This document provides an introduction to basic Python programming concepts like datatypes, functions, modifying lists and dictionaries, and indentation. It explains that Python uses indentation through spaces or tabs to structure code blocks that are executed repeatedly or under certain conditions. Examples are given for defining functions, appending and merging lists, adding keys to dictionaries, and using indentation with for, if/elif/else, and try/except blocks.
This document discusses last week's coding exercise on data harvesting and storage. It provides step-by-step explanations of code used to extract and analyze data from a JSON file. Examples include printing video titles, calculating average tags per video, determining the most commented on porn category, and finding the most frequently used words. The document also covers APIs, scrapers, file formats like JSON and CSV, and how to store extracted data.
This document provides an overview of using statistics in Python with Pandas. It discusses general considerations for using Python for statistics rather than exporting data to another program. Useful Python packages for statistics like NumPy, SciPy, statsmodels, and matplotlib are introduced. The document demonstrates how to work with Pandas dataframes, including descriptive statistics, plotting, and linear regression. An upcoming exercise will provide hands-on practice of these skills.
Slides for the first meeting of the course 'Big Data and Automated Content Analysis' at the Department of Communication Science, University of Amsterdam
This document discusses a lecture on data harvesting and storage. It covers APIs, RSS feeds, scraping and crawling as methods for collecting data from various sources. It also discusses storing data in formats like CSV, JSON, and XML. The document provides code examples for working with JSON data and discusses tools for long-term data collection like DMI-TCAT.
This document summarizes a presentation on the basics of Python programming. It introduces fundamental Python concepts like datatypes, functions, methods, and indentation-based code structuring. It also announces an exercise for the attendees to practice these basics and previews upcoming meetings that will involve working with structured datasets in Python.
Slides for the course Big Data and Automated Content Analysis, in which students of the social sciences (communication science) learn how to conduct analyses using Python.
This document discusses last week's coding exercise on data harvesting and storage. It provides step-by-step explanations of code used to extract and analyze data from a JSON file. Examples include printing video titles, calculating average tags per video, determining the most commented on porn category, and finding the most frequently used words. The document also covers APIs, scrapers, file formats like JSON and CSV, and how to store extracted data.
This document provides an overview of using statistics in Python with Pandas. It discusses general considerations for using Python for statistics rather than exporting data to another program. Useful Python packages for statistics like NumPy, SciPy, statsmodels, and matplotlib are introduced. The document demonstrates how to work with Pandas dataframes, including descriptive statistics, plotting, and linear regression. An upcoming exercise will provide hands-on practice of these skills.
Slides for the first meeting of the course 'Big Data and Automated Content Analysis' at the Department of Communication Science, University of Amsterdam
This document discusses a lecture on data harvesting and storage. It covers APIs, RSS feeds, scraping and crawling as methods for collecting data from various sources. It also discusses storing data in formats like CSV, JSON, and XML. The document provides code examples for working with JSON data and discusses tools for long-term data collection like DMI-TCAT.
This document summarizes a presentation on the basics of Python programming. It introduces fundamental Python concepts like datatypes, functions, methods, and indentation-based code structuring. It also announces an exercise for the attendees to practice these basics and previews upcoming meetings that will involve working with structured datasets in Python.
Slides for the course Big Data and Automated Content Analysis, in which students of the social sciences (communication science) learn how to conduct analyses using Python.
This document provides an overview of a presentation on automated content analysis using regular expressions and natural language processing. The presentation covers topics like bottom-up vs top-down analysis, what regular expressions are and how they can be used in Python, stemming, parsing sentences, and combining techniques like stemming and stopword removal. Examples are given on using regular expressions to count actors in articles and check the number of a document from LexisNexis. The takeaway message is about an upcoming take-home exam and future meetings.
This document summarizes a presentation on analyzing word co-occurrences in text data using network analysis techniques. It discusses counting the frequency of word combinations, representing the co-occurrence data as a network with nodes for words and edges for co-occurrences, and visualizing the network in Gephi. It also provides an example analysis of tweets about a political debate, examining which topics were emphasized by each candidate based on word associations on Twitter.
The document discusses web scraping and outlines a step-by-step process for scraping comments from a Dutch website called GeenStijl. It begins with using regular expressions to scrape the comments, but notes that existing parsers can make the process more elegant, especially for complex websites. It then demonstrates using the lxml module and XPath to scrape reviews from another site in a more structured way. The document provides remarks on regular expressions and XPath, and encourages exploring different scraping techniques.
The document discusses different types of analysis for automated content analysis, including sentiment analysis and stopword removal. It covers bag-of-words approaches to sentiment analysis, which involve comparing words in a text to lists of positive and negative words. More advanced approaches are mentioned that take the structure of text into account, such as identifying sentence structure using linguistic concepts.
This presentation is a part of the COP2271C college level course taught at the Florida Polytechnic University located in Lakeland Florida. The purpose of this course is to introduce Freshmen students to both the process of software development and to the Python language.
The course is one semester in length and meets for 2 hours twice a week. The Instructor is Dr. Jim Anderson.
A video of Dr. Anderson using these slides is available on YouTube at:
https://youtu.be/MamtCCdLnP4
Slides for the course Big Data and Automated Content Analysis, in which students of the social sciences (communication science) learn how to conduct analyses using Python. Fourth meeting.
The document discusses big data and provides examples of how it can be collected and analyzed. It describes a master's thesis that collected 74,000 Dutch news articles over 2 months to analyze rare content. It also describes a bachelor's thesis that automated the coding of tweets to determine the tone politicians used when referring to opponents. The document outlines the typical process of collecting, storing, and analyzing big data and describes the infrastructure used in the workshop to collect Twitter tweets, news articles, and web snapshots.
