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Top 10 Tableau concepts for interviews:

1. Data Connections: Import data from multiple sources like Excel, SQL, and cloud services.


2. Dimensions and Measures: Dimensions categorize data, while measures provide numeric calculations.


3. Filters: Apply data filters at the worksheet, dashboard, or data source level.


4. Calculated Fields: Create custom calculations for advanced analysis.


5. Tableau Joins and Blending: Combine data from multiple sources; joins occur within a source, while blending connects separate sources.


6. Charts and Visualizations: Master bar charts, line charts, scatter plots, heat maps, and dashboards.


7. Table Calculations: Perform operations like running total, percentage difference, and moving average.


8. LOD Expressions: Fixed, Include, and Exclude expressions for granular data control.


9. Dashboards: Combine multiple worksheets into interactive dashboards with filters and actions.


10. Publishing and Sharing: Share insights via Tableau Server, Tableau Online, or Tableau Public.

Best Resources to learn Tableau

Data Analyst Checklist

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Top 10 concepts for Data Analyst interviews 👇👇

1. Data Cleaning: Techniques to handle missing, duplicate, and inconsistent data.


2. SQL: Strong knowledge of Joins, Group By, Window Functions, and Subqueries.


3. Excel: Proficiency in Pivot Tables, VLOOKUP, Conditional Formatting, and advanced formulas.


4. Visualization Tools: Expertise in Tableau, Power BI, or similar tools for dashboards and insights.


5. Data Wrangling: Extracting, transforming, and loading (ETL) data from various sources.


6. Statistics: Basic understanding of mean, median, standard deviation, correlation, and hypothesis testing.


7. Python/R: Ability to use libraries like Pandas, NumPy, and Matplotlib for analysis.


8. Business Acumen: Translate data insights into actionable recommendations for stakeholders.


9. Data Modeling: Create relationships between datasets and understand star/snowflake schema.


10. A/B Testing: Design and interpret experiments to compare group performance.

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Top 15 Excel concepts for Interviews

1. Cell Referencing: Understand absolute ($A$1), relative (A1), and mixed ($A1, A$1) referencing for dynamic formulas.


2. Formulas and Functions: Master key functions like VLOOKUP, HLOOKUP, IF, INDEX, MATCH, TEXT, CONCATENATE, and XLOOKUP.


3. Pivot Tables: Summarize, analyze, and visualize data dynamically; learn grouping and calculated fields.


4. Conditional Formatting: Highlight cells based on specific criteria using colors, icons, or data bars.


5. Data Validation: Restrict inputs using rules like drop-down lists, numerical ranges, or text length.


6. Charts: Create visualizations such as bar charts, pie charts, scatter plots, line graphs, and combo charts.


7. Filters and Sorting: Organize data using filters and multi-level sorting by color, values, or custom lists.


8. Macros: Automate repetitive tasks using VBA or Excel’s macro recorder.


9. What-If Analysis: Use tools like Goal Seek, Scenario Manager, and Data Tables for forecasting.


10. Power Query: Import, clean, and transform data from various sources with ease.


11. Error Handling: Understand and resolve common errors like #DIV/0!, #N/A, #VALUE!, #REF!, and use IFERROR.


12. Dynamic Arrays: Work with functions like SORT, FILTER, SEQUENCE, and UNIQUE for scalable solutions.


13. Advanced Charts: Use sparklines, waterfall charts, heat maps, and histogram charts for advanced visualization.


14. Data Cleaning: Remove duplicates, trim excess spaces, clean inconsistent formatting, and split data with TEXT TO COLUMNS.


15. Workbook/Worksheet Protection: Protect cells, worksheets, or entire workbooks to prevent unintended changes.

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7 Baby Steps to Learn SQL

1. Understand the Basics: Start by learning the foundational concepts of SQL. Understand what SQL is, its role in managing databases, and basic operations like selecting data using SELECT, filtering with WHERE, and sorting with ORDER BY. Familiarize yourself with relational database management systems (RDBMS) such as MySQL, PostgreSQL, or SQLite.


2. Master CRUD Operations: Practice writing SQL queries to perform CRUD operations (Create, Read, Update, Delete). Learn how to:

Insert data using INSERT INTO.

Retrieve data with SELECT.

Update records with UPDATE.

Delete rows using DELETE.



