Data Analyst Interview Questions & Preparation Tips
Be prepared with a mix of technical, analytical, and business-oriented interview questions.
1. Technical Questions (Data Analysis & Reporting)
SQL Questions:
How do you write a query to fetch the top 5 highest revenue-generating customers?
Explain the difference between INNER JOIN, LEFT JOIN, and FULL OUTER JOIN.
How would you optimize a slow-running query?
What are CTEs and when would you use them?
Data Visualization (Power BI / Tableau / Excel)
How would you create a dashboard to track key performance metrics?
Explain the difference between measures and calculated columns in Power BI.
How do you handle missing data in Tableau?
What are DAX functions, and can you give an example?
ETL & Data Processing (Alteryx, Power BI, Excel)
What is ETL, and how does it relate to BI?
Have you used Alteryx for data transformation? Explain a complex workflow you built.
How do you automate reporting using Power Query in Excel?
2. Business and Analytical Questions
How do you define KPIs for a business process?
Give an example of how you used data to drive a business decision.
How would you identify cost-saving opportunities in a reporting process?
Explain a time when your report uncovered a hidden business insight.
3. Scenario-Based & Behavioral Questions
Stakeholder Management:
How do you handle a situation where different business units have conflicting reporting requirements?
How do you explain complex data insights to non-technical stakeholders?
Problem-Solving & Debugging:
What would you do if your report is showing incorrect numbers?
How do you ensure the accuracy of a new KPI you introduced?
Project Management & Process Improvement:
Have you led a project to automate or improve a reporting process?
What steps do you take to ensure the timely delivery of reports?
4. Industry-Specific Questions (Credit Reporting & Financial Services)
What are some key credit risk metrics used in financial services?
How would you analyze trends in customer credit behavior?
How do you ensure compliance and data security in reporting?
5. General HR Questions
Why do you want to work at this company?
Tell me about a challenging project and how you handled it.
What are your strengths and weaknesses?
Where do you see yourself in five years?
How to Prepare?
Brush up on SQL, Power BI, and ETL tools (especially Alteryx).
Learn about key financial and credit reporting metrics.(varies company to company)
Practice explaining data-driven insights in a business-friendly manner.
Be ready to showcase problem-solving skills with real-world examples.
React with ❤️ if you want me to also post sample answer for the above questions
Share with credits: https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
Be prepared with a mix of technical, analytical, and business-oriented interview questions.
1. Technical Questions (Data Analysis & Reporting)
SQL Questions:
How do you write a query to fetch the top 5 highest revenue-generating customers?
Explain the difference between INNER JOIN, LEFT JOIN, and FULL OUTER JOIN.
How would you optimize a slow-running query?
What are CTEs and when would you use them?
Data Visualization (Power BI / Tableau / Excel)
How would you create a dashboard to track key performance metrics?
Explain the difference between measures and calculated columns in Power BI.
How do you handle missing data in Tableau?
What are DAX functions, and can you give an example?
ETL & Data Processing (Alteryx, Power BI, Excel)
What is ETL, and how does it relate to BI?
Have you used Alteryx for data transformation? Explain a complex workflow you built.
How do you automate reporting using Power Query in Excel?
2. Business and Analytical Questions
How do you define KPIs for a business process?
Give an example of how you used data to drive a business decision.
How would you identify cost-saving opportunities in a reporting process?
Explain a time when your report uncovered a hidden business insight.
3. Scenario-Based & Behavioral Questions
Stakeholder Management:
How do you handle a situation where different business units have conflicting reporting requirements?
How do you explain complex data insights to non-technical stakeholders?
Problem-Solving & Debugging:
What would you do if your report is showing incorrect numbers?
How do you ensure the accuracy of a new KPI you introduced?
Project Management & Process Improvement:
Have you led a project to automate or improve a reporting process?
What steps do you take to ensure the timely delivery of reports?
4. Industry-Specific Questions (Credit Reporting & Financial Services)
What are some key credit risk metrics used in financial services?
How would you analyze trends in customer credit behavior?
How do you ensure compliance and data security in reporting?
5. General HR Questions
Why do you want to work at this company?
Tell me about a challenging project and how you handled it.
What are your strengths and weaknesses?
Where do you see yourself in five years?
How to Prepare?
