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company name: Stripe role: Software Engineer Internship batch: 2027/28 passouts Link: https://stripe.com/jobs/listing/software-engineer-intern/8031833

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SQL is the most important tool in any Data Analysis or Data Science project! 🚀 But for most professionals some aspects of SQL are hard to understand. 𝗦𝗼, 𝗜 𝗮𝗺 𝗽𝗿𝗼𝘃𝗶𝗱𝗶𝗻𝗴 𝗮 𝟭𝟬 𝗱𝗮𝘆 𝗽𝗹𝗮𝗻 𝘁𝗼 𝘀𝘁𝗮𝗿𝘁 𝘆𝗼𝘂𝗿 𝗷𝗼𝘂𝗿𝗻𝗲𝘆 𝘄𝗶𝘁𝗵 𝗦𝗤𝗟. 𝗗𝗮𝘆 𝟭 - SQL Basics 𝗗𝗮𝘆 𝟮 - Filtering, Sorting and Operators 𝗗𝗮𝘆 𝟯 - Functions and Aggregations 𝗗𝗮𝘆 𝟰 - Joining tables 𝗗𝗮𝘆 𝟱 - Subqueries and Nested Subqueries 𝗗𝗮𝘆 𝟲 - Set Operations 𝗗𝗮𝘆 𝟳 - Working with Dates and Times 𝗗𝗮𝘆 𝟴 - Advanced SQL Features 𝗗𝗮𝘆 𝟵 - Indexes and Performance Tuning 𝗗𝗮𝘆 𝟭𝟬 - Real World SQL Project So, to help you understand each concept easily here's a resource compiling all of them. 𝗖𝗵𝗲𝗰𝗸 𝘁𝗵𝗲𝗺 𝗵𝗲𝗿𝗲: https://bit.ly/4jYYaM0

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Complete step-by-step syllabus of #Excel for Data Analytics Introduction to Excel for Data Analytics: Overview of Excel's capabilities for data analysis Introduction to Excel's interface: ribbons, worksheets, cells, etc. Differences between Excel desktop version and Excel Online (web version) Data Import and Preparation: Importing data from various sources: CSV, text files, databases, web queries, etc. Data cleaning and manipulation techniques: sorting, filtering, removing duplicates, etc. Data types and formatting in Excel Data validation and error handling Data Analysis Techniques in Excel: Basic formulas and functions: SUM, AVERAGE, COUNT, IF, VLOOKUP, etc. Advanced functions for data analysis: INDEX-MATCH, SUMIFS, COUNTIFS, etc. PivotTables and PivotCharts for summarizing and analyzing data Advanced data analysis tools: Goal Seek, Solver, What-If Analysis, etc. Data Visualization in Excel: Creating basic charts: column, bar, line, pie, scatter, etc. Formatting and customizing charts for better visualization Using sparklines for visualizing trends in data Creating interactive dashboards with slicers and timelines Advanced Data Analysis Features: Data modeling with Excel Tables and Relationships Using Power Query for data transformation and cleaning Introduction to Power Pivot for data modeling and DAX calculations Advanced charting techniques: combination charts, waterfall charts, etc. Statistical Analysis in Excel: Descriptive statistics: mean, median, mode, standard deviation, etc. Hypothesis testing: t-tests, chi-square tests, ANOVA, etc. Regression analysis and correlation Forecasting techniques: moving averages, exponential smoothing, etc. Data Visualization Tools in Excel: Introduction to Excel add-ins for enhanced visualization (e.g., Power Map, Power View) Creating interactive reports with Excel add-ins Introduction to Excel Data Model for handling large datasets Real-world Projects and Case Studies: Analyzing real-world datasets Solving business problems with Excel Portfolio development showcasing Excel skills Best Resources : https://bit.ly/3E5DVgC Hope this helps you 😊

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This Leet Code SQL 50 Challenge can help you Switch to PBCs as a Data Scientist💪 While most candidates focus on basic SQL queries, top product-based companies often test advanced SQL concepts like window functions and query optimization. These are areas where many professionals struggle. It covers real-world scenarios like customer analytics, sales tracking, and data cleansing. Practicing these problems will help you think like a data scientist. Register here: https://bit.ly/3DTlJXm *Download notes* *helpful for Data Field*

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