This is how data analytics teams work!
Example:
1) Senior Management at Swiggy/Infosys/HDFC/XYZ company needs data-driven insights to solve a critical business challenge.
So, they onboard a data analytics team to provide support.
2) A team from Analytics Team/Consulting Firm/Internal Data Science Division is onboarded.
The team typically consists of a Lead Analyst/Manager and 2-3 Data Analysts/Junior Analysts.
3) This data analytics team (1 manager + 2-3 analysts) is part of a bigger ecosystem that they can rely upon:
- A Senior Data Scientist/Analytics Lead who has industry knowledge and experience solving similar problems.
- Subject Matter Experts (SMEs) from various domains like AI, Machine Learning, or industry-specific fields (e.g., Marketing, Supply Chain, Finance).
- Business Intelligence (BI) Experts and Data Engineers who ensure that the data is well-structured and easy to interpret.
- External Tools & Platforms (e.g., Power BI, Tableau, Google Analytics) that can be leveraged for advanced analytics.
- Data Experts who specialize in various data sources, research, and methods to get the right information.
4) Every member of this ecosystem collaborates to create value for the client:
- The entire team works toward solving the client’s business problem using data-driven insights.
- The Manager & Analysts may not be industry experts but have access to the right tools and people to bring the expertise required.
- If help is needed from a Data Scientist sitting in New York or a Cloud Engineer in Singapore, it’s available—collaboration is key!
End of the day:
1) Data analytics teams aren’t just about crunching numbers—they’re about solving problems using data-driven insights.
2) EVERYONE in this ecosystem plays a vital role and is rewarded well because the value they create helps the business make informed decisions!
3) You should consider working in this field for a few years, at least. It’ll teach you how to break down complex business problems and solve them with data. And trust me, data-driven decision-making is one of the most powerful skills to have today!
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Example:
1) Senior Management at Swiggy/Infosys/HDFC/XYZ company needs data-driven insights to solve a critical business challenge.
So, they onboard a data analytics team to provide support.
2) A team from Analytics Team/Consulting Firm/Internal Data Science Division is onboarded.
The team typically consists of a Lead Analyst/Manager and 2-3 Data Analysts/Junior Analysts.
3) This data analytics team (1 manager + 2-3 analysts) is part of a bigger ecosystem that they can rely upon:
- A Senior Data Scientist/Analytics Lead who has industry knowledge and experience solving similar problems.
- Subject Matter Experts (SMEs) from various domains like AI, Machine Learning, or industry-specific fields (e.g., Marketing, Supply Chain, Finance).
- Business Intelligence (BI) Experts and Data Engineers who ensure that the data is well-structured and easy to interpret.
- External Tools & Platforms (e.g., Power BI, Tableau, Google Analytics) that can be leveraged for advanced analytics.
- Data Experts who specialize in various data sources, research, and methods to get the right information.
4) Every member of this ecosystem collaborates to create value for the client:
- The entire team works toward solving the client’s business problem using data-driven insights.
- The Manager & Analysts may not be industry experts but have access to the right tools and people to bring the expertise required.
- If help is needed from a Data Scientist sitting in New York or a Cloud Engineer in Singapore, it’s available—collaboration is key!
End of the day:
1) Data analytics teams aren’t just about crunching numbers—they’re about solving problems using data-driven insights.
2) EVERYONE in this ecosystem plays a vital role and is rewarded well because the value they create helps the business make informed decisions!
3) You should consider working in this field for a few years, at least. It’ll teach you how to break down complex business problems and solve them with data. And trust me, data-driven decision-making is one of the most powerful skills to have today!
I have curated best 80+ top-notch Data Analytics Resources 👇👇
https://news.1rj.ru/str/DataSimplifier
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10 Advanced SQL Concepts For Data Analysts
1. Window Functions for Advanced Analytics:
Calculate running totals, ranks, and moving averages without subqueries.
2. Conditional Aggregation with CASE WHEN:
Segment data within a single query, saving time and creating versatile summaries.
3. CTEs for Modular Queries:
Make complex queries more readable and reusable with CTEs.
4. Optimize with EXISTS vs. IN:
Use EXISTS for better performance in larger datasets.
5. Self Joins for Row Comparisons:
Compare rows within the same table, helpful for changes over time.
6. UNION vs. UNION ALL:
Combine results from multiple queries; UNION ALL is faster as it doesn’t remove duplicates.
7. Handle NULLs with COALESCE:
Replace NULLs with defaults to avoid calculation issues.
8. Pivot Data with CASE Statements:
Transform rows into columns for clearer insights.
9. Extract Data with STRING Functions:
Useful for semi-structured data; extract domains, product codes, etc.
10. Indexing for Faster Queries:
Indexes speed up data retrieval, especially on frequently queried columns.
Mastering these SQL tricks will optimize your queries, simplify logic, and enable complex analyses.
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1. Window Functions for Advanced Analytics:
Calculate running totals, ranks, and moving averages without subqueries.
SELECT date, sales, SUM(sales) OVER (ORDER BY date) AS running_total FROM sales_data;
2. Conditional Aggregation with CASE WHEN:
Segment data within a single query, saving time and creating versatile summaries.
SELECT COUNT(CASE WHEN status = 'Completed' THEN 1 END) AS completed_orders FROM orders;
3. CTEs for Modular Queries:
Make complex queries more readable and reusable with CTEs.
WITH filtered_sales AS (SELECT * FROM sales_data WHERE region = 'North')
SELECT product, SUM(sales) FROM filtered_sales GROUP BY product;
4. Optimize with EXISTS vs. IN:
Use EXISTS for better performance in larger datasets.
SELECT * FROM customers c WHERE EXISTS (SELECT 1 FROM orders o WHERE o.customer_id = c.id);
5. Self Joins for Row Comparisons:
Compare rows within the same table, helpful for changes over time.
SELECT a.date, (a.sales - b.sales) AS sales_diff FROM sales_data a JOIN sales_data b ON a.date = b.date + INTERVAL '1' MONTH;
6. UNION vs. UNION ALL:
Combine results from multiple queries; UNION ALL is faster as it doesn’t remove duplicates.
7. Handle NULLs with COALESCE:
Replace NULLs with defaults to avoid calculation issues.