Brush up on SQL, Power BI, and ETL tools (especially Alteryx).
Learn about key financial and credit reporting metrics.(varies company to company)
Practice explaining data-driven insights in a business-friendly manner.
Be ready to showcase problem-solving skills with real-world examples.
React with ❤️ if you want me to also post sample answer for the above questions
Share with credits: https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
❤4
Q.Autoencoder methods
A. Autoencoder is a type of neural network where the output layer has the same dimensionality as the input layer. In simpler words, the number of output units in the output layer is equal to the number of input units in the input layer. Various techniques exist to prevent autoencoders from learning the identity function and to improve their ability to capture important ' information and learn richer representations. 1.Sparse autoencoder (SAE) 2. Denoising autoencoder (DAE) 3. Contractive autoencoder (CAE) 4. Principal component analysis.
Q. L1 and L2 regularization?
A. L1 regularization gives output in binary weights from 0 to 1 for the model's features and is adopted for decreasing the number of features in a huge dimensional dataset. L2 regularization disperse the error terms in all the weights that leads to more accurate customized final models.
Q. How to measure the Euclidean distance betweeen the two arrays in numpy?
A. Euclidean distance is defined in mathematics as the magnitude or length of the line segment between two points. There are multiple methods for measuring the euclidean methods.
Method 1. In this method, we first initialize two numpy arrays. Then, we use linalg.norm() of numpy basically to compute the euclidean distance directly.
Method 2. In this method, we first initialize two numpy arrays. Then, we take the difference of the two arrays, compute the dot product of the result, and transpose of the result. Then we take the square root of the answer. This is another way to implement Euclidean distance.
Method 3. In this method, we first initialize two numpy arrays. Then, we compute the difference of these arrays and take their square. We take the sum of the squared elements, and after that, we take the square root in the end. This is another way to implement Euclidean distance.
Q.What are the support vectors in SVM?
A. Support vectors are data points that are closer to the hyperplane and influence the position and orientation of the hyperplane. Using these support vectors, we maximize the margin of the classifier. Deleting the support vectors will change the position of the hyperplane. These are the points that help us build our SVM.
Q. How do you handle categorical data?
A. One-Hot Encoding is the most common, correct way to deal with non-ordinal categorical data. It consists of creating an additional feature for each group of the categorical feature and mark each observation belonging (Value=1) or not (Value=0) to that group.
Q. What is coerrelation?
A.Correlation is a statistical measure that expresses the extent to which two variables are linearly related (meaning they change together at a constant rate). It's a common tool for describing simple relationships without making a statement about cause and effects
Q. What is covariance?
A. Covariance is nothing but a measure of correlation. Covariance is a measure of how much two random variables vary together. It’s similar to variance, but where variance tells you how a single variable varies, co variance tells you how two variables vary together
A. Autoencoder is a type of neural network where the output layer has the same dimensionality as the input layer. In simpler words, the number of output units in the output layer is equal to the number of input units in the input layer. Various techniques exist to prevent autoencoders from learning the identity function and to improve their ability to capture important ' information and learn richer representations. 1.Sparse autoencoder (SAE) 2. Denoising autoencoder (DAE) 3. Contractive autoencoder (CAE) 4. Principal component analysis.
Q. L1 and L2 regularization?
A. L1 regularization gives output in binary weights from 0 to 1 for the model's features and is adopted for decreasing the number of features in a huge dimensional dataset. L2 regularization disperse the error terms in all the weights that leads to more accurate customized final models.
Q. How to measure the Euclidean distance betweeen the two arrays in numpy?
A. Euclidean distance is defined in mathematics as the magnitude or length of the line segment between two points. There are multiple methods for measuring the euclidean methods.
Method 1. In this method, we first initialize two numpy arrays. Then, we use linalg.norm() of numpy basically to compute the euclidean distance directly.
Method 2. In this method, we first initialize two numpy arrays. Then, we take the difference of the two arrays, compute the dot product of the result, and transpose of the result. Then we take the square root of the answer. This is another way to implement Euclidean distance.
Method 3. In this method, we first initialize two numpy arrays. Then, we compute the difference of these arrays and take their square. We take the sum of the squared elements, and after that, we take the square root in the end. This is another way to implement Euclidean distance.
Q.What are the support vectors in SVM?
