𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄𝗲𝗿: You have 2 minutes to solve this SQL query.
Retrieve the department name and the highest salary in each department from the
𝗠𝗲: Challenge accepted!
SELECT department, MAX(salary) AS highest_salary
FROM employees
GROUP BY department
HAVING MAX(salary) > 70000;
I used
𝗧𝗶𝗽 𝗳𝗼𝗿 𝗦𝗤𝗟 𝗝𝗼𝗯 𝗦𝗲𝗲𝗸𝗲𝗿𝘀:
It's not about writing complex queries; it's about writing clean, efficient, and scalable code. Focus on mastering subqueries, joins, and aggregation functions to stand out!
I have curated essential SQL Interview Resources👇
https://whatsapp.com/channel/0029VanC5rODzgT6TiTGoa1v
Like this post if you need more 👍❤️
Hope it helps :)
Retrieve the department name and the highest salary in each department from the
employees table, but only for departments where the highest salary is greater than $70,000.𝗠𝗲: Challenge accepted!
SELECT department, MAX(salary) AS highest_salary
FROM employees
GROUP BY department
HAVING MAX(salary) > 70000;
I used
GROUP BY to group employees by department, MAX() to get the highest salary, and HAVING to filter the result based on the condition that the highest salary exceeds $70,000. This solution effectively shows my understanding of aggregation functions and how to apply conditions on the result of those aggregations.𝗧𝗶𝗽 𝗳𝗼𝗿 𝗦𝗤𝗟 𝗝𝗼𝗯 𝗦𝗲𝗲𝗸𝗲𝗿𝘀:
It's not about writing complex queries; it's about writing clean, efficient, and scalable code. Focus on mastering subqueries, joins, and aggregation functions to stand out!
I have curated essential SQL Interview Resources👇
https://whatsapp.com/channel/0029VanC5rODzgT6TiTGoa1v
Like this post if you need more 👍❤️
Hope it helps :)
👍11
How do analysts use SQL in a company?
SQL is every data analyst’s superpower! Here's how they use it in the real world:
Extract Data
Pull data from multiple tables to answer business questions.
Example:
(P.S. Avoid SELECT *—your future self (and the database) will thank you!)
Clean & Transform
Use SQL functions to clean raw data.
Think TRIM(), COALESCE(), CAST()—like giving data a fresh haircut.
Summarize & Analyze
Group and aggregate to spot trends and patterns.
GROUP BY, SUM(), AVG() – your best friends for quick insights.
Build Dashboards
Feed SQL queries into Power BI, Tableau, or Excel to create visual stories that make data talk.
Run A/B Tests
Evaluate product changes and campaigns by comparing user groups.
SQL makes sure your decisions are backed by data, not just gut feeling.
Use Views & CTEs
Simplify complex queries with Views and Common Table Expressions.
Clean, reusable, and boss-approved.
Drive Decisions
SQL powers decisions across Marketing, Product, Sales, and Finance.
When someone asks “What’s working?”—you’ve got the answers.
And remember: write smart queries, not lazy ones. Say no to SELECT * unless you really mean it!
Hit ♥️ if you want me to share more real-world examples to make data analytics easier to understand!
Share with credits: https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
SQL is every data analyst’s superpower! Here's how they use it in the real world:
Extract Data
Pull data from multiple tables to answer business questions.
Example:
SELECT name, revenue FROM sales WHERE region = 'North America';
(P.S. Avoid SELECT *—your future self (and the database) will thank you!)
Clean & Transform
Use SQL functions to clean raw data.
Think TRIM(), COALESCE(), CAST()—like giving data a fresh haircut.
Summarize & Analyze
Group and aggregate to spot trends and patterns.
GROUP BY, SUM(), AVG() – your best friends for quick insights.
Build Dashboards
Feed SQL queries into Power BI, Tableau, or Excel to create visual stories that make data talk.
Run A/B Tests
Evaluate product changes and campaigns by comparing user groups.
SQL makes sure your decisions are backed by data, not just gut feeling.
Use Views & CTEs
Simplify complex queries with Views and Common Table Expressions.
Clean, reusable, and boss-approved.
Drive Decisions
SQL powers decisions across Marketing, Product, Sales, and Finance.
When someone asks “What’s working?”—you’ve got the answers.
And remember: write smart queries, not lazy ones. Say no to SELECT * unless you really mean it!
Hit ♥️ if you want me to share more real-world examples to make data analytics easier to understand!
Share with credits: https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
👍11❤10🥰1
1. What are the ways to detect outliers?
Outliers are detected using two methods:
Box Plot Method: According to this method, the value is considered an outlier if it exceeds or falls below 1.5*IQR (interquartile range), that is, if it lies above the top quartile (Q3) or below the bottom quartile (Q1).
Standard Deviation Method: According to this method, an outlier is defined as a value that is greater or lower than the mean ± (3*standard deviation).
2. What is a Recursive Stored Procedure?
A stored procedure that calls itself until a boundary condition is reached, is called a recursive stored procedure. This recursive function helps the programmers to deploy the same set of code several times as and when required.
3. What is the shortcut to add a filter to a table in EXCEL?
The filter mechanism is used when you want to display only specific data from the entire dataset. By doing so, there is no change being made to the data. The shortcut to add a filter to a table is Ctrl+Shift+L.
4. What is DAX in Power BI?
DAX stands for Data Analysis Expressions. It's a collection of functions, operators, and constants used in formulas to calculate and return values. In other words, it helps you create new info from data you already have.
Outliers are detected using two methods:
Box Plot Method: According to this method, the value is considered an outlier if it exceeds or falls below 1.5*IQR (interquartile range), that is, if it lies above the top quartile (Q3) or below the bottom quartile (Q1).
Standard Deviation Method: According to this method, an outlier is defined as a value that is greater or lower than the mean ± (3*standard deviation).
2. What is a Recursive Stored Procedure?
A stored procedure that calls itself until a boundary condition is reached, is called a recursive stored procedure. This recursive function helps the programmers to deploy the same set of code several times as and when required.
3. What is the shortcut to add a filter to a table in EXCEL?
The filter mechanism is used when you want to display only specific data from the entire dataset. By doing so, there is no change being made to the data. The shortcut to add a filter to a table is Ctrl+Shift+L.
4. What is DAX in Power BI?
DAX stands for Data Analysis Expressions. It's a collection of functions, operators, and constants used in formulas to calculate and return values. In other words, it helps you create new info from data you already have.
👍8❤4
SQL Basics for Data Analysts
SQL (Structured Query Language) is used to retrieve, manipulate, and analyze data stored in databases.
1️⃣ Understanding Databases & Tables
Databases store structured data in tables.
Tables contain rows (records) and columns (fields).