SELECT product, COALESCE(sales, 0) AS sales FROM product_sales;
8. Pivot Data with CASE Statements:
Transform rows into columns for clearer insights.
9. Extract Data with STRING Functions:
Useful for semi-structured data; extract domains, product codes, etc.
SELECT SUBSTRING(email, CHARINDEX('@', email) + 1, LEN(email)) AS domain FROM users;10. Indexing for Faster Queries:
Indexes speed up data retrieval, especially on frequently queried columns.
Mastering these SQL tricks will optimize your queries, simplify logic, and enable complex analyses.
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🧪 Real-world SQL Scenarios & Challenges
Let’s dive into the types of real-world problems you’ll encounter as a data analyst, data scientist , data engineer, or developer.
1. Finding Duplicates
SELECT name, COUNT(*)
FROM employees
GROUP BY name
HAVING COUNT(*) > 1;
Perfect for data cleaning and validation tasks.
2. Get the Second Highest Salary
SELECT MAX(salary) AS second_highest
FROM employees
WHERE salary < (
SELECT MAX(salary)
FROM employees
);
3. Running Totals
SELECT name, salary,
SUM(salary) OVER (ORDER BY id) AS running_total
FROM employees;
Essential in dashboards and financial reports.
4. Customers with No Orders
SELECT c.customer_id, c.name
FROM customers c
LEFT JOIN orders o ON c.customer_id = o.customer_id
WHERE o.order_id IS NULL;
Very common in e-commerce or CRM platforms.
5. Monthly Aggregates
SELECT DATE_TRUNC('month', order_date) AS month,
COUNT(*) AS total_orders
FROM orders
GROUP BY month
ORDER BY month;
Great for trends and time-based reporting.
6. Pivot-like Output (Using CASE)
SELECT
department,
COUNT(CASE WHEN gender = 'Male' THEN 1 END) AS male_count,
COUNT(CASE WHEN gender = 'Female' THEN 1 END) AS female_count
FROM employees
GROUP BY department;
Super useful for dashboards and insights.
7. Recursive Queries (Org Hierarchy or Tree)
WITH RECURSIVE employee_tree AS (
SELECT id, name, manager_id
FROM employees
WHERE manager_id IS NULL
UNION ALL
SELECT e.id, e.name, e.manager_id
FROM employees e
INNER JOIN employee_tree et ON e.manager_id = et.id
)
SELECT * FROM employee_tree;
Used in advanced data modeling and tree structures.
You don’t just need to know how SQL works — you need to know when to use it smartly!
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Hope it helps :)
Let’s dive into the types of real-world problems you’ll encounter as a data analyst, data scientist , data engineer, or developer.
1. Finding Duplicates
SELECT name, COUNT(*)
FROM employees
GROUP BY name
HAVING COUNT(*) > 1;
Perfect for data cleaning and validation tasks.
2. Get the Second Highest Salary
SELECT MAX(salary) AS second_highest
FROM employees
WHERE salary < (
SELECT MAX(salary)
FROM employees
);
3. Running Totals
SELECT name, salary,
SUM(salary) OVER (ORDER BY id) AS running_total
FROM employees;
Essential in dashboards and financial reports.
4. Customers with No Orders
SELECT c.customer_id, c.name
FROM customers c
LEFT JOIN orders o ON c.customer_id = o.customer_id
WHERE o.order_id IS NULL;
Very common in e-commerce or CRM platforms.
5. Monthly Aggregates
SELECT DATE_TRUNC('month', order_date) AS month,
COUNT(*) AS total_orders
FROM orders
GROUP BY month
ORDER BY month;
Great for trends and time-based reporting.
6. Pivot-like Output (Using CASE)
SELECT
department,
COUNT(CASE WHEN gender = 'Male' THEN 1 END) AS male_count,
COUNT(CASE WHEN gender = 'Female' THEN 1 END) AS female_count
FROM employees
GROUP BY department;
Super useful for dashboards and insights.
7. Recursive Queries (Org Hierarchy or Tree)
WITH RECURSIVE employee_tree AS (
SELECT id, name, manager_id
FROM employees
WHERE manager_id IS NULL
UNION ALL
SELECT e.id, e.name, e.manager_id
FROM employees e
INNER JOIN employee_tree et ON e.manager_id = et.id
)
SELECT * FROM employee_tree;
Used in advanced data modeling and tree structures.
You don’t just need to know how SQL works — you need to know when to use it smartly!
React with ❤️ if you’d like me to explain more data analytics topics
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📚🚀Becoming a successful data analyst requires a blend of technical, analytical, and soft skills. Key competencies for excelling in this role include:
Statistical Analysis: Mastery of statistical concepts such as probability, hypothesis testing, and regression analysis is essential.
Data Manipulation: Proficiency in SQL for data querying and manipulation, along with skills in data cleaning and transformation techniques.
Data Visualization: Ability to create insightful visualizations using tools like Tableau, Power BI, or Python libraries such as Matplotlib and Seaborn.
Programming: Strong programming skills in languages like Python or R, along with knowledge of relevant libraries like Pandas and NumPy.
Machine Learning (optional): Understanding of machine learning principles for predictive modeling and classification tasks.
Database Management: Familiarity with database systems such as MySQL, PostgreSQL, or MongoDB for handling large datasets.
Critical Thinking: Ability to analyze data critically, identify patterns, trends, and outliers.
Business Acumen: Understanding the business context and translating data insights into actionable recommendations.
Communication Skills: Effective communication of findings to non-technical stakeholders through both written and verbal means.
Continuous Learning: Commitment to ongoing learning and staying abreast of new tools, techniques, and industry trends to remain competitive.
By honing these skills and gaining practical experience through projects or internships, individuals can build a robust portfolio for a thriving career in data analysis.
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Statistical Analysis: Mastery of statistical concepts such as probability, hypothesis testing, and regression analysis is essential.
Data Manipulation: Proficiency in SQL for data querying and manipulation, along with skills in data cleaning and transformation techniques.
Data Visualization: Ability to create insightful visualizations using tools like Tableau, Power BI, or Python libraries such as Matplotlib and Seaborn.
Programming: Strong programming skills in languages like Python or R, along with knowledge of relevant libraries like Pandas and NumPy.