A. Support vectors are data points that are closer to the hyperplane and influence the position and orientation of the hyperplane. Using these support vectors, we maximize the margin of the classifier. Deleting the support vectors will change the position of the hyperplane. These are the points that help us build our SVM.
Q. How do you handle categorical data?
A. One-Hot Encoding is the most common, correct way to deal with non-ordinal categorical data. It consists of creating an additional feature for each group of the categorical feature and mark each observation belonging (Value=1) or not (Value=0) to that group.
Q. What is coerrelation?
A.Correlation is a statistical measure that expresses the extent to which two variables are linearly related (meaning they change together at a constant rate). It's a common tool for describing simple relationships without making a statement about cause and effects
Q. What is covariance?
A. Covariance is nothing but a measure of correlation. Covariance is a measure of how much two random variables vary together. It’s similar to variance, but where variance tells you how a single variable varies, co variance tells you how two variables vary together
❤5
Data Analyst Interview Questions 👇
1.How to create filters in Power BI?
Filters are an integral part of Power BI reports. They are used to slice and dice the data as per the dimensions we want. Filters are created in a couple of ways.
Using Slicers: A slicer is a visual under Visualization Pane. This can be added to the design view to filter our reports. When a slicer is added to the design view, it requires a field to be added to it. For example- Slicer can be added for Country fields. Then the data can be filtered based on countries.
Using Filter Pane: The Power BI team has added a filter pane to the reports, which is a single space where we can add different fields as filters. And these fields can be added depending on whether you want to filter only one visual(Visual level filter), or all the visuals in the report page(Page level filters), or applicable to all the pages of the report(report level filters)
2.How to sort data in Power BI?
Sorting is available in multiple formats. In the data view, a common sorting option of alphabetical order is there. Apart from that, we have the option of Sort by column, where one can sort a column based on another column. The sorting option is available in visuals as well. Sort by ascending and descending option by the fields and measure present in the visual is also available.
3.How to convert pdf to excel?
Open the PDF document you want to convert in XLSX format in Acrobat DC.
Go to the right pane and click on the “Export PDF” option.
Choose spreadsheet as the Export format.
Select “Microsoft Excel Workbook.”
Now click “Export.”
Download the converted file or share it.
4. How to enable macros in excel?
Click the file tab and then click “Options.”
A dialog box will appear. In the “Excel Options” dialog box, click on the “Trust Center” and then “Trust Center Settings.”
Go to the “Macro Settings” and select “enable all macros.”
Click OK to apply the macro settings.
1.How to create filters in Power BI?
Filters are an integral part of Power BI reports. They are used to slice and dice the data as per the dimensions we want. Filters are created in a couple of ways.
Using Slicers: A slicer is a visual under Visualization Pane. This can be added to the design view to filter our reports. When a slicer is added to the design view, it requires a field to be added to it. For example- Slicer can be added for Country fields. Then the data can be filtered based on countries.
Using Filter Pane: The Power BI team has added a filter pane to the reports, which is a single space where we can add different fields as filters. And these fields can be added depending on whether you want to filter only one visual(Visual level filter), or all the visuals in the report page(Page level filters), or applicable to all the pages of the report(report level filters)
2.How to sort data in Power BI?
Sorting is available in multiple formats. In the data view, a common sorting option of alphabetical order is there. Apart from that, we have the option of Sort by column, where one can sort a column based on another column. The sorting option is available in visuals as well. Sort by ascending and descending option by the fields and measure present in the visual is also available.
3.How to convert pdf to excel?
Open the PDF document you want to convert in XLSX format in Acrobat DC.
Go to the right pane and click on the “Export PDF” option.
Choose spreadsheet as the Export format.
Select “Microsoft Excel Workbook.”
Now click “Export.”
Download the converted file or share it.
4. How to enable macros in excel?
Click the file tab and then click “Options.”
A dialog box will appear. In the “Excel Options” dialog box, click on the “Trust Center” and then “Trust Center Settings.”
Go to the “Macro Settings” and select “enable all macros.”
Click OK to apply the macro settings.
❤1
Quick Power BI Dax Revision
1. Measures: Measures in DAX are calculations that are used in Power BI to perform aggregations, calculations, and comparisons on data. They are defined using the DEFINE MEASURE or CALCULATE functions.