Each column has a specific data type (INTEGER, VARCHAR, DATE, etc.).
2️⃣ Basic SQL Commands
Let's start with some fundamental queries:
🔹 SELECT – Retrieve Data
🔹 WHERE – Filter Data
🔹 ORDER BY – Sort Data
🔹 LIMIT – Restrict Number of Results
🔹 DISTINCT – Remove Duplicates
Mini Task for You: Try to write an SQL query to fetch the top 3 highest-paid employees from an "employees" table.
You can find free SQL Resources here
👇👇
https://news.1rj.ru/str/mysqldata
Like this post if you want me to continue covering all the topics! 👍❤️
Share with credits: https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
#sql
SQL (Structured Query Language) is used to retrieve, manipulate, and analyze data stored in databases.
1️⃣ Understanding Databases & Tables
Databases store structured data in tables.
Tables contain rows (records) and columns (fields).
Each column has a specific data type (INTEGER, VARCHAR, DATE, etc.).
2️⃣ Basic SQL Commands
Let's start with some fundamental queries:
🔹 SELECT – Retrieve Data
SELECT * FROM employees; -- Fetch all columns from 'employees' table SELECT name, salary FROM employees; -- Fetch specific columns
🔹 WHERE – Filter Data
SELECT * FROM employees WHERE department = 'Sales'; -- Filter by department SELECT * FROM employees WHERE salary > 50000; -- Filter by salary
🔹 ORDER BY – Sort Data
SELECT * FROM employees ORDER BY salary DESC; -- Sort by salary (highest first) SELECT name, hire_date FROM employees ORDER BY hire_date ASC; -- Sort by hire date (oldest first)
🔹 LIMIT – Restrict Number of Results
SELECT * FROM employees LIMIT 5; -- Fetch only 5 rows SELECT * FROM employees WHERE department = 'HR' LIMIT 10; -- Fetch first 10 HR employees
🔹 DISTINCT – Remove Duplicates
SELECT DISTINCT department FROM employees; -- Show unique departments
Mini Task for You: Try to write an SQL query to fetch the top 3 highest-paid employees from an "employees" table.
You can find free SQL Resources here
👇👇
https://news.1rj.ru/str/mysqldata
Like this post if you want me to continue covering all the topics! 👍❤️
Share with credits: https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
#sql
👍10❤3
Data Analytics Interview Questions
Q1: Describe a situation where you had to clean a messy dataset. What steps did you take?
Ans: I encountered a dataset with missing values, duplicates, and inconsistent formats. I used Python's Pandas library to identify and handle missing values, standardized data formats using regular expressions, and removed duplicates. I also validated the cleaned data against known benchmarks to ensure accuracy.
Q2: How do you handle outliers in a dataset?
Ans: I start by visualizing the data using box plots or scatter plots to identify potential outliers. Then, depending on the nature of the data and the problem context, I might cap the outliers, transform the data, or even remove them if they're due to errors.
Q3: How would you use data to suggest optimal pricing strategies to Airbnb hosts?
Ans: I'd analyze factors like location, property type, amenities, local events, and historical booking rates. Using regression analysis, I'd model the relationship between these factors and pricing to suggest an optimal price range. Additionally, analyzing competitor pricing in the area can provide insights into market rates.
Q4: Describe a situation where you used data to improve the user experience on the Airbnb platform.
Ans: While analyzing user feedback and platform interaction data, I noticed that users often had difficulty navigating the booking process. Based on this, I suggested streamlining the booking steps and providing clearer instructions. A/B testing confirmed that these changes led to a higher conversion rate and improved user feedback.
Q1: Describe a situation where you had to clean a messy dataset. What steps did you take?
Ans: I encountered a dataset with missing values, duplicates, and inconsistent formats. I used Python's Pandas library to identify and handle missing values, standardized data formats using regular expressions, and removed duplicates. I also validated the cleaned data against known benchmarks to ensure accuracy.
Q2: How do you handle outliers in a dataset?
Ans: I start by visualizing the data using box plots or scatter plots to identify potential outliers. Then, depending on the nature of the data and the problem context, I might cap the outliers, transform the data, or even remove them if they're due to errors.
Q3: How would you use data to suggest optimal pricing strategies to Airbnb hosts?
Ans: I'd analyze factors like location, property type, amenities, local events, and historical booking rates. Using regression analysis, I'd model the relationship between these factors and pricing to suggest an optimal price range. Additionally, analyzing competitor pricing in the area can provide insights into market rates.
Q4: Describe a situation where you used data to improve the user experience on the Airbnb platform.
Ans: While analyzing user feedback and platform interaction data, I noticed that users often had difficulty navigating the booking process. Based on this, I suggested streamlining the booking steps and providing clearer instructions. A/B testing confirmed that these changes led to a higher conversion rate and improved user feedback.
👍7❤1
𝗔𝗰𝗲 𝗬𝗼𝘂𝗿 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝘁 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝘄𝗶𝘁𝗵 𝗧𝗵𝗲𝘀𝗲 𝗠𝘂𝘀𝘁-𝗞𝗻𝗼𝘄 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀! 🔥
Are you preparing for a 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝘁 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄? Hiring managers don’t just want to hear your answers—they want to know if you truly understand data.
Here are 𝗳𝗿𝗲𝗾𝘂𝗲𝗻𝘁𝗹𝘆 𝗮𝘀𝗸𝗲𝗱 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀 (and what they really mean):
📌 "𝗧𝗲𝗹𝗹 𝗺𝗲 𝗮𝗯𝗼𝘂𝘁 𝘆𝗼𝘂𝗿𝘀𝗲𝗹𝗳."
🔍 What they’re really asking: Are you relevant for this role?
✅ Keep it concise—highlight your experience, tools (SQL, Power BI, etc.), and a key impact you made.
📌 "𝗛𝗼𝘄 𝗱𝗼 𝘆𝗼𝘂 𝗵𝗮𝗻𝗱𝗹𝗲 𝗺𝗲𝘀𝘀𝘆 𝗱𝗮𝘁𝗮?"
🔍 What they’re really asking: Do you panic when you see missing values?
✅ Show your structured approach—identify issues, clean with Pandas/SQL, and document your process.
📌 "𝗛𝗼𝘄 𝗱𝗼 𝘆𝗼𝘂 𝗮𝗽𝗽𝗿𝗼𝗮𝗰𝗵 𝗮 𝗱𝗮𝘁𝗮 𝗮𝗻𝗮𝗹𝘆𝘀𝗶𝘀 𝗽𝗿𝗼𝗷𝗲𝗰𝘁?"
🔍 What they’re really asking: Do you have a methodology, or do you just wing it?
✅ Use a structured approach: Define business needs → Clean & explore data → Generate insights → Present effectively.
