How to Clean and Prepare Data in Excel Before Analysis

·1 views

Excel is a widely used tool for data analysis, but raw datasets often contain blank cells, duplicates, inconsistent entries, and formatting issues that must be resolved before any meaningful analysis can begin. Data cleaning involves standardizing inconsistent values, handling missing information appropriately, and removing or investigating duplicate and illogical records. Analysts are advised to work on a duplicate sheet to preserve the original dataset as a reference. Once cleaned, Excel features such as filters, sorting, Pivot Tables, and text functions can be applied to explore and interpret the data more effectively. Multilevel sorting and conditional filters further help users focus on specific subsets within large datasets.

Read the full story at DEV Community

This is an AI-generated summary. ShortSingh links to the original source for the complete article.

Discussion (0)

Developer Documents Deep-Dive Into React's Core Concepts and Mental Models

A developer has begun a blog series on DEV Community aimed at building a deeper conceptual understanding of React beyond surface-level syntax. The series focuses on how React constructs a tree of components whose output describes the UI, rather than directly manipulating the DOM. Key fundamentals covered in the first episode include how JSX produces a React element tree, and how props and state drive re-renders that lead to DOM updates. The author previously used React but now wants to understand the underlying reasoning behind how the framework operates. Future episodes plan to cover hooks, reconciliation, rendering performance, and React internals.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

How developers can optimize content to be cited by AI search systems

A 2025 Pew Research Center study found that users clicked traditional search results in only 8% of Google visits when an AI summary appeared, down from 15% without one, signaling a shift in how web visibility works. Answer Engine Optimization (AEO) is emerging as an additional layer alongside SEO, requiring developers to ensure content is crawlable, consistently structured, and accurately represented across all delivery formats. Google maintains that its AI features still rely on standard indexing and ranking systems, with no special schema required, but inconsistencies between page content, structured data, and metadata can cause AI systems to surface conflicting information. Developers are advised to model each fact once within a shared CMS content model and publish it uniformly across all formats, rather than maintaining separate copies. AI crawlers also use distinct user agents for search retrieval versus model training, and publishers can manage access permissions granularly through robots.txt, CDN rules, and server-level controls, then verify behavior via server logs and emerging AI performance reports in tools like Bing Webmaster Tools and Google Search Console.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

Why 'One-Click Deployment' Is a Myth Built on Invisible Hard Work

Software engineer Sergey Shinder argues that one-click deployments are misleading, as the real effort lies in the preparation and safeguards built long before anyone presses a button. His primary engineering principle is reversibility: if a deployment cannot be rolled back within minutes, he considers it too risky to proceed. Shinder advocates for progressive rollouts, releasing updates to a small percentage of users first and expanding only after verifying stability. He also warns against naive database migration automation, stressing the need for backward-compatible, staged schema changes that allow both old and new code versions to coexist. In his view, a truly successful deployment is a boring one — a sign that all difficult decisions were made carefully in advance.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

Engineer Builds 12-Skill Framework to Fix AI Coding Agents' Process Failures

A developer argues that today's AI coding models fail in production not due to lack of intelligence, but due to poor engineering discipline around process and workflow. Four recurring failures were identified: agents begin coding before requirements are clear, lose all context when a session ends, claim completion without running tests, and write code that clashes with an existing codebase's style and architecture. To address these, the developer built a system called Itqan — an Arabic word meaning mastery of a craft — structured around twelve skills and four core rules. A key design principle is that implementation cannot begin until the user approves a written spec, with approval recorded as a file on disk rather than a chat message, ensuring the gate holds even across resumed sessions. The project reframes AI agent improvement as a discipline problem rather than a capability one, drawing a parallel to how even a brilliant new engineer would fail without proper onboarding and process guardrails.

0 comments Read more at DEV Community