Machine Learning (optional): Understanding of machine learning principles for predictive modeling and classification tasks.
Database Management: Familiarity with database systems such as MySQL, PostgreSQL, or MongoDB for handling large datasets.
Critical Thinking: Ability to analyze data critically, identify patterns, trends, and outliers.
Business Acumen: Understanding the business context and translating data insights into actionable recommendations.
Communication Skills: Effective communication of findings to non-technical stakeholders through both written and verbal means.
Continuous Learning: Commitment to ongoing learning and staying abreast of new tools, techniques, and industry trends to remain competitive.
By honing these skills and gaining practical experience through projects or internships, individuals can build a robust portfolio for a thriving career in data analysis.
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Getting started with SQL comparison operators.
If you're new to SQL, understanding comparison operators is one of the first things you'll need to learn.
They’re really important for filtering and analyzing your data. Let’s break them down with some simple examples.
Comparison operators let you compare values in SQL queries. Here are the basics:
1. = (Equal To): Checks if two values are the same.
Example: SELECT * FROM Employees WHERE Age = 30; (This will find all employees who are exactly 30 years old).
2. <> or != (Not Equal To): Checks if two values are different.
Example: SELECT * FROM Employees WHERE Age <> 30; (This will find all employees who are not 30 years old).
3. > (Greater Than): Checks if a value is larger.
Example: SELECT * FROM Employees WHERE Salary > 50000; (This will list all employees earning more than 50,000).
4. < (Less Than): Checks if a value is smaller.
Example: SELECT * FROM Employees WHERE Salary < 50000; (This will show all employees earning less than 50,000).
5. >= (Greater Than or Equal To): Checks if a value is larger or equal.
Example: SELECT * FROM Employees WHERE Age >= 25; (This will find all employees who are 25 years old or older).
6. <= (Less Than or Equal To): Checks if a value is smaller or equal.
Example: SELECT * FROM Employees WHERE Age <= 30; (This will find all employees who are 30 years old or younger).
These simple operators can help you get more accurate results in your SQL queries.
Keep practicing and you’ll be great at SQL in no time.
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If you're new to SQL, understanding comparison operators is one of the first things you'll need to learn.
They’re really important for filtering and analyzing your data. Let’s break them down with some simple examples.
Comparison operators let you compare values in SQL queries. Here are the basics:
1. = (Equal To): Checks if two values are the same.
Example: SELECT * FROM Employees WHERE Age = 30; (This will find all employees who are exactly 30 years old).
2. <> or != (Not Equal To): Checks if two values are different.
Example: SELECT * FROM Employees WHERE Age <> 30; (This will find all employees who are not 30 years old).
3. > (Greater Than): Checks if a value is larger.
Example: SELECT * FROM Employees WHERE Salary > 50000; (This will list all employees earning more than 50,000).
4. < (Less Than): Checks if a value is smaller.
Example: SELECT * FROM Employees WHERE Salary < 50000; (This will show all employees earning less than 50,000).
5. >= (Greater Than or Equal To): Checks if a value is larger or equal.
Example: SELECT * FROM Employees WHERE Age >= 25; (This will find all employees who are 25 years old or older).
6. <= (Less Than or Equal To): Checks if a value is smaller or equal.
Example: SELECT * FROM Employees WHERE Age <= 30; (This will find all employees who are 30 years old or younger).
These simple operators can help you get more accurate results in your SQL queries.
Keep practicing and you’ll be great at SQL in no time.
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5 Essential Skills Every Data Analyst Must Master in 2025
Data analytics continues to evolve rapidly, and as a data analyst, it's crucial to stay ahead of the curve. In 2025, the skills that were once optional are now essential to stand out in this competitive field. Here are five must-have skills for every data analyst this year.
1. Data Wrangling & Cleaning:
The ability to clean, organize, and prepare data for analysis is critical. No matter how sophisticated your tools are, they can't work with messy, inconsistent data. Mastering data wrangling—removing duplicates, handling missing values, and standardizing formats—will help you deliver accurate and actionable insights.
Tools to master: Python (Pandas), R, SQL
2. Advanced Excel Skills:
Excel remains one of the most widely used tools in the data analysis world. Beyond the basics, you should master advanced formulas, pivot tables, and Power Query. Excel continues to be indispensable for quick analyses and prototype dashboards.
Key skills to learn: VLOOKUP, INDEX/MATCH, Power Pivot, advanced charting
3. Data Visualization:
The ability to convey your findings through compelling data visuals is what sets top analysts apart. Learn how to use tools like Tableau, Power BI, or even D3.js for web-based visualization. Your visuals should tell a story that’s easy for stakeholders to understand at a glance.
Focus areas: Interactive dashboards, storytelling with data, advanced chart types (heat maps, scatter plots)
4. Statistical Analysis & Hypothesis Testing:
Understanding statistics is fundamental for any data analyst. Master concepts like regression analysis, probability theory, and hypothesis testing. This skill will help you not only describe trends but also make data-driven predictions and assess the significance of your findings.
Skills to focus on: T-tests, ANOVA, correlation, regression models
5. Machine Learning Basics:
While you don’t need to be a data scientist, having a basic understanding of machine learning algorithms is increasingly important. Knowledge of supervised vs unsupervised learning, decision trees, and clustering techniques will allow you to push your analysis to the next level.
Begin with: Linear regression, K-means clustering, decision trees (using Python libraries like Scikit-learn)
In 2025, data analysts must embrace a multi-faceted skill set that combines technical expertise, statistical knowledge, and the ability to communicate findings effectively.
Keep learning and adapting to these emerging trends to ensure you're ready for the challenges of tomorrow.
I have curated best 80+ top-notch Data Analytics Resources 👇👇
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Data analytics continues to evolve rapidly, and as a data analyst, it's crucial to stay ahead of the curve. In 2025, the skills that were once optional are now essential to stand out in this competitive field. Here are five must-have skills for every data analyst this year.
1. Data Wrangling & Cleaning:
The ability to clean, organize, and prepare data for analysis is critical. No matter how sophisticated your tools are, they can't work with messy, inconsistent data. Mastering data wrangling—removing duplicates, handling missing values, and standardizing formats—will help you deliver accurate and actionable insights.
