This document discusses various techniques for processing and analyzing text data including:
- Reading in text from various sources like documents, tweets, and audio transcripts.
- Transforming text into numeric feature vectors using bag-of-words and TF-IDF representations.
- Applying machine learning algorithms like classification and clustering to text data.
- Performing sentiment analysis, entity recognition, word colocation analysis, and more using NLP techniques from NLTK.
Machine learning algorithms can be used to make predictions from data. There are several types of algorithms for supervised learning tasks like regression and classification, as well as unsupervised learning tasks like clustering and dimensionality reduction. The scikit-learn library provides popular machine learning algorithms and datasets that can be used to fit models to data and validate performance. Key steps in the machine learning process include getting data, selecting an algorithm, fitting the model to training data, and evaluating performance on test data to avoid overfitting or underfitting. Performance metrics like precision, recall, and F1 score are used to quantify how well models generalize to new data.
This document discusses data cleaning and exploring techniques using Python, OpenRefine, Pandas, Seaborn and R. It describes preparing data by cleaning strings, dates/times and removing junk characters. OpenRefine is used to clean imported data by cleaning columns and using facets. Pandas is used to read in data, explore it by viewing rows and columns, summarizing statistics and creating pivot tables. Seaborn visualizes the Iris dataset with pairplots. R is briefly introduced for matrix analysis, statistics and graphics. The document provides code examples for these techniques.
Python is a programming language that allows users to write instructions for the computer to follow. There are multiple ways to run Python code, including through the terminal window by typing "python" or the name of a Python file, or using iPython notebooks which allow code to be run and formatted notes to be taken in a browser. Python code uses variables to store values of different data types like strings, integers, booleans, lists, and dictionaries. Users can write functions to reuse blocks of code and take input from the user or external files using libraries and conditionals.
This presentation is all about various built in
datastructures which we have in python.
List
Dictionary
Tuple
Set
and various methods present in each data structure
The document discusses arrays in C programming. It defines an array as a collection of similar data elements stored in adjacent memory locations that share a single name. Arrays allow storing multiple values of the same type using this single name. The document covers array declaration syntax, initialization, passing arrays to functions, and multidimensional arrays. It provides examples of one-dimensional and two-dimensional arrays as well as operations like matrix addition and transpose.
An array is a collection of variables of the same type referenced by a common name. A structure groups variables of different types. An array of structures combines these concepts by creating an array where each element is a structure. For example, an array of fraction structures could be defined to hold multiple fractions. Each structure element in the array contains a numerator and denominator integer. The entire array of structures occupies a contiguous block of memory with each structure taking up the same amount of space. Individual structure elements can then be accessed using the array index and dot notation.
This slide brushes up on the concepts of class and templates in C++. It introduces the different sections of the C++ Standard Library and talks about std::pair in further details.
This document discusses various techniques for processing and analyzing text data including:
- Reading in text from various sources like documents, tweets, and audio transcripts.
- Transforming text into numeric feature vectors using bag-of-words and TF-IDF representations.
- Applying machine learning algorithms like classification and clustering to text data.
- Performing sentiment analysis, entity recognition, word colocation analysis, and more using NLP techniques from NLTK.
Machine learning algorithms can be used to make predictions from data. There are several types of algorithms for supervised learning tasks like regression and classification, as well as unsupervised learning tasks like clustering and dimensionality reduction. The scikit-learn library provides popular machine learning algorithms and datasets that can be used to fit models to data and validate performance. Key steps in the machine learning process include getting data, selecting an algorithm, fitting the model to training data, and evaluating performance on test data to avoid overfitting or underfitting. Performance metrics like precision, recall, and F1 score are used to quantify how well models generalize to new data.
This document discusses data cleaning and exploring techniques using Python, OpenRefine, Pandas, Seaborn and R. It describes preparing data by cleaning strings, dates/times and removing junk characters. OpenRefine is used to clean imported data by cleaning columns and using facets. Pandas is used to read in data, explore it by viewing rows and columns, summarizing statistics and creating pivot tables. Seaborn visualizes the Iris dataset with pairplots. R is briefly introduced for matrix analysis, statistics and graphics. The document provides code examples for these techniques.
Python is a programming language that allows users to write instructions for the computer to follow. There are multiple ways to run Python code, including through the terminal window by typing "python" or the name of a Python file, or using iPython notebooks which allow code to be run and formatted notes to be taken in a browser. Python code uses variables to store values of different data types like strings, integers, booleans, lists, and dictionaries. Users can write functions to reuse blocks of code and take input from the user or external files using libraries and conditionals.
This presentation is all about various built in
datastructures which we have in python.
List
Dictionary
Tuple
Set
and various methods present in each data structure
The document discusses arrays in C programming. It defines an array as a collection of similar data elements stored in adjacent memory locations that share a single name. Arrays allow storing multiple values of the same type using this single name. The document covers array declaration syntax, initialization, passing arrays to functions, and multidimensional arrays. It provides examples of one-dimensional and two-dimensional arrays as well as operations like matrix addition and transpose.
An array is a collection of variables of the same type referenced by a common name. A structure groups variables of different types. An array of structures combines these concepts by creating an array where each element is a structure. For example, an array of fraction structures could be defined to hold multiple fractions. Each structure element in the array contains a numerator and denominator integer. The entire array of structures occupies a contiguous block of memory with each structure taking up the same amount of space. Individual structure elements can then be accessed using the array index and dot notation.
This slide brushes up on the concepts of class and templates in C++. It introduces the different sections of the C++ Standard Library and talks about std::pair in further details.
Dev Concepts: Data Structures and AlgorithmsSvetlin Nakov
Brief overview of the "data structures" and "algorithms" concepts.
Watch the video lesson from Svetlin Nakov and learn more at: https://softuni.org/dev-concepts/what-are-data-structures-and-algorithms
Structures in C allow the user to define a custom data type that combines different data types to represent a record. A structure is similar to an array but can contain heterogeneous data types, while an array only holds the same type. Structures are defined using the struct keyword followed by structure tags and member lists. Structure variables are declared like other variables and members can be accessed using the dot operator. Arrays of structures and nested structures are also supported.
Biopython is a set of freely available Python tools for bioinformatics and molecular biology. It provides features like parsing bioinformatics files into Python structures, a sequence class to store sequences and features, and interfaces to popular bioinformatics programs. Biopython can be used to address common bioinformatics problems like sequence manipulation, searching for primers, and running BLAST searches. The current version is 1.53 from December 2009 and future plans include updating the multiple sequence alignment object and adding a Bio.Phylo module.
