Essential Python Libraries for Data Analytics ๐๐
Python Free Resources: https://t.me/pythonresourcestp
1. NumPy:
- Efficient numerical operations and array manipulation.
2. Pandas:
- Data manipulation and analysis with powerful data structures (DataFrame, Series).
3. Matplotlib:
- 2D plotting library for creating visualizations.
4. Scikit-learn:
- Machine learning toolkit for classification, regression, clustering, etc.
5. TensorFlow:
- Open-source machine learning framework for building and deploying ML models.
6. PyTorch:
- Deep learning library, particularly popular for neural network research.
7. Django:
- High-level web framework for building robust, scalable web applications.
8. Flask:
- Lightweight web framework for building smaller web applications and APIs.
9. Requests:
- HTTP library for making HTTP requests.
10. Beautiful Soup:
- Web scraping library for pulling data out of HTML and XML files.
As a beginner, you can start with Pandas and Numpy libraries for data analysis. If you want to transition from Data Analyst to Data Scientist, then you can start applying ML libraries like Scikit-learn, Tensorflow, Pytorch, etc. in your data projects.
SQL for Data Analytics: https://t.me/sqlresourcestp
Hope it helps :)
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Python Free Resources: https://t.me/pythonresourcestp
1. NumPy:
- Efficient numerical operations and array manipulation.
2. Pandas:
- Data manipulation and analysis with powerful data structures (DataFrame, Series).
3. Matplotlib:
- 2D plotting library for creating visualizations.
4. Scikit-learn:
- Machine learning toolkit for classification, regression, clustering, etc.
5. TensorFlow:
- Open-source machine learning framework for building and deploying ML models.
6. PyTorch:
- Deep learning library, particularly popular for neural network research.
7. Django:
- High-level web framework for building robust, scalable web applications.
8. Flask:
- Lightweight web framework for building smaller web applications and APIs.
9. Requests:
- HTTP library for making HTTP requests.
10. Beautiful Soup:
- Web scraping library for pulling data out of HTML and XML files.
As a beginner, you can start with Pandas and Numpy libraries for data analysis. If you want to transition from Data Analyst to Data Scientist, then you can start applying ML libraries like Scikit-learn, Tensorflow, Pytorch, etc. in your data projects.
SQL for Data Analytics: https://t.me/sqlresourcestp
Hope it helps :)
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Data visualization tools
Data visualization tools provide you with an easier way to create visual representations of large data sets. When dealing with data sets that include bunches of data points, automating the process of creating a visualization, at least in part, makes your job significantly easier.
The best data visualization tools on the market should have one important thing in common. It is their ease of use. The best tools can also handle huge sets of data. And the last but not least, they can output an array of different chart, graph, and map types.
There are hundreds, of applications, tools, and scripts available to create visualizations of large data sets. Many are very basic and have a lot of overlapping features.
- Tableau (and Tableau Public (https://www.tableau.com/)
Hundreds of data import options. Mapping capability. Free public version available. Lots of video tutorials to walk you through how to use Tableau.
- Infogram (https://infogram.com/)
Tiered pricing, including a free plan with basic features. Includes 35+ chart types and 550+ map types. Drag and drop editor. API for importing additional data sources.
- ChartBlocks (https://www.chartblocks.com/en)
Free and reasonably priced paid plans are available. Easy to use wizard for importing the necessary data.
- Datawrapper (https://www.datawrapper.de/)
Specifically designed for newsroom data visualization. Free plan is a good fit for smaller sites. Tool includes a built-in color blindness checker.
- D3.js (https://d3js.org/)
A JavaScript library for manipulating documents using data. Very powerful and customizable. Huge number of chart types possible. A focus on web standards. Tools available to let non-programmers create visualizations. Free and open source.
- Looker Studio (Google Data Studio) (https://lookerstudio.google.com/overview)
Free data visualization tool that is specifically for creating interactive charts for embedding online. Easily access a wide variety of data.
- FusionCharts (https://www.fusioncharts.com/)
A JavaScript-based option for creating web and mobile dashboards. Huge number of chart and map format options. More features than most other visualization tools. Integrates with a number of different frameworks and programming languages.
- Chart.js (https://www.chartjs.org/)
A simple but flexible JavaScript charting library. Free and open source. Responsive and cross-browser compatible output.
- Grafana (https://grafana.com/)
Open source, with free and paid options available. Large selection of data sources available. Variety of chart types available. Makes creating dynamic dashboards simple. Can work with mixed data feeds.
4 FREE Data Analytics Courses: https://tinyurl.com/m239d2s8
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Data visualization tools provide you with an easier way to create visual representations of large data sets. When dealing with data sets that include bunches of data points, automating the process of creating a visualization, at least in part, makes your job significantly easier.
The best data visualization tools on the market should have one important thing in common. It is their ease of use. The best tools can also handle huge sets of data. And the last but not least, they can output an array of different chart, graph, and map types.
There are hundreds, of applications, tools, and scripts available to create visualizations of large data sets. Many are very basic and have a lot of overlapping features.
- Tableau (and Tableau Public (https://www.tableau.com/)
Hundreds of data import options. Mapping capability. Free public version available. Lots of video tutorials to walk you through how to use Tableau.
- Infogram (https://infogram.com/)
Tiered pricing, including a free plan with basic features. Includes 35+ chart types and 550+ map types. Drag and drop editor. API for importing additional data sources.
- ChartBlocks (https://www.chartblocks.com/en)
Free and reasonably priced paid plans are available. Easy to use wizard for importing the necessary data.
- Datawrapper (https://www.datawrapper.de/)
Specifically designed for newsroom data visualization. Free plan is a good fit for smaller sites. Tool includes a built-in color blindness checker.
- D3.js (https://d3js.org/)
A JavaScript library for manipulating documents using data. Very powerful and customizable. Huge number of chart types possible. A focus on web standards. Tools available to let non-programmers create visualizations. Free and open source.
- Looker Studio (Google Data Studio) (https://lookerstudio.google.com/overview)
Free data visualization tool that is specifically for creating interactive charts for embedding online. Easily access a wide variety of data.