Tools to master: Python (Pandas), R, SQL
2. Advanced Excel Skills:
Excel remains one of the most widely used tools in the data analysis world. Beyond the basics, you should master advanced formulas, pivot tables, and Power Query. Excel continues to be indispensable for quick analyses and prototype dashboards.
Key skills to learn: VLOOKUP, INDEX/MATCH, Power Pivot, advanced charting
3. Data Visualization:
The ability to convey your findings through compelling data visuals is what sets top analysts apart. Learn how to use tools like Tableau, Power BI, or even D3.js for web-based visualization. Your visuals should tell a story that’s easy for stakeholders to understand at a glance.
Focus areas: Interactive dashboards, storytelling with data, advanced chart types (heat maps, scatter plots)
4. Statistical Analysis & Hypothesis Testing:
Understanding statistics is fundamental for any data analyst. Master concepts like regression analysis, probability theory, and hypothesis testing. This skill will help you not only describe trends but also make data-driven predictions and assess the significance of your findings.
Skills to focus on: T-tests, ANOVA, correlation, regression models
5. Machine Learning Basics:
While you don’t need to be a data scientist, having a basic understanding of machine learning algorithms is increasingly important. Knowledge of supervised vs unsupervised learning, decision trees, and clustering techniques will allow you to push your analysis to the next level.
Begin with: Linear regression, K-means clustering, decision trees (using Python libraries like Scikit-learn)
In 2025, data analysts must embrace a multi-faceted skill set that combines technical expertise, statistical knowledge, and the ability to communicate findings effectively.
Keep learning and adapting to these emerging trends to ensure you're ready for the challenges of tomorrow.
I have curated best 80+ top-notch Data Analytics Resources 👇👇
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Junior-level Data Analyst interview questions:
Introduction and Background
1. Can you tell me about your background and how you became interested in data analysis?
2. What do you know about our company/organization?
3. Why do you want to work as a data analyst?
Data Analysis and Interpretation
1. What is your experience with data analysis tools like Excel, SQL, or Tableau?
2. How would you approach analyzing a large dataset to identify trends and patterns?
3. Can you explain the concept of correlation versus causation?
4. How do you handle missing or incomplete data?
5. Can you walk me through a time when you had to interpret complex data results?
Technical Skills
1. Write a SQL query to extract data from a database.
2. How do you create a pivot table in Excel?
3. Can you explain the difference between a histogram and a box plot?
4. How do you perform data visualization using Tableau or Power BI?
5. Can you write a simple Python or R noscript to manipulate data?
Statistics and Math
1. What is the difference between mean, median, and mode?
2. Can you explain the concept of standard deviation and variance?
3. How do you calculate probability and confidence intervals?
4. Can you describe a time when you applied statistical concepts to a real-world problem?
5. How do you approach hypothesis testing?
Communication and Storytelling
1. Can you explain a complex data concept to a non-technical person?
2. How do you present data insights to stakeholders?
3. Can you walk me through a time when you had to communicate data results to a team?
4. How do you create effective data visualizations?
5. Can you tell a story using data?
Case Studies and Scenarios
1. You are given a dataset with customer purchase history. How would you analyze it to identify trends?
2. A company wants to increase sales. How would you use data to inform marketing strategies?
3. You notice a discrepancy in sales data. How would you investigate and resolve the issue?
4. Can you describe a time when you had to work with a stakeholder to understand their data needs?
5. How would you prioritize data projects with limited resources?
Behavioral Questions
1. Can you describe a time when you overcame a difficult data analysis challenge?
2. How do you handle tight deadlines and multiple projects?
3. Can you tell me about a project you worked on and your role in it?
4. How do you stay up-to-date with new data tools and technologies?
5. Can you describe a time when you received feedback on your data analysis work?
Final Questions
1. Do you have any questions about the company or role?
2. What do you think sets you apart from other candidates?
3. Can you summarize your experience and qualifications?
4. What are your long-term career goals?
Hope this helps you 😊
Introduction and Background
1. Can you tell me about your background and how you became interested in data analysis?
2. What do you know about our company/organization?
3. Why do you want to work as a data analyst?
Data Analysis and Interpretation
1. What is your experience with data analysis tools like Excel, SQL, or Tableau?
2. How would you approach analyzing a large dataset to identify trends and patterns?
3. Can you explain the concept of correlation versus causation?
4. How do you handle missing or incomplete data?
5. Can you walk me through a time when you had to interpret complex data results?
Technical Skills
1. Write a SQL query to extract data from a database.
2. How do you create a pivot table in Excel?
3. Can you explain the difference between a histogram and a box plot?
4. How do you perform data visualization using Tableau or Power BI?
5. Can you write a simple Python or R noscript to manipulate data?
Statistics and Math
1. What is the difference between mean, median, and mode?
2. Can you explain the concept of standard deviation and variance?
3. How do you calculate probability and confidence intervals?
4. Can you describe a time when you applied statistical concepts to a real-world problem?
5. How do you approach hypothesis testing?
Communication and Storytelling
1. Can you explain a complex data concept to a non-technical person?
2. How do you present data insights to stakeholders?
3. Can you walk me through a time when you had to communicate data results to a team?
4. How do you create effective data visualizations?
5. Can you tell a story using data?
Case Studies and Scenarios
1. You are given a dataset with customer purchase history. How would you analyze it to identify trends?
2. A company wants to increase sales. How would you use data to inform marketing strategies?
3. You notice a discrepancy in sales data. How would you investigate and resolve the issue?
4. Can you describe a time when you had to work with a stakeholder to understand their data needs?
5. How would you prioritize data projects with limited resources?
Behavioral Questions
1. Can you describe a time when you overcame a difficult data analysis challenge?
2. How do you handle tight deadlines and multiple projects?
3. Can you tell me about a project you worked on and your role in it?
4. How do you stay up-to-date with new data tools and technologies?
5. Can you describe a time when you received feedback on your data analysis work?
Final Questions
1. Do you have any questions about the company or role?
2. What do you think sets you apart from other candidates?
3. Can you summarize your experience and qualifications?
4. What are your long-term career goals?
Hope this helps you 😊
❤3
Excel Cheat Sheet 📔
This Excel cheatsheet is designed to be your quick reference guide for using Microsoft Excel efficiently.
