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Python Mindmap 👇👇 https://www.linkedin.com/posts/sql-analysts_how-to-master-python-from-scratch-1-setup-activity-7208056029719195648-IYWF?utm_source=share&utm_medium=member_android

I finally got my first Data Analytics JOB ....said no one ever after ⟶ Applying to just a handful of jobs ⟶ Overlooking the importance of problem-solving skills ⟶ Ignoring business acumen ⟶ Dismissing the advice of industry experts ⟶ Enrolling in a "JOB GUARANTEE Course" ⟶ Spending a fortune on courses with minimal hands-on experience The reality is If you want your FIRST job, you need to ⟶ gain practical experience ⟶ develop a strong understanding of business acumen ⟶ master "problem-solving" and "attention to detail" skills You may not be an expert in EVERYTHING, but you should at least be proficient in the basics and have a broad understanding of key concepts. If you know WHAT to do, you can figure out HOW to do it later. I have created 100-Day Roadmap & Resources for Data Analyst 👇👇 https://topmate.io/analyst/981703 Hope it helps :)

The key to starting your data analysis career: ❌It's not your education ❌It's not your experience It's how you apply these principles: 1. Learn the job through "doing" 2. Build a portfolio 3. Make yourself known No one starts an expert, but everyone can become one. If you're looking for a career in data analysis, start by: ⟶ Watching videos ⟶ Reading experts advice ⟶ Doing internships ⟶ Building a portfolio ⟶ Learning from seniors You'll be amazed at how fast you'll learn and how quickly you'll become an expert. So, start today and let the data analysis career begin I have curated best 80+ top-notch Data Analytics Resources 👇👇 https://topmate.io/analyst/861634 Hope it helps :)

Here's how you may start your career in data analytics 1. Learn Excel 2. Learn SQL 3. Learn a BI tool (Tableau or Power BI) 4. Create 1 Excel project 5. Create 1 SQL project 6. Create 3 dashboards 7. Create a data portfolio 8. Add projects to your resume 9. Link portfolio on LinkedIn and resume 10. Practice data related STAR questions 11. Set time to connect with recruiters 12. Set time to connect with others within a company 13. Set time to connect with other Data Analysts Get through this as fast as possible so you can spend all of your time applying Applying is the hardest and most time consuming part So you need to put all your energy into that, NOT perfecting tools I have created 100-Day Roadmap & Resources for Data Analyst 👇👇 https://topmate.io/analyst/981703 Hope it helps :)

How do you handle null, 0, and blank values in your data during the cleaning process? Sometimes interview questions are also based on this topic. Many data aspirants or even some professionals sometimes make the mistake of simply deleting missing values or trying to fill them without proper analysis.This can damage the integrity of the analysis. It’s essential to ask or find out the reason behind missing values in the data whether from the project head, client, or through own investigation. 𝘼𝙣𝙨𝙬𝙚𝙧: Handling null, 0, and blank values is crucial for ensuring the accuracy and reliability of data analysis. Here’s how to approach it: 1. 𝙄𝙙𝙚𝙣𝙩𝙞𝙛𝙮𝙞𝙣𝙜 𝙖𝙣𝙙 𝙐𝙣𝙙𝙚𝙧𝙨𝙩𝙖𝙣𝙙𝙞𝙣𝙜 𝙩𝙝𝙚 𝘾𝙤𝙣𝙩𝙚𝙭𝙩: - 𝙉𝙪𝙡𝙡 𝙑𝙖𝙡𝙪𝙚𝙨: These represent missing or undefined data. Identify them using functions like 'ISNULL' or filters in Power Query. - 0 𝙑𝙖𝙡𝙪𝙚𝙨: These can be legitimate data points but may also indicate missing data in some contexts. Understanding the context is important. - 𝘽𝙡𝙖𝙣𝙠 𝙑𝙖𝙡𝙪𝙚𝙨: These can be spaces or empty strings. Identify them using 'LEN', 'TRIM', or filters. 2. 𝙃𝙖𝙣𝙙𝙡𝙞𝙣𝙜 𝙏𝙝𝙚𝙨𝙚 𝙑𝙖𝙡𝙪𝙚𝙨 𝙐𝙨𝙞𝙣𝙜 𝙋𝙧𝙤𝙥𝙚𝙧 𝙏𝙚𝙘𝙝𝙣𝙞𝙦𝙪𝙚𝙨: - 𝙉𝙪𝙡𝙡 𝙑𝙖𝙡𝙪𝙚𝙨: Typically decide whether to impute, remove, or leave them based on the dataset’s context and the analysis requirements. Common imputation methods include using mean, median, or a placeholder. - 0 𝙑𝙖𝙡𝙪𝙚𝙨: If 0s are valid data, leave them as is. If they indicate missing data, treat them similarly to null values. - 𝘽𝙡𝙖𝙣𝙠 𝙑𝙖𝙡𝙪𝙚𝙨: Convert blanks to nulls or handle them as needed. This involves using 'IF' statements or Power Query transformations. 3. 𝙐𝙨𝙞𝙣𝙜 𝙀𝙭𝙘𝙚𝙡 𝙖𝙣𝙙 𝙋𝙤𝙬𝙚𝙧 𝙌𝙪𝙚𝙧𝙮: - 𝙀𝙭𝙘𝙚𝙡: Use formulas like 'IFERROR', 'IF', and 'VLOOKUP' to handle these values. - 𝙋𝙤𝙬𝙚𝙧 𝙌𝙪𝙚𝙧𝙮: Use transformations to filter, replace, or fill null and blank values. Steps like 'Fill Down', 'Replace Values', and custom columns help automate the process. By carefully considering the context and using appropriate methods, the data cleaning process maintains the integrity and quality of the data. I have curated best 80+ top-notch Data Analytics Resources 👇👇 https://topmate.io/analyst/861634 Hope it helps :)