This presentation is a part of the COP2271C college level course taught at the Florida Polytechnic University located in Lakeland Florida. The purpose of this course is to introduce Freshmen students to both the process of software development and to the Python language.
The course is one semester in length and meets for 2 hours twice a week. The Instructor is Dr. Jim Anderson.
A video of Dr. Anderson using these slides is available on YouTube at:
https://www.youtube.com/watch?v=AOchqjVB_1o
http://youtu.be/4EaTej-aH0M
This document provides an overview of the curriculum for a Python with AI course. The 8 sessions cover Python basics like conditionals, loops, operators and data structures. Sessions also focus on REST APIs, data visualization, connecting multiple AIs, and final projects. Key concepts taught include printing output, taking user input, for/while loops, writing to and appending files, lists, dictionaries, functions, and using external modules like NumPy and Pandas.
Text Analysis: Latent Topics and Annotated DocumentsNelson Auner
This document describes a cluster model for combining latent topics with document attributes in text analysis. It introduces topic models and describes how metadata can be incorporated. The model restricts each document to one topic to allow collapsing observations. An algorithm is provided and applied to congressional speech and restaurant review data. Results show the model can recover topics similarly to topic models, while also capturing variation explained by metadata like political affiliation or review rating.
This document discusses Python data types and handling data in Python. It covers the different types of numbers in Python including integers, floating point numbers, and complex numbers. It also discusses Boolean, string, and tuple data types. Methods for accessing and slicing strings are provided along with examples. Mutable and immutable objects are defined, with lists and tuples given as examples. Operator precedence in Python is also discussed.
Slides for Lecture 3 of the course: Introduction to Programming with Python offered at ICCBS.
It covers the following topics:
Strings useful string operations.
Introduction To Programming with PythonSushant Mane
The document provides an introduction to the Python programming language. It discusses Python's core features like being an interpreted, object-oriented, and dynamic language. It covers basic Python concepts like data types, variables, operators, control flow, functions, modules, file handling, and object-oriented programming. The document contains examples and explanations of built-in types like numbers, strings, lists, tuples, and dictionaries. It also discusses control structures, functions, modules, and classes in Python.
In this section, we will see advanced concepts related to Python. We will introduce you to different types of data structures, like: lists, tuples, and dictionaries.
We will also define an important concept in programming which is control flow statements. We will show how to use conditional and repetitive statements.
Finally, we will talk about different concepts of object oriented programming (beside other concepts), and how to implement them in python.
[Notebook link] (https://drive.google.com/file/d/11AjOGxmhz-YOHVVqDVQVlpjVJMrSrDn2/view?usp=drivesdk)
The document discusses different approaches to meta-learning, or learning to learn. It begins by explaining how humans are able to learn new tasks more quickly by leveraging prior knowledge from similar tasks. It then covers three main approaches to meta-learning for machine learning models: 1) Starting with what generally works based on previous task performance data, 2) Starting from what is most likely to work for similar tasks based on task meta-features, and 3) Starting from previously trained models on very similar tasks via transfer learning. The document dives into various techniques within each of these three approaches, such as warm-starting optimization searches, learning task embeddings, and few-shot learning.
This document introduces Naive Bayes classification. It discusses using Bayes' rule to calculate the probability of an email being spam given the presence of a word. An example is worked out classifying an email as spam or ham based on the word "meeting". The document then expands on this to consider multiple words using a naive Bayes model that treats words as independent predictors. It notes that wrangling data is important and discusses extracting and classifying articles using Naive Bayes as an example.
This document outlines an introductory Python programming course, including discussions of:
1. Python data types like integers, floats, booleans, and strings.
2. Python structures like lists, dictionaries, functions, and methods.
3. Using indentation in Python to structure programs with blocks like for loops and if/else statements.
4. An upcoming class will cover data harvesting, storage formats, APIs, scrapers, databases, and an assigned reading.
Slides for the second meeting of the course 'Big Data and Automated Content Analysis' at the Department of Communication Science, University of Amsterdam
This document provides a cheat sheet for Python basics. It begins with an introduction to Python and its advantages. It then covers key Python data types like strings, integers, floats, lists, tuples, and dictionaries. It explains how to define variables, functions, conditional statements, and loops. The document also demonstrates built-in functions, methods for manipulating common data structures, and other Python programming concepts in a concise and easy to understand manner.
Presentation on using the Arrow library for enhanced Functional Programming in the Kotlin Language. Delivered at the Northern Ireland Developer Conference 2018.
The document provides an introduction to Python programming. It discusses installing and running Python, basic Python syntax like variables, data types, conditionals, and functions. It emphasizes that Python uses references rather than copying values, so assigning one variable to another causes both to refer to the same object.
This document provides an overview of a presentation on automated content analysis using regular expressions and natural language processing. The presentation covers topics like bottom-up vs top-down analysis, what regular expressions are and how they can be used in Python, stemming, parsing sentences, and combining techniques like stemming and stopword removal. Examples are given on using regular expressions to count actors in articles and check the number of a document from LexisNexis. The takeaway message is about an upcoming take-home exam and future meetings.
This document summarizes a presentation on analyzing word co-occurrences in text data using network analysis techniques. It discusses counting the frequency of word combinations, representing the co-occurrence data as a network with nodes for words and edges for co-occurrences, and visualizing the network in Gephi. It also provides an example analysis of tweets about a political debate, examining which topics were emphasized by each candidate based on word associations on Twitter.
The document discusses web scraping and outlines a step-by-step process for scraping comments from a Dutch website called GeenStijl. It begins with using regular expressions to scrape the comments, but notes that existing parsers can make the process more elegant, especially for complex websites. It then demonstrates using the lxml module and XPath to scrape reviews from another site in a more structured way. The document provides remarks on regular expressions and XPath, and encourages exploring different scraping techniques.
