Featured Post

Claude Code for Beginners: Step-by-Step AI Coding Tutorial

-
Image
 Artificial Intelligence is changing how developers write software. From generating code to fixing bugs and explaining complex logic, AI tools are becoming everyday companions for programmers. One such powerful tool is Claude Code , powered by Anthropic’s Claude AI model. If you’re a beginner or  an experienced developer looking to improve productivity, this guide will help you understand  what Claude Code is, how it works, and how to use it step-by-step . Let’s get started. What is Claude Code? Claude Code is an AI-powered coding assistant built on top of Anthropic’s Claude models. It helps developers by: Writing code from natural language prompts Explaining existing code Debugging errors Refactoring code for better readability Generating tests and documentation In simple words, you describe what you want in plain English, and Claude Code helps turn that into working code. It supports multiple programming languages, such as: Python JavaScri...
Post a Comment

5 Emerging Trends in Data Visualization

-
Nowadays, we deeply drowned in data of diverse kinds due to the increased computational power and accessibility.





Specifically, in addition to public data available on the Internet (e.g., census, demographics, environmental data), data pertaining personal daily activities are now more easily collected.




For example, through mobile devices that can log people's running distances and time or their manual record of nutrition consumption. Due to such expanded sources of data, there appear new applications that involve data collection, visualization, exploration, and distribution in daily contexts.


These applications do, not only display static information but also let users navigate the data in forms of interactive visualizations.


5 Emerging Trends in Data Visualization.



#1: This emerging trend has brought both opportunities and challenges to interaction designers to develop new approaches to designing data-based applications.




#2: Conveying information has been one of main functions of graphic and communication design since the analog printing era. The focus of information design is the communicative and aesthetic presentation of structured data as in an example of subway route map.




#3: In treating the increasing volumes of unprocessed data accessible either from public media or personal devices, the approaches of information design are now more diverse with the influence of other disciplines (Pousman & Stasko, 2007).




#4: Specifically, unlike information design in a traditional and confined manner, data visualization starts with the data that did not proceed through structuring and exist often in large volumes and complicated formats (Manovich, 2010).




#5: Thus, data visualization requires designers to obtain diverse knowledge and skill sets in addition to their visual aesthetic senses. The new requirements include human visual perception and cognition, statistics, and computational data mining.

How Visualization Applied in End user services 


Moreover, as data visualization has been more broadly applied to end-user services, interaction and experiential qualities need to be more critically considered.




Those qualities of data application do not only rely on the usability of data perception or task-based navigation but also build up on aesthetics that affords engaging and exploratory data navigation.




The latter have been remained as a less investigated area than the former due to the strong disciplinary tradition of data visualization in computer science and cognitive science.



Furthermore, visualizations are presented, used, and shared in diverse contexts from science labs to online journalism sites, to personal mobile devices.




This means that data visualization has become a truly interdisciplinary field of research and practice by weaving informatics, programming, graphic design, and even media art

Comments

Post a Comment

Thanks for your message. We will get back you.

Popular posts from this blog

SQL Query: 3 Methods for Calculating Cumulative SUM

-
Image
SQL provides various constructs for calculating cumulative sums, offering flexibility and efficiency in data analysis. In this article, we explore three distinct SQL queries that facilitate the computation of cumulative sums. Each query leverages different SQL constructs to achieve the desired outcome, catering to diverse analytical needs and preferences. Using Window Functions (e.g., PostgreSQL, SQL Server, Oracle) SELECT id, value, SUM(value) OVER (ORDER BY id) AS cumulative_sum  FROM your_table; This query uses the SUM() window function with the OVER clause to calculate the cumulative sum of the value column ordered by the id column. Using Subqueries (e.g., MySQL, SQLite): SELECT t1.id, t1.value, SUM(t2.value) AS cumulative_sum FROM your_table t1 JOIN your_table t2 ON t1.id >= t2.id GROUP BY t1.id, t1.value ORDER BY t1.id; This query uses a self-join to calculate the cumulative sum. It joins the table with itself, matching rows where the id in the first table is greater than...
Read more

Step-by-Step Guide to Reading Different Files in Python

-
Image
 In the world of data science, automation, and general programming, working with files is unavoidable. Whether you’re dealing with CSV reports, JSON APIs, Excel sheets, or text logs, Python provides rich and easy-to-use libraries for reading different file formats. In this guide, we’ll explore how to read different files in Python , with code examples and best practices. 1. Reading Text Files ( .txt ) Text files are the simplest form of files. Python’s built-in open() function handles them effortlessly. Example: # Open and read a text file with open ( "sample.txt" , "r" ) as file: content = file.read() print (content) Explanation: "r" mode means read . with open() automatically closes the file when done. Best Practice: Always use with to handle files to avoid memory leaks. 2. Reading CSV Files ( .csv ) CSV files are widely used for storing tabular data. Python has a built-in csv module and a powerful pandas library. Using cs...
Read more

5 SQL Queries That Popularly Used in Data Analysis

-
Image
 Here are five popular SQL queries frequently used in data analysis. 1. SELECT with Aggregations Summarize data by calculating aggregates like counts, sums, averages, etc. SELECT department, COUNT(*) as employee_count, AVG(salary) as average_salary FROM employees GROUP BY department; 2. JOIN Operations  Combine data from multiple tables based on a related column. SELECT e.employee_id, e.name, d.department_name FROM employees e JOIN departments d ON e.department_id = d.department_id; 3. WHERE Clause for Filtering Filter records based on specified conditions. SELECT * FROM sales WHERE sale_date BETWEEN '2024-01-01' AND '2024-12-31'   AND amount > 1000; 4. ORDER BY Clause for Sorting Sort results in ascending or descending order based on one or more columns. SELECT product_name, price FROM products ORDER BY price DESC; 5. GROUP BY with HAVING Clause Group records and apply conditions to the aggregated results. SELECT department, SUM(salary) as total_salaries FROM employ...
Read more