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
Share with credits: https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
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
Share with credits: https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
❤14👍10
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.
You can refer these Power BI Interview Resources to learn more: https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02
Like this post if you want me to continue this Power BI series 👍♥️
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.
You can refer these Power BI Interview Resources to learn more: https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02
Like this post if you want me to continue this Power BI series 👍♥️
👍28❤13🥰3
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.
Python Interview Resources: https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02
Join for more: https://news.1rj.ru/str/sqlspecialist
ENJOY LEARNING 👍👍
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.
Python Interview Resources: https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02
Join for more: https://news.1rj.ru/str/sqlspecialist
ENJOY LEARNING 👍👍
👍32❤13🔥4👏2🎉2🥰1
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.
I have curated best 80+ top-notch Data Analytics Resources 👇👇
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Like this post for more content like this 👍♥️
Share with credits: https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
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.
I have curated best 80+ top-notch Data Analytics Resources 👇👇
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Like this post for more content like this 👍♥️
Share with credits: https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
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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
I have curated best 80+ top-notch Data Analytics Resources 👇👇
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Like this post for more content like this 👍♥️
Share with credits: https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
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
I have curated best 80+ top-notch Data Analytics Resources 👇👇
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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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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!
Here you can find SQL Interview Resources👇
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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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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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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.
Here you can find essential Python Interview Resources👇
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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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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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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! 🚀
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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 👇👇
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👍25❤12🔥2👏2
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.
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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.
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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 💪
Work hard, stay focused, and change your life.
Happy New Year! May this year bring you success and new opportunities 💪
❤67👍16🥰5🎉4👏3
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.
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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
👇👇
https://whatsapp.com/channel/0029VaxaFzoEQIaujB31SO34
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Advanced SQL Optimization Tips for Data Analysts
Use Proper Indexing: Create indexes for frequently queried columns.
Avoid SELECT *: Specify only required columns to improve performance.
Use WHERE Instead of HAVING: Filter data early in the query.
Limit Joins: Avoid excessive joins to reduce query complexity.
Apply LIMIT or TOP: Retrieve only the required rows.
Optimize Joins: Use INNER JOIN over OUTER JOIN where applicable.
Use Temporary Tables: Break complex queries into smaller parts.
Avoid Functions on Indexed Columns: It prevents index usage.
Use CTEs for Readability: Simplify nested queries using Common Table Expressions.
Analyze Execution Plans: Identify bottlenecks and optimize queries.
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Use Proper Indexing: Create indexes for frequently queried columns.
Avoid SELECT *: Specify only required columns to improve performance.
Use WHERE Instead of HAVING: Filter data early in the query.
Limit Joins: Avoid excessive joins to reduce query complexity.
Apply LIMIT or TOP: Retrieve only the required rows.
Optimize Joins: Use INNER JOIN over OUTER JOIN where applicable.
Use Temporary Tables: Break complex queries into smaller parts.
Avoid Functions on Indexed Columns: It prevents index usage.
Use CTEs for Readability: Simplify nested queries using Common Table Expressions.
Analyze Execution Plans: Identify bottlenecks and optimize queries.
Here you can find SQL Interview Resources👇
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Top Python Libraries for Data Analysis
Pandas: For data manipulation and analysis.
NumPy: For numerical computations and array operations.
Matplotlib: For creating static visualizations.
Seaborn: For statistical data visualization.
SciPy: For advanced mathematical and scientific computations.
Scikit-learn: For machine learning tasks.
Statsmodels: For statistical modeling and hypothesis testing.
Plotly: For interactive visualizations.
OpenPyXL: For working with Excel files.
PySpark: For big data processing.
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Pandas: For data manipulation and analysis.
NumPy: For numerical computations and array operations.
Matplotlib: For creating static visualizations.
Seaborn: For statistical data visualization.
SciPy: For advanced mathematical and scientific computations.
Scikit-learn: For machine learning tasks.
Statsmodels: For statistical modeling and hypothesis testing.
Plotly: For interactive visualizations.
OpenPyXL: For working with Excel files.
PySpark: For big data processing.