1. Basic Functions
- SUM:
- AVERAGE:
- COUNT:
- MAX:
- MIN:
2. Text Functions
- CONCATENATE:
- LEFT:
- RIGHT:
- MID:
- TRIM:
3. Logical Functions
- IF:
- AND:
- OR:
- NOT:
4. Lookup Functions
- VLOOKUP:
- HLOOKUP:
- INDEX:
- MATCH:
5. Data Sorting & Filtering
- Sort: *Data > Sort*
- Filter: *Data > Filter*
- Advanced Filter: *Data > Advanced*
6. Conditional Formatting
- Apply Formatting: *Home > Conditional Formatting > New Rule*
- Highlight Cells: *Home > Conditional Formatting > Highlight Cells Rules*
7. Charts and Graphs
- Insert Chart: *Insert > Select Chart Type*
- Customize Chart: *Chart Tools > Design/Format*
8. PivotTables
- Create PivotTable: *Insert > PivotTable*
- Refresh PivotTable: *Right-click on PivotTable > Refresh*
9. Data Validation
- Set Validation: *Data > Data Validation*
- List: *Allow: List > Source: range or items*
10. Protecting Data
- Protect Sheet: *Review > Protect Sheet*
- Protect Workbook: *Review > Protect Workbook*
11. Shortcuts
- Copy:
- Paste:
- Undo:
- Redo:
- Save:
12. Printing Options
- Print Area: *Page Layout > Print Area > Set Print Area*
- Page Setup: *Page Layout > Page Setup*
Checklist for Data Analyst: https://dataanalytics.beehiiv.com/p/data
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This Excel cheatsheet is designed to be your quick reference guide for using Microsoft Excel efficiently.
1. Basic Functions
- SUM:
=SUM(range)- AVERAGE:
=AVERAGE(range)- COUNT:
=COUNT(range)- MAX:
=MAX(range)- MIN:
=MIN(range)2. Text Functions
- CONCATENATE:
=CONCATENATE(text1, text2, ...) or =TEXTJOIN(delimiter, ignore_empty, text1, text2, ...)- LEFT:
=LEFT(text, num_chars)- RIGHT:
=RIGHT(text, num_chars)- MID:
=MID(text, start_num, num_chars)- TRIM:
=TRIM(text)3. Logical Functions
- IF:
=IF(condition, true_value, false_value)- AND:
=AND(condition1, condition2, ...)- OR:
=OR(condition1, condition2, ...)- NOT:
=NOT(condition)4. Lookup Functions
- VLOOKUP:
=VLOOKUP(lookup_value, table_array, col_index_num, [range_lookup])- HLOOKUP:
=HLOOKUP(lookup_value, table_array, row_index_num, [range_lookup])- INDEX:
=INDEX(array, row_num, [column_num])- MATCH:
=MATCH(lookup_value, lookup_array, [match_type])5. Data Sorting & Filtering
- Sort: *Data > Sort*
- Filter: *Data > Filter*
- Advanced Filter: *Data > Advanced*
6. Conditional Formatting
- Apply Formatting: *Home > Conditional Formatting > New Rule*
- Highlight Cells: *Home > Conditional Formatting > Highlight Cells Rules*
7. Charts and Graphs
- Insert Chart: *Insert > Select Chart Type*
- Customize Chart: *Chart Tools > Design/Format*
8. PivotTables
- Create PivotTable: *Insert > PivotTable*
- Refresh PivotTable: *Right-click on PivotTable > Refresh*
9. Data Validation
- Set Validation: *Data > Data Validation*
- List: *Allow: List > Source: range or items*
10. Protecting Data
- Protect Sheet: *Review > Protect Sheet*
- Protect Workbook: *Review > Protect Workbook*
11. Shortcuts
- Copy:
Ctrl + C- Paste:
Ctrl + V- Undo:
Ctrl + Z- Redo:
Ctrl + Y- Save:
Ctrl + S12. Printing Options
- Print Area: *Page Layout > Print Area > Set Print Area*
- Page Setup: *Page Layout > Page Setup*
Checklist for Data Analyst: https://dataanalytics.beehiiv.com/p/data
I have curated best 80+ top-notch Data Analytics Resources 👇👇
https://news.1rj.ru/str/DataSimplifier
Like for more Interview Resources ♥️
Share with credits: https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
❤2
Machine Learning – Essential Concepts 🚀
1️⃣ Types of Machine Learning
Supervised Learning – Uses labeled data to train models.
Examples: Linear Regression, Decision Trees, Random Forest, SVM
Unsupervised Learning – Identifies patterns in unlabeled data.
Examples: Clustering (K-Means, DBSCAN), PCA
Reinforcement Learning – Models learn through rewards and penalties.
Examples: Q-Learning, Deep Q Networks
2️⃣ Key Algorithms
Regression – Predicts continuous values (Linear Regression, Ridge, Lasso).
Classification – Categorizes data into classes (Logistic Regression, Decision Tree, SVM, Naïve Bayes).
Clustering – Groups similar data points (K-Means, Hierarchical Clustering, DBSCAN).
Dimensionality Reduction – Reduces the number of features (PCA, t-SNE, LDA).
3️⃣ Model Training & Evaluation
Train-Test Split – Dividing data into training and testing sets.
Cross-Validation – Splitting data multiple times for better accuracy.
Metrics – Evaluating models with RMSE, Accuracy, Precision, Recall, F1-Score, ROC-AUC.
4️⃣ Feature Engineering
Handling missing data (mean imputation, dropna()).
Encoding categorical variables (One-Hot Encoding, Label Encoding).
Feature Scaling (Normalization, Standardization).
5️⃣ Overfitting & Underfitting
Overfitting – Model learns noise, performs well on training but poorly on test data.
Underfitting – Model is too simple and fails to capture patterns.
Solution: Regularization (L1, L2), Hyperparameter Tuning.
6️⃣ Ensemble Learning
Combining multiple models to improve performance.
Bagging (Random Forest)
Boosting (XGBoost, Gradient Boosting, AdaBoost)
7️⃣ Deep Learning Basics
Neural Networks (ANN, CNN, RNN).
Activation Functions (ReLU, Sigmoid, Tanh).
Backpropagation & Gradient Descent.