- FusionCharts (https://www.fusioncharts.com/)
A JavaScript-based option for creating web and mobile dashboards. Huge number of chart and map format options. More features than most other visualization tools. Integrates with a number of different frameworks and programming languages.
- Chart.js (https://www.chartjs.org/)
A simple but flexible JavaScript charting library. Free and open source. Responsive and cross-browser compatible output.
- Grafana (https://grafana.com/)
Open source, with free and paid options available. Large selection of data sources available. Variety of chart types available. Makes creating dynamic dashboards simple. Can work with mixed data feeds.
4 FREE Data Analytics Courses: https://tinyurl.com/m239d2s8
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Useful websites to practice and enhance your Data Analytics skills
1. SQL
https://mode.com/sql-tutorial/introduction-to-sql
https://t.me/sqlresourcestp/10
2. Python
https://www.learnpython.org/
https://t.me/pythonresourcestp/6
https://www.geeksforgeeks.org/python-programming-language/learn-python-tutorial
3. R
https://www.datacamp.com/courses/free-introduction-to-r
4. Data Structures
https://leetcode.com/study-plan/data-structure/
https://t.me/techpsyche/241
https://www.udacity.com/course/data-structures-and-algorithms-in-python--ud513
5. Data Visualization
https://www.freecodecamp.org/learn/data-visualization/
https://t.me/dataanalysisresourcestp/47
https://www.tableau.com/learn/training/20223
https://www.workout-wednesday.com/power-bi-challenges/
6. Excel
https://excel-practice-online.com/
https://t.me/dataanalysisresourcestp/36
https://www.w3schools.com/EXCEL/index.php
ENJOY LEARNING ๐๐
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1. SQL
https://mode.com/sql-tutorial/introduction-to-sql
https://t.me/sqlresourcestp/10
2. Python
https://www.learnpython.org/
https://t.me/pythonresourcestp/6
https://www.geeksforgeeks.org/python-programming-language/learn-python-tutorial
3. R
https://www.datacamp.com/courses/free-introduction-to-r
4. Data Structures
https://leetcode.com/study-plan/data-structure/
https://t.me/techpsyche/241
https://www.udacity.com/course/data-structures-and-algorithms-in-python--ud513
5. Data Visualization
https://www.freecodecamp.org/learn/data-visualization/
https://t.me/dataanalysisresourcestp/47
https://www.tableau.com/learn/training/20223
https://www.workout-wednesday.com/power-bi-challenges/
6. Excel
https://excel-practice-online.com/
https://t.me/dataanalysisresourcestp/36
https://www.w3schools.com/EXCEL/index.php
ENJOY LEARNING ๐๐
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https://whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R
Essential tools and skills required to become a data analyst ๐๐
### Data Analysis and Visualization:
1. Microsoft Excel: Essential for data manipulation, analysis, and basic modeling.
2. SQL (Structured Query Language): Crucial for querying databases and extracting data for analysis.
3. Tableau or Power BI: Powerful tools for creating interactive dashboards and visualizing data.
### Programming and Data Manipulation:(Optional)
4. Python: Used for data manipulation, scripting, and automation.
5. R: Useful for statistical computing, data visualization, and basic analytics.
### Statistical Analysis:
6. Statistical Software (SPSS, SAS): Tools for advanced statistical analysis and modeling.(Optional)
7. Advanced Excel Functions: Proficiency in pivot tables, VLOOKUP, statistical functions, and data cleaning techniques.
### Project Management and Collaboration:(Optional)
8. Jira or Trello: Tools for project management, task tracking, and collaboration.
9. Confluence or SharePoint: Platforms for documentation, collaboration, and knowledge sharing.
### Business Process Management:(Optional)
10. Business Process Modeling Tools (Visio, Lucidchart): Used for modeling, analyzing, and optimizing business processes.
### Additional Skills:
11. Google Analytics: Important for understanding website traffic and user behavior. (Optional)
12. CRM Systems (Salesforce, HubSpot): Knowledge of these systems aids in analyzing sales data and customer interactions.(Optional)
13. Version Control (Git): Helps manage changes in analytical projects and ensures versioning control. (Optional)
### Data Warehousing and Database Management:
14. Data Warehousing (Amazon Redshift, Google BigQuery): Knowledge of these platforms for handling large-scale datasets and optimizing queries. (Optional)
### Soft Skills:
15. Communication: Clear and concise communication of findings and recommendations.
16. Problem-Solving & Critical Thinking: Ability to analyze complex problems and derive actionable insights.
I know this list might seem extensive, so it's best to begin with mastering Excel, Power BI, and SQL. As you progress, you can gradually add other tools from the list based on specific project needs and requirements.
Here are some essential telegram channels with important resources:
โฏ SQL โ t.me/sqlresourcestp
โฏ Data Analysis โ t.me/dataanalysisresourcestp
Also, try building projects & data portfolio while learning these skills. Creating data analytics projects will help you in showcasing the skills while giving job interviews.
Join t.me/techpsyche for more resources
ENJOY LEARNING๐๐
Share our channel link with your friends:
https://whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R
### Data Analysis and Visualization:
1. Microsoft Excel: Essential for data manipulation, analysis, and basic modeling.
2. SQL (Structured Query Language): Crucial for querying databases and extracting data for analysis.
3. Tableau or Power BI: Powerful tools for creating interactive dashboards and visualizing data.
### Programming and Data Manipulation:(Optional)
4. Python: Used for data manipulation, scripting, and automation.
5. R: Useful for statistical computing, data visualization, and basic analytics.
### Statistical Analysis:
6. Statistical Software (SPSS, SAS): Tools for advanced statistical analysis and modeling.(Optional)
7. Advanced Excel Functions: Proficiency in pivot tables, VLOOKUP, statistical functions, and data cleaning techniques.
### Project Management and Collaboration:(Optional)
8. Jira or Trello: Tools for project management, task tracking, and collaboration.
9. Confluence or SharePoint: Platforms for documentation, collaboration, and knowledge sharing.
### Business Process Management:(Optional)
10. Business Process Modeling Tools (Visio, Lucidchart): Used for modeling, analyzing, and optimizing business processes.