📌 "𝗖𝗮𝗻 𝘆𝗼𝘂 𝗲𝘅𝗽𝗹𝗮𝗶𝗻 𝗮 𝗰𝗼𝗺𝗽𝗹𝗲𝘅 𝗰𝗼𝗻𝗰𝗲𝗽𝘁 𝘁𝗼 𝗮 𝗻𝗼𝗻-𝘁𝗲𝗰𝗵𝗻𝗶𝗰𝗮𝗹
𝘀𝘁𝗮𝗸𝗲𝗵𝗼𝗹𝗱𝗲𝗿?"
🔍 What they’re really asking: Can you simplify data without oversimplifying?
✅ Use storytelling—focus on actionable insights rather than jargon.
📌 "𝗧𝗲𝗹𝗹 𝗺𝗲 𝗮𝗯𝗼𝘂𝘁 𝗮 𝘁𝗶𝗺𝗲 𝘆𝗼𝘂 𝗺𝗮𝗱𝗲 𝗮 𝗺𝗶𝘀𝘁𝗮𝗸𝗲."
🔍 What they’re really asking: Can you learn from failure?
✅ Own your mistake, explain how you fixed it, and share what you do differently now.
💡 𝗣𝗿𝗼 𝗧𝗶𝗽: The best candidates don’t just answer questions—they tell stories that demonstrate problem-solving, clarity, and impact.
🔄 Save this for later & share with someone preparing for interviews!
Are you preparing for a 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝘁 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄? Hiring managers don’t just want to hear your answers—they want to know if you truly understand data.
Here are 𝗳𝗿𝗲𝗾𝘂𝗲𝗻𝘁𝗹𝘆 𝗮𝘀𝗸𝗲𝗱 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀 (and what they really mean):
📌 "𝗧𝗲𝗹𝗹 𝗺𝗲 𝗮𝗯𝗼𝘂𝘁 𝘆𝗼𝘂𝗿𝘀𝗲𝗹𝗳."
🔍 What they’re really asking: Are you relevant for this role?
✅ Keep it concise—highlight your experience, tools (SQL, Power BI, etc.), and a key impact you made.
📌 "𝗛𝗼𝘄 𝗱𝗼 𝘆𝗼𝘂 𝗵𝗮𝗻𝗱𝗹𝗲 𝗺𝗲𝘀𝘀𝘆 𝗱𝗮𝘁𝗮?"
🔍 What they’re really asking: Do you panic when you see missing values?
✅ Show your structured approach—identify issues, clean with Pandas/SQL, and document your process.
📌 "𝗛𝗼𝘄 𝗱𝗼 𝘆𝗼𝘂 𝗮𝗽𝗽𝗿𝗼𝗮𝗰𝗵 𝗮 𝗱𝗮𝘁𝗮 𝗮𝗻𝗮𝗹𝘆𝘀𝗶𝘀 𝗽𝗿𝗼𝗷𝗲𝗰𝘁?"
🔍 What they’re really asking: Do you have a methodology, or do you just wing it?
✅ Use a structured approach: Define business needs → Clean & explore data → Generate insights → Present effectively.
📌 "𝗖𝗮𝗻 𝘆𝗼𝘂 𝗲𝘅𝗽𝗹𝗮𝗶𝗻 𝗮 𝗰𝗼𝗺𝗽𝗹𝗲𝘅 𝗰𝗼𝗻𝗰𝗲𝗽𝘁 𝘁𝗼 𝗮 𝗻𝗼𝗻-𝘁𝗲𝗰𝗵𝗻𝗶𝗰𝗮𝗹
𝘀𝘁𝗮𝗸𝗲𝗵𝗼𝗹𝗱𝗲𝗿?"
🔍 What they’re really asking: Can you simplify data without oversimplifying?
✅ Use storytelling—focus on actionable insights rather than jargon.
📌 "𝗧𝗲𝗹𝗹 𝗺𝗲 𝗮𝗯𝗼𝘂𝘁 𝗮 𝘁𝗶𝗺𝗲 𝘆𝗼𝘂 𝗺𝗮𝗱𝗲 𝗮 𝗺𝗶𝘀𝘁𝗮𝗸𝗲."
🔍 What they’re really asking: Can you learn from failure?
✅ Own your mistake, explain how you fixed it, and share what you do differently now.
💡 𝗣𝗿𝗼 𝗧𝗶𝗽: The best candidates don’t just answer questions—they tell stories that demonstrate problem-solving, clarity, and impact.
🔄 Save this for later & share with someone preparing for interviews!
👍16
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.
👍5
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15. Excel➝
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16. Generative AI➝
◀️ https://news.1rj.ru/str/generativeai_gpt
17. Java
◀️ https://news.1rj.ru/str/Java_Programming_Notes
18. Artificial Intelligence
◀️ https://news.1rj.ru/str/machinelearning_deeplearning
19. Data Structure & Algorithms
◀️ https://news.1rj.ru/str/dsabooks
20. Backend Development
◀️ https://imp.i115008.net/rn2nyy
21. Python for AI
◀️ https://deeplearning.ai/short-courses/ai-python-for-beginners/
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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 👇👇
https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02
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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 👇👇
https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02
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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!
I have curated best 80+ top-notch Data Analytics Resources 👇👇
https://news.1rj.ru/str/DataSimplifier
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Hope it helps :)
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
Like this post for more content like this 👍♥️
Share with credits: https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
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SQL Interview Questions
1. How would you find duplicate records in SQL?
2.What are various types of SQL joins?
3.What is a trigger in SQL?
4.What are different DDL,DML commands in SQL?
5.What is difference between Delete, Drop and Truncate?
6.What is difference between Union and Union all?
7.Which command give Unique values?
8. What is the difference between Where and Having Clause?
9.Give the execution of keywords in SQL?
10. What is difference between IN and BETWEEN Operator?
11. What is primary and Foreign key?
12. What is an aggregate Functions?
13. What is the difference between Rank and Dense Rank?
14. List the ACID Properties and explain what they are?
15. What is the difference between % and _ in like operator?
16. What does CTE stands for?
17. What is database?what is DBMS?What is RDMS?
18.What is Alias in SQL?
19. What is Normalisation?Describe various form?
20. How do you sort the results of a query?
21. Explain the types of Window functions?
22. What is limit and offset?
23. What is candidate key?
24. Describe various types of Alter command?
25. What is Cartesian product?
Like this post if you need more content like this ❤️
1. How would you find duplicate records in SQL?
2.What are various types of SQL joins?
3.What is a trigger in SQL?
4.What are different DDL,DML commands in SQL?
5.What is difference between Delete, Drop and Truncate?
6.What is difference between Union and Union all?
7.Which command give Unique values?