The document discusses different types of analysis for automated content analysis, including sentiment analysis and stopword removal. It covers bag-of-words approaches to sentiment analysis, which involve comparing words in a text to lists of positive and negative words. More advanced approaches are mentioned that take the structure of text into account, such as identifying sentence structure using linguistic concepts.
This presentation is a part of the COP2271C college level course taught at the Florida Polytechnic University located in Lakeland Florida. The purpose of this course is to introduce Freshmen students to both the process of software development and to the Python language.
The course is one semester in length and meets for 2 hours twice a week. The Instructor is Dr. Jim Anderson.
A video of Dr. Anderson using these slides is available on YouTube at:
https://youtu.be/MamtCCdLnP4
Slides for the course Big Data and Automated Content Analysis, in which students of the social sciences (communication science) learn how to conduct analyses using Python. Fourth meeting.
The document discusses big data and provides examples of how it can be collected and analyzed. It describes a master's thesis that collected 74,000 Dutch news articles over 2 months to analyze rare content. It also describes a bachelor's thesis that automated the coding of tweets to determine the tone politicians used when referring to opponents. The document outlines the typical process of collecting, storing, and analyzing big data and describes the infrastructure used in the workshop to collect Twitter tweets, news articles, and web snapshots.
This presentation is a part of the COP2271C college level course taught at the Florida Polytechnic University located in Lakeland Florida. The purpose of this course is to introduce Freshmen students to both the process of software development and to the Python language.
The course is one semester in length and meets for 2 hours twice a week. The Instructor is Dr. Jim Anderson.
A video of Dr. Anderson using these slides is available on YouTube at:
https://www.youtube.com/watch?v=AOchqjVB_1o
http://youtu.be/4EaTej-aH0M
This document provides an overview of the curriculum for a Python with AI course. The 8 sessions cover Python basics like conditionals, loops, operators and data structures. Sessions also focus on REST APIs, data visualization, connecting multiple AIs, and final projects. Key concepts taught include printing output, taking user input, for/while loops, writing to and appending files, lists, dictionaries, functions, and using external modules like NumPy and Pandas.
Text Analysis: Latent Topics and Annotated DocumentsNelson Auner
This document describes a cluster model for combining latent topics with document attributes in text analysis. It introduces topic models and describes how metadata can be incorporated. The model restricts each document to one topic to allow collapsing observations. An algorithm is provided and applied to congressional speech and restaurant review data. Results show the model can recover topics similarly to topic models, while also capturing variation explained by metadata like political affiliation or review rating.
This document discusses Python data types and handling data in Python. It covers the different types of numbers in Python including integers, floating point numbers, and complex numbers. It also discusses Boolean, string, and tuple data types. Methods for accessing and slicing strings are provided along with examples. Mutable and immutable objects are defined, with lists and tuples given as examples. Operator precedence in Python is also discussed.
Slides for Lecture 3 of the course: Introduction to Programming with Python offered at ICCBS.
It covers the following topics:
Strings useful string operations.
Introduction To Programming with PythonSushant Mane
The document provides an introduction to the Python programming language. It discusses Python's core features like being an interpreted, object-oriented, and dynamic language. It covers basic Python concepts like data types, variables, operators, control flow, functions, modules, file handling, and object-oriented programming. The document contains examples and explanations of built-in types like numbers, strings, lists, tuples, and dictionaries. It also discusses control structures, functions, modules, and classes in Python.
In this section, we will see advanced concepts related to Python. We will introduce you to different types of data structures, like: lists, tuples, and dictionaries.
We will also define an important concept in programming which is control flow statements. We will show how to use conditional and repetitive statements.
Finally, we will talk about different concepts of object oriented programming (beside other concepts), and how to implement them in python.
[Notebook link] (https://drive.google.com/file/d/11AjOGxmhz-YOHVVqDVQVlpjVJMrSrDn2/view?usp=drivesdk)
The document discusses different approaches to meta-learning, or learning to learn. It begins by explaining how humans are able to learn new tasks more quickly by leveraging prior knowledge from similar tasks. It then covers three main approaches to meta-learning for machine learning models: 1) Starting with what generally works based on previous task performance data, 2) Starting from what is most likely to work for similar tasks based on task meta-features, and 3) Starting from previously trained models on very similar tasks via transfer learning. The document dives into various techniques within each of these three approaches, such as warm-starting optimization searches, learning task embeddings, and few-shot learning.
This document introduces Naive Bayes classification. It discusses using Bayes' rule to calculate the probability of an email being spam given the presence of a word. An example is worked out classifying an email as spam or ham based on the word "meeting". The document then expands on this to consider multiple words using a naive Bayes model that treats words as independent predictors. It notes that wrangling data is important and discusses extracting and classifying articles using Naive Bayes as an example.
This document outlines an introductory Python programming course, including discussions of:
1. Python data types like integers, floats, booleans, and strings.
2. Python structures like lists, dictionaries, functions, and methods.
3. Using indentation in Python to structure programs with blocks like for loops and if/else statements.
4. An upcoming class will cover data harvesting, storage formats, APIs, scrapers, databases, and an assigned reading.
Slides for the second meeting of the course 'Big Data and Automated Content Analysis' at the Department of Communication Science, University of Amsterdam
This document provides a cheat sheet for Python basics. It begins with an introduction to Python and its advantages. It then covers key Python data types like strings, integers, floats, lists, tuples, and dictionaries. It explains how to define variables, functions, conditional statements, and loops. The document also demonstrates built-in functions, methods for manipulating common data structures, and other Python programming concepts in a concise and easy to understand manner.
Presentation on using the Arrow library for enhanced Functional Programming in the Kotlin Language. Delivered at the Northern Ireland Developer Conference 2018.