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Data Visualization Tools & Best Practices
1. Power BI:
Purpose: Powerful business analytics tool to visualize and share insights from your data.
Best Practices:
Use simple visuals (avoid overloading with data).
Choose the right chart type (e.g., bar chart for comparisons, line chart for trends).
Use slicers and filters to allow users to explore data interactively.
Keep your color schemes consistent and avoid too many colors.
Use Tooltips for additional context without cluttering the visual.
2. Tableau:
Purpose: Data visualization tool used for creating interactive and shareable dashboards.
Best Practices:
Minimize clutter by reducing non-essential elements (e.g., gridlines, unnecessary labels).
Ensure readability with a clean and intuitive layout.
Use dual-axis charts when comparing two measures in a single visual.
Keep noscripts and labels concise; avoid redundant information.
Prioritize data integrity (avoid misleading visualizations).
3. Matplotlib & Seaborn (Python):
Purpose: Python libraries for static, animated, and interactive visualizations.
Best Practices:
Use subplots to visualize multiple charts together for comparison.
Keep axes readable with appropriate noscripts and labels.
Choose appropriate color palettes (e.g., Seaborn has good built-in color schemes).
Annotations can help clarify key points on the chart.
Use log scaling for large numerical ranges to make the data more interpretable.
4. Excel:
Purpose: Widely used tool for simple data analysis and visualization.
Best Practices:
Use pivot charts to summarize data interactively.
Stick to basic chart types (e.g., bar, line, pie) for easy-to-understand visuals.
Use conditional formatting to highlight key trends or outliers.
Label charts clearly (noscripts, axis names, and legends).
Limit the number of chart elements (don’t overcrowd your chart).
5. Google Data Studio:
Purpose: Free tool for creating dashboards and reports, often integrated with Google products.
Best Practices:
Link to live data sources for automatic updates (e.g., Google Sheets, Google Analytics).
Use dynamic filters to give users control over what data is shown.
Utilize templates for consistent reports and visuals.
Keep reports simple and focused on key metrics.
Design with mobile responsiveness in mind for accessibility.
6. Best Practices for Data Visualization:
Clarity over complexity: Simplify your visuals, removing unnecessary elements.
Choose the right chart: Select charts that best represent the data (e.g., bar for comparisons, line for trends).
Tell a story: Your visual should communicate a clear message or insight.
Consistency in design: Maintain a consistent style for fonts, colors, and layout across all visuals.
Be mindful of colorblindness: Use color schemes that are accessible to all viewers.
Provide context: Include clear noscripts, labels, and legends for better understanding.
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1. Power BI:
Purpose: Powerful business analytics tool to visualize and share insights from your data.
Best Practices:
Use simple visuals (avoid overloading with data).
Choose the right chart type (e.g., bar chart for comparisons, line chart for trends).
Use slicers and filters to allow users to explore data interactively.
Keep your color schemes consistent and avoid too many colors.
Use Tooltips for additional context without cluttering the visual.
2. Tableau:
Purpose: Data visualization tool used for creating interactive and shareable dashboards.
Best Practices:
Minimize clutter by reducing non-essential elements (e.g., gridlines, unnecessary labels).
Ensure readability with a clean and intuitive layout.
Use dual-axis charts when comparing two measures in a single visual.
Keep noscripts and labels concise; avoid redundant information.
Prioritize data integrity (avoid misleading visualizations).
3. Matplotlib & Seaborn (Python):
Purpose: Python libraries for static, animated, and interactive visualizations.
Best Practices:
Use subplots to visualize multiple charts together for comparison.
Keep axes readable with appropriate noscripts and labels.
Choose appropriate color palettes (e.g., Seaborn has good built-in color schemes).
Annotations can help clarify key points on the chart.
Use log scaling for large numerical ranges to make the data more interpretable.
4. Excel:
Purpose: Widely used tool for simple data analysis and visualization.
Best Practices:
Use pivot charts to summarize data interactively.
Stick to basic chart types (e.g., bar, line, pie) for easy-to-understand visuals.
Use conditional formatting to highlight key trends or outliers.