### Additional Skills:
11. Google Analytics: Important for understanding website traffic and user behavior. (Optional)
12. CRM Systems (Salesforce, HubSpot): Knowledge of these systems aids in analyzing sales data and customer interactions.(Optional)
13. Version Control (Git): Helps manage changes in analytical projects and ensures versioning control. (Optional)
### Data Warehousing and Database Management:
14. Data Warehousing (Amazon Redshift, Google BigQuery): Knowledge of these platforms for handling large-scale datasets and optimizing queries. (Optional)
### Soft Skills:
15. Communication: Clear and concise communication of findings and recommendations.
16. Problem-Solving & Critical Thinking: Ability to analyze complex problems and derive actionable insights.
I know this list might seem extensive, so it's best to begin with mastering Excel, Power BI, and SQL. As you progress, you can gradually add other tools from the list based on specific project needs and requirements.
Here are some essential telegram channels with important resources:
โฏ SQL โ t.me/sqlresourcestp
โฏ Data Analysis โ t.me/dataanalysisresourcestp
Also, try building projects & data portfolio while learning these skills. Creating data analytics projects will help you in showcasing the skills while giving job interviews.
Join t.me/techpsyche for more resources
ENJOY LEARNING๐๐
Share our channel link with your friends:
https://whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R
There has never been a better time to become a data analyst.
Tackle the tools:
- Excel
- SQL
- PowerBI/Tableau
- Python/R
Sharpen these soft skills:
- Communication
- Storytelling
- Critical thinking
- Business acumen
And let your journey begin.
Tackle the tools:
- Excel
- SQL
- PowerBI/Tableau
- Python/R
Sharpen these soft skills:
- Communication
- Storytelling
- Critical thinking
- Business acumen
And let your journey begin.
Power BI Questions Scenario based questions ๐๐
๐ Scenario 1:Question: Imagine you need to visualize year-over-year growth in product sales. What approach would you take to calculate and present this information effectively in Power BI?
Answer: To visualize year-over-year growth in product sales, I would first calculate the sales for each product for the current year and the previous year using DAX measures in Power BI. Then, I would create a line chart visual where the x-axis represents the months or quarters, and the y-axis represents the sales amount. I would plot two lines on the chart, one for the current year's sales and one for the previous year's sales, allowing stakeholders to easily compare the growth trends over time.
๐ Scenario 2: Question: You're working with a dataset that requires extensive data cleaning and transformation before analysis. Describe your process for cleaning and preparing the data in Power BI, ensuring accuracy and efficiency.
Answer: For cleaning and preparing the dataset in Power BI, I would start by identifying and addressing missing or duplicate values, outliers, and inconsistencies in data formats. I would use Power Query Editor to perform data cleaning operations such as removing null values, renaming columns, and applying transformations like data type conversion and standardization. Additionally, I would create calculated columns or measures as needed to derive new insights from the cleaned data.
๐ Scenario 3: Question: Your organization wants to incorporate real-time data updates into their Power BI reports. How would you set up and manage live data connections in Power BI to ensure timely insights?
Answer: To incorporate real-time data updates into Power BI reports, I would utilize Power BI's streaming datasets feature. I would set up a data streaming connection to the source system, such as a database or API, and configure the dataset to receive real-time data updates at specified intervals. Then, I would design reports and visuals based on the streaming dataset, enabling stakeholders to view and analyze the latest data as it is updated in real-time.
โก๏ธ Scenario 4: Question: You've noticed that your Power BI reports are taking longer to load and refresh than usual. How would you diagnose and address performance issues to optimize report performance?
Answer: If Power BI reports are experiencing performance issues, I would first identify potential bottlenecks by analyzing factors such as data volume, query complexity, and visual design. Then, I would optimize report performance by applying techniques such as data model optimization, query optimization, and visualization best practices.
Power BI Learning Plan: https://t.me/dataanalysisresourcestp/39
Hope you'll like it
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๐ Scenario 1:Question: Imagine you need to visualize year-over-year growth in product sales. What approach would you take to calculate and present this information effectively in Power BI?
Answer: To visualize year-over-year growth in product sales, I would first calculate the sales for each product for the current year and the previous year using DAX measures in Power BI. Then, I would create a line chart visual where the x-axis represents the months or quarters, and the y-axis represents the sales amount. I would plot two lines on the chart, one for the current year's sales and one for the previous year's sales, allowing stakeholders to easily compare the growth trends over time.
๐ Scenario 2: Question: You're working with a dataset that requires extensive data cleaning and transformation before analysis. Describe your process for cleaning and preparing the data in Power BI, ensuring accuracy and efficiency.
Answer: For cleaning and preparing the dataset in Power BI, I would start by identifying and addressing missing or duplicate values, outliers, and inconsistencies in data formats. I would use Power Query Editor to perform data cleaning operations such as removing null values, renaming columns, and applying transformations like data type conversion and standardization. Additionally, I would create calculated columns or measures as needed to derive new insights from the cleaned data.
๐ Scenario 3: Question: Your organization wants to incorporate real-time data updates into their Power BI reports. How would you set up and manage live data connections in Power BI to ensure timely insights?
Answer: To incorporate real-time data updates into Power BI reports, I would utilize Power BI's streaming datasets feature. I would set up a data streaming connection to the source system, such as a database or API, and configure the dataset to receive real-time data updates at specified intervals. Then, I would design reports and visuals based on the streaming dataset, enabling stakeholders to view and analyze the latest data as it is updated in real-time.
โก๏ธ Scenario 4: Question: You've noticed that your Power BI reports are taking longer to load and refresh than usual. How would you diagnose and address performance issues to optimize report performance?
Answer: If Power BI reports are experiencing performance issues, I would first identify potential bottlenecks by analyzing factors such as data volume, query complexity, and visual design. Then, I would optimize report performance by applying techniques such as data model optimization, query optimization, and visualization best practices.
Power BI Learning Plan: https://t.me/dataanalysisresourcestp/39
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Power BI interview questions and answers ๐๐
1. Question: What is Power BI?
Answer: Power BI is a business analytics service by Microsoft that provides interactive visualizations and business intelligence capabilities with an interface simple enough for end-users to create their reports and dashboards.