2. Calculated Columns: Calculated columns are columns that are created in a table by using DAX expressions. They are calculated row by row when the data is loaded into the model.
3. DAX Functions: DAX provides a wide range of functions for data manipulation and calculation. Some common functions include SUM, AVERAGE, COUNT, FILTER, CALCULATE, RELATED, ALL, ALLEXCEPT, and many more.
4. Context: DAX calculations are performed within a context, which can be row context or filter context. Understanding how context works is crucial for writing accurate DAX expressions.
5. Relationships: Power BI data models are built on relationships between tables. DAX expressions can leverage these relationships to perform calculations across related tables.
6. Time Intelligence Functions: DAX includes a set of time intelligence functions that enable you to perform calculations based on dates and time periods. Examples include TOTALYTD, SAMEPERIODLASTYEAR, DATESBETWEEN, etc.
7. Variables: DAX allows you to declare and use variables within expressions to improve readability and performance of complex calculations.
8. Aggregation Functions: DAX provides aggregation functions like SUMX, AVERAGEX, COUNTX that allow you to iterate over a table and perform aggregations based on specified conditions.
9. Logical Functions: DAX includes logical functions such as IF, AND, OR, SWITCH that help in implementing conditional logic within calculations.
10. Error Handling: DAX provides functions like ISBLANK, IFERROR, BLANK, etc., for handling errors and missing data in calculations.
1. Measures: Measures in DAX are calculations that are used in Power BI to perform aggregations, calculations, and comparisons on data. They are defined using the DEFINE MEASURE or CALCULATE functions.
2. Calculated Columns: Calculated columns are columns that are created in a table by using DAX expressions. They are calculated row by row when the data is loaded into the model.
3. DAX Functions: DAX provides a wide range of functions for data manipulation and calculation. Some common functions include SUM, AVERAGE, COUNT, FILTER, CALCULATE, RELATED, ALL, ALLEXCEPT, and many more.
4. Context: DAX calculations are performed within a context, which can be row context or filter context. Understanding how context works is crucial for writing accurate DAX expressions.
5. Relationships: Power BI data models are built on relationships between tables. DAX expressions can leverage these relationships to perform calculations across related tables.
6. Time Intelligence Functions: DAX includes a set of time intelligence functions that enable you to perform calculations based on dates and time periods. Examples include TOTALYTD, SAMEPERIODLASTYEAR, DATESBETWEEN, etc.
7. Variables: DAX allows you to declare and use variables within expressions to improve readability and performance of complex calculations.
8. Aggregation Functions: DAX provides aggregation functions like SUMX, AVERAGEX, COUNTX that allow you to iterate over a table and perform aggregations based on specified conditions.
9. Logical Functions: DAX includes logical functions such as IF, AND, OR, SWITCH that help in implementing conditional logic within calculations.
10. Error Handling: DAX provides functions like ISBLANK, IFERROR, BLANK, etc., for handling errors and missing data in calculations.
❤2
7 Must-Have Tools for Data Analysts in 2025:
✅ SQL – Still the #1 skill for querying and managing structured data
✅ Excel / Google Sheets – Quick analysis, pivot tables, and essential calculations
✅ Python (Pandas, NumPy) – For deep data manipulation and automation
✅ Power BI – Transform data into interactive dashboards
✅ Tableau – Visualize data patterns and trends with ease
✅ Jupyter Notebook – Document, code, and visualize all in one place
✅ Looker Studio – A free and sleek way to create shareable reports with live data.
Perfect blend of code, visuals, and storytelling.
React with ❤️ for free tutorials on each tool
Share with credits: https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
✅ SQL – Still the #1 skill for querying and managing structured data
✅ Excel / Google Sheets – Quick analysis, pivot tables, and essential calculations
✅ Python (Pandas, NumPy) – For deep data manipulation and automation
✅ Power BI – Transform data into interactive dashboards
✅ Tableau – Visualize data patterns and trends with ease
✅ Jupyter Notebook – Document, code, and visualize all in one place
✅ Looker Studio – A free and sleek way to create shareable reports with live data.
Perfect blend of code, visuals, and storytelling.