3. Work with Functions and Aggregations: Dive into SQL functions and aggregate queries. Understand how to use functions like MIN, MAX, AVG, COUNT, and SUM. Practice grouping data with GROUP BY and filtering aggregated data using HAVING.


4. Explore Joins and Relationships: Learn to combine data from multiple tables using different types of joins (INNER JOIN, LEFT JOIN, RIGHT JOIN, FULL JOIN). Understand table relationships (one-to-one, one-to-many, many-to-many) and how to leverage them effectively in queries.


5. Write Complex Queries: Advance to writing more complex SQL queries, including subqueries, Common Table Expressions (CTEs), and nested queries. Practice scenarios like finding duplicate entries, ranking data, or retrieving hierarchical data.


6. Understand Database Design: Learn about database normalization and denormalization to design efficient database schemas. Understand primary keys, foreign keys, constraints, and indexing to optimize query performance.


7. Engage with SQL Communities: Join SQL forums, GitHub repositories, and platforms like StackOverflow, or WhatsApp's SQL community. Participate in SQL challenges on websites like HackerRank, LeetCode, or Stratascrach to sharpen your skills and get feedback from experienced developers.


Additional Tips:

- Work on real-world datasets to understand practical applications.
- Explore advanced concepts like stored procedures, triggers, and views as you progress.
- Regularly review your queries to find optimization opportunities.

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7 Baby Steps to Learn Excel

1. Understand the Basics: Start by getting familiar with Excel's interface, including workbooks, worksheets, cells, rows, and columns. Learn basic operations like entering and editing data, formatting cells, and using basic formulas (e.g., SUM, AVERAGE, COUNT).

2. Master Essential Functions: Excel's power lies in its functions. Focus on learning frequently used ones like:

Mathematical: SUM, AVERAGE, ROUND

Text: CONCATENATE, LEFT, RIGHT, LEN

Logical: IF, AND, OR

Lookup: VLOOKUP, HLOOKUP, INDEX, MATCH

3. Work with Data: Learn how to organize, sort, and filter data effectively. Practice creating and formatting tables to handle structured data, and explore data validation to restrict input values.

4. Visualize with Charts: Understand how to create charts like bar, line, and pie charts to represent data visually. Learn the importance of choosing the right chart type and practice customizing them for clarity and impact.

5. Explore Pivot Tables: Pivot tables are essential for summarizing large datasets. Learn how to create pivot tables, use slicers for dynamic filtering, and analyze data using fields like Rows, Columns, Values, and Filters.

6. Use Advanced Features: Dive into advanced features like conditional formatting, macros, and Excel's built-in tools for data analysis (e.g., Goal Seek, Solver, and Data Analysis ToolPak). Learn how to work with Array Formulas and explore the power of XLOOKUP (in newer versions).

7. Engage with Excel Communities: Join Excel communities on forums like Reddit’s r/Excel, or Microsoft’s Excel Community. Participate in challenges on platforms like ExcelJet, LeetCode, or Kaggle to improve your problem-solving skills and get insights from experts.

Additional Tips:

- Regularly practice on real-world datasets.

- Learn keyboard shortcuts to speed up your work.

- Explore Microsoft Excel's official documentation and free online tutorials for deeper insights.

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7 Baby Steps to Learn Tableau

1. Understand the Basics:

Familiarize yourself with Tableau's ecosystem, including Tableau Desktop, Tableau Public, Tableau Server, and Tableau Online.

Learn the Tableau interface: dimensions, measures, rows, columns, and marks.

Connect Tableau to different data sources (Excel, SQL, CSV, etc.) and experiment with drag-and-drop functionality to build your first visualization.



2. Master Data Connections and Preparation:

Learn how to connect to multiple data sources and work with joins, unions, and data blending.

Use Tableau's Data Interpreter to clean raw data.

Practice creating calculated fields, such as calculated columns and aggregated measures, to enhance your data.



3. Create Basic Visualizations:

Build fundamental charts, such as:

Bar charts

Line charts

Pie charts

Scatter plots


Explore the Show Me feature for guidance on choosing the best visualization for your data.

Customize your charts with formatting, labels, colors, and tooltips.



4. Learn Advanced Visualization Techniques:

Work on advanced visualizations like:

Heatmaps

Tree maps

Dual-axis charts

Bullet graphs


Create hierarchies and drilldowns for in-depth analysis.