The document provides an introduction to Python programming. It discusses installing and running Python, basic Python syntax like variables, data types, conditionals, and functions. It emphasizes that Python uses references rather than copying values, so assigning one variable to another causes both to refer to the same object.
The document provides an introduction to programming in Python. It discusses how Python can be used for web development, desktop applications, data science, machine learning, and more. It also covers executing Python programs, reading keyboard input, decision making and loops in Python, standard data types like numbers, strings, lists, tuples and dictionaries. Additionally, it describes functions, opening and reading/writing files, regular expressions, and provides examples of SQLite database connections in Python projects.
This presentation about Python Interview Questions will help you crack your next Python interview with ease. The video includes interview questions on Numbers, lists, tuples, arrays, functions, regular expressions, strings, and files. We also look into concepts such as multithreading, deep copy, and shallow copy, pickling and unpickling. This video also covers Python libraries such as matplotlib, pandas, numpy,scikit and the programming paradigms followed by Python. It also covers Python library interview questions, libraries such as matplotlib, pandas, numpy and scikit. This video is ideal for both beginners as well as experienced professionals who are appearing for Python programming job interviews. Learn what are the most important Python interview questions and answers and know what will set you apart in the interview process.
Simplilearn’s Python Training Course is an all-inclusive program that will introduce you to the Python development language and expose you to the essentials of object-oriented programming, web development with Django and game development. Python has surpassed Java as the top language used to introduce U.S. students to programming and computer science. This course will give you hands-on development experience and prepare you for a career as a professional Python programmer.
What is this course about?
The All-in-One Python course enables you to become a professional Python programmer. Any aspiring programmer can learn Python from the basics and go on to master web development & game development in Python. Gain hands on experience creating a flappy bird game clone & website functionalities in Python.
What are the course objectives?
By the end of this online Python training course, you will be able to:
1. Internalize the concepts & constructs of Python
2. Learn to create your own Python programs
3. Master Python Django & advanced web development in Python
4. Master PyGame & game development in Python
5. Create a flappy bird game clone
The Python training course is recommended for:
1. Any aspiring programmer can take up this bundle to master Python
2. Any aspiring web developer or game developer can take up this bundle to meet their training needs
Learn more at https://www.simplilearn.com/mobile-and-software-development/python-development-training
Write better python code with these 10 tricks | by yong cui, ph.d. | aug, 202...amit kuraria
This document provides a summary of techniques for coding in a Pythonic way. It discusses negative indexing in Python to access elements from the end of a sequence, using the length of a container to check if it is empty, creating lists of strings using the split() method, using ternary expressions for conditional assignments, opening files using the with statement for automatic closing, chaining comparisons and using the in keyword to test for multiple equality conditions. The document provides examples of each technique and explains how they make for more concise and Pythonic code.
James Jesus Bermas on Crash Course on PythonCP-Union
This document provides an overview of the Python programming language. It introduces Python, discusses its uses in industries like Google and Industrial Light & Magic, and covers key Python concepts like data types, functions, object-oriented programming, modules, and tools. The document is intended to explain what Python is and give an introduction to programming in Python.
Slides for the first meeting of the course 'Big Data and Automated Content Analysis' at the Department of Communication Science, University of Amsterdam
A Gentle Introduction to Coding ... with PythonTariq Rashid
A gentle introduction to coding (programming) for complete beginners. Starting from then basics - electrical wires - proceeding through variables, data structures, loops, functions, and exploring libraries for visualisation and specialist tools. Finally we use flask to make a very simple twitter clone web application.
This document provides a summary of Python concepts including:
1. Python is an interpreted, object-oriented, and high-level programming language with features like being easy to read, productive, portable and having a big library.
2. Key Python concepts covered include variables, data types, objects, lists, dictionaries, tuples, control structures, functions and files.
3. The document uses examples and explanations to introduce Python building blocks like variables, data types, lists, dictionaries, control flow and functions. It also discusses how Python interacts with files.
This document provides an overview of learning Python in three hours. It covers Python's history, installing and running Python, basic data types like integers, floats and strings. It also discusses sequence types like lists, tuples and strings, and how lists are mutable while tuples are immutable. The document includes examples of basic syntax like assignment, conditionals, functions and modules. It provides guidance on naming conventions and discusses the Python interpreter, editors and development environments.
The Ring programming language version 1.5.4 book - Part 6 of 185Mahmoud Samir Fayed
This document provides an overview of the Ring programming language. Key features include native object-oriented support with encapsulation, inheritance, polymorphism and composition. It also supports reflection, exception handling, runtime code evaluation, I/O, math functions, strings, lists, files, databases, security, internet, zip, and CGI functionality. The language aims to have clear structure, be compact, encourage organization, and support both procedural and object-oriented paradigms. It can be used to create applications, libraries, games and more.
This document provides an overview of machine learning applications in natural language processing and text classification. It discusses common machine learning tasks like part-of-speech tagging, named entity extraction, and text classification. Popular machine learning algorithms for classification are described, including k-nearest neighbors, Rocchio classification, support vector machines, bagging, and boosting. The document argues that machine learning can be used to solve complex real-world problems and that text processing is one area with many potential applications of these techniques.
The document provides an overview of the Python programming language. It discusses Python's history, how to install and run Python, basic data types like integers, floats, strings, lists and tuples. It explains key Python concepts like variable assignment, conditional statements, functions, modules and packages. The document also compares mutable lists and immutable tuples, and covers common list operations.
The Ring programming language version 1.5.3 book - Part 6 of 184Mahmoud Samir Fayed
- Ring is a simple, dynamically typed scripting language designed for productivity. It aims to have clear program structure and encourage organization.