The document discusses various Python datatypes. It explains that Python supports built-in and user-defined datatypes. The main built-in datatypes are None, numeric, sequence, set and mapping types. Numeric types include int, float and complex. Common sequence types are str, bytes, list, tuple and range. Sets can be created using set and frozenset datatypes. Mapping types represent a group of key-value pairs like dictionaries.
These are the slides of the second part of this multi-part series, from Learn Python Den Haag meetup group. It covers List comprehensions, Dictionary comprehensions and functions.
This document provides information on arrays in C programming. It defines an array as a collection of data storage locations with the same name and data type. It discusses array declaration, initialization, accessing elements using indexes, insertion and deletion of elements, and multi-dimensional arrays. Code examples are provided to demonstrate printing the contents of 1D, 2D and 3D arrays. The document also lists some reference books for learning C programming.
1. Arrays are structured data types that allow storing and accessing related data elements by index.
2. A one-dimensional array stores elements of the same type and provides indexed access to individual elements.
3. Arrays in C++ must be declared with a size and individual elements can only be accessed by integer indices corresponding to their position in the array.
This document provides an overview of arrays in the C programming language. It defines arrays as collections of variables of the same data type stored in contiguous memory locations. The document discusses declaring, initializing, and accessing array elements using indices. It provides examples of inserting elements into arrays, deleting elements from arrays, and printing the contents of 1D and 2D arrays. The document is intended to teach the fundamentals of array programming in C.
An array is a container that holds a fixed number of elements of the same type. An array's length is established at creation and cannot be changed. Each element has an index number starting from 0. The document demonstrates how to declare, initialize, access, and copy array elements in Java. It also discusses multidimensional arrays.
This document provides an overview of the C programming language. It discusses the basics of C programming including data types, variables, constants, keywords, operators, input/output statements, decision-making statements, and looping statements. It also covers basic C program structure, storage classes, and introduces different programming paradigms like procedural, structured, object-oriented and monolithic programming.
This document provides an overview of C++ data types. It discusses fundamental data types like integer, character, float, and double. It also covers type modifiers, derived data types like arrays and functions, and other concepts like pointers, references, constants, classes, structures, unions, and enumerations. The document aims to explain the different data types and how they are used in C++.
The document discusses reading and manipulating data in R. It describes how to read different file types like CSV, Excel, SAS, Stata and text files into R objects using various packages and functions. It also explains different data objects in R like vectors, arrays, lists and data frames, and how to access elements within these objects using indexes or names.
This document provides an overview of data types in Java, including primitive and reference data types. It discusses the eight primitive data types (byte, short, int, long, float, double, boolean, char), their purpose, ranges of values, default values, and examples. Reference data types refer to objects created from classes. The document also covers literals, which directly represent fixed values in source code, and escape sequences for string and char literals.
Is this good Python? PyCon WEB 2017 Lightning TalkSteffen Wenz
Lightning talk I held at https://pyconweb.com/ about how my Python idioms changed over the years, and how trying to write smart (but unreadable) code is bad :)
This presentation educates you about the types o python variables, Assigning Values to Variables, Multiple Assignment, Standard Data Types, Data Type Conversion and Function & Description.
For more topics stay tuned with Learnbay.
The document contains two passages about water and climate issues in western United States. Passage 1 discusses the significant differences in climate between eastern and western US, noting that the West does not receive adequate rainfall to sustain agriculture without irrigation. It also describes the extremes in rainfall amounts caused by mountain ranges like the Sierra Nevada and Cascades. Passage 2 provides more details on specific factors like the Colorado River and Ogallala Aquifer that impact water availability in the arid West, and stresses that solving the water problem is critical for the region to remain habitable.
This document summarizes a catering and decoration company that was started in 1992 and provides spices, pickles, papads, chutneys, and other Indian food products. The company has a variety of clients including large corporations and schools. It aims to be a trusted and reliable supplier known for hygienic and high quality products. The company has various departments to oversee production, research and development, storage, quality control, packing, and more. It is focused on meeting customer needs through competitive pricing, timely deliveries, and customization.
The Big-M method is a variation of the simplex method for solving linear programming problems with "greater-than" constraints. It works by introducing artificial variables with a large coefficient M to transform inequality constraints into equality constraints, creating an initial feasible solution. The transformed problem is then solved via simplex elimination to arrive at an optimal solution while eliminating artificial variables. The document provides an example problem demonstrating the step-by-step Big-M method process.
Dev Concepts: Data Structures and AlgorithmsSvetlin Nakov
Brief overview of the "data structures" and "algorithms" concepts.
Watch the video lesson from Svetlin Nakov and learn more at: https://softuni.org/dev-concepts/what-are-data-structures-and-algorithms
Structures in C allow the user to define a custom data type that combines different data types to represent a record. A structure is similar to an array but can contain heterogeneous data types, while an array only holds the same type. Structures are defined using the struct keyword followed by structure tags and member lists. Structure variables are declared like other variables and members can be accessed using the dot operator. Arrays of structures and nested structures are also supported.
Biopython is a set of freely available Python tools for bioinformatics and molecular biology. It provides features like parsing bioinformatics files into Python structures, a sequence class to store sequences and features, and interfaces to popular bioinformatics programs. Biopython can be used to address common bioinformatics problems like sequence manipulation, searching for primers, and running BLAST searches. The current version is 1.53 from December 2009 and future plans include updating the multiple sequence alignment object and adding a Bio.Phylo module.
The document discusses various Python datatypes. It explains that Python supports built-in and user-defined datatypes. The main built-in datatypes are None, numeric, sequence, set and mapping types. Numeric types include int, float and complex. Common sequence types are str, bytes, list, tuple and range. Sets can be created using set and frozenset datatypes. Mapping types represent a group of key-value pairs like dictionaries.
These are the slides of the second part of this multi-part series, from Learn Python Den Haag meetup group. It covers List comprehensions, Dictionary comprehensions and functions.
This document provides information on arrays in C programming. It defines an array as a collection of data storage locations with the same name and data type. It discusses array declaration, initialization, accessing elements using indexes, insertion and deletion of elements, and multi-dimensional arrays. Code examples are provided to demonstrate printing the contents of 1D, 2D and 3D arrays. The document also lists some reference books for learning C programming.