React with ❤️ for free tutorials on each tool
Share with credits: https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
❤5
To transition from Data Analyst ➡️ Data Scientist, you will have to focus on building relevant projects! 🎯
✅ Predictive Analytics Project
→ Built a model to predict customer behaviour by analyzing past purchase patterns and used time series forecasting to predict future trends.
✅ Sentiment Analysis using NLP
→ Developed a sentiment analysis model that categorized customer feedback into positive, neutral, and negative sentiments to improve products.
✅ Personalized Recommendation Engine
→ Created a recommendation engine using collaborative and content-based filtering to give personalized suggestions based on user’s browsing history and preferences.
Tailor every project to focus on business impact and user experience, which can help you stand out to recruiters. 💪🏻
✅ Predictive Analytics Project
→ Built a model to predict customer behaviour by analyzing past purchase patterns and used time series forecasting to predict future trends.
✅ Sentiment Analysis using NLP
→ Developed a sentiment analysis model that categorized customer feedback into positive, neutral, and negative sentiments to improve products.
✅ Personalized Recommendation Engine
→ Created a recommendation engine using collaborative and content-based filtering to give personalized suggestions based on user’s browsing history and preferences.
Tailor every project to focus on business impact and user experience, which can help you stand out to recruiters. 💪🏻
❤2👌1
Q. Explain the data preprocessing steps in data analysis.
Ans. Data preprocessing transforms the data into a format that is more easily and effectively processed in data mining, machine learning and other data science tasks.
1. Data profiling.
2. Data cleansing.
3. Data reduction.
4. Data transformation.
5. Data enrichment.
6. Data validation.
Q. What Are the Three Stages of Building a Model in Machine Learning?
Ans. The three stages of building a machine learning model are:
Model Building: Choosing a suitable algorithm for the model and train it according to the requirement
Model Testing: Checking the accuracy of the model through the test data
Applying the Model: Making the required changes after testing and use the final model for real-time projects
Q. What are the subsets of SQL?
Ans. The following are the four significant subsets of the SQL:
Data definition language (DDL): It defines the data structure that consists of commands like CREATE, ALTER, DROP, etc.
Data manipulation language (DML): It is used to manipulate existing data in the database. The commands in this category are SELECT, UPDATE, INSERT, etc.
Data control language (DCL): It controls access to the data stored in the database. The commands in this category include GRANT and REVOKE.
Transaction Control Language (TCL): It is used to deal with the transaction operations in the database. The commands in this category are COMMIT, ROLLBACK, SET TRANSACTION, SAVEPOINT, etc.
Q. What is a Parameter in Tableau? Give an Example.
Ans. A parameter is a dynamic value that a customer could select, and you can use it to replace constant values in calculations, filters, and reference lines.
For example, when creating a filter to show the top 10 products based on total profit instead of the fixed value, you can update the filter to show the top 10, 20, or 30 products using a parameter.
Ans. Data preprocessing transforms the data into a format that is more easily and effectively processed in data mining, machine learning and other data science tasks.
1. Data profiling.
2. Data cleansing.
3. Data reduction.
4. Data transformation.
5. Data enrichment.
6. Data validation.
Q. What Are the Three Stages of Building a Model in Machine Learning?
Ans. The three stages of building a machine learning model are:
Model Building: Choosing a suitable algorithm for the model and train it according to the requirement
Model Testing: Checking the accuracy of the model through the test data
Applying the Model: Making the required changes after testing and use the final model for real-time projects
Q. What are the subsets of SQL?
Ans. The following are the four significant subsets of the SQL:
Data definition language (DDL): It defines the data structure that consists of commands like CREATE, ALTER, DROP, etc.
Data manipulation language (DML): It is used to manipulate existing data in the database. The commands in this category are SELECT, UPDATE, INSERT, etc.
Data control language (DCL): It controls access to the data stored in the database. The commands in this category include GRANT and REVOKE.
Transaction Control Language (TCL): It is used to deal with the transaction operations in the database. The commands in this category are COMMIT, ROLLBACK, SET TRANSACTION, SAVEPOINT, etc.
Q. What is a Parameter in Tableau? Give an Example.
Ans. A parameter is a dynamic value that a customer could select, and you can use it to replace constant values in calculations, filters, and reference lines.
For example, when creating a filter to show the top 10 products based on total profit instead of the fixed value, you can update the filter to show the top 10, 20, or 30 products using a parameter.