- Key features include object-oriented support, reflection, exception handling, math/string/file functions, and embedding capabilities. It can be used to create applications, games, and declarative domain-specific languages.
- The language focuses on transparency - its implementation and each compiler stage can be clearly seen. It also aims to have natural, minimal syntax and encourage nesting and organization of code.
The document discusses several key points about Python:
1. It summarizes praise for Python from programmers and companies like Google, NASA, and CCP Games, highlighting Python's simplicity, compactness, and ability to quickly develop applications.
2. It introduces common Python concepts like strings, lists, sequences, namespaces, polymorphism, and duck typing. Strings can be manipulated using slicing and methods. Lists and other sequences support indexing, slicing, and iteration.
3. Python uses name-based rather than type-based polymorphism through duck typing - an object's capabilities are defined by its methods and properties rather than its class.
These questions will be a bit advanced level 2sadhana312471
These questions will be a bit advanced(Intermediate) in terms of Python interview.
This is the continuity of Nail the Python Interview Questions.
The fields that these questions will help you in are:
• Python Developer
• Data Analyst
• Research Analyst
• Data Scientist
The document discusses techniques for scaling up automated content analysis projects. It begins by looking back at the workflow and techniques covered in previous sessions, such as developing components separately, writing functions, and making the code robust. It then looks forward by discussing additional techniques that were not covered, such as using Selenium for dynamic web scraping, databases for storing large datasets, word embeddings, and more advanced natural language processing and machine learning models. The document also introduces the INCA project, which aims to scale up content analysis by collecting data in a way that allows for reuse across multiple projects, using a database backend and reusable preprocessing and analysis code. The goal is to make automated content analysis usable with minimal Python knowledge.
This document provides a summary of a meeting on machine learning. It recaps unsupervised and supervised machine learning techniques. Unsupervised techniques discussed include principal component analysis (PCA) and latent Dirichlet allocation (LDA). PCA is used to find how words co-occur in documents. LDA can be implemented in Python using gensim to infer topics in a collection of documents. Supervised machine learning techniques the audience has previously used are regression models. The document concludes by noting models will only use a portion of available data for training and validation.
This document outlines an introductory course on big data and automated content analysis. It covers using the Linux command line, writing and running Python code, and announces upcoming meetings. The course will introduce tools like the Linux terminal and Python, explain why they are useful for big data tasks, and provide exercises for students to practice these skills, such as writing simple Python programs. Upcoming meetings are scheduled for weeks 2 and 3 to continue lectures and lab sessions on using Python.
This document provides an overview of a course on Big Data and Automated Content Analysis. It introduces the instructor, Damian Trilling, and a PhD student, Joanna Strycharz. It then discusses definitions of Big Data, implications and criticisms, and whether the techniques used in the course constitute Big Data research. Next, it outlines methods that will be covered, including data collection, analysis techniques, and the programming language Python. Finally, it discusses reasons for building one's own tools rather than using commercial software.
This document summarizes a presentation on unsupervised and supervised machine learning techniques for automated content analysis. It recaps types of automated content analysis, describes unsupervised techniques like principal component analysis (PCA) and latent Dirichlet allocation (LDA), and supervised machine learning techniques like regression. It provides examples of applying these techniques to cluster Facebook messages and predict newspaper reading. The document concludes by noting the presenter will use a portion of labeled data to estimate models and check predictions against the remaining labeled data.
1) Traditional assumptions about how people consume news through a fixed set of outlets are incorrect in today's fragmented media environment.
2) News consumption involves three layers - media type, individual outlets, and the gateway through which people access the outlet (e.g. website, app, social media).
3) Researchers need to account for this third layer when studying people's news repertoires to fully capture how news flows in the digital age.
This document proposes conceptualizing and measuring news exposure as a network of users and news items. It outlines some common assumptions about news consumption that are outdated, such as people using a fixed set of news outlets. The document then presents a model where news items and users are represented as nodes, and their connections as edges. For example, an edge between a user and news item would indicate the user has read that item. Implementing this model involves collecting data on users, news items, and their connections to build a graph database that can be used to analyze news diffusion and exposure. This network approach is presented as an improved way to measure individual-level exposure to specific news items in today's unbundled media environment.
This document summarizes a presentation on analyzing political communication data from Twitter. It discusses analyzing the structure of Twitter data by examining things like retweet networks and interaction patterns, versus analyzing the content of tweets by looking at topics, sentiments, and word frequencies. It provides examples of studies that take both structural and content-based approaches. Specifically, it examines studies that analyzed how Twitter discussions relate to televised political debates and who engages in uncivil language online. The presentation concludes that the most insightful approach is often to combine structural and content-based analyses.
This document introduces the topic of studying social media in political communication. It discusses how social media may contribute to selective exposure, fragmentation, and polarization due to people selectively exposing themselves to ideologically congruent content. It also examines how politicians are using social media to directly communicate with citizens. The case study aims to leverage computational social science methods and large-scale data analysis to provide new insights into these topics. Students will work on a project analyzing political content on social media over the course of the case study.
Slides for the sixth meeting of the course 'Big Data and Automated Content Analysis' at the Department of Communication Science, University of Amsterdam
Slides for the first meeting of the course 'Big Data and Automated Content Analysis' at the Department of Communication Science, University of Amsterdam
Slides for the first meeting of the course 'Big Data and Automated Content Analysis' at the Department of Communication Science, University of Amsterdam
This document summarizes research on filter bubbles and personalized communication. It finds that while people do engage in some self-selected personalization of news sources, exposure to diverse opinions through traditional and social media is still common. Personalization appears to have small effects on polarization and political learning, but its long term impacts are uncertain due to changing media consumption patterns. Overall, the evidence does not strongly support concerns about "filter bubbles" isolating the public sphere.