1. Arrays are structured data types that allow storing and accessing related data elements by index.
2. A one-dimensional array stores elements of the same type and provides indexed access to individual elements.
3. Arrays in C++ must be declared with a size and individual elements can only be accessed by integer indices corresponding to their position in the array.
This document provides an overview of arrays in the C programming language. It defines arrays as collections of variables of the same data type stored in contiguous memory locations. The document discusses declaring, initializing, and accessing array elements using indices. It provides examples of inserting elements into arrays, deleting elements from arrays, and printing the contents of 1D and 2D arrays. The document is intended to teach the fundamentals of array programming in C.
An array is a container that holds a fixed number of elements of the same type. An array's length is established at creation and cannot be changed. Each element has an index number starting from 0. The document demonstrates how to declare, initialize, access, and copy array elements in Java. It also discusses multidimensional arrays.
This document provides an overview of the C programming language. It discusses the basics of C programming including data types, variables, constants, keywords, operators, input/output statements, decision-making statements, and looping statements. It also covers basic C program structure, storage classes, and introduces different programming paradigms like procedural, structured, object-oriented and monolithic programming.
This document provides an overview of C++ data types. It discusses fundamental data types like integer, character, float, and double. It also covers type modifiers, derived data types like arrays and functions, and other concepts like pointers, references, constants, classes, structures, unions, and enumerations. The document aims to explain the different data types and how they are used in C++.
The document discusses reading and manipulating data in R. It describes how to read different file types like CSV, Excel, SAS, Stata and text files into R objects using various packages and functions. It also explains different data objects in R like vectors, arrays, lists and data frames, and how to access elements within these objects using indexes or names.
This document provides an overview of data types in Java, including primitive and reference data types. It discusses the eight primitive data types (byte, short, int, long, float, double, boolean, char), their purpose, ranges of values, default values, and examples. Reference data types refer to objects created from classes. The document also covers literals, which directly represent fixed values in source code, and escape sequences for string and char literals.
Is this good Python? PyCon WEB 2017 Lightning TalkSteffen Wenz
Lightning talk I held at https://pyconweb.com/ about how my Python idioms changed over the years, and how trying to write smart (but unreadable) code is bad :)
This presentation educates you about the types o python variables, Assigning Values to Variables, Multiple Assignment, Standard Data Types, Data Type Conversion and Function & Description.
For more topics stay tuned with Learnbay.
The document contains two passages about water and climate issues in western United States. Passage 1 discusses the significant differences in climate between eastern and western US, noting that the West does not receive adequate rainfall to sustain agriculture without irrigation. It also describes the extremes in rainfall amounts caused by mountain ranges like the Sierra Nevada and Cascades. Passage 2 provides more details on specific factors like the Colorado River and Ogallala Aquifer that impact water availability in the arid West, and stresses that solving the water problem is critical for the region to remain habitable.
This document summarizes a catering and decoration company that was started in 1992 and provides spices, pickles, papads, chutneys, and other Indian food products. The company has a variety of clients including large corporations and schools. It aims to be a trusted and reliable supplier known for hygienic and high quality products. The company has various departments to oversee production, research and development, storage, quality control, packing, and more. It is focused on meeting customer needs through competitive pricing, timely deliveries, and customization.
The Big-M method is a variation of the simplex method for solving linear programming problems with "greater-than" constraints. It works by introducing artificial variables with a large coefficient M to transform inequality constraints into equality constraints, creating an initial feasible solution. The transformed problem is then solved via simplex elimination to arrive at an optimal solution while eliminating artificial variables. The document provides an example problem demonstrating the step-by-step Big-M method process.
This document analyzes and defines 61 medical terms by breaking them down into their prefixes, roots, suffixes, and overall definitions. It examines terms related to anatomy, medical procedures, and conditions, including glenohumeral, scapular, osteoporosis, osteoarthritis, cholecystectomy, hypertension, and adenocarcinoma. For each term, the document identifies the relevant prefix, root(s), and suffix, and provides a definition based on the breakdown. The purpose is to understand medical terminology through word analysis.
The passage discusses the two main types of diabetes - Type I and Type II. Type II diabetes, which accounts for 90-95% of diabetes cases, results from the body becoming resistant to insulin or not properly using glucose. Both types of diabetes prevent the body from properly using blood sugar for energy after eating, though their specific causes and treatments differ. A balanced diet high in complex carbohydrates and low in fat is recommended to manage Type II diabetes symptoms.
El documento detalla las tareas a realizar en 5 semanas para un curso universitario. Cada semana, los estudiantes completarán tareas como definir conceptos, elaborar cronogramas, leer artículos, tomar notas, escribir ensayos con referencias APA e incluir una entrevista, presentar oralmente, demostrar pensamiento crítico y solución de problemas, y completar un examen escrito y portafolio final. También deben llevar un diario reflexivo y notar puntos confusos.
Project design for change .doc pums kollimalaidesigntn
1. The document outlines a plan for a school program called "Bird School" to teach students social and emotional skills.
2. Activities included imagining classroom lessons on feelings, cooperation, and conflict resolution. Students would learn through games and role plays.
3. The plan was implemented, with teachers and students participating in daily activities. Forty-three social-emotional skills were taught through interactive exercises. Feedback was positive.
The document is about simple harmonic motion (SHM). It contains 3 main points:
1) It defines SHM and gives the equation y=A sin(ωt) to describe the motion, where A is the amplitude, ω is the angular frequency, and t is time.
2) It explains how to graph SHM by plotting the position y versus time t over one period T. The motion is periodic, repeating every period T.
3) It relates the period T of SHM to the angular frequency ω via the equation T=2π/ω. The period is the time taken for one complete oscillation.
The document discusses the benefits of exercise for mental health. Regular physical activity can help reduce anxiety and depression and improve mood and cognitive function. Exercise causes chemical changes in the brain that may help protect against mental illness and improve symptoms for those who already suffer from conditions like depression and anxiety.
1) Audience feedback from test screenings of the film trailer criticized the killer's motivation for murdering victims as it did not seem reasonable for the character to kill because his mother left the family. However, the filmmakers decided to keep their original plot after researching that killers in slasher films often have psychological problems from troubled pasts.
2) Other negative feedback was that the trailer showed too many long scenes revealing future plot points and not enough focus on the killer stalking victims. This made it lack tension between the killer and victims.