8️⃣ Model Deployment
Deploy models using Flask, FastAPI, or Streamlit.
Model versioning with MLflow.
Cloud deployment (AWS SageMaker, Google Vertex AI).
Join our WhatsApp channel: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
1️⃣ Types of Machine Learning
Supervised Learning – Uses labeled data to train models.
Examples: Linear Regression, Decision Trees, Random Forest, SVM
Unsupervised Learning – Identifies patterns in unlabeled data.
Examples: Clustering (K-Means, DBSCAN), PCA
Reinforcement Learning – Models learn through rewards and penalties.
Examples: Q-Learning, Deep Q Networks
2️⃣ Key Algorithms
Regression – Predicts continuous values (Linear Regression, Ridge, Lasso).
Classification – Categorizes data into classes (Logistic Regression, Decision Tree, SVM, Naïve Bayes).
Clustering – Groups similar data points (K-Means, Hierarchical Clustering, DBSCAN).
Dimensionality Reduction – Reduces the number of features (PCA, t-SNE, LDA).
3️⃣ Model Training & Evaluation
Train-Test Split – Dividing data into training and testing sets.
Cross-Validation – Splitting data multiple times for better accuracy.
Metrics – Evaluating models with RMSE, Accuracy, Precision, Recall, F1-Score, ROC-AUC.
4️⃣ Feature Engineering
Handling missing data (mean imputation, dropna()).
Encoding categorical variables (One-Hot Encoding, Label Encoding).
Feature Scaling (Normalization, Standardization).
5️⃣ Overfitting & Underfitting
Overfitting – Model learns noise, performs well on training but poorly on test data.
Underfitting – Model is too simple and fails to capture patterns.
Solution: Regularization (L1, L2), Hyperparameter Tuning.
6️⃣ Ensemble Learning
Combining multiple models to improve performance.
Bagging (Random Forest)
Boosting (XGBoost, Gradient Boosting, AdaBoost)
7️⃣ Deep Learning Basics
Neural Networks (ANN, CNN, RNN).
Activation Functions (ReLU, Sigmoid, Tanh).
Backpropagation & Gradient Descent.
8️⃣ Model Deployment
Deploy models using Flask, FastAPI, or Streamlit.
Model versioning with MLflow.
Cloud deployment (AWS SageMaker, Google Vertex AI).
Join our WhatsApp channel: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
❤3
Complete Roadmap to learn Excel in 2025 👇👇
1. Basic Excel Skills:
- Familiarize yourself with Excel's interface and navigation.
- Learn basic formulas (SUM, AVERAGE, COUNT, etc.).
- Understand cell referencing (absolute vs. relative).
2. Data Entry and Formatting:
- Practice entering and formatting data efficiently.
- Explore cell formatting options for a clean and organized dataset.
3. Advanced Formulas:
- Master more advanced formulas like VLOOKUP, HLOOKUP, INDEX-MATCH.
- Learn logical functions (IF, AND, OR).
- Understand array formulas for complex calculations.
4. Pivot Tables:
- Gain proficiency in creating Pivot Tables for data summarization.
- Learn to customize and format Pivot Tables effectively.
5. Data Cleaning:
- Acquire skills in cleaning and transforming data.
- Explore text-to-columns, remove duplicates, and data validation.
6. Charts and Graphs:
- Learn to create various charts (bar, line, pie) for data visualization.
- Understand chart formatting and customization.
7. Dashboard Creation:
- Combine charts and tables to build basic dashboards.
- Explore dynamic dashboards using Excel features.
8. Macros and VBA:
- Dive into basic automation using Excel macros.
- Learn Visual Basic for Applications (VBA) for more advanced automation.
9. Power Query:
- Introduce yourself to Power Query for enhanced data manipulation.
- Learn to import, transform, and load data efficiently.
10. Advanced Excel Techniques:
- Explore advanced features like Goal Seek, Solver, and Scenario Manager.
- Master the use of data tables for sensitivity analysis.
11. Real-world Projects:
- Apply your skills to real-world projects or datasets.
- Practice solving analytical problems using Excel.
Remember to practice consistently, as hands-on experience is crucial for mastering Excel. This roadmap will provide a solid foundation for your journey into data analysis using Excel.
5️⃣ Free resources to practice Excel
https://www.w3schools.com/EXCEL/index.php
https://bit.ly/3PSorPT
http://learn.microsoft.com/en-gb/training/paths/modern-analytics/
https://news.1rj.ru/str/excel_analyst/52
https://excel-practice-online.com/
Join for more: https://news.1rj.ru/str/free4unow_backup
ENJOY LEARNING 👍👍
1. Basic Excel Skills:
- Familiarize yourself with Excel's interface and navigation.
- Learn basic formulas (SUM, AVERAGE, COUNT, etc.).
- Understand cell referencing (absolute vs. relative).
2. Data Entry and Formatting:
- Practice entering and formatting data efficiently.
- Explore cell formatting options for a clean and organized dataset.
3. Advanced Formulas:
- Master more advanced formulas like VLOOKUP, HLOOKUP, INDEX-MATCH.
- Learn logical functions (IF, AND, OR).
- Understand array formulas for complex calculations.
4. Pivot Tables:
- Gain proficiency in creating Pivot Tables for data summarization.
- Learn to customize and format Pivot Tables effectively.
5. Data Cleaning:
- Acquire skills in cleaning and transforming data.
- Explore text-to-columns, remove duplicates, and data validation.
6. Charts and Graphs:
- Learn to create various charts (bar, line, pie) for data visualization.
- Understand chart formatting and customization.
7. Dashboard Creation:
- Combine charts and tables to build basic dashboards.
- Explore dynamic dashboards using Excel features.
8. Macros and VBA:
- Dive into basic automation using Excel macros.
- Learn Visual Basic for Applications (VBA) for more advanced automation.
9. Power Query:
- Introduce yourself to Power Query for enhanced data manipulation.
- Learn to import, transform, and load data efficiently.
10. Advanced Excel Techniques:
- Explore advanced features like Goal Seek, Solver, and Scenario Manager.
- Master the use of data tables for sensitivity analysis.
11. Real-world Projects:
- Apply your skills to real-world projects or datasets.
- Practice solving analytical problems using Excel.