2. Question: Differentiate between Power BI Desktop, Power BI Service, and Power BI Mobile.
Answer: Power BI Desktop is used for creating reports, Power BI Service (or Power BI Online) is the cloud service for sharing and collaborating on reports, and Power BI Mobile allows users to access reports on mobile devices.
3. Question: Explain the role of Power Query in Power BI.
Answer: Power Query is used for data transformation and shaping. It allows users to connect to various data sources, clean and transform data before loading it into Power BI for analysis.
4. Question: What is DAX in Power BI, and why is it important?
Answer: DAX (Data Analysis Expressions) is a formula language used for creating custom calculations in Power BI. It is important as it enables users to create sophisticated measures and calculated columns.
5. Question: How do you create relationships between tables in Power BI?
Answer: In Power BI Desktop, go to the "Model" view, drag and drop fields from one table to another to create relationships based on common keys.
6. Question: What is the difference between a calculated column and a measure in Power BI?
Answer: A calculated column is a column added to a table, computed row by row, while a measure is a formula applied to a set of data, providing a dynamic calculation based on the context.
7. Question: How can you implement row-level security in Power BI?
Answer: Row-level security in Power BI can be implemented by creating roles in Power BI Desktop and defining filters at the row level based on user roles.
8. Question: Explain the purpose of the Power BI Gateway.
Answer: The Power BI Gateway allows for a secure connection between Power BI services and on-premises data sources. It facilitates refreshing datasets and running scheduled refreshes.
9. Question: What is a Power BI dashboard?
Answer: A Power BI dashboard is a single-page, interactive view of your data that provides a consolidated and visualized summary of key metrics. It can include visuals, images, and live data.
10. Question: How can you share a Power BI report with others?
Answer: Power BI reports can be shared through the Power BI service. Publish the report to the Power BI service, and then share it with specific users or distribute it widely within an organization.
Power BI Questions Scenario based questions ๐๐
https://t.me/dataanalysisresourcestp/52
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1. Question: What is Power BI?
Answer: Power BI is a business analytics service by Microsoft that provides interactive visualizations and business intelligence capabilities with an interface simple enough for end-users to create their reports and dashboards.
2. Question: Differentiate between Power BI Desktop, Power BI Service, and Power BI Mobile.
Answer: Power BI Desktop is used for creating reports, Power BI Service (or Power BI Online) is the cloud service for sharing and collaborating on reports, and Power BI Mobile allows users to access reports on mobile devices.
3. Question: Explain the role of Power Query in Power BI.
Answer: Power Query is used for data transformation and shaping. It allows users to connect to various data sources, clean and transform data before loading it into Power BI for analysis.
4. Question: What is DAX in Power BI, and why is it important?
Answer: DAX (Data Analysis Expressions) is a formula language used for creating custom calculations in Power BI. It is important as it enables users to create sophisticated measures and calculated columns.
5. Question: How do you create relationships between tables in Power BI?
Answer: In Power BI Desktop, go to the "Model" view, drag and drop fields from one table to another to create relationships based on common keys.
6. Question: What is the difference between a calculated column and a measure in Power BI?
Answer: A calculated column is a column added to a table, computed row by row, while a measure is a formula applied to a set of data, providing a dynamic calculation based on the context.
7. Question: How can you implement row-level security in Power BI?
Answer: Row-level security in Power BI can be implemented by creating roles in Power BI Desktop and defining filters at the row level based on user roles.
8. Question: Explain the purpose of the Power BI Gateway.
Answer: The Power BI Gateway allows for a secure connection between Power BI services and on-premises data sources. It facilitates refreshing datasets and running scheduled refreshes.
9. Question: What is a Power BI dashboard?
Answer: A Power BI dashboard is a single-page, interactive view of your data that provides a consolidated and visualized summary of key metrics. It can include visuals, images, and live data.
10. Question: How can you share a Power BI report with others?
Answer: Power BI reports can be shared through the Power BI service. Publish the report to the Power BI service, and then share it with specific users or distribute it widely within an organization.
Power BI Questions Scenario based questions ๐๐
https://t.me/dataanalysisresourcestp/52
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Top 15 advanced Power BI interview questions
1. Explain the concept of row-level security in Power BI and how to implement it.
2. What are calculated tables in Power BI, and when would you use them?
3. Describe the differences between DirectQuery, Live Connection, and Import Data storage modes in Power BI.
4. How can you optimize the performance of a Power BI report or dashboard with large datasets?
5. What is the DAX language, and how is it used in Power BI? Provide an example of a complex DAX calculation.
6. Explain the role of Power Query in data transformation within Power BI. What are some common data cleansing techniques in Power Query?
7. What is the purpose of the Power BI Data Model, and how do relationships between tables impact report development?
8. How can you create custom visuals or extensions in Power BI? Provide an example of when you would use custom visuals.
9. Describe the steps involved in setting up Power BI Gateway and its significance in a corporate environment.
10. What are the differences between Power BI Desktop, Power BI Service, and Power BI Mobile? How do they work together in a typical Power BI workflow?
11. Discuss the process of incremental data refresh in Power BI and its benefits.
12. How can you implement dynamic security roles in Power BI, and why might you need them in a multi-user environment?
13. What are Power BI paginated reports, and when would you choose to use them over standard interactive reports?
14. Explain the concept of drill-through in Power BI, including its configuration and use cases.
15. How can you integrate Power BI with other Microsoft products, such as Azure Data Lake Storage or SharePoint?
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Scenario Based Questions: https://t.me/dataanalysisresourcestp/52
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1. Explain the concept of row-level security in Power BI and how to implement it.
2. What are calculated tables in Power BI, and when would you use them?
3. Describe the differences between DirectQuery, Live Connection, and Import Data storage modes in Power BI.
4. How can you optimize the performance of a Power BI report or dashboard with large datasets?
5. What is the DAX language, and how is it used in Power BI? Provide an example of a complex DAX calculation.
6. Explain the role of Power Query in data transformation within Power BI. What are some common data cleansing techniques in Power Query?