3) A classmate commented that the killer's casual clothing did not suit his character, but the filmmakers chose to keep it to make him seem mysterious to viewers.
El documento habla sobre el voluntariado de Cruz Roja Argentina, los efectos negativos de la exposición solar como quemaduras e insolaciones, y las medidas de prevención y primeros auxilios relacionados con problemas de salud causados por el sol y el agua como picaduras de medusas.
Este blog trata sobre la importancia de cuidar el medio ambiente y promover la sostenibilidad en los colegios. Alienta a los estudiantes a reducir, reutilizar y reciclar los recursos, y propone actividades prácticas como limpiezas de playas y siembra de árboles para crear conciencia sobre la protección del planeta.
2014.2 journal of literature and art studiesDoris Carly
This document summarizes two short story anthologies - Voices Made Night by Mia Couto and Tales of Tenderness and Power by Bessie Head. Both anthologies explore the effects of political violence and poverty in Mozambique and South Africa during periods of conflict and oppression. Couto examines the civil war between FRELIMO and RENAMO in Mozambique, while Head depicts the violence and tensions between races in apartheid South Africa. The summaries use memory and history as lenses to analyze how mismanagement of political power impacted the socioeconomic conditions of the local populations in both countries during these tumultuous times.
Creating web sites using datalife engineJapprend.Com
1. The document discusses how to make over $200 per month using the Datalife Engine content management system. It provides instructions on installing Datalife Engine, finding content, promoting websites, and monetizing websites using advertising platforms like Clicksor and Adbrite.
2. The author details their own earnings of over $300 per month from using these platforms and hosting websites built with Datalife Engine.
3. The document then provides a step-by-step guide to installing and setting up Datalife Engine on a hosting account.
Oedipus is the son of King Laius and Queen Jocasta of Thebes. An oracle prophesied that Oedipus would kill his father and marry his mother. To avoid this fate, Oedipus was abandoned as an infant but was rescued and raised in Corinth. As a young man, a new oracle tells Oedipus of his destined patricide and incest. He leaves Corinth in an attempt to avoid this prophecy. On the road to Thebes, Oedipus kills an older man in a quarrel, not knowing it was his birth father Laius. He solves the riddle of the Sphinx and becomes king of Thebes, unwittingly fulfilling the first
This short document promotes creating presentations using Haiku Deck on SlideShare. It encourages the reader to get started making their own Haiku Deck presentation by providing a button to click to begin the process. The document is advertising the creation of presentations on Haiku Deck and SlideShare.
Key processes are well defined and have performance standards. Employees have clear responsibilities and customer needs drive work design. Performance focuses on productivity and financial results are regularly tracked, reported, and shared. Key metrics are used for control and improvement and other organizations are studied to set long term goals. Changes eliminate recurring problems and employees are involved in systematic improvement activities along with tradespeople.
Ejercicios de estilo en la programaciónSoftware Guru
El escritor francés Raymond Queneau escribió a mediados del siglo XX un libro llamado "Ejercicios de Estilo" donde mostraba una misma historia corta, redactada de 99 formas distintas.
En esta plática realizaremos el mismo ejercicio con un programa de software. Abarcaremos distintos estilos y paradigmas: programación monolítica, orientada a objetos, relacional, orientada a aspectos, monadas, map-reduce, y muchos otros, a través de los cuales podremos apreciar la riqueza del pensamiento humano aplicado a la computación.
Esto va mucho más allá de un ejercicio académico; el diseño de sistemas de gran escala se alimenta de esta variedad de estilos. También platicaremos sobre los peligros de quedar atrapado bajo un conjunto reducido de estilos a lo largo de tu carrera, y la necesidad de verdaderamente entender distintos estilos al diseñar arquitecturas de sistemas de software.
Semblanza del conferencista:
Crista Lopez es profesora en la Facultad de Ciencias Computacionales de la Universidad de California en Irvine. Su investigación se enfoca en prácticas de ingeniería de software para sistemas de gran escala. Previamente, fue miembro fundador del equipo en Xerox PARC creador del paradigma de programación orientado a aspectos (AOP). Crista es una de las desarrolladoras principales de OpenSimulator, una plataforma open source para crear mundos virtuales 3D. También es fundadora de Encitra, empresa especializada en la utilización de la realidad virtual para proyectos de desarrollo urbano sustentable. @cristalopes
The document provides a cheat sheet on the pandas DataFrame object. It discusses importing pandas, creating DataFrames from various data sources like CSVs, Excel, and dictionaries. It covers common operations on DataFrames like selecting, filtering, and transforming columns; handling indexes; and saving DataFrames. The DataFrame is a two-dimensional data structure with labeled columns that can be manipulated using various methods.
The document discusses various Python libraries used for data science tasks. It describes NumPy for numerical computing, SciPy for algorithms, Pandas for data structures and analysis, Scikit-Learn for machine learning, Matplotlib for visualization, and Seaborn which builds on Matplotlib. It also provides examples of loading data frames in Pandas, exploring and manipulating data, grouping and aggregating data, filtering, sorting, and handling missing values.
This document provides an overview of the Python programming language. It includes several code examples and summaries of key Python concepts like strings, lists, tuples, dictionaries, files, regular expressions, and object-oriented programming. It also lists some common Python functions and modules for tasks like HTTP requests and database access. The document aims to introduce Python's main features and provide basic code samples to help new Python learners.
This document provides an overview of the course "Data Structures and Applications" including the module topics, definitions, and classifications of data structures. The first module covers introduction to data structures, including definitions of primitive and non-primitive data structures, data structure operations, arrays, structures, stacks, and queues. Key concepts like dynamic memory allocation and various data structure implementations are also summarized.
Pandas Dataframe reading data Kirti final.pptxKirti Verma
Pandas is a Python library used for data manipulation and analysis. It provides data structures like Series and DataFrames that make working with structured data easy. A DataFrame is a two-dimensional data structure that can store data of different types in columns. DataFrames can be created from dictionaries, lists, CSV files, JSON files and other sources. They allow indexing, selecting, adding and deleting of rows and columns. Pandas provides useful methods for data cleaning, manipulation and analysis tasks on DataFrames.
fINAL Lesson_5_Data_Manipulation_using_R_v1.pptxdataKarthik
Anna is a junior data scientist working on a customer retention strategy. She needs to analyze data from different sources to understand customer value. To efficiently perform her job, she needs to learn techniques for reading, merging, summarizing and preparing data for analysis in R. These include reading data from files and databases, merging tables, summarizing data using functions like mean, median, and aggregate, and exporting cleaned data.