SQL Cheatsheet 👇👇 https://www.linkedin.com/posts/sql-analysts_sql-activity-7206875465700061185-Jj8h

SAMPLE RESUME TEMPLATE FOR A DATA ANALYST(FRESHER) Creating a resume as a fresher data analyst involves highlighting your education, skills, projects, and any relevant experience you have gained through internships, coursework, or personal projects. Here’s a structured resume template tailored for a fresher in data analysis: [Your Name] [Your Address] [City, State, Zip Code] [Your Email Address] [Your Phone Number] [LinkedIn Profile] [GitHub Profile (if applicable)] Objective:- A motivated and detail-oriented data analyst with a strong foundation in statistics, data manipulation, and visualization. Seeking to leverage technical and analytical skills to solve complex problems and drive business insights in an entry-level data analyst role. Education:- Bachelor of Science in [Your Major] [Your University], [City, State] Graduation Date: [Month, Year] ● Relevant Coursework: Data Structures, Statistics, Data Mining, Machine Learning, Database Management, Business Analytics Technical Skills:- ● Programming Languages: Python, R, SQL ● Data Manipulation: pandas, NumPy ● Data Visualization: matplotlib, seaborn, ggplot2, Tableau, Power BI ● Databases: MySQL, PostgreSQL ● Tools: Excel, Jupyter Notebook, RStudio ● Other Skills: Data Cleaning, Data Wrangling, Exploratory Data Analysis (EDA), Statistical Analysis, Machine Learning Basics Projects:- Project Title 1 ● Description: [Brief description of the project, the problem you solved, and the tools/technologies you used.] ● Key Achievements: [Highlight specific outcomes, insights derived, or skills applied.] Project Title 2 ● Description: [Brief description of the project, the problem you solved, and the tools/technologies you used.] ● Key Achievements: [Highlight specific outcomes, insights derived, or skills applied.] Project Title 3 ● Description: [Brief description of the project, the problem you solved, and the tools/technologies you used.] ● Key Achievements: [Highlight specific outcomes, insights derived, or skills applied.] Internships and Experience:- Data Analyst Intern [Company Name], [City, State] [Month, Year] – [Month, Year] ● Assisted in collecting, cleaning, and analyzing large datasets to support business decision-making. ● Developed dashboards and visualizations to present data insights to stakeholders. ● Conducted statistical analyses to identify trends and patterns in data. Research Assistant [University Department or Lab], [City, State] [Month, Year] – [Month, Year] ● Collaborated on research projects involving data collection, data entry, and preliminary data analysis. ● Used statistical software to analyze research data and prepare reports. Certifications:- ● Google Data Analytics Professional Certificate ● Microsoft Certified: Data Analyst Associate ● [Any other relevant certification] Extracurricular Activities:- Member, Data Science Club, [Your University] ● Participated in data analysis competitions and hackathons. ● Attended workshops and seminars on data science and analytics. Volunteer, [Organization Name] ● Contributed to data-driven projects that helped the organization improve its operations and outreach. Additional Information:- ● Languages: [Any languages you speak other than English, if applicable] ● Interests: [Relevant interests that can show your passion for data and analysis, e.g., participating in Kaggle competitions, blogging about data science, etc.] Data Analyst Jobs -> t.me/jobs_SQL

➡️ Master Power BI in 2 hours. ➡️ Become python expert in 3 hours. ➡️ Become Data Analyst in a week. Hold on.!! 2 min me sirf maggi banta h career nahi. Take your time..