8. What is the difference between Where and Having Clause?
9.Give the execution of keywords in SQL?
10. What is difference between IN and BETWEEN Operator?
11. What is primary and Foreign key?
12. What is an aggregate Functions?
13. What is the difference between Rank and Dense Rank?
14. List the ACID Properties and explain what they are?
15. What is the difference between % and _ in like operator?
16. What does CTE stands for?
17. What is database?what is DBMS?What is RDMS?
18.What is Alias in SQL?
19. What is Normalisation?Describe various form?
20. How do you sort the results of a query?
21. Explain the types of Window functions?
22. What is limit and offset?
23. What is candidate key?
24. Describe various types of Alter command?
25. What is Cartesian product?
Like this post if you need more content like this ❤️
👍15❤1
Quick SQL functions cheat sheet for beginners ✍
Aggregate Functions
COUNT(*): Counts rows.
SUM(column): Total sum.
AVG(column): Average value.
MAX(column): Maximum value.
MIN(column): Minimum value.
String Functions
CONCAT(a, b, …): Concatenates strings.
SUBSTRING(s, start, length): Extracts part of a string.
UPPER(s) / LOWER(s): Converts string case.
TRIM(s): Removes leading/trailing spaces.
Date & Time Functions
CURRENT_DATE / CURRENT_TIME / CURRENT_TIMESTAMP: Current date/time.
EXTRACT(unit FROM date): Retrieves a date part (e.g., year, month).
DATE_ADD(date, INTERVAL n unit): Adds an interval to a date.
Numeric Functions
ROUND(num, decimals): Rounds to a specified decimal.
CEIL(num) / FLOOR(num): Rounds up/down.
ABS(num): Absolute value.
MOD(a, b): Returns the remainder.
Control Flow Functions
CASE: Conditional logic.
COALESCE(val1, val2, …): Returns the first non-null value.
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#dataanalytics
Aggregate Functions
COUNT(*): Counts rows.
SUM(column): Total sum.
AVG(column): Average value.
MAX(column): Maximum value.
MIN(column): Minimum value.
String Functions
CONCAT(a, b, …): Concatenates strings.
SUBSTRING(s, start, length): Extracts part of a string.
UPPER(s) / LOWER(s): Converts string case.
TRIM(s): Removes leading/trailing spaces.
Date & Time Functions
CURRENT_DATE / CURRENT_TIME / CURRENT_TIMESTAMP: Current date/time.
EXTRACT(unit FROM date): Retrieves a date part (e.g., year, month).
DATE_ADD(date, INTERVAL n unit): Adds an interval to a date.
Numeric Functions
ROUND(num, decimals): Rounds to a specified decimal.
CEIL(num) / FLOOR(num): Rounds up/down.
ABS(num): Absolute value.
MOD(a, b): Returns the remainder.
Control Flow Functions
CASE: Conditional logic.
COALESCE(val1, val2, …): Returns the first non-null value.
Like for more free Cheatsheets ❤️
Share with credits: https://news.1rj.ru/str/sqlspecialist
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#dataanalytics
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Power BI DAX Cheatsheet 🚀
1️⃣ Basics of DAX (Data Analysis Expressions)
DAX is used to create custom calculations in Power BI.
It works with tables and columns, not individual cells.
Functions in DAX are similar to Excel but optimized for relational data.
2️⃣ Aggregation Functions
SUM(ColumnName): Adds all values in a column.
AVERAGE(ColumnName): Finds the mean of values.
MIN(ColumnName): Returns the smallest value.
MAX(ColumnName): Returns the largest value.
COUNT(ColumnName): Counts non-empty values.
COUNTROWS(TableName): Counts rows in a table.
3️⃣ Logical Functions
IF(condition, result_if_true, result_if_false): Conditional statement.
SWITCH(expression, value1, result1, value2, result2, default): Alternative to nested IF.
AND(condition1, condition2): Returns TRUE if both conditions are met.
OR(condition1, condition2): Returns TRUE if either condition is met.
4️⃣ Time Intelligence Functions
TODAY(): Returns the current date.
YEAR(TODAY()): Extracts the year from a date.
TOTALYTD(SUM(Sales[Amount]), Date[Date]): Year-to-date total.
SAMEPERIODLASTYEAR(Date[Date]): Returns values from the same period last year.
DATEADD(Date[Date], -1, MONTH): Shifts dates by a specified interval.
5️⃣ Filtering Functions
FILTER(Table, Condition): Returns a filtered table.
ALL(TableName): Removes all filters from a table.
ALLEXCEPT(TableName, Column1, Column2): Removes all filters except specified columns.
KEEPFILTERS(FilterExpression): Keeps filters applied while using other functions.
6️⃣ Ranking & Row Context Functions
RANKX(Table, Expression, [Value], [Order]): Ranks values in a column.
TOPN(N, Table, OrderByExpression): Returns the top N rows based on an expression.
7️⃣ Iterators (Row-by-Row Calculations)
SUMX(Table, Expression): Iterates over a table and sums calculated values.
AVERAGEX(Table, Expression): Iterates over a table and finds the average.
MAXX(Table, Expression): Finds the maximum value based on an expression.
8️⃣ Relationships & Lookup Functions
RELATED(ColumnName): Fetches a related column from another table.
LOOKUPVALUE(ColumnName, SearchColumn, SearchValue): Returns a value from a column where another column matches a value.
9️⃣ Variables in DAX
VAR variableName = Expression RETURN variableName
Improves performance by reducing redundant calculations.
🔟 Advanced DAX Concepts
Calculated Columns: Created at the column level, stored in the data model.
Measures: Dynamic calculations based on user interactions in Power BI visuals.
Row Context vs. Filter Context: Understanding how DAX applies calculations at different levels.
Free Power BI Resources: https://whatsapp.com/channel/0029Vai1xKf1dAvuk6s1v22c
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1️⃣ Basics of DAX (Data Analysis Expressions)
DAX is used to create custom calculations in Power BI.
It works with tables and columns, not individual cells.
Functions in DAX are similar to Excel but optimized for relational data.
2️⃣ Aggregation Functions
SUM(ColumnName): Adds all values in a column.
AVERAGE(ColumnName): Finds the mean of values.
MIN(ColumnName): Returns the smallest value.
MAX(ColumnName): Returns the largest value.
COUNT(ColumnName): Counts non-empty values.
COUNTROWS(TableName): Counts rows in a table.
3️⃣ Logical Functions
IF(condition, result_if_true, result_if_false): Conditional statement.
SWITCH(expression, value1, result1, value2, result2, default): Alternative to nested IF.
AND(condition1, condition2): Returns TRUE if both conditions are met.
OR(condition1, condition2): Returns TRUE if either condition is met.