Morel, a data-parallel programming languageJulian Hyde
This document discusses Morel, a data-parallel programming language that is an extension of Standard ML with relational operators. Morel aims to provide the expressiveness of a functional programming language, the power and conciseness of SQL, and efficient execution on different hardware. It is implemented on top of Apache Calcite's relational algebra framework. The talk describes Morel's evolution and how it is pushing Calcite's capabilities with graph and recursive queries. Standard ML concepts like functions, recursion, and higher-order functions are extended in Morel with relational operators like "from" to enable data-parallel programming over immutable datasets. Functions can also be treated as values in Morel.
Control structures allow programmers to control the flow of execution of code in R. This document discusses various control structures like if-else loops, for loops, while loops, and functions. It also covers various data types in R like vectors, lists, data frames, factors and how to handle vectors through operations like addition, indexing, etc. It provides examples for each concept discussed.
The document discusses an R programming module that will cover getting started with R, data types and structures, control flow and functions, and scalability. It compares R to MATLAB and Python, describing their similarities as interactive shells for data manipulation but noting differences in popularity across fields and open-source availability. Base graphics and ggplot2 for data visualization are introduced. Sample datasets are also mentioned.
Vectorization refers to performing operations on entire NumPy arrays or sequences of data without using explicit loops. This allows computations to be performed more efficiently by leveraging optimized low-level code. Traditional Python code may use loops to perform operations element-wise, whereas NumPy allows the same operations to be performed vectorized on entire arrays. Broadcasting rules allow operations between arrays of different shapes by automatically expanding dimensions. Vectorization is a key technique for speeding up numerical Python code using NumPy.
Python Workshop - Learn Python the Hard WayUtkarsh Sengar
This document provides an introduction to learning Python. It discusses prerequisites for Python, basic Python concepts like variables, data types, operators, conditionals and loops. It also covers functions, files, classes and exceptions handling in Python. The document demonstrates these concepts through examples and exercises learners to practice char frequency counting and Caesar cipher encoding/decoding in Python. It encourages learners to practice more to master the language and provides additional learning resources.
This document discusses various methods for importing, exporting, and summarizing data in Python using the Pandas library. It covers reading and writing CSV, TXT, and XLSX files with Pandas, checking the structure and dimensions of data frames, handling missing values, and modifying data through functions like rename(). The key methods described are read_csv(), read_table(), read_excel(), to_csv(), info(), isnull(), sum(), head(), tail(), and describe().
XII - 2022-23 - IP - RAIPUR (CBSE FINAL EXAM).pdfKrishnaJyotish1
The document provides study material and sample papers for Class XII students of Kendriya Vidyalaya Sangathan Regional Office Raipur for the 2022-23 session. It lists the subject coordination by Mrs. Sandhya Lakra, Principal of KV No. 4 Korba and the content team comprising of 7 PGT Computer Science teachers from different KVs. The compilation, review and vetting is done by Mr. Sumit Kumar Choudhary, PGT CS of KV No. 2 Korba NTPC. The document contains introduction and concepts related to data handling using Pandas and Matplotlib libraries in Python.
R is an open source programming language used for data analysis and visualization. It allows users to process raw data into meaningful assets through packages that provide functions for tasks like data cleaning, modeling, and graphic creation. The document provides an introduction to R for beginners, including how to install R, basic commands and their uses, how to work with common data structures in R like vectors, matrices, data frames and lists, how to create user-defined functions, and how to import data into R.
Plunging Into Perl While Avoiding the Deep End (mostly)Roy Zimmer
This document provides an introduction to the Perl programming language. It discusses Perl nomenclature, attributes, variables, scopes, file input/output, string manipulation, regular expressions, and the DBI module for connecting to databases from Perl scripts. Examples are provided for common Perl programming tasks like reading files, splitting strings, formatting output, and executing SQL queries.
This document provides an overview of Python libraries for data analysis and data science. It discusses popular Python libraries such as NumPy, Pandas, SciPy, Scikit-Learn and visualization libraries like matplotlib and Seaborn. It describes the functionality of these libraries for tasks like reading and manipulating data, descriptive statistics, inferential statistics, machine learning and data visualization. It also provides examples of using these libraries to explore a sample dataset and perform operations like data filtering, aggregation, grouping and missing value handling.
This document provides a summary of a seminar presentation on robotic process automation and virtual internships. It introduces popular Python libraries for data science like NumPy, SciPy, Pandas, matplotlib and Seaborn. It covers reading, exploring and manipulating data frames; filtering and selecting data; grouping; descriptive statistics. It also discusses missing value handling and aggregation functions. The goal is to provide an overview of key Python tools and techniques for data analysis.
Pandas is an open-source Python library that provides high-performance data manipulation and analysis tools using powerful data structures like DataFrame. It allows users to load, prepare, manipulate, model, and analyze data regardless of its source through these five typical steps of data processing. Pandas contains data structures like Series and DataFrame, and methods for data loading, merging, sorting, filtering and handling missing data.
Distributed defense against disinformation: disinformation risk management an...Sara-Jayne Terp
This document discusses distributed defense against disinformation through cognitive security operations centers (CogSecCollab). It proposes a multi-pronged approach involving platforms, law enforcement, government, and other actors to address the complex problem of online disinformation. Key aspects include establishing disinformation security operations centers to conduct threat intelligence, incident response, risk mitigation, and enablement activities like training, tools, and processes. The centers would use frameworks to model disinformation campaigns and share indicators across heterogeneous teams in a collaborative manner. Simulations, red teaming, and other techniques are recommended to test defenses and learn from examples.
Risk, SOCs, and mitigations: cognitive security is coming of ageSara-Jayne Terp
This document discusses cognitive security and disinformation risk assessments. It outlines three layers of security - physical, cyber, and cognitive. It describes various disinformation strategies and risks, including different types of misleading information like disinformation, misinformation, and malinformation. It then discusses approaches for assessing and managing disinformation risks, including analyzing the information, threat, and response landscapes in a country. It provides frameworks for classifying disinformation incidents and objects. Finally, it discusses how to set up a cognitive security operations center (CogSOC) to conduct near real-time monitoring, analysis, and response to disinformation threats.
disinformation risk management: leveraging cyber security best practices to s...Sara-Jayne Terp
This document discusses leveraging cybersecurity best practices to support cognitive security goals related to disinformation and misinformation. It outlines three layers of security - physical, cyber, and cognitive security. It then provides examples of cognitive security risk assessment and mapping the risk landscape. Next, it discusses working together to mitigate and respond to risks through proposed cognitive security operations centers. Finally, it provides a hypothetical example of conducting a country-level risk assessment and designing a response strategy. The document advocates adapting frameworks and standards from cybersecurity to help conceptualize and coordinate cognitive security challenges and responses.