❤3
SQL Interview Questions with Answers
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Hey Guys👋,
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𝐁𝐞𝐜𝐨𝐦𝐞 𝐚 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐞𝐝 𝐃𝐚𝐭𝐚 𝐒𝐜𝐢𝐞𝐧𝐭𝐢𝐬𝐭 𝐈𝐧 𝐓𝐨𝐩 𝐌𝐍𝐂𝐬😍
We help you master the required skills.
Learn by doing, build Industry level projects
👩🎓 1500+ Students Placed
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Planning for Data Science or Data Engineering Interview.
Focus on SQL & Python first. Here are some important questions which you should know.
𝐈𝐦𝐩𝐨𝐫𝐭𝐚𝐧𝐭 𝐒𝐐𝐋 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬
1- Find out nth Order/Salary from the tables.
2- Find the no of output records in each join from given Table 1 & Table 2
3- YOY,MOM Growth related questions.
4- Find out Employee ,Manager Hierarchy (Self join related question) or
Employees who are earning more than managers.
5- RANK,DENSERANK related questions
6- Some row level scanning medium to complex questions using CTE or recursive CTE, like (Missing no /Missing Item from the list etc.)
7- No of matches played by every team or Source to Destination flight combination using CROSS JOIN.
8-Use window functions to perform advanced analytical tasks, such as calculating moving averages or detecting outliers.
9- Implement logic to handle hierarchical data, such as finding all descendants of a given node in a tree structure.
10-Identify and remove duplicate records from a table.
𝐈𝐦𝐩𝐨𝐫𝐭𝐚𝐧𝐭 𝐏𝐲𝐭𝐡𝐨𝐧 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬
1- Reversing a String using an Extended Slicing techniques.
2- Count Vowels from Given words .
3- Find the highest occurrences of each word from string and sort them in order.
4- Remove Duplicates from List.
5-Sort a List without using Sort keyword.
6-Find the pair of numbers in this list whose sum is n no.
7-Find the max and min no in the list without using inbuilt functions.
8-Calculate the Intersection of Two Lists without using Built-in Functions
9-Write Python code to make API requests to a public API (e.g., weather API) and process the JSON response.
10-Implement a function to fetch data from a database table, perform data manipulation, and update the database.
Join for more: https://news.1rj.ru/str/datasciencefun
ENJOY LEARNING 👍👍
Focus on SQL & Python first. Here are some important questions which you should know.
𝐈𝐦𝐩𝐨𝐫𝐭𝐚𝐧𝐭 𝐒𝐐𝐋 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬
1- Find out nth Order/Salary from the tables.
2- Find the no of output records in each join from given Table 1 & Table 2
3- YOY,MOM Growth related questions.
4- Find out Employee ,Manager Hierarchy (Self join related question) or
Employees who are earning more than managers.
5- RANK,DENSERANK related questions
6- Some row level scanning medium to complex questions using CTE or recursive CTE, like (Missing no /Missing Item from the list etc.)
7- No of matches played by every team or Source to Destination flight combination using CROSS JOIN.
8-Use window functions to perform advanced analytical tasks, such as calculating moving averages or detecting outliers.
9- Implement logic to handle hierarchical data, such as finding all descendants of a given node in a tree structure.
10-Identify and remove duplicate records from a table.
𝐈𝐦𝐩𝐨𝐫𝐭𝐚𝐧𝐭 𝐏𝐲𝐭𝐡𝐨𝐧 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬
1- Reversing a String using an Extended Slicing techniques.
2- Count Vowels from Given words .
3- Find the highest occurrences of each word from string and sort them in order.
4- Remove Duplicates from List.
5-Sort a List without using Sort keyword.
6-Find the pair of numbers in this list whose sum is n no.
7-Find the max and min no in the list without using inbuilt functions.
8-Calculate the Intersection of Two Lists without using Built-in Functions
9-Write Python code to make API requests to a public API (e.g., weather API) and process the JSON response.
10-Implement a function to fetch data from a database table, perform data manipulation, and update the database.