4️⃣ Time Intelligence Functions
TODAY(): Returns the current date.
YEAR(TODAY()): Extracts the year from a date.
TOTALYTD(SUM(Sales[Amount]), Date[Date]): Year-to-date total.
SAMEPERIODLASTYEAR(Date[Date]): Returns values from the same period last year.
DATEADD(Date[Date], -1, MONTH): Shifts dates by a specified interval.
5️⃣ Filtering Functions
FILTER(Table, Condition): Returns a filtered table.
ALL(TableName): Removes all filters from a table.
ALLEXCEPT(TableName, Column1, Column2): Removes all filters except specified columns.
KEEPFILTERS(FilterExpression): Keeps filters applied while using other functions.
6️⃣ Ranking & Row Context Functions
RANKX(Table, Expression, [Value], [Order]): Ranks values in a column.
TOPN(N, Table, OrderByExpression): Returns the top N rows based on an expression.
7️⃣ Iterators (Row-by-Row Calculations)
SUMX(Table, Expression): Iterates over a table and sums calculated values.
AVERAGEX(Table, Expression): Iterates over a table and finds the average.
MAXX(Table, Expression): Finds the maximum value based on an expression.
8️⃣ Relationships & Lookup Functions
RELATED(ColumnName): Fetches a related column from another table.
LOOKUPVALUE(ColumnName, SearchColumn, SearchValue): Returns a value from a column where another column matches a value.
9️⃣ Variables in DAX
VAR variableName = Expression RETURN variableName
Improves performance by reducing redundant calculations.
🔟 Advanced DAX Concepts
Calculated Columns: Created at the column level, stored in the data model.
Measures: Dynamic calculations based on user interactions in Power BI visuals.
Row Context vs. Filter Context: Understanding how DAX applies calculations at different levels.
Free Power BI Resources: https://whatsapp.com/channel/0029Vai1xKf1dAvuk6s1v22c
React with ❤️ for free cheatsheets
Share with credits: https://news.1rj.ru/str/sqlspecialist
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Data Analytics Interview Preparation Part-2
[Questions with Answers]
How did you get your job?
I was hired after an internship.
To get the internship, I prepared a bunch for general Python questions (LeetCode etc.) and studied the basics of machine learning (several different algorithms, how they work, when they're useful, metrics
to measure their performance, how to train them in practice etc.).
To get the internship I had to pass a technical interview as well as a take-home machine learning (ML) exercise. Then, it was just a question of doing a good job in the internship!
What are your data related responsibilities in your job?
I work on our recommendation system. It’s deep learning based. I work on a lot of features to try and
improve it (reinforcement learning & NLP etc). Since I'm in a start-up, it's also up to our team to put the models we design into production. So, after a phase of research & development and model design, in notebooks, it's time to create a real pipeline, by creating noscripts.
This enables us to define, train, replace, compare and check the status of the models in production. It's basically all in Python, using Keras/TensorFlow, Pandas, Scikit-learn and NumPy. We also do a lot of analysis for the business team to help them compute metrics of interest (related to
revenue, acquisition etc.). For that, we use an external utility called Metabase. It is is hooked up to our database where we write SQL queries and visualize the results and create dashboards (using
Tableau/Looker etc).
I would say my role is quite "full-stack" since we are all involved from the phase of R&D to deployment on our cluster.
Was it difficult to get this role?
I got hired after an internship. If you come from a scientific background, it's not that hard to transition into data science. All the math is something you will probably have seen already (especially if you're
doing maths or physics). So, with some preparation and coding practice, you can start applying to internships.
It took me maybe a month or two of preparation to get some basic ideas of the typical Python data stack (Pandas, Keras, SciKit-learn etc) before I started to send out CVs. Then, if you get an internship, try your best to do the best you can and then maybe you'll be hired after!
I have curated best 80+ top-notch Data Analytics Resources 👇👇
https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02
Hope it helps :)
[Questions with Answers]
How did you get your job?
I was hired after an internship.
To get the internship, I prepared a bunch for general Python questions (LeetCode etc.) and studied the basics of machine learning (several different algorithms, how they work, when they're useful, metrics
to measure their performance, how to train them in practice etc.).
To get the internship I had to pass a technical interview as well as a take-home machine learning (ML) exercise. Then, it was just a question of doing a good job in the internship!
What are your data related responsibilities in your job?
I work on our recommendation system. It’s deep learning based. I work on a lot of features to try and
improve it (reinforcement learning & NLP etc). Since I'm in a start-up, it's also up to our team to put the models we design into production. So, after a phase of research & development and model design, in notebooks, it's time to create a real pipeline, by creating noscripts.
This enables us to define, train, replace, compare and check the status of the models in production. It's basically all in Python, using Keras/TensorFlow, Pandas, Scikit-learn and NumPy. We also do a lot of analysis for the business team to help them compute metrics of interest (related to
revenue, acquisition etc.). For that, we use an external utility called Metabase. It is is hooked up to our database where we write SQL queries and visualize the results and create dashboards (using
Tableau/Looker etc).
I would say my role is quite "full-stack" since we are all involved from the phase of R&D to deployment on our cluster.
Was it difficult to get this role?
I got hired after an internship. If you come from a scientific background, it's not that hard to transition into data science. All the math is something you will probably have seen already (especially if you're
doing maths or physics). So, with some preparation and coding practice, you can start applying to internships.
It took me maybe a month or two of preparation to get some basic ideas of the typical Python data stack (Pandas, Keras, SciKit-learn etc) before I started to send out CVs. Then, if you get an internship, try your best to do the best you can and then maybe you'll be hired after!
I have curated best 80+ top-notch Data Analytics Resources 👇👇
https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02
Hope it helps :)
❤3👍3
7 Baby Steps to Become a Data Analyst 👇👇
1. Understand the Role of a Data Analyst:
Learn what a data analyst does, including collecting, cleaning, analyzing, and interpreting data to support decision-making.
Familiarize yourself with key terms like KPIs, dashboards, and business intelligence.
Research industries where data analysts work, such as finance, marketing, healthcare, and e-commerce.
2. Learn the Essential Tools:
Excel: Start with basics like formulas, functions, and pivot tables, then advance to using Power Query and macros.
SQL: Learn to write queries for retrieving, filtering, and aggregating data from databases.
Data Visualization Tools: Master tools like Power BI or Tableau to create dashboards and reports.
3. Develop Analytical Thinking:
Practice identifying trends, patterns, and outliers in datasets.
Learn to ask the right questions about what the data reveals and how it can guide decision-making.
Strengthen problem-solving skills through real-world case studies or challenges.
4. Master a Programming Language (Python or R):
Learn Python libraries like pandas, NumPy, and matplotlib for data manipulation and visualization.