Use Tableau's geospatial features to create maps and visualize location-based data.



5. Master Filters, Groups, and Sets:

Apply various types of filters: extract filters, context filters, and quick filters.

Create groups to combine categories and sets for advanced filtering and segmentation.

Work with Parameters to build dynamic dashboards and calculations.



6. Build Dashboards and Stories:

Combine multiple sheets to create interactive dashboards.

Add interactivity with filters, actions, and highlight features.

Explore creating Stories to present data insights in a narrative format.



7. Engage with the Tableau Community:

Participate in Tableau forums, Reddit’s r/Tableau, and the Tableau Community Hub.

Take part in Tableau Public challenges to showcase your skills and build a portfolio.

Follow Tableau blogs, webinars, and YouTube channels to stay updated with new features and best practices.


Additional Tips:

Work on real-world datasets (e.g., sales data, survey results) to build hands-on experience.

Learn Tableau keyboard shortcuts to enhance efficiency.

Explore advanced topics like Tableau Prep for data preparation and Tableau Server for sharing and collaboration.

Best Resources to learn Tableau

Data Analyst Checklist

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7 Baby Steps to Learn Power BI

1. Understand the Basics:

Get familiar with Power BI Desktop, Power BI Service, and Power BI Mobile.

Explore Power BI’s interface, including the Fields pane, Visualizations pane, and Report view.

Learn key terms like datasets, reports, dashboards, and workspaces.

Create a simple report by importing an Excel dataset.



2. Learn to Import and Transform Data:

Use Power Query Editor for data cleaning and transformation.

Practice operations like:

Removing duplicates and filtering rows.

Splitting/merging columns.

Changing data types.


Explore connecting to various data sources, including Excel, SQL Server, and APIs.



3. Master Data Modeling:

Understand relationships between tables using Model View.

Learn the difference between one-to-one and one-to-many relationships.

Create calculated columns, measures, and hierarchies to enhance your models.

Explore the importance of star schema for efficient data modeling.



4. Get Comfortable with DAX (Data Analysis Expressions):

Learn how to write basic DAX formulas for calculations and measures.

Start with functions like SUM, AVERAGE, COUNT, and DISTINCTCOUNT.

Advance to logical functions like IF, SWITCH, and CALCULATE.

Use time intelligence functions (e.g., DATEADD, TOTALYTD) for date-based analysis.

5. Create Visualizations:

Learn to use various visualizations like bar charts, line charts, slicers, and tables.

Customize visuals with formatting options to make reports more interactive and user-friendly.

Practice creating KPIs and cards to highlight key metrics.

Explore custom visuals from the Microsoft AppSource.

6. Publish and Share Reports:

Publish your reports to the Power BI Service to share them with others.

Learn how to create and manage dashboards by pinning visuals.

Understand Power BI Gateways for refreshing on-premises data sources.

Explore sharing options, such as sharing reports, embedding in websites, or exporting to PowerPoint.



7. Engage with the Power BI Community:

Join forums like Microsoft Power BI Community, Whatsapp's Power BI, or StackOverflow for support.

Participate in Power BI challenges to practice real-world scenarios.

Follow Power BI blogs and YouTube channels for tips, tricks, and updates.


Additional Tips:

Work on real-world datasets to build practical projects like sales dashboards, financial reports, or marketing analytics.

Learn keyboard shortcuts and performance optimization techniques for faster development.

Explore advanced features like Row-Level Security (RLS), Paginated Reports, and Power BI API as you grow.

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7 Baby Steps to Learn Python

1. Grasp the Basics: Start with Python fundamentals. Learn how to install Python, set up a code editor (like VS Code or PyCharm), and write your first Python noscript. Focus on understanding:

Syntax and indentation

Variables and data types (e.g., strings, integers, floats, lists)

Operators, control flow (if, for, while), and input/output functions


2. Practice Writing Simple Programs: Apply your basics by writing simple programs like:

A calculator for arithmetic operations

A program to find the largest number in a list

A noscript to reverse a string or check if it’s a palindrome


3. Explore Python’s Core Libraries: Familiarize yourself with Python’s built-in libraries such as math, random, and datetime. Learn to handle files using open() and write(), and understand how to work with exceptions using try...except.