Join for more: https://news.1rj.ru/str/datasciencefun
ENJOY LEARNING 👍👍
❤4
Keyboard #Shortcut Keys
Ctrl+A - Select All
Ctrl+B - Bold
Ctrl+C - Copy
Ctrl+D - Fill Down
Ctrl+F - Find
Ctrl+G - Goto
Ctrl+H - Replace
Ctrl+I - Italic
Ctrl+K - Insert Hyperlink
Ctrl+N - New Workbook
Ctrl+O - Open
Ctrl+P - Print
Ctrl+R - Fill Right
Ctrl+S - Save
Ctrl+U - Underline
Ctrl+V - Paste
Ctrl W - Close
Ctrl+X - Cut
Ctrl+Y - Repeat
Ctrl+Z - Undo
F1 - Help
F2 - Edit
F3 - Paste Name
F4 - Repeat last action
F4 - While typing a formula, switch between absolute/relative refs
F5 - Goto
F6 - Next Pane
F7 - Spell check
F8 - Extend mode
F9 - Recalculate all workbooks
F10 - Activate Menu bar
F11 - New Chart
F12 - Save As
Ctrl+: - Insert Current Time
Ctrl+; - Insert Current Date
Ctrl+" - Copy Value from Cell Above
Ctrl+’ - Copy Formula from Cell Above
Shift - Hold down shift for additional functions in Excel’s menu
Shift+F1 - What’s This?
Shift+F2 - Edit cell comment
Shift+F3 - Paste function into formula
Shift+F4 - Find Next
Shift+F5 - Find
Shift+F6 - Previous Pane
Shift+F8 - Add to selection
Shift+F9 - Calculate active worksheet
Shift+F10 - Display shortcut menu
Shift+F11 - New worksheet
Ctrl+F3 - Define name
Ctrl+F4 - Close
Ctrl+F5 - XL, Restore window size
Ctrl+F6 - Next workbook window
Shift+Ctrl+F6 - Previous workbook window
Ctrl+F7 - Move window
Ctrl+F8 - Resize window
Ctrl+F9 - Minimize workbook
Ctrl+F10 - Maximize or restore window
Ctrl+F11 - Inset 4.0 Macro sheet
Ctrl+F1 - File Open
Alt+F1 - Insert Chart
Alt+F2 - Save As
Alt+F4 - Exit
Alt+Down arrow - Display AutoComplete list
Alt+’ - Format Style dialog box
Ctrl+Shift+~ - General format
Ctrl+Shift+! - Comma format
Ctrl+Shift+@ - Time format
Ctrl+Shift+# - Date format
Ctrl+Shift+$ - Currency format
Ctrl+Shift+% - Percent format
Ctrl+Shift+^ - Exponential format
Ctrl+Shift+& - Place outline border around selected cells
Ctrl+Shift+_ - Remove outline border
Ctrl+Shift+* - Select current region
Ctrl++ - Insert
Ctrl+- - Delete
Ctrl+1 - Format cells dialog box
Ctrl+2 - Bold
Ctrl+3 - Italic
Ctrl+4 - Underline
Ctrl+5 - Strikethrough
Ctrl+6 - Show/Hide objects
Ctrl+7 - Show/Hide Standard toolbar
Ctrl+8 - Toggle Outline symbols
Ctrl+9 - Hide rows
Ctrl+0 - Hide columns
Ctrl+Shift+( - Unhide rows
Ctrl+Shift+) - Unhide columns
Alt or F10 - Activate the menu
Ctrl+Tab - In toolbar: next toolbar
Shift+Ctrl+Tab - In toolbar: previous toolbar
Ctrl+Tab - In a workbook: activate next workbook
Shift+Ctrl+Tab - In a workbook: activate previous workbook
Tab - Next tool
Shift+Tab - Previous tool
Enter - Do the command
Shift+Ctrl+F - Font Drop down List
Shift+Ctrl+F+F - Font tab of Format Cell Dialog box
Shift+Ctrl+P - Point size Drop down List
Ctrl + E - Align center
Ctrl + J - justify
Ctrl + L - align
Ctrl + R - align right
Alt + Tab - switch applications
Windows + P - Project screen
Windows + E - open file explorer
Windows + D - go to desktop
Windows + M - minimize all windows
Windows + S - search
Ctrl+A - Select All
Ctrl+B - Bold
Ctrl+C - Copy
Ctrl+D - Fill Down
Ctrl+F - Find
Ctrl+G - Goto
Ctrl+H - Replace
Ctrl+I - Italic
Ctrl+K - Insert Hyperlink
Ctrl+N - New Workbook
Ctrl+O - Open
Ctrl+P - Print
Ctrl+R - Fill Right
Ctrl+S - Save
Ctrl+U - Underline
Ctrl+V - Paste
Ctrl W - Close
Ctrl+X - Cut
Ctrl+Y - Repeat
Ctrl+Z - Undo
F1 - Help
F2 - Edit
F3 - Paste Name
F4 - Repeat last action
F4 - While typing a formula, switch between absolute/relative refs
F5 - Goto
F6 - Next Pane
F7 - Spell check
F8 - Extend mode
F9 - Recalculate all workbooks
F10 - Activate Menu bar
F11 - New Chart
F12 - Save As
Ctrl+: - Insert Current Time
Ctrl+; - Insert Current Date
Ctrl+" - Copy Value from Cell Above
Ctrl+’ - Copy Formula from Cell Above
Shift - Hold down shift for additional functions in Excel’s menu
Shift+F1 - What’s This?