Remember to practice consistently, as hands-on experience is crucial for mastering Excel. This roadmap will provide a solid foundation for your journey into data analysis using Excel.
5️⃣ Free resources to practice Excel
https://www.w3schools.com/EXCEL/index.php
https://bit.ly/3PSorPT
http://learn.microsoft.com/en-gb/training/paths/modern-analytics/
https://news.1rj.ru/str/excel_analyst/52
https://excel-practice-online.com/
Join for more: https://news.1rj.ru/str/free4unow_backup
ENJOY LEARNING 👍👍
❤2
SQL From Basic to Advanced level
Basic SQL is ONLY 7 commands:
- SELECT
- FROM
- WHERE (also use SQL comparison operators such as =, <=, >=, <> etc.)
- ORDER BY
- Aggregate functions such as SUM, AVERAGE, COUNT etc.
- GROUP BY
- CREATE, INSERT, DELETE, etc.
You can do all this in just one morning.
Once you know these, take the next step and learn commands like:
- LEFT JOIN
- INNER JOIN
- LIKE
- IN
- CASE WHEN
- HAVING (undertstand how it's different from GROUP BY)
- UNION ALL
This should take another day.
Once both basic and intermediate are done, start learning more advanced SQL concepts such as:
- Subqueries (when to use subqueries vs CTE?)
- CTEs (WITH AS)
- Stored Procedures
- Triggers
- Window functions (LEAD, LAG, PARTITION BY, RANK, DENSE RANK)
These can be done in a couple of days.
Learning these concepts is NOT hard at all
- what takes time is practice and knowing what command to use when. How do you master that?
- First, create a basic SQL project
- Then, work on an intermediate SQL project (search online) -
Lastly, create something advanced on SQL with many CTEs, subqueries, stored procedures and triggers etc.
This is ALL you need to become a badass in SQL, and trust me when I say this, it is not rocket science. It's just logic.
Remember that practice is the key here. It will be more clear and perfect with the continous practice
Best telegram channel to learn SQL: https://news.1rj.ru/str/sqlanalyst
Data Analyst Jobs👇
https://news.1rj.ru/str/jobs_SQL
Join @free4unow_backup for more free resources.
Like this post if it helps 😄❤️
ENJOY LEARNING 👍👍
Basic SQL is ONLY 7 commands:
- SELECT
- FROM
- WHERE (also use SQL comparison operators such as =, <=, >=, <> etc.)
- ORDER BY
- Aggregate functions such as SUM, AVERAGE, COUNT etc.
- GROUP BY
- CREATE, INSERT, DELETE, etc.
You can do all this in just one morning.
Once you know these, take the next step and learn commands like:
- LEFT JOIN
- INNER JOIN
- LIKE
- IN
- CASE WHEN
- HAVING (undertstand how it's different from GROUP BY)
- UNION ALL
This should take another day.
Once both basic and intermediate are done, start learning more advanced SQL concepts such as:
- Subqueries (when to use subqueries vs CTE?)
- CTEs (WITH AS)
- Stored Procedures
- Triggers
- Window functions (LEAD, LAG, PARTITION BY, RANK, DENSE RANK)
These can be done in a couple of days.
Learning these concepts is NOT hard at all
- what takes time is practice and knowing what command to use when. How do you master that?
- First, create a basic SQL project
- Then, work on an intermediate SQL project (search online) -
Lastly, create something advanced on SQL with many CTEs, subqueries, stored procedures and triggers etc.
This is ALL you need to become a badass in SQL, and trust me when I say this, it is not rocket science. It's just logic.
Remember that practice is the key here. It will be more clear and perfect with the continous practice
Best telegram channel to learn SQL: https://news.1rj.ru/str/sqlanalyst
Data Analyst Jobs👇
https://news.1rj.ru/str/jobs_SQL
Join @free4unow_backup for more free resources.
Like this post if it helps 😄❤️
ENJOY LEARNING 👍👍
❤1
1. Define the term 'Data Wrangling.
Data Wrangling is the process wherein raw data is cleaned, structured, and enriched into a desired usable format for better decision making. It involves discovering, structuring, cleaning, enriching, validating, and analyzing data. This process can turn and map out large amounts of data extracted from various sources into a more useful format.
2. What are the best methods for data cleaning?
Create a data cleaning plan by understanding where the common errors take place and keep all the communications open. Before working with the data, identify and remove the duplicates. This will lead to an easy and effective data analysis process.Focus on the accuracy of the data. Set cross-field validation, maintain the value types of data, and provide mandatory constraints.Normalize the data at the entry point so that it is less chaotic. You will be able to ensure that all information is standardized, leading to fewer errors on entry.
3. Explain 4 steps to use CTE in sql.
All CTE starts with "with" clause.
After with you need to define CTE name and the field names. For instance in the below code snippet I have 3 fields Count,Column and Id. The name of CTE is "MyTemp".
Once you have defined CTE we need to specify the SQL which will give the result for the CTE.
Finally you can use the CTE in your SQL query.
Data Wrangling is the process wherein raw data is cleaned, structured, and enriched into a desired usable format for better decision making. It involves discovering, structuring, cleaning, enriching, validating, and analyzing data. This process can turn and map out large amounts of data extracted from various sources into a more useful format.
2. What are the best methods for data cleaning?
Create a data cleaning plan by understanding where the common errors take place and keep all the communications open. Before working with the data, identify and remove the duplicates. This will lead to an easy and effective data analysis process.Focus on the accuracy of the data. Set cross-field validation, maintain the value types of data, and provide mandatory constraints.Normalize the data at the entry point so that it is less chaotic. You will be able to ensure that all information is standardized, leading to fewer errors on entry.
3. Explain 4 steps to use CTE in sql.
All CTE starts with "with" clause.
After with you need to define CTE name and the field names. For instance in the below code snippet I have 3 fields Count,Column and Id. The name of CTE is "MyTemp".
Once you have defined CTE we need to specify the SQL which will give the result for the CTE.
Finally you can use the CTE in your SQL query.
❤2
Dreaming of a perfect day as a data analyst?
Here is the reality check:
• You arrive at the office, grab a coffee, and dive deep into solving complex problems.
𝗕𝘂𝘁, you spend the first hour trying to figure out why one of your dashboards shows outdated data.