4. Learn Key Data Structures: Master Python’s key data structures like:

Lists: Learn slicing, appending, and iterating

Dictionaries: Understand key-value pairs and their applications

Sets & Tuples: Learn their use cases and differences
Practice solving problems like removing duplicates from a list or counting word frequencies.


5. Understand Functions and Modules: Learn how to write reusable code using functions. Understand how to:

Define and call functions

Use *args and **kwargs

Import and create your own modules for better code organization


6. Work on Real-World Projects: Start with small, practical projects to apply your skills, such as:

A to-do list manager using text files

A web scraper using BeautifulSoup

A data visualization project using matplotlib and pandas


7. Engage with Python Communities: Join Python forums and communities like Reddit’s r/learnpython, StackOverflow, or Python Discord. Participate in coding challenges on HackerRank, LeetCode, or Kaggle. These platforms will help you practice problem-solving and get feedback from others.


Additional Tips:

Explore Python’s vast ecosystem, including libraries like NumPy, pandas, and Flask, depending on your goals.

Practice regularly to reinforce your understanding and grow as a Python developer.

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7 Baby Steps to Become a Data Analyst 👇👇

1. Understand the Role of a Data Analyst:

Learn what a data analyst does, including collecting, cleaning, analyzing, and interpreting data to support decision-making.

Familiarize yourself with key terms like KPIs, dashboards, and business intelligence.

Research industries where data analysts work, such as finance, marketing, healthcare, and e-commerce.


2. Learn the Essential Tools:

Excel: Start with basics like formulas, functions, and pivot tables, then advance to using Power Query and macros.

SQL: Learn to write queries for retrieving, filtering, and aggregating data from databases.

Data Visualization Tools: Master tools like Power BI or Tableau to create dashboards and reports.


3. Develop Analytical Thinking:

Practice identifying trends, patterns, and outliers in datasets.

Learn to ask the right questions about what the data reveals and how it can guide decision-making.

Strengthen problem-solving skills through real-world case studies or challenges.


4. Master a Programming Language (Python or R):

Learn Python libraries like pandas, NumPy, and matplotlib for data manipulation and visualization.

Alternatively, learn R for statistical analysis and its packages like ggplot2 and dplyr.

Work on projects like cleaning messy datasets or creating automated analysis noscripts.


5. Work with Real-World Data:

Explore open datasets from platforms like Kaggle or Google Dataset Search.

Practice analyzing datasets related to your area of interest (e.g., sales, customer feedback, or healthcare).

Create sample reports or dashboards to showcase insights.


6. Build a Portfolio:

Document your projects in a way that demonstrates your skills. Include:

Data cleaning and transformation examples.

Visualization dashboards using Power BI, Tableau, or Excel.

Analysis reports with actionable insights.


Use GitHub or Tableau Public to showcase your work.


7. Engage with the Data Analytics Community:

Join forums like Kaggle, Reddit’s r/dataanalysis, or LinkedIn groups.

Participate in challenges to solve real-world problems, such as Kaggle competitions.

Additional Tips:

Gain domain knowledge relevant to your target industry (e.g., marketing analytics or financial analysis).

Focus on communication skills to present insights effectively to non-technical stakeholders.

Continuously learn and upskill as new tools and techniques emerge in the data analytics field.

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7 Baby Steps to Become a Business Analyst

1. Understand the Role of a Business Analyst:

Learn what a business analyst (BA) does: bridging the gap between business needs and technology solutions.

Understand the key responsibilities, such as gathering requirements, documenting processes, analyzing data, and ensuring project goals align with business objectives.

Familiarize yourself with BA deliverables like business requirements documents (BRDs), use case diagrams, and process flowcharts.


2. Learn Core Business Analysis Skills:

Develop strong communication and interpersonal skills for stakeholder management.

Practice creating clear and concise documentation.

Learn problem-solving and critical thinking to analyze complex business challenges and propose effective solutions.

Understand business process modeling and mapping using tools like Lucidchart or Visio.


3. Master Essential Tools and Techniques:

Data Analysis: Learn tools like Excel, SQL, and basic data visualization tools (Power BI/Tableau) to analyze and interpret data.

Requirement Elicitation Techniques: Practice interviews, workshops, brainstorming, and surveys to gather requirements effectively.