More from Department of Communication Science, University of Amsterdam (17)
Strategies for Effective Upskilling is a presentation by Chinwendu Peace in a Your Skill Boost Masterclass organisation by the Excellence Foundation for South Sudan on 08th and 09th June 2024 from 1 PM to 3 PM on each day.
How to Fix the Import Error in the Odoo 17Celine George
An import error occurs when a program fails to import a module or library, disrupting its execution. In languages like Python, this issue arises when the specified module cannot be found or accessed, hindering the program's functionality. Resolving import errors is crucial for maintaining smooth software operation and uninterrupted development processes.
LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UPRAHUL
This Dissertation explores the particular circumstances of Mirzapur, a region located in the
core of India. Mirzapur, with its varied terrains and abundant biodiversity, offers an optimal
environment for investigating the changes in vegetation cover dynamics. Our study utilizes
advanced technologies such as GIS (Geographic Information Systems) and Remote sensing to
analyze the transformations that have taken place over the course of a decade.
The complex relationship between human activities and the environment has been the focus
of extensive research and worry. As the global community grapples with swift urbanization,
population expansion, and economic progress, the effects on natural ecosystems are becoming
more evident. A crucial element of this impact is the alteration of vegetation cover, which plays a
significant role in maintaining the ecological equilibrium of our planet.Land serves as the foundation for all human activities and provides the necessary materials for
these activities. As the most crucial natural resource, its utilization by humans results in different
'Land uses,' which are determined by both human activities and the physical characteristics of the
land.
The utilization of land is impacted by human needs and environmental factors. In countries
like India, rapid population growth and the emphasis on extensive resource exploitation can lead
to significant land degradation, adversely affecting the region's land cover.
Therefore, human intervention has significantly influenced land use patterns over many
centuries, evolving its structure over time and space. In the present era, these changes have
accelerated due to factors such as agriculture and urbanization. Information regarding land use and
cover is essential for various planning and management tasks related to the Earth's surface,
providing crucial environmental data for scientific, resource management, policy purposes, and
diverse human activities.
Accurate understanding of land use and cover is imperative for the development planning
of any area. Consequently, a wide range of professionals, including earth system scientists, land
and water managers, and urban planners, are interested in obtaining data on land use and cover
changes, conversion trends, and other related patterns. The spatial dimensions of land use and
cover support policymakers and scientists in making well-informed decisions, as alterations in
these patterns indicate shifts in economic and social conditions. Monitoring such changes with the
help of Advanced technologies like Remote Sensing and Geographic Information Systems is
crucial for coordinated efforts across different administrative levels. Advanced technologies like
Remote Sensing and Geographic Information Systems
9
Changes in vegetation cover refer to variations in the distribution, composition, and overall
structure of plant communities across different temporal and spatial scales. These changes can
occur natural.
How to Make a Field Mandatory in Odoo 17Celine George
In Odoo, making a field required can be done through both Python code and XML views. When you set the required attribute to True in Python code, it makes the field required across all views where it's used. Conversely, when you set the required attribute in XML views, it makes the field required only in the context of that particular view.
This presentation includes basic of PCOS their pathology and treatment and also Ayurveda correlation of PCOS and Ayurvedic line of treatment mentioned in classics.
A workshop hosted by the South African Journal of Science aimed at postgraduate students and early career researchers with little or no experience in writing and publishing journal articles.
Walmart Business+ and Spark Good for Nonprofits.pdfTechSoup
"Learn about all the ways Walmart supports nonprofit organizations.
You will hear from Liz Willett, the Head of Nonprofits, and hear about what Walmart is doing to help nonprofits, including Walmart Business and Spark Good. Walmart Business+ is a new offer for nonprofits that offers discounts and also streamlines nonprofits order and expense tracking, saving time and money.
The webinar may also give some examples on how nonprofits can best leverage Walmart Business+.
The event will cover the following::
Walmart Business + (https://business.walmart.com/plus) is a new shopping experience for nonprofits, schools, and local business customers that connects an exclusive online shopping experience to stores. Benefits include free delivery and shipping, a 'Spend Analytics” feature, special discounts, deals and tax-exempt shopping.
Special TechSoup offer for a free 180 days membership, and up to $150 in discounts on eligible orders.
Spark Good (walmart.com/sparkgood) is a charitable platform that enables nonprofits to receive donations directly from customers and associates.
Answers about how you can do more with Walmart!"
हिंदी वर्णमाला पीपीटी, hindi alphabet PPT presentation, hindi varnamala PPT, Hindi Varnamala pdf, हिंदी स्वर, हिंदी व्यंजन, sikhiye hindi varnmala, dr. mulla adam ali, hindi language and literature, hindi alphabet with drawing, hindi alphabet pdf, hindi varnamala for childrens, hindi language, hindi varnamala practice for kids, https://www.drmullaadamali.com
বাংলাদেশের অর্থনৈতিক সমীক্ষা ২০২৪ [Bangladesh Economic Review 2024 Bangla.pdf] কম্পিউটার , ট্যাব ও স্মার্ট ফোন ভার্সন সহ সম্পূর্ণ বাংলা ই-বুক বা pdf বই " সুচিপত্র ...বুকমার্ক মেনু 🔖 ও হাইপার লিংক মেনু 📝👆 যুক্ত ..
আমাদের সবার জন্য খুব খুব গুরুত্বপূর্ণ একটি বই ..বিসিএস, ব্যাংক, ইউনিভার্সিটি ভর্তি ও যে কোন প্রতিযোগিতা মূলক পরীক্ষার জন্য এর খুব ইম্পরট্যান্ট একটি বিষয় ...তাছাড়া বাংলাদেশের সাম্প্রতিক যে কোন ডাটা বা তথ্য এই বইতে পাবেন ...