This document discusses cognitive security, which involves defending against attempts to intentionally or unintentionally manipulate cognition and sensemaking at scale. It covers various topics related to cognitive security including actors, channels, influencers, groups, messaging, and tools used in disinformation campaigns. Frameworks are presented for analyzing disinformation incidents, adapting concepts from information security like the cyber kill chain. Response strategies are discussed, drawing from fields like information operations, crisis management, and risk management. The need for a common language and ongoing monitoring and evaluation is emphasized.
2021 IWC presentation: Risk, SOCs and Mitigations: Cognitive Security is Comi...Sara-Jayne Terp
This document discusses cognitive security and disinformation risk assessments. It outlines three layers of security - physical, cyber, and cognitive. It describes various disinformation strategies and risks, including different types of misleading information like disinformation, misinformation, and malinformation. It then discusses approaches for assessing and managing disinformation risks, including analyzing the information, threat, and response landscapes in a country. It provides frameworks for classifying disinformation incidents and objects. Finally, it discusses how to set up a cognitive security operations center (CogSOC) to conduct near real-time monitoring, analysis, and response to disinformation threats.
This document discusses distributed defense against disinformation through cognitive security operations centers (CogSecCollab). It proposes a multi-pronged approach involving platforms, law enforcement, government, and other actors to address the complex problem of online disinformation. Key aspects include establishing disinformation security operations centers to conduct threat intelligence, incident response, risk mitigation, and enablement activities. The centers would use frameworks like AMITT to analyze disinformation techniques, track narratives and artifacts, and share intelligence. A variety of tactics are outlined, including detecting, denying, disrupting, and deceiving disinformation actors, as well as developing counter-narratives. Machine learning and automation could help with tasks like graph analysis, text analysis, and
1) The document discusses frameworks for understanding and responding to disinformation, including the AMITT and ATT&CK frameworks.
2) It describes various types of actors involved in spreading disinformation and proposes establishing Disinformation Security Operations Centers to facilitate collaboration between response efforts.
3) The goals of a CogSec SOC are outlined as informing about ongoing incidents, neutralizing disinformation, preventing future incidents, supporting organizations, and acting as a clearinghouse for incident data.
This document discusses lessons learned from the CTI League's Disinformation Team in responding to disinformation incidents related to COVID-19. It outlines key aspects of disinformation response including identifying common COVID-19 narratives, understanding motivations like money and geopolitics, and evolving tactics used by disinformation actors. It also describes the incident response process involving triaging incidents, conducting analysis to understand the situation, and considering options for countermeasures. Collaboration is emphasized as critical to effectively countering this complex, global problem.
1. The document outlines plans for an Information Sharing and Analysis Organization (ISAO) focused on countering misinformation.
2. It proposes building a global infrastructure and connecting public and private stakeholders to facilitate information sharing and developing collaborative capabilities to define, disseminate and apply best practices for cognitive security.
3. The ISAO would identify risks, protect information systems, detect threats and incidents, respond with countermeasures, and help recovery through lessons learned - extending the MITRE ATT&CK framework to analyze misinformation campaigns and techniques.
This document summarizes a presentation about social engineering at scale on the internet. The presentation discusses how social media platforms like Facebook have been used by groups to spread misinformation and manipulate public opinion at a massive scale through inauthentic accounts and posts. It also examines common human vulnerabilities that are exploited, such as biases and emotions. The presentation then outlines some responses from different groups to address this issue, including tech companies, journalists, and politicians. It concludes by suggesting ways to better design systems to reduce manipulation and abuse while coexisting with social bots.
The document discusses social engineering at scale on social media platforms like Facebook, the vulnerabilities it exploits in human cognition, and various responses to the spread of misinformation and disinformation online. It notes how certain Facebook groups have achieved massive shares and interactions while spreading untruths, and outlines cognitive biases and effects that make misinformation spread widely. Responses discussed include fact-checking organizations, changes by tech companies and social networks, and efforts by journalists, politicians, and hackers. It suggests this issue significantly changes the nature of the internet and human interactions online.
This document discusses belief hacking and how beliefs can be influenced through the use of algorithms, data analytics, and artificial intelligence on social networks. It describes how beliefs can be modeled and adjusted by optimizing for what people want to believe, using propaganda techniques to undermine opposing views, and leveraging social contagion to spread ideas. The document warns that while misinformation can be disruptive, understanding the systems already in place to influence beliefs is needed before attempting to counter or limit their effects.
This document discusses risks and mitigations when releasing data. It defines risk as the probability of something happening multiplied by the resulting cost or benefit. There are risks of physical harm, legal harm, reputational harm, and privacy breaches to data subjects, collectors, processors, and those releasing the data. Risk levels can be low, medium, or high. The document provides strategies for mitigating risks such as considering partial data releases, including locals to assess risks in local languages/contexts, and being aware of how data may interact with other datasets. It emphasizes the responsibility to do no harm when releasing datasets.
End-to-end pipeline agility - Berlin Buzzwords 2024Lars Albertsson
We describe how we achieve high change agility in data engineering by eliminating the fear of breaking downstream data pipelines through end-to-end pipeline testing, and by using schema metaprogramming to safely eliminate boilerplate involved in changes that affect whole pipelines.
A quick poll on agility in changing pipelines from end to end indicated a huge span in capabilities. For the question "How long time does it take for all downstream pipelines to be adapted to an upstream change," the median response was 6 months, but some respondents could do it in less than a day. When quantitative data engineering differences between the best and worst are measured, the span is often 100x-1000x, sometimes even more.
A long time ago, we suffered at Spotify from fear of changing pipelines due to not knowing what the impact might be downstream. We made plans for a technical solution to test pipelines end-to-end to mitigate that fear, but the effort failed for cultural reasons. We eventually solved this challenge, but in a different context. In this presentation we will describe how we test full pipelines effectively by manipulating workflow orchestration, which enables us to make changes in pipelines without fear of breaking downstream.