Project Management Tools: Get familiar with tools like Jira, Trello, or MS Project to manage tasks and requirements.


4. Learn Business Frameworks and Methodologies:

Understand methodologies like Agile, Waterfall, and Scrum.

Learn frameworks such as SWOT analysis, PESTLE analysis, and process improvement methodologies like Six Sigma.

Study how BAs fit into the SDLC (Software Development Life Cycle) and how to contribute during each phase.


5. Work on Real-World Scenarios:

Practice writing user stories, functional requirements, and acceptance criteria.

Use case studies or hypothetical projects to create process models and propose solutions.

Work on building mock dashboards or reports to present insights effectively to stakeholders.


6. Build a Portfolio:

Document your projects, case studies, or hypothetical solutions. Include:

Process diagrams and models.

Requirement gathering documents.

Data analysis reports or dashboards.


Use platforms like GitHub, Tableau Public, or personal blogs to showcase your work.


7. Engage with the Business Analyst Community:

Participate in webinars, workshops, or business analysis meetups.

Stay updated with blogs, podcasts, and books on BA practices and trends.


Additional Tips:

- Consider earning certifications like CBAP (Certified Business Analysis Professional) or ECBA (Entry Certificate in Business Analysis) to boost your credibility.

- Gain domain knowledge in industries like finance, healthcare, or IT, depending on your interest.

- Develop strong storytelling skills to communicate findings and recommendations effectively to stakeholders.

- Join telegram channels specifically for business analysts

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Essential SQL Shortcut Keys for Data Analysts

Ctrl + Enter: Execute query in SQL Editor.

Alt + F1: Get object details (SQL Server).

Ctrl + K + C: Comment selected lines.

Ctrl + K + U: Uncomment selected lines.

F5: Refresh query results.

Alt + Shift + Arrow Keys: Select columns in grid mode.

Ctrl + Shift + R: Refresh IntelliSense cache.

Ctrl + Tab: Switch between open tabs in SQL Server.

Ctrl + L: Display estimated execution plan.

Ctrl + R: Toggle results pane visibility.


Pro Tip: Memorize the most-used shortcuts for faster debugging and query optimization!

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Essential Excel Shortcut Keys for Data Analysts

Ctrl + N: Create a new workbook.

Ctrl + S: Save the current workbook.

Ctrl + C / Ctrl + V: Copy/Paste.

Ctrl + Z / Ctrl + Y: Undo/Redo.

Ctrl + F: Find specific text or values.

Ctrl + T: Convert data into a table.

Ctrl + Shift + L: Apply/remove filters.

Alt + =: Auto-sum selected cells.

Ctrl + Shift + Arrow Keys: Select continuous data.

Ctrl + `: Show formulas in cells.

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Essential Python Shortcut Keys for Data Analysts

Ctrl + N: Create a new noscript.

Ctrl + S: Save the current noscript.

Ctrl + Enter: Run the current cell in Jupyter Notebook.

Shift + Enter: Run the cell and move to the next in Jupyter.

Ctrl + /: Comment/Uncomment selected lines.

Ctrl + F: Find specific text.

Ctrl + H: Replace text.

Alt + Shift + Up/Down: Duplicate the current line in VS Code.

F5: Run the program.

Ctrl + Shift + L: Select all occurrences of a variable in VS Code.

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Essential Jupyter Notebook Shortcut Keys

Mode Switching:

Enter: Switch to Edit Mode (write code/text).

Esc: Switch to Command Mode (navigate and execute commands).


Cell Operations:

A: Insert a new cell above.

B: Insert a new cell below.

D, D: Delete the selected cell.

Z: Undo the last cell deletion.


Run and Execution:

Shift + Enter: Run the current cell and move to the next one.

Ctrl + Enter: Run the current cell without moving.

Alt + Enter: Run the current cell and insert a new one below.


Text Formatting (Markdown):

M: Convert cell to Markdown.

Y: Convert cell to Code.


Navigation:

Up/Down Arrow: Move between cells in Command Mode.

Ctrl + Shift + -: Split the cell at the cursor position.


Other Useful Commands:

Ctrl + S: Save the notebook.

Shift + Tab: View function or method documentation.

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Essential Tableau Shortcut Keys for Data Analysts

Ctrl + N: Create a new workbook.

Ctrl + O: Open an existing workbook.