Label charts clearly (noscripts, axis names, and legends).
Limit the number of chart elements (don’t overcrowd your chart).
5. Google Data Studio:
Purpose: Free tool for creating dashboards and reports, often integrated with Google products.
Best Practices:
Link to live data sources for automatic updates (e.g., Google Sheets, Google Analytics).
Use dynamic filters to give users control over what data is shown.
Utilize templates for consistent reports and visuals.
Keep reports simple and focused on key metrics.
Design with mobile responsiveness in mind for accessibility.
6. Best Practices for Data Visualization:
Clarity over complexity: Simplify your visuals, removing unnecessary elements.
Choose the right chart: Select charts that best represent the data (e.g., bar for comparisons, line for trends).
Tell a story: Your visual should communicate a clear message or insight.
Consistency in design: Maintain a consistent style for fonts, colors, and layout across all visuals.
Be mindful of colorblindness: Use color schemes that are accessible to all viewers.
Provide context: Include clear noscripts, labels, and legends for better understanding.
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🌟 Data Cleaning Best Practices 🌟
✅ Remove Duplicates: Ensure data accuracy by eliminating duplicate rows.
✅ Handle Missing Values: Use imputation or remove rows/columns with missing data.
✅ Standardize Formats: Ensure consistency in date, time, and number formats.
✅ Remove Outliers: Identify and handle outliers to improve data quality.
✅ Trim Whitespace: Clean leading or trailing spaces in text fields.
✅ Correct Data Types: Convert columns to appropriate data types (e.g., numbers, dates).
✅ Normalize Data: Scale numerical values to a common range for better analysis.
✅ Use Consistent Naming: Standardize naming conventions for columns and variables.
✅ Check for Inconsistencies: Identify and correct mismatched categories or values.
✅ Validate Data: Cross-check data with original sources to ensure accuracy.
Data Cleaning WhatsApp Channel: https://whatsapp.com/channel/0029VarxgFqATRSpdUeHUA27
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✅ Remove Duplicates: Ensure data accuracy by eliminating duplicate rows.
✅ Handle Missing Values: Use imputation or remove rows/columns with missing data.
✅ Standardize Formats: Ensure consistency in date, time, and number formats.
✅ Remove Outliers: Identify and handle outliers to improve data quality.
✅ Trim Whitespace: Clean leading or trailing spaces in text fields.
✅ Correct Data Types: Convert columns to appropriate data types (e.g., numbers, dates).
✅ Normalize Data: Scale numerical values to a common range for better analysis.
✅ Use Consistent Naming: Standardize naming conventions for columns and variables.
✅ Check for Inconsistencies: Identify and correct mismatched categories or values.
✅ Validate Data: Cross-check data with original sources to ensure accuracy.
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Key Power BI Functions Every Analyst Should Master
DAX Functions:
1. CALCULATE():
Purpose: Modify context or filter data for calculations.
Example: CALCULATE(SUM(Sales[Amount]), Sales[Region] = "East")
2. SUM():
Purpose: Adds up column values.
Example: SUM(Sales[Amount])
3. AVERAGE():
Purpose: Calculates the mean of column values.
Example: AVERAGE(Sales[Amount])
4. RELATED():
Purpose: Fetch values from a related table.
Example: RELATED(Customers[Name])
5. FILTER():
Purpose: Create a subset of data for calculations.
Example: FILTER(Sales, Sales[Amount] > 100)
6. IF():
Purpose: Apply conditional logic.
Example: IF(Sales[Amount] > 1000, "High", "Low")
7. ALL():
Purpose: Removes filters to calculate totals.
Example: ALL(Sales[Region])
8. DISTINCT():
Purpose: Return unique values in a column.
Example: DISTINCT(Sales[Product])
9. RANKX():
Purpose: Rank values in a column.
Example: RANKX(ALL(Sales[Region]), SUM(Sales[Amount]))
10. FORMAT():
Purpose: Format numbers or dates as text.
Example: FORMAT(TODAY(), "MM/DD/YYYY")
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DAX Functions:
1. CALCULATE():
Purpose: Modify context or filter data for calculations.