7. What is the purpose of the Power BI Data Model, and how do relationships between tables impact report development?
8. How can you create custom visuals or extensions in Power BI? Provide an example of when you would use custom visuals.
9. Describe the steps involved in setting up Power BI Gateway and its significance in a corporate environment.
10. What are the differences between Power BI Desktop, Power BI Service, and Power BI Mobile? How do they work together in a typical Power BI workflow?
11. Discuss the process of incremental data refresh in Power BI and its benefits.
12. How can you implement dynamic security roles in Power BI, and why might you need them in a multi-user environment?
13. What are Power BI paginated reports, and when would you choose to use them over standard interactive reports?
14. Explain the concept of drill-through in Power BI, including its configuration and use cases.
15. How can you integrate Power BI with other Microsoft products, such as Azure Data Lake Storage or SharePoint?
Hope you'll like it
Scenario Based Questions: https://t.me/dataanalysisresourcestp/52
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Short roadmap to learn Tableau ๐๐
1. Getting Started:
- Download and install Tableau Public (free) or Tableau Desktop (trial version).
- Explore the Tableau interface to get familiar with its components.
2. Data Connection:
- Learn to connect Tableau to your data sources like Excel, CSV, databases, or cloud services.
3. Data Preparation:
- Understand how to clean and shape data in Tableau using the Data Source tab.
4. Basic Visualization:
- Create simple visualizations like bar charts, line charts, and scatter plots.
5. Calculations:
- Learn about calculated fields and basic functions for more complex data transformations.
6. Dashboards and Stories:
- Explore creating interactive dashboards and stories to present your insights effectively.
7. Advanced Visualizations:
- Dive into more advanced charts and graphs, such as heat maps, treemaps, and dual-axis charts.
8. Advanced Calculations:
- Master advanced calculations, such as level of detail (LOD) expressions and table calculations.
9. Mapping:
- Learn how to create maps and geospatial visualizations using Tableau's mapping features.
10. Data Blending:
- Understand how to blend data from multiple sources for comprehensive analysis.
11. Performance Optimization:
- Optimize the performance of your Tableau workbooks for larger datasets.
12. Tableau Server (Optional):
- If needed, explore Tableau Server for collaboration and sharing.
13. Online Resources:
- Utilize online tutorials, documentation, and forums to expand your knowledge.
14. Practice:
- Work on real-world projects to apply what you've learned. Remember to practice and apply your knowledge as you progress through each stage.
15. Certification (Optional):
- Consider pursuing Tableau certification for formal recognition of your skills.
Websites to enhance your Data Analytics Skill: https://t.me/dataanalysisresourcestp/48
Hope it helps :)
WhatsApp Channel:
https://whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R
1. Getting Started:
- Download and install Tableau Public (free) or Tableau Desktop (trial version).
- Explore the Tableau interface to get familiar with its components.
2. Data Connection:
- Learn to connect Tableau to your data sources like Excel, CSV, databases, or cloud services.
3. Data Preparation:
- Understand how to clean and shape data in Tableau using the Data Source tab.
4. Basic Visualization:
- Create simple visualizations like bar charts, line charts, and scatter plots.
5. Calculations:
- Learn about calculated fields and basic functions for more complex data transformations.
6. Dashboards and Stories:
- Explore creating interactive dashboards and stories to present your insights effectively.
7. Advanced Visualizations:
- Dive into more advanced charts and graphs, such as heat maps, treemaps, and dual-axis charts.
8. Advanced Calculations:
- Master advanced calculations, such as level of detail (LOD) expressions and table calculations.
9. Mapping:
- Learn how to create maps and geospatial visualizations using Tableau's mapping features.
10. Data Blending:
- Understand how to blend data from multiple sources for comprehensive analysis.
11. Performance Optimization:
- Optimize the performance of your Tableau workbooks for larger datasets.
12. Tableau Server (Optional):
- If needed, explore Tableau Server for collaboration and sharing.
13. Online Resources:
- Utilize online tutorials, documentation, and forums to expand your knowledge.
14. Practice:
- Work on real-world projects to apply what you've learned. Remember to practice and apply your knowledge as you progress through each stage.
15. Certification (Optional):
- Consider pursuing Tableau certification for formal recognition of your skills.
Websites to enhance your Data Analytics Skill: https://t.me/dataanalysisresourcestp/48
Hope it helps :)
WhatsApp Channel:
https://whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R
Important Power BI Interview Questions
1. What techniques can be used to reduce the file size of a Power BI report?
2. Describe the different layers involved in Power BI architecture.
3. How do you implement data lineage in Power BI?
4. What is the role of the M language in Power BI?
5. How do you create a gauge chart in Power BI?
6. What is the difference between a heat map and a filled map in Power BI?
7. Explain the concept of data masking in Power BI and its use cases.
8. What is the function of the "Append Queries" feature in Power BI, and how is it used?
9. How can you ensure that Power BI recognizes a specific column as a date column if it doesn't do so automatically?
10. Describe the process Power BI uses to handle large datasets exceeding the in-memory capacity.
11. Can you explain the role of the Power BI service in the overall Power BI architecture?
12. What are the key components of data modeling in Power BI?
13. What is the purpose of the VertiPaq engine in Power BI?
14. How do you create a stacked area chart in Power BI?
15. What is the difference between a clustered bar chart and a stacked bar chart?
16. Explain the concept of role-based access control (RBAC) in Power BI.
17. What is a calculated column in Power BI, and how is it different from a measure?
18. How can you create and apply a custom data category in Power BI?
19. What are the different methods to optimize data load performance in Power BI?
20. Can you outline the Power BI ecosystem and its major components?
21. What is the difference between a dataflow and a dataset in Power BI?
22. How does the DirectQuery mode work in Power BI, and when would you use it?
23. How do you create a waterfall chart in Power BI?
24. What are the advantages and disadvantages of using a scatter plot in Power BI?
25. Explain the concept of incremental refresh in Power BI.
26. What is the purpose of the "Group By" feature in Power BI, and how is it used?
27. How can you handle time zone conversions in Power BI?
Power BI Learning Resources ๐๐
https://www.freecodecamp.org/learn/data-visualization/
https://t.me/dataanalysisresourcestp/7
https://www.workout-wednesday.com/power-bi-challenges/
Hope you'll like it
Like this post if you need more content like this ๐โค๏ธ
WhatsApp Channel:
https://whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R
1. What techniques can be used to reduce the file size of a Power BI report?
2. Describe the different layers involved in Power BI architecture.
3. How do you implement data lineage in Power BI?
4. What is the role of the M language in Power BI?
5. How do you create a gauge chart in Power BI?
6. What is the difference between a heat map and a filled map in Power BI?
7. Explain the concept of data masking in Power BI and its use cases.
8. What is the function of the "Append Queries" feature in Power BI, and how is it used?
9. How can you ensure that Power BI recognizes a specific column as a date column if it doesn't do so automatically?
10. Describe the process Power BI uses to handle large datasets exceeding the in-memory capacity.
11. Can you explain the role of the Power BI service in the overall Power BI architecture?
12. What are the key components of data modeling in Power BI?
13. What is the purpose of the VertiPaq engine in Power BI?
14. How do you create a stacked area chart in Power BI?
15. What is the difference between a clustered bar chart and a stacked bar chart?
16. Explain the concept of role-based access control (RBAC) in Power BI.
17. What is a calculated column in Power BI, and how is it different from a measure?
18. How can you create and apply a custom data category in Power BI?
19. What are the different methods to optimize data load performance in Power BI?
20. Can you outline the Power BI ecosystem and its major components?
21. What is the difference between a dataflow and a dataset in Power BI?
22. How does the DirectQuery mode work in Power BI, and when would you use it?
23. How do you create a waterfall chart in Power BI?
24. What are the advantages and disadvantages of using a scatter plot in Power BI?
25. Explain the concept of incremental refresh in Power BI.
26. What is the purpose of the "Group By" feature in Power BI, and how is it used?
27. How can you handle time zone conversions in Power BI?
Power BI Learning Resources ๐๐
https://www.freecodecamp.org/learn/data-visualization/
https://t.me/dataanalysisresourcestp/7
https://www.workout-wednesday.com/power-bi-challenges/
Hope you'll like it
Like this post if you need more content like this ๐โค๏ธ
WhatsApp Channel:
https://whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R
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GLBC in UK is Inviting Applications For: 2025 Global Leadership & Business Certification, Cambridge
- Type: Training
- Location: Clare College, Cambridge
- Duration: 5 Days
- Eligible Countries : All
- Deadline: 18th April, 2025
Funding Type:
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Benefits:
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Apply here:
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- Type: Training
- Location: Clare College, Cambridge
- Duration: 5 Days
- Eligible Countries : All
- Deadline: 18th April, 2025
Funding Type:
- Fully
- Partial and
- Self Funded
Benefits:
- Air ticket
- Accommodation
- Certificate
- Conference kits, etc
Grab the Fully Funded seat on time because the seat is limited!
Apply here:
https://kenyatrends.co.ke/glbc-in-uk-is-inviting-applications-for-2025-global-leadership-business-certification-cambridge
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In ๐๐จ๐ฐ๐๐ซ ๐๐, data transformation, and cleaning are crucial steps in preparing your data for analysis and visualization. Power BI provides a range of tools and functionalities to perform these tasks efficiently.
1- ๐๐๐ญ๐ ๐๐จ๐ฎ๐ซ๐๐ ๐๐จ๐ง๐ง๐๐๐ญ๐ข๐ฏ๐ข๐ญ๐ฒ:
Connect to your data source(s) by selecting the appropriate connector from Power BI's extensive list. This can include databases, files (such as Excel or CSV), online services, or even custom data sources.
2- ๐๐๐ญ๐ ๐๐จ๐๐ ๐๐ง๐ ๐๐ฎ๐๐ซ๐ฒ ๐๐๐ข๐ญ๐จ๐ซ:
Once connected, Power BI's Query Editor provides a user-friendly interface for transforming and cleaning your data before loading it into your data model.
Click on "Transform Data" or "Edit Queries" to open the Query Editor.
3 - ๐๐๐ญ๐ ๐๐ซ๐๐ง๐ฌ๐๐จ๐ซ๐ฆ๐๐ญ๐ข๐จ๐ง:
Use the Query Editor's transformation capabilities to perform various data manipulation tasks,
such as:
- ๐๐๐ง๐๐ฆ๐ข๐ง๐ ๐๐จ๐ฅ๐ฎ๐ฆ๐ง๐ฌ : Rename columns to make them more descriptive.
- ๐๐๐ฆ๐จ๐ฏ๐ข๐ง๐ ๐๐จ๐ฅ๐ฎ๐ฆ๐ง๐ฌ : Remove unnecessary columns from your dataset.
- ๐๐ก๐๐ง๐ ๐ข๐ง๐ ๐๐๐ญ๐ ๐ญ๐ฒ๐ฉ๐๐ฌ : Convert data types (e.g., from text to date or number).
- ๐๐๐๐ข๐ง๐ ๐จ๐ซ ๐ซ๐๐ฆ๐จ๐ฏ๐ข๐ง๐ ๐ซ๐จ๐ฐ๐ฌ : Filter out unwanted rows or add calculated rows.
- ๐๐ฉ๐ฅ๐ข๐ญ๐ญ๐ข๐ง๐ ๐จ๐ซ ๐ฆ๐๐ซ๐ ๐ข๐ง๐ ๐๐จ๐ฅ๐ฎ๐ฆ๐ง๐ฌ : Split columns based on delimiters or merge columns together.
- ๐๐ฉ๐ฉ๐ฅ๐ฒ๐ข๐ง๐ ๐ญ๐ซ๐๐ง๐ฌ๐๐จ๐ซ๐ฆ๐๐ญ๐ข๐จ๐ง๐ฌ : Apply standard transformations such as sorting, filtering, and grouping.