Ctrl + S: Save the workbook.

F11: Toggle Full Screen Mode.

Ctrl + D: Duplicate the current worksheet.

Ctrl + W: Close the current workbook.

Alt + Shift + D: Toggle the Data Pane.

Alt + Shift + F: Toggle the Analytics Pane.

Ctrl + T: Open the Format Pane.

Ctrl + Shift + B: Show/Hide the Toolbar.

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🌟 Data Analyst vs Business Analyst: Quick comparison 🌟

1. Data Analyst: Dives into data, cleans it up, and finds hidden insights like Sherlock Holmes. 🕵️‍♂️

Business Analyst: Talks to stakeholders, defines requirements, and ensures everyone’s on the same page. The diplomat. 🤝


2. Data Analyst: Master of Excel, SQL, Python, and dashboards. Their life is rows, columns, and code. 📊

Business Analyst: Fluent in meetings, presentations, and documentation. Their life is all about people and processes. 🗂️


3. Data Analyst: Focuses on numbers, patterns, and trends to tell a story with data. 📈

Business Analyst: Focuses on the "why" behind the numbers to help the business make decisions. 💡


4. Data Analyst: Creates beautiful Power BI or Tableau dashboards that wow stakeholders. 🎨

Business Analyst: Uses those dashboards to present actionable insights to the C-suite. 🎤


5. Data Analyst: SQL queries, Python noscripts, and statistical models are their weapons. 🛠️

Business Analyst: Process diagrams, requirement docs, and communication are their superpowers. 🦸‍♂️


6. Data Analyst: “Why is revenue declining? Let me analyze the sales data.”

Business Analyst: “Why is revenue declining? Let’s talk to the sales team and fix the process.”


7. Data Analyst: Works behind the scenes, crunching data and making sense of numbers. 🔢

Business Analyst: Works with teams to ensure that processes, strategies, and technologies align with business goals. 🎯


8. Data Analyst: Uses data to make decisions—raw data is their best friend. 📉

Business Analyst: Uses data to support business decisions and recommends solutions to improve processes. 📝


9. Data Analyst: Aims for accuracy, precision, and statistical significance in every analysis. 🧮

Business Analyst: Aims to understand business needs, optimize workflows, and align solutions with business objectives. 🏢


10. Data Analyst: Focuses on extracting insights from data for current or historical analysis. 🔍

Business Analyst: Looks forward, aligning business strategies with long-term goals and improvements. 🌱

Both roles are vital, but they approach the data world in their unique ways.

Choose your path wisely! 🚀

Data Analytics Resources 👇👇
https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02

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Essential Power BI Shortcut Keys for Data Analysts

Ctrl + N: Create a new report.

Ctrl + O: Open an existing report.

Ctrl + S: Save the report.

Ctrl + Z / Ctrl + Y: Undo/Redo actions.

Ctrl + C / Ctrl + V: Copy/Paste visuals or data.

Ctrl + X: Cut selected items.

Ctrl + Shift + L: Open the Filters Pane.

Ctrl + T: Add a new table visual.

Alt + Shift + Arrow Keys: Nudge visuals in small increments.

Ctrl + Shift + F: Toggle between Full-Screen and Normal view.

You can refer these Power BI Interview Resources to learn more: https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02

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If you dream of becoming a data analyst, let 2025 be the year you make it happen.

Work hard, stay focused, and change your life.

Happy New Year! May this year bring you success and new opportunities 💪

Essential Data Visualization Tips for Analysts

Simplify Your Visuals: Avoid overcrowding with too much data.

Use Consistent Colors: Maintain uniformity for better readability.

Leverage Contrast: Highlight key insights using contrast.

Focus on Audience Needs: Tailor visuals for your target audience.

Label Clearly: Use concise and clear labels for charts and graphs.

Avoid Unnecessary 3D Effects: Stick to 2D for accurate representation.

Maintain Alignment: Ensure visuals are properly aligned for a professional look.

Tell a Story: Present insights in a logical flow for better comprehension.

Limit Chart Types: Use the right chart for the right data (e.g., bar, line, scatter).

Validate Data Accuracy: Always double-check your data sources and calculations.

Free Data Visualization Resources on WhatsApp
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https://whatsapp.com/channel/0029VaxaFzoEQIaujB31SO34

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