Alternatively, learn R for statistical analysis and its packages like ggplot2 and dplyr.
Work on projects like cleaning messy datasets or creating automated analysis noscripts.
5. Work with Real-World Data:
Explore open datasets from platforms like Kaggle or Google Dataset Search.
Practice analyzing datasets related to your area of interest (e.g., sales, customer feedback, or healthcare).
Create sample reports or dashboards to showcase insights.
6. Build a Portfolio:
Document your projects in a way that demonstrates your skills. Include:
Data cleaning and transformation examples.
Visualization dashboards using Power BI, Tableau, or Excel.
Analysis reports with actionable insights.
Use GitHub or Tableau Public to showcase your work.
7. Engage with the Data Analytics Community:
Join forums like Kaggle, Reddit’s r/dataanalysis, or LinkedIn groups.
Participate in challenges to solve real-world problems, such as Kaggle competitions.
Additional Tips:
Gain domain knowledge relevant to your target industry (e.g., marketing analytics or financial analysis).
Focus on communication skills to present insights effectively to non-technical stakeholders.
Continuously learn and upskill as new tools and techniques emerge in the data analytics field.
Join our WhatsApp channel 👇
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Like this post for more content like this 👍♥️
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Hope it helps :)
1. Understand the Role of a Data Analyst:
Learn what a data analyst does, including collecting, cleaning, analyzing, and interpreting data to support decision-making.
Familiarize yourself with key terms like KPIs, dashboards, and business intelligence.
Research industries where data analysts work, such as finance, marketing, healthcare, and e-commerce.
2. Learn the Essential Tools:
Excel: Start with basics like formulas, functions, and pivot tables, then advance to using Power Query and macros.
SQL: Learn to write queries for retrieving, filtering, and aggregating data from databases.
Data Visualization Tools: Master tools like Power BI or Tableau to create dashboards and reports.
3. Develop Analytical Thinking:
Practice identifying trends, patterns, and outliers in datasets.
Learn to ask the right questions about what the data reveals and how it can guide decision-making.
Strengthen problem-solving skills through real-world case studies or challenges.
4. Master a Programming Language (Python or R):
Learn Python libraries like pandas, NumPy, and matplotlib for data manipulation and visualization.
Alternatively, learn R for statistical analysis and its packages like ggplot2 and dplyr.
Work on projects like cleaning messy datasets or creating automated analysis noscripts.
5. Work with Real-World Data:
Explore open datasets from platforms like Kaggle or Google Dataset Search.
Practice analyzing datasets related to your area of interest (e.g., sales, customer feedback, or healthcare).
Create sample reports or dashboards to showcase insights.
6. Build a Portfolio:
Document your projects in a way that demonstrates your skills. Include:
Data cleaning and transformation examples.
Visualization dashboards using Power BI, Tableau, or Excel.
Analysis reports with actionable insights.
Use GitHub or Tableau Public to showcase your work.
7. Engage with the Data Analytics Community:
Join forums like Kaggle, Reddit’s r/dataanalysis, or LinkedIn groups.
Participate in challenges to solve real-world problems, such as Kaggle competitions.
Additional Tips:
Gain domain knowledge relevant to your target industry (e.g., marketing analytics or financial analysis).
Focus on communication skills to present insights effectively to non-technical stakeholders.
Continuously learn and upskill as new tools and techniques emerge in the data analytics field.
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Step-by-Step Approach to Learn Data Analytics
➊ Learn Programming Language → SQL & Python
↓
➋ Master Excel & Spreadsheets → Pivot Tables, VLOOKUP, Data Cleaning
↓
➌ SQL for Data Analysis → SELECT, JOINS, GROUP BY, Window Functions
↓
➍ Data Manipulation & Processing → Pandas, NumPy
↓
➎ Data Visualization → Power BI, Tableau, Matplotlib, Seaborn
↓
➏ Exploratory Data Analysis (EDA) → Missing Values, Outliers, Feature Engineering
↓
➐ Business Intelligence & Reporting → Dashboards, Storytelling with Data
↓
➑ Advanced Concepts → A/B Testing, Statistical Analysis, Machine Learning Basics
React with ❤️ for detailed explanation
Share with credits: https://news.1rj.ru/str/sqlspecialist
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➊ Learn Programming Language → SQL & Python
↓
➋ Master Excel & Spreadsheets → Pivot Tables, VLOOKUP, Data Cleaning
↓
➌ SQL for Data Analysis → SELECT, JOINS, GROUP BY, Window Functions
↓
➍ Data Manipulation & Processing → Pandas, NumPy
↓
➎ Data Visualization → Power BI, Tableau, Matplotlib, Seaborn
↓
➏ Exploratory Data Analysis (EDA) → Missing Values, Outliers, Feature Engineering
↓
➐ Business Intelligence & Reporting → Dashboards, Storytelling with Data
↓
➑ Advanced Concepts → A/B Testing, Statistical Analysis, Machine Learning Basics
React with ❤️ for detailed explanation
Share with credits: https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
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SQL Cheatsheet 📝
This SQL cheatsheet is designed to be your quick reference guide for SQL programming. Whether you’re a beginner learning how to query databases or an experienced developer looking for a handy resource, this cheatsheet covers essential SQL topics.
1. Database Basics
-
-
2. Tables
- Create Table:
- Drop Table:
- Alter Table:
3. Insert Data
-
4. Select Queries
- Basic Select:
- Select Specific Columns:
- Select with Condition:
5. Update Data
-
6. Delete Data
-
7. Joins
- Inner Join:
- Left Join:
- Right Join:
8. Aggregations
- Count:
- Sum:
- Group By:
9. Sorting & Limiting
- Order By:
- Limit Results:
10. Indexes
- Create Index:
- Drop Index:
11. Subqueries
-
12. Views
- Create View:
- Drop View:
Here you can find SQL Interview Resources👇
https://news.1rj.ru/str/DataSimplifier
Share with credits: https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
This SQL cheatsheet is designed to be your quick reference guide for SQL programming. Whether you’re a beginner learning how to query databases or an experienced developer looking for a handy resource, this cheatsheet covers essential SQL topics.