Example: CALCULATE(SUM(Sales[Amount]), Sales[Region] = "East")
2. SUM():
Purpose: Adds up column values.
Example: SUM(Sales[Amount])
3. AVERAGE():
Purpose: Calculates the mean of column values.
Example: AVERAGE(Sales[Amount])
4. RELATED():
Purpose: Fetch values from a related table.
Example: RELATED(Customers[Name])
5. FILTER():
Purpose: Create a subset of data for calculations.
Example: FILTER(Sales, Sales[Amount] > 100)
6. IF():
Purpose: Apply conditional logic.
Example: IF(Sales[Amount] > 1000, "High", "Low")
7. ALL():
Purpose: Removes filters to calculate totals.
Example: ALL(Sales[Region])
8. DISTINCT():
Purpose: Return unique values in a column.
Example: DISTINCT(Sales[Product])
9. RANKX():
Purpose: Rank values in a column.
Example: RANKX(ALL(Sales[Region]), SUM(Sales[Amount]))
10. FORMAT():
Purpose: Format numbers or dates as text.
Example: FORMAT(TODAY(), "MM/DD/YYYY")
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Power BI DAX Functions Every Analyst Should Know
SUM(): Adds all values in a column.
AVERAGE(): Returns the average of a column.
COUNT(): Counts the number of rows in a column.
IF(): Performs conditional logic (True/False).
CALCULATE(): Modifies the context of a calculation.
FILTER(): Returns a table that represents a subset of another table.
ALL(): Removes filters from a table or column.
RELATED(): Retrieves related values from another table.
DISTINCT(): Returns unique values in a column.
DATEADD(): Shifts dates by a specified number of intervals (days, months, etc.).
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SUM(): Adds all values in a column.
AVERAGE(): Returns the average of a column.
COUNT(): Counts the number of rows in a column.
IF(): Performs conditional logic (True/False).
CALCULATE(): Modifies the context of a calculation.
FILTER(): Returns a table that represents a subset of another table.
ALL(): Removes filters from a table or column.
RELATED(): Retrieves related values from another table.
DISTINCT(): Returns unique values in a column.
DATEADD(): Shifts dates by a specified number of intervals (days, months, etc.).
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👍18❤10
Data Visualization Tools Comparison
Power BI:
Best for: Interactive dashboards and reports.
Strengths: Seamless integration with Microsoft products, strong DAX functions.
Weaknesses: Can be resource-heavy with large datasets.
Tableau:
Best for: Advanced data visualizations and storytelling.
Strengths: User-friendly drag-and-drop interface, powerful visual capabilities.
Weaknesses: Higher cost, steeper learning curve for complex analyses.
Excel:
Best for: Quick data analysis and small-scale visualizations.
Strengths: Widely used, simple to learn, great for quick charts.
Weaknesses: Limited in handling large datasets, fewer customization options.
Google Data Studio:
Best for: Free, cloud-based visualizations.
Strengths: Easy collaboration, integrates well with Google products.
Weaknesses: Fewer advanced features compared to Tableau and Power BI.
Free Resources: https://news.1rj.ru/str/PowerBI_analyst
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Power BI:
Best for: Interactive dashboards and reports.
Strengths: Seamless integration with Microsoft products, strong DAX functions.
Weaknesses: Can be resource-heavy with large datasets.
Tableau:
Best for: Advanced data visualizations and storytelling.
Strengths: User-friendly drag-and-drop interface, powerful visual capabilities.
Weaknesses: Higher cost, steeper learning curve for complex analyses.
Excel:
Best for: Quick data analysis and small-scale visualizations.
Strengths: Widely used, simple to learn, great for quick charts.
Weaknesses: Limited in handling large datasets, fewer customization options.
Google Data Studio:
Best for: Free, cloud-based visualizations.
Strengths: Easy collaboration, integrates well with Google products.
Weaknesses: Fewer advanced features compared to Tableau and Power BI.
Free Resources: https://news.1rj.ru/str/PowerBI_analyst
You can refer these Power BI Interview Resources to learn more: https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02
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Hope it helps :)
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