Thinking about becoming a Data Engineer? Here's the roadmap to avoid pitfalls & master the essential skills for a successful career. 👇👇 https://t.me/sql_engineer/62

Scenario-based questions related to Power BI and SQL 1.Scenario: You have been given a dataset in Power BI that contains sales data for a company. Your task is to create a report that shows the total sales by product category and region. How would you approach this task in Power BI? 2.Scenario: You are working on a SQL database that stores customer information. The database has a table called "Orders" that contains order details. Your task is to write a SQL query to retrieve the total number of orders placed by each customer. How would you approach this task in SQL? 3.Scenario: You have been asked to create a Power BI dashboard that displays real-time stock prices for a set of companies. The stock prices are available through an API. How would you connect Power BI to the API and update the dashboard with the latest stock prices? 4.Scenario: You are working on a SQL database that stores employee information. The database has a table called "Employees" that contains employee details. Your task is to write a SQL query to retrieve the names of all employees who have been with the company for more than 5 years. How would you approach this task in SQL? 5.Scenario: You have been given a Power BI report that contains multiple visualizations. The report is taking a long time to load and is impacting the performance of the application. How would you optimize the Power BI report to improve its performance? I have curated best 80+ top-notch Data Analytics Resources 👇👇 https://topmate.io/analyst/861634 Hope it helps :)

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Scenario based question and Answers 1. Scenario: Creating a Dynamic Sales Growth Report in Power BI Approach: Load Data: Import sales data and calendar tables. Data Model: Establish a relationship between the sales and calendar tables. Create Measures: Current Sales: Current Sales = SUM(Sales[Amount]). Previous Year Sales: Previous Year Sales = CALCULATE(SUM(Sales[Amount]), DATEADD(Calendar[Date], -1, YEAR)). Sales Growth: Sales Growth = [Current Sales] - [Previous Year Sales]. Visualization: Use Line Chart for trends. Use Card Visual for displaying numeric growth values. Slicers and Filters: Add slicers for selecting specific time periods. 2. Scenario: Identifying Top 5 Customers by Revenue in SQL Approach: Understand the Schema: Know the relevant tables and columns, e.g., Orders table with CustomerID and Revenue. SQL Query: SELECT TOP 5 CustomerID, SUM(Revenue) AS TotalRevenue FROM Orders GROUP BY CustomerID ORDER BY TotalRevenue DESC; 3. Scenario: Creating a Monthly Sales Forecast in Power BI Approach: Load Historical Data: Import historical sales data. Data Model: Ensure proper relationships. Time Series Analysis: Use built-in Power BI forecasting features. Create measures for historical and forecasted sales. Visualization: Use a Line Chart to display historical and forecasted sales. Adjust Forecast Parameters: Customize the forecast length and confidence intervals. 4. Scenario: Updating a SQL Table with New Data Approach: Understand the Schema: Identify the table and columns to be updated. SQL Query: UPDATE Employees SET JobTitle = 'Senior Developer' WHERE EmployeeID = 1234; 5. Scenario: Creating a Custom KPI in Power BI Approach: Define KPI: Identify the key performance indicators. Create Measures: Define the KPI measure using DAX. Visualization: Use KPI Visual or Card Visual. Configure the target and actual values. Conditional Formatting: Apply conditional formatting based on the KPI thresholds.

Excel Useful Shortcuts 👇👇 https://www.linkedin.com/posts/sql-analysts_excel-activity-7205903255766282240-HH5D

You don't need to know everything about every data tool. Focus on what will help land you your job. For Excel: - IFS (all variations) - XLOOKUP - IMPORTRANGE (in GSheets) - Pivot Tables - Dynamic functions like TODAY() For SQL: - Sum - Group By - Window Functions - CTEs - Joins For Tableau: - Calculated Columns - Sets - Groups - Formatting For Power BI: - Power Query for data transformation - DAX (Data Analysis Expressions) for creating custom calculations - Relationships between tables - Creating interactive and dynamic dashboards - Utilizing slicers and filters effectively

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