1. Database Basics
-
CREATE DATABASE db_name;-
USE db_name;2. Tables
- Create Table:
CREATE TABLE table_name (col1 datatype, col2 datatype);- Drop Table:
DROP TABLE table_name;- Alter Table:
ALTER TABLE table_name ADD column_name datatype;3. Insert Data
-
INSERT INTO table_name (col1, col2) VALUES (val1, val2);4. Select Queries
- Basic Select:
SELECT * FROM table_name;- Select Specific Columns:
SELECT col1, col2 FROM table_name;- Select with Condition:
SELECT * FROM table_name WHERE condition;5. Update Data
-
UPDATE table_name SET col1 = value1 WHERE condition;6. Delete Data
-
DELETE FROM table_name WHERE condition;7. Joins
- Inner Join:
SELECT * FROM table1 INNER JOIN table2 ON table1.col = table2.col;- Left Join:
SELECT * FROM table1 LEFT JOIN table2 ON table1.col = table2.col;- Right Join:
SELECT * FROM table1 RIGHT JOIN table2 ON table1.col = table2.col;8. Aggregations
- Count:
SELECT COUNT(*) FROM table_name;- Sum:
SELECT SUM(col) FROM table_name;- Group By:
SELECT col, COUNT(*) FROM table_name GROUP BY col;9. Sorting & Limiting
- Order By:
SELECT * FROM table_name ORDER BY col ASC|DESC;- Limit Results:
SELECT * FROM table_name LIMIT n;10. Indexes
- Create Index:
CREATE INDEX idx_name ON table_name (col);- Drop Index:
DROP INDEX idx_name;11. Subqueries
-
SELECT * FROM table_name WHERE col IN (SELECT col FROM other_table);12. Views
- Create View:
CREATE VIEW view_name AS SELECT * FROM table_name;- Drop View:
DROP VIEW view_name;Here you can find SQL Interview Resources👇
https://news.1rj.ru/str/DataSimplifier
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Amazon Data Analyst Interview Questions for 1-3 years of experience role :-
A. SQL:
1. You have two tables: Employee and Department.
- Employee Table Columns: Employee_id, Employee_Name, Department_id, Salary
- Department Table Columns: Department_id, Department_Name, Location
Write an SQL query to find the name of the employee with the highest salary in each location.
2. You have two tables: Orders and Customers.
- Orders Table Columns: Order_id, Customer_id, Order_Date, Amount
- Customers Table Columns: Customer_id, Customer_Name, Join_Date
Write an SQL query to calculate the total order amount for each customer who joined in the current year. The output should contain Customer_Name and the total amount.
B. Python:
1. Basic oral questions on NumPy (e.g., array creation, slicing, broadcasting) and Matplotlib (e.g., plot types, customization).
2. Basic oral questions on pandas (like: groupby, loc/iloc, merge & join, etc.)
2. Write the code in NumPy and Pandas to replicate the functionality of your answer to the second SQL question.
C. Leadership or Situational Questions:
(Based on the leadership principle of Bias for Action)
- Describe a situation where you had to make a quick decision with limited information. How did you proceed, and what was the outcome?
(Based on the leadership principle of Dive Deep)
- Can you share an example of a project where you had to delve deeply into the data to uncover insights or solve a problem? What steps did you take, and what were the results?
(Based on the leadership principle of Customer Obsession)
- Tell us about a time when you went above and beyond to meet a customer's needs or expectations. How did you identify their requirements, and what actions did you take to deliver exceptional service?
D. Excel:
Questions on advanced functions like VLOOKUP, XLookup, SUMPRODUCT, INDIRECT, TEXT functions, SUMIFS, COUNTIFS, LOOKUPS, INDEX & MATCH, AVERAGEIFS. Plus, some basic questions on pivot tables, conditional formatting, data validation, and charts.
I have curated top-notch Data Analytics Resources 👇👇
https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02
Like if it helps :)
A. SQL:
1. You have two tables: Employee and Department.
- Employee Table Columns: Employee_id, Employee_Name, Department_id, Salary
- Department Table Columns: Department_id, Department_Name, Location
Write an SQL query to find the name of the employee with the highest salary in each location.
2. You have two tables: Orders and Customers.
- Orders Table Columns: Order_id, Customer_id, Order_Date, Amount
- Customers Table Columns: Customer_id, Customer_Name, Join_Date
Write an SQL query to calculate the total order amount for each customer who joined in the current year. The output should contain Customer_Name and the total amount.
B. Python:
1. Basic oral questions on NumPy (e.g., array creation, slicing, broadcasting) and Matplotlib (e.g., plot types, customization).
2. Basic oral questions on pandas (like: groupby, loc/iloc, merge & join, etc.)
2. Write the code in NumPy and Pandas to replicate the functionality of your answer to the second SQL question.
C. Leadership or Situational Questions:
(Based on the leadership principle of Bias for Action)
- Describe a situation where you had to make a quick decision with limited information. How did you proceed, and what was the outcome?
(Based on the leadership principle of Dive Deep)
- Can you share an example of a project where you had to delve deeply into the data to uncover insights or solve a problem? What steps did you take, and what were the results?
(Based on the leadership principle of Customer Obsession)
- Tell us about a time when you went above and beyond to meet a customer's needs or expectations. How did you identify their requirements, and what actions did you take to deliver exceptional service?
D. Excel:
Questions on advanced functions like VLOOKUP, XLookup, SUMPRODUCT, INDIRECT, TEXT functions, SUMIFS, COUNTIFS, LOOKUPS, INDEX & MATCH, AVERAGEIFS. Plus, some basic questions on pivot tables, conditional formatting, data validation, and charts.
I have curated top-notch Data Analytics Resources 👇👇
https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02
Like if it helps :)
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Must important topics to look before any excel interview for Data/Business Analyst role :-
Data Handling: Cell formatting, rows/columns, basic functions (SUM, AVERAGE, COUNT etc).
Data Management Mastery: Sorting, filtering, data validation, diverse cell references. Function Proficiency: Explore SUMIF, (V & X)LOOKUP, INDEX, MATCH, IF, and advanced function nesting.
Advanced Analytics: Master PivotTables for dynamic data analysis and various chart creation.
Advanced Analysis Techniques: Conditional formatting, goal-seeking, in-depth what-if analysis.
Advanced Functions: COUNTIF/IFS, SUMIFS, AVERAGEIF/IFS, CONCATENATE, date/time functions.
These are the most important one's which I tried to summarise in the best possible way, please let me know in the comments if I have missed something important.
Data Handling: Cell formatting, rows/columns, basic functions (SUM, AVERAGE, COUNT etc).
Data Management Mastery: Sorting, filtering, data validation, diverse cell references. Function Proficiency: Explore SUMIF, (V & X)LOOKUP, INDEX, MATCH, IF, and advanced function nesting.
Advanced Analytics: Master PivotTables for dynamic data analysis and various chart creation.
Advanced Analysis Techniques: Conditional formatting, goal-seeking, in-depth what-if analysis.
Advanced Functions: COUNTIF/IFS, SUMIFS, AVERAGEIF/IFS, CONCATENATE, date/time functions.
These are the most important one's which I tried to summarise in the best possible way, please let me know in the comments if I have missed something important.