4 - ๐๐๐ญ๐ ๐๐ฅ๐๐๐ง๐ข๐ง๐ :
Clean your data to ensure accuracy and consistency, which may include:
- ๐๐๐ง๐๐ฅ๐ข๐ง๐ ๐ฆ๐ข๐ฌ๐ฌ๐ข๐ง๐ ๐ฏ๐๐ฅ๐ฎ๐๐ฌ: Replace or remove missing values as appropriate.
- ๐๐ญ๐๐ง๐๐๐ซ๐๐ข๐ณ๐ข๐ง๐ ๐๐๐ญ๐ ๐๐จ๐ซ๐ฆ๐๐ญ๐ฌ: Ensure consistency in date formats, text capitalization, etc.
- ๐๐๐ฆ๐จ๐ฏ๐ข๐ง๐ ๐๐ฎ๐ฉ๐ฅ๐ข๐๐๐ญ๐๐ฌ: Identify and remove duplicate records from your dataset.
- ๐๐จ๐ซ๐ซ๐๐๐ญ๐ข๐ง๐ ๐๐ซ๐ซ๐จ๐ซ๐ฌ: Identify and correct any errors or inconsistencies in your data.
- ๐๐๐ง๐๐ฅ๐ข๐ง๐ ๐จ๐ฎ๐ญ๐ฅ๐ข๐๐ซ๐ฌ: Address outliers or anomalies in your data through filtering or transformations.
5 - ๐๐๐ฏ๐๐ง๐๐๐ ๐๐ซ๐๐ง๐ฌ๐๐จ๐ซ๐ฆ๐๐ญ๐ข๐จ๐ง๐ฌ:
Power BI's Query Editor also supports more advanced transformations using Power Query M language or DAX expressions. This allows for complex data manipulation and calculations tailored to your specific requirements.
Data Load:
Once you've completed your transformations and cleaning, click on "Close & Load" to load the cleaned data into Power BI's data model for analysis and visualization.
6- ๐๐ฎ๐ญ๐จ๐ฆ๐๐ญ๐ข๐ง๐ ๐๐๐๐ซ๐๐ฌ๐ก:
Set up automated data refresh schedules to ensure that your data stays up-to-date with the latest changes from your data sources.
I have curated the best interview resources to crack Power BI Interviews ๐๐
https://t.me/dataanalysisresourcestp/58
Hope you'll like it
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1- ๐๐๐ญ๐ ๐๐จ๐ฎ๐ซ๐๐ ๐๐จ๐ง๐ง๐๐๐ญ๐ข๐ฏ๐ข๐ญ๐ฒ:
Connect to your data source(s) by selecting the appropriate connector from Power BI's extensive list. This can include databases, files (such as Excel or CSV), online services, or even custom data sources.
2- ๐๐๐ญ๐ ๐๐จ๐๐ ๐๐ง๐ ๐๐ฎ๐๐ซ๐ฒ ๐๐๐ข๐ญ๐จ๐ซ:
Once connected, Power BI's Query Editor provides a user-friendly interface for transforming and cleaning your data before loading it into your data model.
Click on "Transform Data" or "Edit Queries" to open the Query Editor.
3 - ๐๐๐ญ๐ ๐๐ซ๐๐ง๐ฌ๐๐จ๐ซ๐ฆ๐๐ญ๐ข๐จ๐ง:
Use the Query Editor's transformation capabilities to perform various data manipulation tasks,
such as:
- ๐๐๐ง๐๐ฆ๐ข๐ง๐ ๐๐จ๐ฅ๐ฎ๐ฆ๐ง๐ฌ : Rename columns to make them more descriptive.
- ๐๐๐ฆ๐จ๐ฏ๐ข๐ง๐ ๐๐จ๐ฅ๐ฎ๐ฆ๐ง๐ฌ : Remove unnecessary columns from your dataset.
- ๐๐ก๐๐ง๐ ๐ข๐ง๐ ๐๐๐ญ๐ ๐ญ๐ฒ๐ฉ๐๐ฌ : Convert data types (e.g., from text to date or number).
- ๐๐๐๐ข๐ง๐ ๐จ๐ซ ๐ซ๐๐ฆ๐จ๐ฏ๐ข๐ง๐ ๐ซ๐จ๐ฐ๐ฌ : Filter out unwanted rows or add calculated rows.
- ๐๐ฉ๐ฅ๐ข๐ญ๐ญ๐ข๐ง๐ ๐จ๐ซ ๐ฆ๐๐ซ๐ ๐ข๐ง๐ ๐๐จ๐ฅ๐ฎ๐ฆ๐ง๐ฌ : Split columns based on delimiters or merge columns together.
- ๐๐ฉ๐ฉ๐ฅ๐ฒ๐ข๐ง๐ ๐ญ๐ซ๐๐ง๐ฌ๐๐จ๐ซ๐ฆ๐๐ญ๐ข๐จ๐ง๐ฌ : Apply standard transformations such as sorting, filtering, and grouping.
4 - ๐๐๐ญ๐ ๐๐ฅ๐๐๐ง๐ข๐ง๐ :
Clean your data to ensure accuracy and consistency, which may include:
- ๐๐๐ง๐๐ฅ๐ข๐ง๐ ๐ฆ๐ข๐ฌ๐ฌ๐ข๐ง๐ ๐ฏ๐๐ฅ๐ฎ๐๐ฌ: Replace or remove missing values as appropriate.
- ๐๐ญ๐๐ง๐๐๐ซ๐๐ข๐ณ๐ข๐ง๐ ๐๐๐ญ๐ ๐๐จ๐ซ๐ฆ๐๐ญ๐ฌ: Ensure consistency in date formats, text capitalization, etc.
- ๐๐๐ฆ๐จ๐ฏ๐ข๐ง๐ ๐๐ฎ๐ฉ๐ฅ๐ข๐๐๐ญ๐๐ฌ: Identify and remove duplicate records from your dataset.
- ๐๐จ๐ซ๐ซ๐๐๐ญ๐ข๐ง๐ ๐๐ซ๐ซ๐จ๐ซ๐ฌ: Identify and correct any errors or inconsistencies in your data.
- ๐๐๐ง๐๐ฅ๐ข๐ง๐ ๐จ๐ฎ๐ญ๐ฅ๐ข๐๐ซ๐ฌ: Address outliers or anomalies in your data through filtering or transformations.