Making schema changes that affect many jobs also involves a lot of toil and boilerplate. Using schema-on-read mitigates some of it, but has drawbacks since it makes it more difficult to detect errors early. We will describe how we have rejected this tradeoff by applying schema metaprogramming, eliminating boilerplate but keeping the protection of static typing, thereby further improving agility to quickly modify data pipelines without fear.
Build applications with generative AI on Google CloudMárton Kodok
We will explore Vertex AI - Model Garden powered experiences, we are going to learn more about the integration of these generative AI APIs. We are going to see in action what the Gemini family of generative models are for developers to build and deploy AI-driven applications. Vertex AI includes a suite of foundation models, these are referred to as the PaLM and Gemini family of generative ai models, and they come in different versions. We are going to cover how to use via API to: - execute prompts in text and chat - cover multimodal use cases with image prompts. - finetune and distill to improve knowledge domains - run function calls with foundation models to optimize them for specific tasks. At the end of the session, developers will understand how to innovate with generative AI and develop apps using the generative ai industry trends.
Orchestrating the Future: Navigating Today's Data Workflow Challenges with Ai...Kaxil Naik
Navigating today's data landscape isn't just about managing workflows; it's about strategically propelling your business forward. Apache Airflow has stood out as the benchmark in this arena, driving data orchestration forward since its early days. As we dive into the complexities of our current data-rich environment, where the sheer volume of information and its timely, accurate processing are crucial for AI and ML applications, the role of Airflow has never been more critical.
In my journey as the Senior Engineering Director and a pivotal member of Apache Airflow's Project Management Committee (PMC), I've witnessed Airflow transform data handling, making agility and insight the norm in an ever-evolving digital space. At Astronomer, our collaboration with leading AI & ML teams worldwide has not only tested but also proven Airflow's mettle in delivering data reliably and efficiently—data that now powers not just insights but core business functions.
This session is a deep dive into the essence of Airflow's success. We'll trace its evolution from a budding project to the backbone of data orchestration it is today, constantly adapting to meet the next wave of data challenges, including those brought on by Generative AI. It's this forward-thinking adaptability that keeps Airflow at the forefront of innovation, ready for whatever comes next.
The ever-growing demands of AI and ML applications have ushered in an era where sophisticated data management isn't a luxury—it's a necessity. Airflow's innate flexibility and scalability are what makes it indispensable in managing the intricate workflows of today, especially those involving Large Language Models (LLMs).
This talk isn't just a rundown of Airflow's features; it's about harnessing these capabilities to turn your data workflows into a strategic asset. Together, we'll explore how Airflow remains at the cutting edge of data orchestration, ensuring your organization is not just keeping pace but setting the pace in a data-driven future.
Session in https://budapestdata.hu/2024/04/kaxil-naik-astronomer-io/ | https://dataml24.sessionize.com/session/667627
"Financial Odyssey: Navigating Past Performance Through Diverse Analytical Lens"sameer shah
Embark on a captivating financial journey with 'Financial Odyssey,' our hackathon project. Delve deep into the past performance of two companies as we employ an array of financial statement analysis techniques. From ratio analysis to trend analysis, uncover insights crucial for informed decision-making in the dynamic world of finance."
2. Lab 5: your 5-7 things
Data cleaning
Basic data cleaning with Python
Using OpenRefine
Exploring Data
The Pandas library
The Seaborn library
The R language
4. Algorithms want their data to be:
Machine-readable
Consistent format (e.g. text is all lowercase)
Consistent labels (e.g. use M/F, Male/Female, 0/1/2, but not *all* of these)
No whitespace hiding in number or text cells
No junk characters
No strange outliers (e.g. 200 year old living people)
In vectors and matrices
Normalised
6. Cleaning Strings
Removing capitals and whitespace:
mystring = " CApiTalIsaTion Sucks "
mystring.lower().strip()
original text is - CApiTalIsaTion Sucks -
lowercased text is - capitalisation sucks -
Text without whitespace is -capitalisation sucks-
7. Regular Expressions: repeated spaces
There’s a repeated space in capitalisation sucks
import re
re.sub(r's', '.', 'this is a string')
re.sub(r's+', '.', 'this is a string')
'this.is..a.string'
'this.is.a.string'
8. Regular Expressions: junk
import re
string1 = “This is a! sentence&& with junk!@“
cleanstring1 = re.sub(r'[^w ]', '', string1)
This is a sentence with junk
9. Converting Date/Times
European vs American? Name of month vs number? Python comes with a bunch
of date reformatting libraries that can convert between these. For example:
import datetime
date_string = “14/03/48"
datetime.datetime.strptime(date_string, ‘%m/%d/%y').strftime('%m/%d/%Y')
19. Exploring Data
Eyeball your data
Plot your data - visually look for trends and outliers
Get the basics statistics (mean, sd etc) of your data
Create pivot tables to help understand how columns interact
Do more cleaning if you need to (e.g. those outliers)
21. Reading in data files with Pandas
read_csv
read_excel
read_sql
read_json
read_html
read_stata
read_clipboard
22. Eyeballing rows
How many rows are there in this dataset?
len(df)
What do my data rows look like?
df.head(5)
df.tail()
df[10:20]
23. Eyeballing columns
What’s in these columns?
df[‘sourceid’]
df[[‘sourceid’,’ag12a_01','ag12a_02_2']]
What’s in the columns when these are true?
df[df.ag12a_01 == ‘YES’]
df[(df.ag12a_01 == 'YES') & (df.ag12a_02_1 == 'NO')]
24. Summarising columns
What are my column names and types?
df.columns
df.dtypes
Which labels do I have in this column?