👍12❤2
Hey guys,
Today, let’s talk about some of the Python questions you might face during a data analyst interview. Below, I’ve compiled the most commonly asked Python questions you should be prepared for in your interviews.
1. Why is Python used in data analysis?
Python is popular for data analysis due to its simplicity, readability, and vast ecosystem of libraries like Pandas, NumPy, Matplotlib, and Scikit-learn. It allows for quick prototyping, data manipulation, and visualization. Moreover, Python integrates seamlessly with other tools like SQL, Excel, and cloud platforms, making it highly versatile for both small-scale analysis and large-scale data engineering.
2. What are the essential libraries used for data analysis in Python?
Some key libraries you’ll use frequently are:
- Pandas: For data manipulation and analysis. It provides data structures like DataFrames, which are perfect for handling tabular data.
- NumPy: For numerical operations. It supports arrays and matrices and includes mathematical functions.
- Matplotlib/Seaborn: For data visualization. Matplotlib allows for creating static, interactive, and animated visualizations, while Seaborn makes creating complex plots easier.
- Scikit-learn: For machine learning. It provides tools for data mining and analysis.
3. What is a Python dictionary, and how is it used in data analysis?
A dictionary in Python is an unordered collection of key-value pairs. It’s extremely useful in data analysis for storing mappings (like labels to corresponding values) or for quick lookups.
Example:
4. Explain the difference between a list and a tuple in Python.
- List: Mutable, meaning you can modify (add, remove, or change) elements. It’s written in square brackets
Example:
- Tuple: Immutable, meaning once defined, you cannot modify it. It’s written in parentheses
Example:
5. How would you handle missing data in a dataset using Python?
Handling missing data is critical in data analysis, and Python’s Pandas library makes it easy. Here are some common methods:
- Drop missing data:
- Fill missing data with a specific value:
- Forward-fill or backfill missing values:
6. How do you merge/join two datasets in Python?
- pd.merge(): For SQL-style joins (inner, outer, left, right).
- pd.concat(): For concatenating along rows or columns.
7. What is the purpose of lambda functions in Python?
A lambda function is an anonymous, single-line function that can be used for quick, simple operations. They are useful when you need a short, throwaway function.
Example:
Lambdas are often used in data analysis for quick transformations or filtering operations within functions like
If you’re preparing for interviews, focus on writing clean, optimized code and understand how Python fits into the larger data ecosystem.
Here you can find essential Python Interview Resources👇
https://news.1rj.ru/str/DataSimplifier
Like for more resources like this 👍 ♥️
Share with credits: https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
Today, let’s talk about some of the Python questions you might face during a data analyst interview. Below, I’ve compiled the most commonly asked Python questions you should be prepared for in your interviews.
1. Why is Python used in data analysis?
Python is popular for data analysis due to its simplicity, readability, and vast ecosystem of libraries like Pandas, NumPy, Matplotlib, and Scikit-learn. It allows for quick prototyping, data manipulation, and visualization. Moreover, Python integrates seamlessly with other tools like SQL, Excel, and cloud platforms, making it highly versatile for both small-scale analysis and large-scale data engineering.
2. What are the essential libraries used for data analysis in Python?
Some key libraries you’ll use frequently are:
- Pandas: For data manipulation and analysis. It provides data structures like DataFrames, which are perfect for handling tabular data.
- NumPy: For numerical operations. It supports arrays and matrices and includes mathematical functions.
- Matplotlib/Seaborn: For data visualization. Matplotlib allows for creating static, interactive, and animated visualizations, while Seaborn makes creating complex plots easier.
- Scikit-learn: For machine learning. It provides tools for data mining and analysis.
3. What is a Python dictionary, and how is it used in data analysis?
A dictionary in Python is an unordered collection of key-value pairs. It’s extremely useful in data analysis for storing mappings (like labels to corresponding values) or for quick lookups.
Example:
sales = {"January": 12000, "February": 15000, "March": 17000}
print(sales["February"]) # Output: 150004. Explain the difference between a list and a tuple in Python.
- List: Mutable, meaning you can modify (add, remove, or change) elements. It’s written in square brackets
[ ].Example:
my_list = [10, 20, 30]
my_list.append(40)
- Tuple: Immutable, meaning once defined, you cannot modify it. It’s written in parentheses
( ).Example:
my_tuple = (10, 20, 30)
5. How would you handle missing data in a dataset using Python?
Handling missing data is critical in data analysis, and Python’s Pandas library makes it easy. Here are some common methods:
- Drop missing data:
df.dropna()
- Fill missing data with a specific value:
df.fillna(0)
- Forward-fill or backfill missing values:
df.fillna(method='ffill') # Forward-fill
df.fillna(method='bfill') # Backfill
6. How do you merge/join two datasets in Python?
- pd.merge(): For SQL-style joins (inner, outer, left, right).
df_merged = pd.merge(df1, df2, on='common_column', how='inner')
- pd.concat(): For concatenating along rows or columns.
df_concat = pd.concat([df1, df2], axis=1)
7. What is the purpose of lambda functions in Python?
A lambda function is an anonymous, single-line function that can be used for quick, simple operations. They are useful when you need a short, throwaway function.
Example:
add = lambda x, y: x + y
print(add(10, 20)) # Output: 30
Lambdas are often used in data analysis for quick transformations or filtering operations within functions like
map() or filter().If you’re preparing for interviews, focus on writing clean, optimized code and understand how Python fits into the larger data ecosystem.
Here you can find essential Python Interview Resources👇
https://news.1rj.ru/str/DataSimplifier
Like for more resources like this 👍 ♥️
Share with credits: https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
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Data Analytics isn't rocket science. It's just a different language.
Here's a beginner's guide to the world of data analytics:
1) Understand the fundamentals:
- Mathematics
- Statistics
- Technology
2) Learn the tools:
- SQL
- Python
- Excel (yes, it's still relevant!)
3) Understand the data:
- What do you want to measure?
- How are you measuring it?
- What metrics are important to you?
4) Data Visualization:
- A picture is worth a thousand words
5) Practice:
- There's no better way to learn than to do it yourself.
Data Analytics is a valuable skill that can help you make better decisions, understand your audience better, and ultimately grow your business.
It's never too late to start learning!
Here's a beginner's guide to the world of data analytics:
1) Understand the fundamentals:
- Mathematics
- Statistics
- Technology
2) Learn the tools:
- SQL
- Python
- Excel (yes, it's still relevant!)
3) Understand the data:
- What do you want to measure?
- How are you measuring it?
- What metrics are important to you?
4) Data Visualization:
- A picture is worth a thousand words
5) Practice:
- There's no better way to learn than to do it yourself.
Data Analytics is a valuable skill that can help you make better decisions, understand your audience better, and ultimately grow your business.
It's never too late to start learning!
❤8👍4