তাই একজন নাগরিক হিসাবে এই তথ্য গুলো আপনার জানা প্রয়োজন ...।
বিসিএস ও ব্যাংক এর লিখিত পরীক্ষা ...+এছাড়া মাধ্যমিক ও উচ্চমাধ্যমিকের স্টুডেন্টদের জন্য অনেক কাজে আসবে ...
Natural birth techniques - Mrs.Akanksha Trivedi Rama University
BDACA - Lecture2
1. You are encouraged to start up
a Python environment (like
Spyder or Jupyter Notebook). If
you do so, you can try out the
examples while listening. If you prefer
to listen only, that’s fine as well.
2. Basics Exercise Next meetings
Big Data and Automated Content Analysis
Week 2 – Monday
»Getting started with Python«
Damian Trilling
d.c.trilling@uva.nl
@damian0604
www.damiantrilling.net
Afdeling Communicatiewetenschap
Universiteit van Amsterdam
12 February 2018
Big Data and Automated Content Analysis Damian Trilling
3. Basics Exercise Next meetings
Today
1 The very, very, basics of programming with Python
Datatypes
Functions and methods
Modifying lists and dictionaries
Indention: The Python way of structuring your program
2 Exercise
3 Next meetings
Big Data and Automated Content Analysis Damian Trilling
5. Basics Exercise Next meetings
Datatypes
Python lingo
Basic datatypes (variables)
int 32
float 1.75
bool True, False
string "Damian"
Big Data and Automated Content Analysis Damian Trilling
6. Basics Exercise Next meetings
Datatypes
Python lingo
Basic datatypes (variables)
int 32
float 1.75
bool True, False
string "Damian"
(variable name firstname)
"firstname" and firstname is not the same.
Big Data and Automated Content Analysis Damian Trilling
7. Basics Exercise Next meetings
Datatypes
Python lingo
Basic datatypes (variables)
int 32
float 1.75
bool True, False
string "Damian"
(variable name firstname)
"firstname" and firstname is not the same.
"5" and 5 is not the same.
But you can transform it: int("5") will return 5.
You cannot calculate 3 * "5" (In fact, you can. It’s "555").
But you can calculate 3 * int("5")
Big Data and Automated Content Analysis Damian Trilling
8. Basics Exercise Next meetings
Datatypes
Python lingo
More advanced datatypes
Note that the elements of a list, the keys of a dict, and the values
of a dict can have any datatype! (Better to be consistent, though!)
Big Data and Automated Content Analysis Damian Trilling
9. Basics Exercise Next meetings
Datatypes
Python lingo
More advanced datatypes
list firstnames = [’Damian’,’Lori’,’Bjoern’]
lastnames =
[’Trilling’,’Meester’,’Burscher’]
Note that the elements of a list, the keys of a dict, and the values
of a dict can have any datatype! (Better to be consistent, though!)
Big Data and Automated Content Analysis Damian Trilling
10. Basics Exercise Next meetings
Datatypes
Python lingo
More advanced datatypes
list firstnames = [’Damian’,’Lori’,’Bjoern’]
lastnames =
[’Trilling’,’Meester’,’Burscher’]
list ages = [18,22,45,23]
Note that the elements of a list, the keys of a dict, and the values
of a dict can have any datatype! (Better to be consistent, though!)
Big Data and Automated Content Analysis Damian Trilling
11. Basics Exercise Next meetings
Datatypes
Python lingo
More advanced datatypes
list firstnames = [’Damian’,’Lori’,’Bjoern’]
lastnames =
[’Trilling’,’Meester’,’Burscher’]
list ages = [18,22,45,23]
dict familynames= {’Bjoern’: ’Burscher’,
’Damian’: ’Trilling’, ’Lori’: ’Meester’}
dict {’Bjoern’: 26, ’Damian’: 31, ’Lori’:
25}
Note that the elements of a list, the keys of a dict, and the values
of a dict can have any datatype! (Better to be consistent, though!)
Big Data and Automated Content Analysis Damian Trilling
12. Basics Exercise Next meetings
Functions and methods
Python lingo
Functions
Big Data and Automated Content Analysis Damian Trilling
13. Basics Exercise Next meetings
Functions and methods
Python lingo
Functions
functions Take an input and return something else
int(32.43) returns the integer 32. len("Hello")
returns the integer 5.
Big Data and Automated Content Analysis Damian Trilling
14. Basics Exercise Next meetings
Functions and methods
Python lingo
Functions
functions Take an input and return something else
int(32.43) returns the integer 32. len("Hello")
returns the integer 5.
methods are similar to functions, but directly associated with
an object. "SCREAM".lower() returns the string
"scream"
Big Data and Automated Content Analysis Damian Trilling
15. Basics Exercise Next meetings
Functions and methods
Python lingo
Functions
functions Take an input and return something else
int(32.43) returns the integer 32. len("Hello")
returns the integer 5.
methods are similar to functions, but directly associated with
an object. "SCREAM".lower() returns the string
"scream"
Both functions and methods end with (). Between the (),
arguments can (sometimes have to) be supplied.
Big Data and Automated Content Analysis Damian Trilling
16. Basics Exercise Next meetings
Functions and methods
Writing own functions
You can write an own function:
1 def addone(x):
2 y = x + 1
3 return y
Functions take some input (“argument”) (in this example, we
called it x) and return some result.
Thus, running
1 addone(5)
returns 6.
Big Data and Automated Content Analysis Damian Trilling
18. Basics Exercise Next meetings
Modifying lists and dictionaries
Modifying lists
Appending to a list
1 mijnlijst = ["element 1", "element 2"]
2 anotherone = "element 3" # note that this is a string, not a list!