5 - ๐๐๐ฏ๐๐ง๐๐๐ ๐๐ซ๐๐ง๐ฌ๐๐จ๐ซ๐ฆ๐๐ญ๐ข๐จ๐ง๐ฌ:
Power BI's Query Editor also supports more advanced transformations using Power Query M language or DAX expressions. This allows for complex data manipulation and calculations tailored to your specific requirements.
Data Load:
Once you've completed your transformations and cleaning, click on "Close & Load" to load the cleaned data into Power BI's data model for analysis and visualization.
6- ๐๐ฎ๐ญ๐จ๐ฆ๐๐ญ๐ข๐ง๐ ๐๐๐๐ซ๐๐ฌ๐ก:
Set up automated data refresh schedules to ensure that your data stays up-to-date with the latest changes from your data sources.
I have curated the best interview resources to crack Power BI Interviews ๐๐
https://t.me/dataanalysisresourcestp/58
Hope you'll like it
Like this post if you need more content like this ๐โค๏ธ
Follow this WhatsApp Channel for More Resources:
https://whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R
โค1
Complete structured Power BI syllabus for data analysts
1. Introduction to Power BI
Overview of Power BI Desktop, Service, and licensing. Power BI workflow: data import, transform, model, visualize, and publish.
2. Connecting to Data Sources
Connecting to Excel, CSV, SQL Server, and others. Import vs. Direct Query, data refresh, and scheduling.
3. Power Query Editor (ETL)
Data transformation: removing duplicates, filtering, changing data types. Merging and appending queries, introduction to the M Language.
4. Data Modeling
Creating relationships between tables, star schema design, calculated columns/tables. Understanding cardinality, cross-filtering, and data categorization.
5. DAX (Data Analysis Expressions)
Basic DAX syntax, measures, and calculated columns. Aggregation, logical, and filter functions (SUM, IF, CALCULATE). Time intelligence functions for dynamic calculations (YTD, MTD).
6. Visualizations
Building visuals (bar, line, pie, etc.), custom visuals. Formatting and interactions (drill-down, cross-filtering). Maps and geographical visualizations, hierarchies.
7. Filters and Slicers
Using filters at different levels (report, page, visual). Slicers and drill-through filters for report interaction.
8. Publishing and Sharing
Publishing reports, creating dashboards, sharing reports. Workspaces, embedding reports, Power BI Apps.
9. Row-Level Security (RLS)
Implementing RLS to manage user access to data.
10. Power BI Mobile
Optimizing reports for mobile view.
11. Dataflows and Datasets
Creating reusable dataflows, linking datasets, and composite models.
12. Bookmarks and Buttons
Using bookmarks for report storytelling, buttons for navigation.
13. AI Capabilities
AI visuals (Decomposition Tree, Key Influencers), integrating Azure AI.
14. Paginated Reports
Creating pixel-perfect paginated reports for detailed data representation.
15. Data Refresh
Scheduled/manual refresh, data gateways for on-premises data.
16. Power BI and SQL
Direct connection to SQL, using stored procedures with Power BI.
17. Performance Optimization
Best practices for optimizing performance and report load times.
18. Real-Time Dashboards
Streaming datasets, live dashboards with APIs, and real-time data integration.
19. Case Studies and Projects
Building industry-specific dashboards (finance, sales, marketing), real-world scenarios.
I have curated the best interview resources to crack Power BI Interviews ๐๐
https://t.me/dataanalysisresourcestp/54
Hope you'll like it
Like this post if you need more content like this ๐โค๏ธ
Follow this WhatsApp Channel for More Resources:
https://whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R
1. Introduction to Power BI
Overview of Power BI Desktop, Service, and licensing. Power BI workflow: data import, transform, model, visualize, and publish.
2. Connecting to Data Sources
Connecting to Excel, CSV, SQL Server, and others. Import vs. Direct Query, data refresh, and scheduling.
3. Power Query Editor (ETL)
Data transformation: removing duplicates, filtering, changing data types. Merging and appending queries, introduction to the M Language.
4. Data Modeling
Creating relationships between tables, star schema design, calculated columns/tables. Understanding cardinality, cross-filtering, and data categorization.
5. DAX (Data Analysis Expressions)
Basic DAX syntax, measures, and calculated columns. Aggregation, logical, and filter functions (SUM, IF, CALCULATE). Time intelligence functions for dynamic calculations (YTD, MTD).
6. Visualizations
Building visuals (bar, line, pie, etc.), custom visuals. Formatting and interactions (drill-down, cross-filtering). Maps and geographical visualizations, hierarchies.
7. Filters and Slicers
Using filters at different levels (report, page, visual). Slicers and drill-through filters for report interaction.
8. Publishing and Sharing
Publishing reports, creating dashboards, sharing reports. Workspaces, embedding reports, Power BI Apps.
9. Row-Level Security (RLS)
Implementing RLS to manage user access to data.
10. Power BI Mobile
Optimizing reports for mobile view.
11. Dataflows and Datasets
Creating reusable dataflows, linking datasets, and composite models.
12. Bookmarks and Buttons
Using bookmarks for report storytelling, buttons for navigation.
13. AI Capabilities
AI visuals (Decomposition Tree, Key Influencers), integrating Azure AI.
14. Paginated Reports
Creating pixel-perfect paginated reports for detailed data representation.
15. Data Refresh
Scheduled/manual refresh, data gateways for on-premises data.
16. Power BI and SQL
Direct connection to SQL, using stored procedures with Power BI.
17. Performance Optimization
Best practices for optimizing performance and report load times.
18. Real-Time Dashboards
Streaming datasets, live dashboards with APIs, and real-time data integration.
19. Case Studies and Projects
Building industry-specific dashboards (finance, sales, marketing), real-world scenarios.
I have curated the best interview resources to crack Power BI Interviews ๐๐
https://t.me/dataanalysisresourcestp/54
Hope you'll like it
Like this post if you need more content like this ๐โค๏ธ
Follow this WhatsApp Channel for More Resources:
https://whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R