• You present impactful insights to a room full of executives, who trust your recommendations and are eager to execute your ideas.
𝗕𝘂𝘁, you will explain for the 10th time why Excel isn’t the best tool for running the complex analysis they are requesting.
• You use the latest machine learning models to accurately predict future trends.
𝗕𝘂𝘁, you will spend whole days wrangling messy, incomplete datasets.
• You collaborate with a team of data scientists to create innovative solutions.
𝗕𝘂𝘁, you will have to send a dozen Slack messages to IT just to get access to the data you need.
• You spend the afternoon writing elegant, and efficient Python code.
𝗕𝘂𝘁, you will google basic pandas function more times than you’d like to admit.
Manage your expectations and find humor in your daily work. It’s all part of the journey to those moments where you will drive real business impact as a data analyst!
Here is the reality check:
• You arrive at the office, grab a coffee, and dive deep into solving complex problems.
𝗕𝘂𝘁, you spend the first hour trying to figure out why one of your dashboards shows outdated data.
• You present impactful insights to a room full of executives, who trust your recommendations and are eager to execute your ideas.
𝗕𝘂𝘁, you will explain for the 10th time why Excel isn’t the best tool for running the complex analysis they are requesting.
• You use the latest machine learning models to accurately predict future trends.
𝗕𝘂𝘁, you will spend whole days wrangling messy, incomplete datasets.
• You collaborate with a team of data scientists to create innovative solutions.
𝗕𝘂𝘁, you will have to send a dozen Slack messages to IT just to get access to the data you need.
• You spend the afternoon writing elegant, and efficient Python code.
𝗕𝘂𝘁, you will google basic pandas function more times than you’d like to admit.
Manage your expectations and find humor in your daily work. It’s all part of the journey to those moments where you will drive real business impact as a data analyst!
❤1
Junior-level Data Analyst interview questions:
Introduction and Background
1. Can you tell me about your background and how you became interested in data analysis?
2. What do you know about our company/organization?
3. Why do you want to work as a data analyst?
Data Analysis and Interpretation
1. What is your experience with data analysis tools like Excel, SQL, or Tableau?
2. How would you approach analyzing a large dataset to identify trends and patterns?
3. Can you explain the concept of correlation versus causation?
4. How do you handle missing or incomplete data?
5. Can you walk me through a time when you had to interpret complex data results?
Technical Skills
1. Write a SQL query to extract data from a database.
2. How do you create a pivot table in Excel?
3. Can you explain the difference between a histogram and a box plot?
4. How do you perform data visualization using Tableau or Power BI?
5. Can you write a simple Python or R noscript to manipulate data?
Statistics and Math
1. What is the difference between mean, median, and mode?
2. Can you explain the concept of standard deviation and variance?
3. How do you calculate probability and confidence intervals?
4. Can you describe a time when you applied statistical concepts to a real-world problem?
5. How do you approach hypothesis testing?
Communication and Storytelling
1. Can you explain a complex data concept to a non-technical person?
2. How do you present data insights to stakeholders?
3. Can you walk me through a time when you had to communicate data results to a team?
4. How do you create effective data visualizations?
5. Can you tell a story using data?
Case Studies and Scenarios
1. You are given a dataset with customer purchase history. How would you analyze it to identify trends?
2. A company wants to increase sales. How would you use data to inform marketing strategies?
3. You notice a discrepancy in sales data. How would you investigate and resolve the issue?
4. Can you describe a time when you had to work with a stakeholder to understand their data needs?
5. How would you prioritize data projects with limited resources?
Behavioral Questions
1. Can you describe a time when you overcame a difficult data analysis challenge?
2. How do you handle tight deadlines and multiple projects?
3. Can you tell me about a project you worked on and your role in it?
4. How do you stay up-to-date with new data tools and technologies?
5. Can you describe a time when you received feedback on your data analysis work?
Final Questions
1. Do you have any questions about the company or role?
2. What do you think sets you apart from other candidates?
3. Can you summarize your experience and qualifications?
4. What are your long-term career goals?
Hope this helps you 😊
Introduction and Background
1. Can you tell me about your background and how you became interested in data analysis?
2. What do you know about our company/organization?
3. Why do you want to work as a data analyst?
Data Analysis and Interpretation
1. What is your experience with data analysis tools like Excel, SQL, or Tableau?
2. How would you approach analyzing a large dataset to identify trends and patterns?
3. Can you explain the concept of correlation versus causation?
4. How do you handle missing or incomplete data?
5. Can you walk me through a time when you had to interpret complex data results?
Technical Skills
1. Write a SQL query to extract data from a database.
2. How do you create a pivot table in Excel?
3. Can you explain the difference between a histogram and a box plot?
4. How do you perform data visualization using Tableau or Power BI?
5. Can you write a simple Python or R noscript to manipulate data?
Statistics and Math
1. What is the difference between mean, median, and mode?
2. Can you explain the concept of standard deviation and variance?
3. How do you calculate probability and confidence intervals?
4. Can you describe a time when you applied statistical concepts to a real-world problem?
5. How do you approach hypothesis testing?
Communication and Storytelling
1. Can you explain a complex data concept to a non-technical person?
2. How do you present data insights to stakeholders?
3. Can you walk me through a time when you had to communicate data results to a team?
4. How do you create effective data visualizations?
5. Can you tell a story using data?
Case Studies and Scenarios
1. You are given a dataset with customer purchase history. How would you analyze it to identify trends?
2. A company wants to increase sales. How would you use data to inform marketing strategies?
3. You notice a discrepancy in sales data. How would you investigate and resolve the issue?
4. Can you describe a time when you had to work with a stakeholder to understand their data needs?
5. How would you prioritize data projects with limited resources?
Behavioral Questions
1. Can you describe a time when you overcame a difficult data analysis challenge?
2. How do you handle tight deadlines and multiple projects?
3. Can you tell me about a project you worked on and your role in it?
4. How do you stay up-to-date with new data tools and technologies?
5. Can you describe a time when you received feedback on your data analysis work?
Final Questions
1. Do you have any questions about the company or role?
2. What do you think sets you apart from other candidates?
3. Can you summarize your experience and qualifications?
4. What are your long-term career goals?
Hope this helps you 😊
❤2🤔1