df['ag12a_03'].unique()
df['ag12a_03'].value_counts()
25. Pivot Tables: Combining data from one dataframe
● pd.pivot_table(df, index=[‘sourceid’, ‘ag12a_03’])
26. Merge: Combining data from multiple frames
longnames = pd.DataFrame({ 'country' : pd.Series(['United States of America', 'Zaire', 'Egypt']),
'longname' : pd.Series([True, True, False])})
merged_data = pd.merge(
left=popstats,
right=longnames,
left_on='Country/territory of residence',
right_on='country')
merged_data[['Year', 'Country/territory of residence', 'longname', 'Total population', 'Origin / Returned from']]
27. Left Joins: Keep everything from the left table…
longnames = pd.DataFrame({ 'country' : pd.Series(['United States of America', 'Zaire', 'Egypt']),
'longname' : pd.Series([True, True, False])})
merged_data = pd.merge(
left=popstats,
right=longnames,
how='left',
left_on='Country/territory of residence',
right_on='country')
merged_data[['Year', 'Country/territory of residence', 'longname', 'Total population', 'Origin / Returned from']]
33. R
Matrix analysis (similar to Pandas)
Good at:
Rapid statistical analysis (4000+ R libraries)
Rapidly-created static graphics
Not so good at:
Non-statistical things (e.g. GIS data analysis)
34. Running R code
● Running R files:
○ From the terminal window: “R <myscript.r —no-save”
○ From inside another R program: source('myscript.r')
● Writing your own R code:
○ iPython notebooks: create “R” notebook (instead of python3)
○ Terminal window: type “r” (and “q()” to quit)
We’re talking today about cleaning and exploring data. What we’re really talking about is making friends with your data; understanding it yourself before you run any algorithms (e.g. machine learning algorithms) on it. We do this because a) it’s hard to run algorithms on badly-formatted data, and b) discovering data issues when you’re trying to train a classifier sucks - you have enough on your hands without dealing with outliers and spelling errors too.
Data cleaning is the process of removing errors (spelling mistakes, extraneous characters, corrupted data etc) from datafiles, to prepare them for use in algorithms and visualisation. Data cleaning is sometimes also called data scrubbing or cleansing.
Normalised: each datapoint has its own row in the data matrix.
Basic text cleaning
We'll spend a lot of time cleaning up text. Mostly this is because:
A) although you see 'Capital' and 'capital' as the same words, an algorithm will see these as different because of the capital letter in one of them
B) people leave a lot of invisible characters in their data (NB they do this to string representations of numerical data too - and many spreadsheet programs will store numbers as strings) - this is known as "whitespace", and can really mess up your day because "whitespace" and "whitespace " look the same to you, but an algorithm will see as different.
In the example, lower() converts a string into lowercase (upper() converts it into uppercase, but the convention in data science is to use all-lowercase, probably because it's less shouty to read), and strip() removes any whitespace before the first character, and after the last character (a-z etc) in the string.
Use the RE (regular expression) library to clean up strings. To use a regular expression (aka RegEx), you need to specify two patterns: one input pattern that will be applied repeatedly to the input text, and one output pattern that will be used in place of anything that matches the input pattern. Regular expressions can be very powerful and can take a while to learn, but here are a couple of patterns that you’ll probably find helpful at some point.
^\w = everything that isn’t a character or number.
[] = a group of possible characters, e.g. [^\w ] = alphanumeric plus space.
\s+,\s+ = one or more spaces followed by a comma then one or more spaces
More about date formats in https://docs.python.org/3/library/datetime.html
Open refine is a powerful data cleaning tool. It doesn’t do the cleaning for you, but it does make doing repeated tasks much much easier.
This is file 2009_2013_popstats_PSQ_POC.csv in directory notebooks/example_data
First, click on the OpenRefine icon. This will start a webpage for you, at URL 127.0.0.1:3333
Click on create project, then select a file. Click “next”.
I’ve selected file Notebooks/example_data/2009_2013_popstats_PSQ_POC.csv
Now I can see a preview of the data as it will be fed into the system, and a set of buttons for changing that import.
And it’s a mess. OpenRefine has put all the data into one column - it’s ignored the commas that separate columns, and it’s used the first row (which is a comment about the file) as the column headings.
Fortunately, OpenRefine has ways to start cleaning as you import the file. Here, we’ve selected “commas” as the column separators, and told OpenRefine to ignore the first 4 lines in the file. Now all we’ve got left to do is to clean up those annoying “*”s.
First, click on “create project”.
Here’s your data. You can do many things with this: clean text en-masse, move columns around (or add and delete columns), split or merge data in columns. We’ll play with just a few of these things.
If you right-click on a cell that you want to change, a little blue “edit” box will appear. Click on this, then edit the box that appears.
Now a powerful thing happens. You can apply whatever you did to that cell, to all other identical cells in this column. So if I want to remove cells with ‘*’ in them, I click on one of them, edit out the star, then click on “apply to all identical cells”.
Facets are also really powerful ways to look at and edit the data in cells. For instance, if you click on the arrow next to “column 3”, you’ll get a popup menu. Click on “facet” then “text facet”, and a facet box will appear on the left side of the screen. Now, if you want to change every instance of “Viet Nam” to “Vietnam”, you just need to edit the text here (hover over the word “Viet Nam” and an “edit” box will appear.
If you have really messy inputs, you can cluster them (see the “cluster” button?) into similar fields, and assign the text that should replace everything in the cluster. This is a useful way to deal with spelling variations, misspellings, spaces etc.
When you’re finished, click “export” (top right side of the page) to write your data out to CSV etc.
I’ve just shown you some of OpenRefine’s power. For example, there are a bunch of OpenRefine recipes at https://github.com/OpenRefine/OpenRefine/wiki/Recipes
Get to know your dataset, before you ask a machine to understand it.
Pandas is a Python data analysis library.
Read_sql reads database files; read_html reads tables from html pages; read_clipboard reads from your PC’s clipboard.
Beware: if you have more columns of data than you have column headings, Pandas read_csv can fail. If this happens, there are lots of optional parameters to read_csv that can help, but in practice it’s better to feed in clean data.
df[k].unique()
NB df.describe will only find mean and SD for numerical columns - which is reasonable.
This is very similar to the pivot tables in Excel. More at http://pbpython.com/pandas-pivot-table-explained.html
Those NaNs? “Not a numbers”.
Pandas merge defaults to an ‘inner join’: only keep the rows where data exists in *both* tables.
See e.g. http://www.datacarpentry.org/python-ecology/04-merging-data
Left join: keep all rows from the first table; combine rows where you can, put “NaN”s in the rows when you can’t.
Some great visuals about joins: http://www.codeproject.com/Articles/33052/Visual-Representation-of-SQL-Joins
You can normalise your tables in Pandas by using the stack function - see e.g. http://pandas.pydata.org/pandas-docs/stable/reshaping.html
Image: UNICEF state of the world’s children report.
You might want to do “sns.set()” before plotting…
You can also run R from Python code, using the rpy2 library.