This presentation was presented for explained how to create photo archives system to Thai Redcross MIS team on May 10, 2010 at NECTEC, Thailand Science Park, Pathumthani, Thailand.
This presentation was presented for explained how to create photo archives system to Thai Redcross MIS team on May 10, 2010 at NECTEC, Thailand Science Park, Pathumthani, Thailand.
Word segmentation using Deep Learning (Deep cut) บรรยายโดย Rakpong Kittinaradorn จาก True Corporation ในงาน the second business analytics and data science contest/conference
This file is presented to include lectures on topics. "Digital Museum Development" for TOT/CAT digital museum development working group at Telephone Organization of Thailand Museum.
This document summarizes a presentation on unlocking the power of qualitative data through visualization. It discusses defining qualitative data and its benefits, frameworks for qualitative visualization like word clouds and matrices, and tools for sharing qualitative insights. Examples from Humans of New York and Colorado's early childhood outcomes data are provided. The presentation aims to help audiences better understand and communicate qualitative information.
The document discusses software engineering and defines software as instructions, data structures, and documents that provide desired functions when executed on a computer. It notes that software is engineered rather than physical, does not wear out in the way hardware does, and is complex and custom-built for specific uses. The document also outlines different types of software applications and common myths about software development.
This document discusses random functions in Python. It explains how to import the random module and describes functions like random(), randrange(), and randint() to generate random floats, integers within a range, and random selection from lists. random() generates a random number between 0 and 1, while randrange() and randint() are used to get random integers within a specified range. Examples are provided to demonstrate how to generate random numbers between certain values and with certain step increments.
This document discusses strings in Python. It defines strings as sequences of characters that can contain letters, numbers, and special characters. Strings are immutable and can be manipulated using built-in functions like len(), min(), max() as well as string methods. Common string operations include concatenation, slicing, formatting, comparison, searching, and conversion methods. The document provides examples of using these string functions and methods.
This document provides an outline and overview of key Python concepts including operators, data types, variables, functions, and program flow. It introduces Python as an interpreted programming language with a strict syntax. Operators like +, -, *, / perform actions on operands to produce new values. Data types include integers, floats, booleans and strings. Variables are used to store and reference data. Functions allow for code reuse and abstraction by defining reusable blocks of code. Program flow can be controlled using conditional statements like if/else.
The document discusses functions in Python. It defines a function as a block of code that performs a specific task and only runs when called. Functions can take parameters as input and return values. Some key points covered include:
- User-defined functions can be created in Python in addition to built-in functions.
- Functions make code reusable, readable, and modular. They allow for easier testing and maintenance of code.
- Variables can have local, global, or non-local scope depending on where they are used.
- Functions can take positional/required arguments, keyword arguments, default arguments, and variable length arguments.
- Objects passed to functions can be mutable like lists, causing pass by
Word segmentation using Deep Learning (Deep cut) บรรยายโดย Rakpong Kittinaradorn จาก True Corporation ในงาน the second business analytics and data science contest/conference
This file is presented to include lectures on topics. "Digital Museum Development" for TOT/CAT digital museum development working group at Telephone Organization of Thailand Museum.
This document summarizes a presentation on unlocking the power of qualitative data through visualization. It discusses defining qualitative data and its benefits, frameworks for qualitative visualization like word clouds and matrices, and tools for sharing qualitative insights. Examples from Humans of New York and Colorado's early childhood outcomes data are provided. The presentation aims to help audiences better understand and communicate qualitative information.
The document discusses software engineering and defines software as instructions, data structures, and documents that provide desired functions when executed on a computer. It notes that software is engineered rather than physical, does not wear out in the way hardware does, and is complex and custom-built for specific uses. The document also outlines different types of software applications and common myths about software development.
This document discusses random functions in Python. It explains how to import the random module and describes functions like random(), randrange(), and randint() to generate random floats, integers within a range, and random selection from lists. random() generates a random number between 0 and 1, while randrange() and randint() are used to get random integers within a specified range. Examples are provided to demonstrate how to generate random numbers between certain values and with certain step increments.
This document discusses strings in Python. It defines strings as sequences of characters that can contain letters, numbers, and special characters. Strings are immutable and can be manipulated using built-in functions like len(), min(), max() as well as string methods. Common string operations include concatenation, slicing, formatting, comparison, searching, and conversion methods. The document provides examples of using these string functions and methods.
This document provides an outline and overview of key Python concepts including operators, data types, variables, functions, and program flow. It introduces Python as an interpreted programming language with a strict syntax. Operators like +, -, *, / perform actions on operands to produce new values. Data types include integers, floats, booleans and strings. Variables are used to store and reference data. Functions allow for code reuse and abstraction by defining reusable blocks of code. Program flow can be controlled using conditional statements like if/else.
The document discusses functions in Python. It defines a function as a block of code that performs a specific task and only runs when called. Functions can take parameters as input and return values. Some key points covered include:
- User-defined functions can be created in Python in addition to built-in functions.
- Functions make code reusable, readable, and modular. They allow for easier testing and maintenance of code.
- Variables can have local, global, or non-local scope depending on where they are used.
- Functions can take positional/required arguments, keyword arguments, default arguments, and variable length arguments.
- Objects passed to functions can be mutable like lists, causing pass by
Functions are blocks of code that perform tasks and are called when needed. User-defined functions in Python are created using the def keyword. Functions make code reusable, increase readability and modularity. Variables inside functions have local scope unless declared as global or nonlocal. Functions can take arguments and return values. Libraries contain many built-in functions for tasks like math operations and string manipulation.
This document summarizes a lecture on statistical inference and exploratory data analysis. It includes announcements about the class, an overview of the data science workflow and statistical inference. The lecture covers modeling data and uncertainty, populations and samples, probability distributions and fitting models. It concludes with an introduction to exploratory data analysis and an activity to perform EDA in a Jupyter notebook.
This document provides an overview of the CS639: Data Management for Data Science course. It discusses that data science is becoming increasingly important as more fields utilize data-driven approaches. The course will teach students the basics of managing and analyzing data to obtain useful insights. It will cover topics like data storage, predictive analytics, data integration, and communicating findings. The goal is for students to learn fundamental concepts and design data science workflows and pipelines. The course will include lectures, programming assignments, a midterm, and final exam.