python, Scala, C#, Closure i JavaScript. Podczas szkolenia uczestnicy zdobywają ogólną wiedzę zarówno ze wzorców programowania, dobrych praktyk, powszechnie stosowanych rozwiązań jak i przegląd realizacji omawianych tematów przez różne platformy. Każde z zagadnień omówionych podczas szkolenia ilustrowane jest przykładami zarówno najbardziej podstawowymi, jak i bardziej zaawansowanymi i opartymi o rzeczywiste problemy. Wprowadzenie Czym jest programowanie i dlaczego warto poświęcić mu uwagę Historia programowania Możliwości automatyzacji zadań za pomocą oprogramowania Rola programisty i komputera w przedsiębiorstwie Programowanie dziś, trendy rozwoju obecnego rynku Programowanie deklaratywne i imperatywne. Jak czy Co? Maszyna Turinga Konsolidacja, kompilacja i interpretowanie "w locie" Przypomnienie zagadnień logiki i algebry Boole'a Predykaty Zdania logiczne Tautologie Algebra Boole'a Pierwszy program Strukturalnie Funkcyjnie Obiektowo I jak jeszcze? Typy proste Reprezentacja ciągów znakowych Liczby całkowite Liczby zmiennoprzecinkowe Typy logiczne Typ Null Wartość pusta czy niezainicjalizowana Silne i słabe typowanie Struktury danych Pojęcia FIFO i FILO Stosy Kolejki Deklaracja tablic i list Indeksowanie Mapy Rekordy Drzewa Operatory Operatory przypisania Operatory arytmetyczne Operatory porównania Porównanie typu i wartości w różnych językach Operatory bitowe Konkatenacja Operatory inkrementacji i dekrementacji Najczęściej popełniane błędy Sterowanie przebiegiem programu Instrukcje if, if else Instrukcja goto, omówienie problemów zastosowania Instrukcja switch Pętla for, for-in Pętla while, do-while Pętla foreach Przerywanie pętli Tworzenie kodu wielokrotnego użycia Programowanie funkcyjne Programowanie obiektowe Paradygmaty programowania funkcyjnego Czym jest funkcja Funkcja a procedura Podstawy rachunku lambda Argumenty funkcji Zwracanie wartości Funkcje jako argumenty Funkcje anonimowe Domknięcia Rekurencja Paradygmaty programowania obiektowego Reprezentacja bytów z realnego świata, byty w filozofii, ontologie i idee, potrzeba objektowości Podejmowanie decyzji co ma być objektem, czyli własne typy Deklaracja klas Tworzenie instancji klas Pola, jako stan obiektu Metody, jako zachowanie obiektu Abstrakcja Hermetyzacja Dziedziczenie Polimorfizm Asocjacja i agregacja Delegacja i rozdzielenie zależności pomiędzy obiektami Moduły, pakiety, biblioteki Udostępnianie API Modelowanie systemu jako klas i obiektów Opisywanie i programowanie relacji pomiędzy klasami Program z perspektywy biznesowej Dobre praktyki programowania Pułapki i najczęściej popełniane błędy Kod wysokopoziomowy w interpretacji niskopoziomowej Optymalizacja kodu Zasada KISS Zasada DRY Zasada Worse is Better Separacja abstrakcji od implementacji Sposoby detekcji błędów logicznych programów Konwencje godowania Komentowanie kodu Metryki oprogramowania Przegląd technologii i omawianych języków Obszar zastosowań omawianych języków Główne cechy języków Perspektywy rozwoju Dalszy kierunek rozwoju: algorytmika, optymalizacja kodu, wzorce implementacyjne, wzorce projektowe, wzorce architektoniczne, wzorce analityczne Redukcja konstrukcji sterujących - zastosowanie algorytmów sztucznej inteligencji i automatyczne podejmowanie decyzji Którą platformę wybrać? Indywidualne konsultacje |
Najbliższe szkolenia
| Szkolenie | Data Kursu | Cena szkolenia [Zdalne / Stacjonarne] |
|---|---|---|
| Machine Learning Fundamentals with Python - Bydgoszcz, ul. Dworcowa 94 | pon., 2017-10-09 09:00 | 8050PLN / 2939PLN |
| Introduction to Programming - Kraków, ul. Rzemieślnicza 1 | pon., 2017-10-16 09:00 | 5800PLN / 4900PLN |
| Machine Learning Fundamentals with Scala and Apache Spark - Częstochowa, ul. Wały Dwernickiego 117/121 | pon., 2017-10-23 09:00 | 16450PLN / 5685PLN |
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