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How Git Works - From Working Directory to Remote Repository [1]. Working Directory: Your project starts here. The working directory is where you actively make changes to your files. [2]. Staging Area (Index): After modifying files, use git add to stage changes. This prepares them for the next commit, acting as a checkpoint. [3]. Local Repository: Upon staging, execute git commit to record changes in the local repository. Commits create snapshots of your project at specific points. [4]. Stash (Optional): If needed, use git stash to temporarily save changes without committing. Useful when switching branches or performing other tasks. [5]. Remote Repository: The remote repository, hosted on platforms like GitHub, is a version of your project accessible to others. Use git push to send local commits and git pull to fetch remote changes. [6]. Remote Branch Tracking: Local branches can be set to track corresponding branches on the remote. This eases synchronization with git pull or git push.

The 'bias machine': How Google tells you what you want to hear "We're at the mercy of Google." Undecided voters in the US who turn to Google may see dramatically different views of the world – even when they're asking the exact same question. Type in "Is Kamala Harris a good Democratic candidate", and Google paints a rosy picture. Search results are constantly changing, but last week, the first link was a Pew Research Center poll showing that "Harris energises Democrats". Next is an Associated Press article titled "Majority of Democrats think Kamala Harris would make a good president", and the following links were similar. But if you've been hearing negative things about Harris, you might ask if she's a "bad" Democratic candidate instead. Fundamentally, that's an identical question, but Google's results are far more pessimistic. "It's been easy to forget how bad Kamala Harris is," said an article from Reason Magazine in the top spot. Source-Link: BBC

10 Advanced Excel Concepts for Data Analysts 1. VLOOKUP & XLOOKUP for Fast Data Retrieval: Quickly find data from different sheets with VLOOKUP or XLOOKUP for flexible lookups and defaults when no match is found. 2. Pivot Tables for Summarizing Data: Quickly summarize, explore, and analyze large datasets with drag-and-drop ease. 3. Conditional Formatting for Key Insights: Highlight trends and outliers automatically with conditional formatting, like Color Scales for instant data visualization. 4. Data Validation for Consistent Entries: Use dropdowns and set criteria to avoid entry errors and maintain data consistency. 5. IFERROR for Clean Formulas: Replace errors with default values like "N/A" for cleaner, more professional sheets. 6. INDEX-MATCH for Advanced Lookups: INDEX-MATCH is more flexible than VLOOKUP, allowing lookups in any direction and handling large datasets effectively. 7. TEXT Functions for Data Cleaning: Use LEFT, RIGHT, and TEXT functions to clean up inconsistent data formats or extract specific data elements. 8. Sparklines for Mini Data Visuals: Insert mini line or bar charts directly in cells to show trends at a glance without taking up space. 9. Array Formulas (UNIQUE, FILTER, SORT): Create dynamic lists and automatically update data with array formulas, perfect for unique values or filtered results. 10. Power Query for Efficient Data Transformation: Use Power Query to clean and reshape data from multiple sources effortlessly, making data prep faster. Hope it helps :)

𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄𝗲𝗿: You have only 2 minutes to solve this Python task. Retrieve the department name and the highest salary in each department from the employee dataset, but only for departments where the highest salary is greater than $70,000. 𝗠𝗲: Challenge accepted! 1️⃣ Import Libraries and Create DataFrame: import pandas as pd # Sample data data = {'Department': ['Sales', 'Sales', 'HR', 'HR', 'Engineering', 'Engineering'], 'Salary': [60000, 80000, 75000, 65000, 72000, 90000]} df = pd.DataFrame(data) 2️⃣ Group and Filter: Use groupby() to find the highest salary in each department, then filter based on the condition. # Group by department and find max salary result = df.groupby('Department')['Salary'].max().reset_index() # Filter departments with highest salary > 70000 result = result[result['Salary'] > 70000] print(result) This solution shows my understanding of pandas functions like groupby(), max(), and data filtering to meet specific requirements in a short time. 𝗧𝗶𝗽 𝗳𝗼𝗿 𝗣𝘆𝘁𝗵𝗼𝗻 𝗝𝗼𝗯 𝗦𝗲𝗲𝗸𝗲𝗿𝘀: Don’t focus only on syntax; practice efficient data manipulation with libraries like pandas and numpy. They’re essential for data analytics and solving real-world problems quickly! Hope it helps! :)

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◾HANDWRITTEN NOTES ✍️◾️ 🔺DATA STRUCTURE SHORT NOTES 🔺DATA STRUCTURE INTERVIEW SERIES 🔹(PART - 1) 🔺DATA STRUCTURE INTERVIEW SERIES 🔹(PART - 2) 🔺DATA STRUCTURE INTERVIEW SERIES 🔹(PART - 3) 🔺DBMS (DATABASE MANAGEMENT SYSTEM)NOTES 🔺C PROGRAMMING SHORT NOTES

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𝐏𝐨𝐰𝐞𝐫 𝐁𝐈 𝐑𝐞𝐚𝐥 𝐓𝐢𝐦𝐞 𝐏𝐫𝐨𝐣𝐞𝐜𝐭 𝐒𝐞𝐫𝐢𝐞𝐬 📊 𝐃𝐢𝐟𝐟𝐞𝐫𝐞𝐧𝐜𝐞 𝐛𝐞𝐭𝐰𝐞𝐞𝐧 𝐑𝐞𝐩𝐨𝐫𝐭𝐬 𝐯𝐬 𝐃𝐚𝐬𝐡𝐛𝐨𝐚𝐫𝐝𝐬 𝐑𝐞𝐩𝐨𝐫𝐭: 𝐖𝐡𝐞𝐫𝐞 𝐲𝐨𝐮 𝐦𝐚𝐤𝐞 𝐢𝐭: Power BI Desktop, 𝐖𝐡𝐚𝐭 𝐢𝐭 𝐬𝐡𝐨𝐰𝐬: Reports in Power BI are detailed documents that use charts, graphs, and tables to explain your data. They help you analyze trends and find insights. 𝐃𝐚𝐬𝐡𝐛𝐨𝐚𝐫𝐝: 𝐖𝐡𝐞𝐫𝐞 𝐲𝐨𝐮 𝐦𝐚𝐤𝐞 𝐢𝐭: Power BI service 𝐖𝐡𝐚𝐭 𝐢𝐭 𝐬𝐡𝐨𝐰𝐬: Dashboards is a display of key metrics and KPIs from multiple reports. They give you a quick overview of your data. 𝐘𝐨𝐮 𝐜𝐚𝐧 𝐬𝐞𝐥𝐞𝐜𝐭 𝐯𝐢𝐬𝐮𝐚𝐥𝐬 𝐟𝐫𝐨𝐦 𝐦𝐮𝐥𝐭𝐢𝐩𝐥𝐞 𝐫𝐞𝐩𝐨𝐫𝐭𝐬 𝐭𝐨 𝐜𝐫𝐞𝐚𝐭𝐞 𝐚 𝐬𝐢𝐧𝐠𝐥𝐞 𝐝𝐚𝐬𝐡𝐛𝐨𝐚𝐫𝐝.

2. Use relationships and filtering carefully to minimize the amount of data processed. 3. Avoid using complex DAX calculations in visuals; instead, create calculated columns or tables if needed. 4. Use aggregate tables or pre-aggregated data to reduce the volume of data processed in visuals. 5. Ensure that your data source is optimized for performance, such as indexing important columns or partitioning large tables. 6. Use Power BI Performance Analyzer to identify and troubleshoot performance bottlenecks in your report.