3 mijnlijst.append(anotherone)
4 print(mijnlijst)
gives you:
1 ["element 1", "element 2", "element 3"]
Big Data and Automated Content Analysis Damian Trilling
19. Basics Exercise Next meetings
Modifying lists and dictionaries
Modifying lists
Merging two lists
1 mijnlijst = ["element 1", "element 2"]
2 anotherone = ["element 3", "element 4"]
3 mijnlist.extend(anotherone) # or simply: mijnlijst += anotherone
4 print(mijnlijst)
gives you:
1 ["element 1", "element 2", "element 3", "element 4]
Big Data and Automated Content Analysis Damian Trilling
20. Basics Exercise Next meetings
Modifying lists and dictionaries
Modifying dicts
Adding a key to a dict (or changing the value of an existing
key)
1 mydict = {"whatever": 42, "something": 11}
2 mydict["somethingelse"] = 76
3 print(mydict)
gives you:
1 {’whatever’: 42, ’somethingelse’: 76, ’something’: 11}
If a key already exists, its value is simply replaced.
Big Data and Automated Content Analysis Damian Trilling
22. Basics Exercise Next meetings
Indention
Indention
Structure
The program is structured by TABs or SPACEs
Big Data and Automated Content Analysis Damian Trilling
23.
24. Basics Exercise Next meetings
Indention
Indention
Structure
The program is structured by TABs or SPACEs
1 firstnames=[’Damian’,’Lori’,’Bjoern’]
2 age={’Bjoern’: 27, ’Damian’: 32, ’Lori’: 26}
3 print ("The names and ages of these people:")
4 for naam in firstnames:
5 print (naam,age[naam])
Big Data and Automated Content Analysis Damian Trilling
25. Basics Exercise Next meetings
Indention
Indention
Structure
The program is structured by TABs or SPACEs
1 firstnames=[’Damian’,’Lori’,’Bjoern’]
2 age={’Bjoern’: 27, ’Damian’: 32, ’Lori’: 26}
3 print ("The names and ages of these people:")
4 for naam in firstnames:
5 print (naam,age[naam])
Don’t mix up TABs and spaces! Both are valid, but you have
to be consequent!!! Best: always use 4 spaces!
Big Data and Automated Content Analysis Damian Trilling
26. Basics Exercise Next meetings
Indention
Indention
Structure
The program is structured by TABs or SPACEs
1 print ("The names and ages of all these people:")
2 for naam in firstnames:
3 print (naam,age[naam])
4 if naam=="Damian":
5 print ("He teaches this course")
6 elif naam=="Lori":
7 print ("She is a former assistant")
8 elif naam=="Bjoern":
9 print ("He helped teaching this course in the past")
10 else:
11 print ("No idea who this is")
Big Data and Automated Content Analysis Damian Trilling
27. Basics Exercise Next meetings
Indention
Indention
The line before an indented block starts with a statement
indicating what should be done with the block and ends with a :
Big Data and Automated Content Analysis Damian Trilling
28. Basics Exercise Next meetings
Indention
Indention
The line before an indented block starts with a statement
indicating what should be done with the block and ends with a :
Indention of the block indicates that
Big Data and Automated Content Analysis Damian Trilling
29. Basics Exercise Next meetings
Indention
Indention
The line before an indented block starts with a statement
indicating what should be done with the block and ends with a :
Indention of the block indicates that
• it is to be executed repeatedly (for statement) – e.g., for
each element from a list
Big Data and Automated Content Analysis Damian Trilling
30. Basics Exercise Next meetings
Indention
Indention
The line before an indented block starts with a statement
indicating what should be done with the block and ends with a :
Indention of the block indicates that
• it is to be executed repeatedly (for statement) – e.g., for
each element from a list
• it is only to be executed under specific conditions (if, elif,
and else statements)
Big Data and Automated Content Analysis Damian Trilling
31. Basics Exercise Next meetings
Indention
Indention
The line before an indented block starts with a statement
indicating what should be done with the block and ends with a :
Indention of the block indicates that
• it is to be executed repeatedly (for statement) – e.g., for
each element from a list
• it is only to be executed under specific conditions (if, elif,
and else statements)
• an alternative block should be executed if an error occurs
(try and except statements)
Big Data and Automated Content Analysis Damian Trilling
32. Basics Exercise Next meetings
Indention
Indention
The line before an indented block starts with a statement
indicating what should be done with the block and ends with a :
Indention of the block indicates that
• it is to be executed repeatedly (for statement) – e.g., for
each element from a list
• it is only to be executed under specific conditions (if, elif,
and else statements)
• an alternative block should be executed if an error occurs
(try and except statements)
• a file is opened, but should be closed again after the block has
been executed (with statement)
Big Data and Automated Content Analysis Damian Trilling
33. Basics Exercise Next meetings
Exercise
We’ll now together do some simple exercises . . .
Big Data and Automated Content Analysis Damian Trilling
34. Basics Exercise Next meetings
Exercise
Exercises
1. Warming up
• Create a list, loop over the list, and do something with each
value (you’re free to choose).
2. Did you pass?
• Think of a way to determine for a list of grades whether they
are a pass (>5.5) or fail.
• Can you make that program robust enough to handle invalid
input (e.g., a grade as ’ewghjieh’)?
• How does your program deal with impossible grades (e.g., 12
or -3)?
• . . .
Big Data and Automated Content Analysis Damian Trilling
35. Basics Exercise Next meetings
Next meetings
Big Data and Automated Content Analysis Damian Trilling
36. Basics Exercise Next meetings
Wednesday
We will work together on “Describing an existing structured
dataset” (Appendix A).
Preparation: Make sure you understood all of today’s
concepts!
Big Data and Automated Content Analysis Damian Trilling