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Essential Skills for Data Analysis ☝️

Data Analytics Interview Questions

Starting your journey as a data analyst is an amazing start for your career. As you progress, you might find new areas that pique your interest: • Data Science: If you enjoy diving deep into statistics, predictive modeling, and machine learning, this could be your next challenge. • Data Engineering: If building and optimizing data pipelines excites you, this might be the path for you. • Business Analysis: If you're passionate about translating data into strategic business insights, consider transitioning to a business analyst role. But remember, even if you stick with data analysis, there's always room for growth, especially with the evolving landscape of AI. No matter where your path leads, the key is to start now.

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SQL Basics for Beginners: Must-Know Concepts 1. What is SQL?     SQL (Structured Query Language) is a standard language used to communicate with databases. It allows you to query, update, and manage relational databases by writing simple or complex queries. 2. SQL Syntax     SQL is written using statements, which consist of keywords like SELECT, FROM, WHERE, etc., to perform operations on the data.    - SQL keywords are not case-sensitive, but it's common to write them in uppercase (e.g., SELECT, FROM). 3. SQL Data Types     Databases store data in different formats. The most common data types are:    - INT (Integer): For whole numbers.    - VARCHAR(n) or TEXT: For storing text data.    - DATE: For dates.    - DECIMAL: For precise decimal values, often used in financial calculations. 4. Basic SQL Queries     Here are some fundamental SQL operations:    - SELECT Statement: Used to retrieve data from a database.    

    
             SELECT column1, column2 FROM table_name;
     {}
    

   - WHERE Clause: Filters data based on conditions.    

    
             SELECT * FROM table_name WHERE condition;
     {}
    

   - ORDER BY: Sorts data in ascending (ASC) or descending (DESC) order.    

    
             SELECT column1, column2 FROM table_name ORDER BY column1 ASC;
     {}
    

   - LIMIT: Limits the number of rows returned.    

    
             SELECT * FROM table_name LIMIT 5;
     {}
    

5. Filtering Data with WHERE Clause     The WHERE clause helps you filter data based on a condition:  

    
           SELECT * FROM employees WHERE salary > 50000;
   {}
    

   You can use comparison operators like:    - =: Equal to    - >: Greater than    - <: Less than    - LIKE: For pattern matching 6. Aggregating Data     SQL provides functions to summarize or aggregate data:    - COUNT(): Counts the number of rows.    

    
             SELECT COUNT(*) FROM table_name;
     {}
    

   - SUM(): Adds up values in a column.    

    
             SELECT SUM(salary) FROM employees;
     {}
    

   - AVG(): Calculates the average value.    

    
             SELECT AVG(salary) FROM employees;
     {}
    

   - GROUP BY: Groups rows that have the same values into summary rows.    

    
             SELECT department, AVG(salary) FROM employees GROUP BY department;
     {}
    

7. Joins in SQL     Joins combine data from two or more tables:    - INNER JOIN: Retrieves records with matching values in both tables.    

    
             SELECT employees.name, departments.department
     FROM employees
     INNER JOIN departments
     ON employees.department_id = departments.id;
     {}
    

   - LEFT JOIN: Retrieves all records from the left table and matched records from the right table.    

    
             SELECT employees.name, departments.department
     FROM employees
     LEFT JOIN departments
     ON employees.department_id = departments.id;
     {}
    

8. Inserting Data    To add new data to a table, you use the INSERT INTO statement:  

    
           INSERT INTO employees (name, position, salary) VALUES ('John Doe', 'Analyst', 60000);
   {}
    

9. Updating Data    You can update existing data in a table using the UPDATE statement:  

    
           UPDATE employees SET salary = 65000 WHERE name = 'John Doe';
   {}
    

10. Deleting Data     To remove data from a table, use the DELETE statement:   

    
            DELETE FROM employees WHERE name = 'John Doe';
   {}
    

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The Real Truth About Junior Data Analytics Interviews DataAnalytics (From someone who's interviewed 50+ analysts) Let me save you hours of interview prep... SQL Round WHAT THEY SAY: "Complex SQL knowledge" WHAT THEY ACTUALLY TEST: Can you clean messy data Do you check for NULL values How do you handle duplicates Can you explain your logic Do you verify results REAL QUESTIONS: "Find duplicate transactions" "Calculate monthly sales" "Show top customers" That's it. Really. ⤵️ Excel Interview WHAT THEY SAY: "Advanced Excel skills" WHAT THEY ACTUALLY TEST: VLOOKUP/XLOOKUP usage Pivot Table comfort Basic formulas Data cleaning approach Problem-solving process Business Case WHAT THEY SAY: "Data analysis presentation" WHAT THEY REALLY WANT: Can you explain simply Do you ask good questions Can you structure analysis Do you focus on impact Are you confident with data ⤵️ Common Scenarios The "Messy Data" Test They give you: Inconsistent formats Missing values Duplicate records They watch: How you spot issues What questions you ask Your cleaning approach The "Explain It" Challenge They ask: "Walk me through your analysis" They assess: Communication clarity Technical understanding Business thinking Confidence level ⤵️ How to Actually Prepare Practice Basics: Simple SQL queries Excel fundamentals Clear explanation Business Understanding: Read company metrics Understand industry Know basic KPIs Prepare good questions Real Scenarios to Practice: Monthly sales analysis Customer segmentation Product performance Marketing campaign results Reality Check: They care more about: How you think How you communicate How you solve problems Than: Perfect technical knowledge Complex code Advanced statistics

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Data Analyst Roadmap: - Tier 1: Excel & SQL - Tier 2: Data Cleaning & Exploratory Data Analysis (EDA) - Tier 3: Data Visualization & Business Intelligence (BI) Tools - Tier 4: Statistical Analysis & Machine Learning Basics Then build projects that include: - Data Collection - Data Cleaning - Data Analysis - Data Visualization And if you want to make your portfolio stand out more: - Solve real business problems - Provide clear, impactful insights - Create a presentation - Record a video presentation - Target specific industries - Reach out to companies Hope this helps you 😊