Shift+F2 - Edit cell comment
Shift+F3 - Paste function into formula
Shift+F4 - Find Next
Shift+F5 - Find
Shift+F6 - Previous Pane
Shift+F8 - Add to selection
Shift+F9 - Calculate active worksheet
Shift+F10 - Display shortcut menu
Shift+F11 - New worksheet
Ctrl+F3 - Define name
Ctrl+F4 - Close
Ctrl+F5 - XL, Restore window size
Ctrl+F6 - Next workbook window
Shift+Ctrl+F6 - Previous workbook window
Ctrl+F7 - Move window
Ctrl+F8 - Resize window
Ctrl+F9 - Minimize workbook
Ctrl+F10 - Maximize or restore window
Ctrl+F11 - Inset 4.0 Macro sheet
Ctrl+F1 - File Open
Alt+F1 - Insert Chart
Alt+F2 - Save As
Alt+F4 - Exit
Alt+Down arrow - Display AutoComplete list
Alt+’ - Format Style dialog box
Ctrl+Shift+~ - General format
Ctrl+Shift+! - Comma format
Ctrl+Shift+@ - Time format
Ctrl+Shift+# - Date format
Ctrl+Shift+$ - Currency format
Ctrl+Shift+% - Percent format
Ctrl+Shift+^ - Exponential format
Ctrl+Shift+& - Place outline border around selected cells
Ctrl+Shift+_ - Remove outline border
Ctrl+Shift+* - Select current region
Ctrl++ - Insert
Ctrl+- - Delete
Ctrl+1 - Format cells dialog box
Ctrl+2 - Bold
Ctrl+3 - Italic
Ctrl+4 - Underline
Ctrl+5 - Strikethrough
Ctrl+6 - Show/Hide objects
Ctrl+7 - Show/Hide Standard toolbar
Ctrl+8 - Toggle Outline symbols
Ctrl+9 - Hide rows
Ctrl+0 - Hide columns
Ctrl+Shift+( - Unhide rows
Ctrl+Shift+) - Unhide columns
Alt or F10 - Activate the menu
Ctrl+Tab - In toolbar: next toolbar
Shift+Ctrl+Tab - In toolbar: previous toolbar
Ctrl+Tab - In a workbook: activate next workbook
Shift+Ctrl+Tab - In a workbook: activate previous workbook
Tab - Next tool
Shift+Tab - Previous tool
Enter - Do the command
Shift+Ctrl+F - Font Drop down List
Shift+Ctrl+F+F - Font tab of Format Cell Dialog box
Shift+Ctrl+P - Point size Drop down List
Ctrl + E - Align center
Ctrl + J - justify
Ctrl + L - align
Ctrl + R - align right
Alt + Tab - switch applications
Windows + P - Project screen
Windows + E - open file explorer
Windows + D - go to desktop
Windows + M - minimize all windows
Windows + S - search
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AccioJob x Sceniuz are hiring for Data Analyst & Data Engineer roles!
* Graduation Year: Open to All
* Degree: BTech / BE / BCA / BSC / MTech /ME / MCA / MSC
* CTC: 3–6 LPA
* Offline Assesment at AccioJob partnered campus in Mumbai
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