Soft skills questions will be part of your next data job interview!
Here is what you should prepare for:
1. 𝗖𝗼𝗺𝗺𝘂𝗻𝗶𝗰𝗮𝘁𝗶𝗼𝗻: Be ready to discuss how you explain complex data insights to non-technical stakeholders.
𝘌𝘹𝘢𝘮𝘱𝘭𝘦 𝘲𝘶𝘦𝘴𝘵𝘪𝘰𝘯:
“How do you ensure that your data insights are understood and get used by non-technical stakeholders?”
2. 𝗧𝗲𝗮𝗺 𝗖𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗶𝗼𝗻: Show your ability to work well with others.
𝘌𝘹𝘢𝘮𝘱𝘭𝘦 𝘲𝘶𝘦𝘴𝘵𝘪𝘰𝘯:
“Can you talk about a time when you had to manage a conflict within a team? How did you resolve it?”
3. 𝗣𝗿𝗼𝗯𝗹𝗲𝗺-𝗦𝗼𝗹𝘃𝗶𝗻𝗴: Highlight your critical thinking and problem-solving skills.
𝘌𝘹𝘢𝘮𝘱𝘭𝘦 𝘲𝘶𝘦𝘴𝘵𝘪𝘰𝘯:
“Describe a situation where you had to make a quick decision based on incomplete data. What was the outcome?”
4. 𝗔𝗱𝗮𝗽𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆: Demonstrate your flexibility and openness to change.
𝘌𝘹𝘢𝘮𝘱𝘭𝘦 𝘲𝘶𝘦𝘴𝘵𝘪𝘰𝘯:
“How do you handle sudden changes in project priorities or scope?”
5. 𝗧𝗶𝗺𝗲 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁: Prove your ability to manage multiple tasks and deadlines.
𝘌𝘹𝘢𝘮𝘱𝘭𝘦 𝘲𝘶𝘦𝘴𝘵𝘪𝘰𝘯:
“Tell me about a time when you were under tight deadlines. How did you manage to meet them?”
6. 𝗘𝗺𝗽𝗮𝘁𝗵𝘆 𝗮𝗻𝗱 𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴: Show your ability to understand stakeholder needs.
𝘌𝘹𝘢𝘮𝘱𝘭𝘦 𝘲𝘶𝘦𝘴𝘵𝘪𝘰𝘯:
“How do you approach understanding the needs of different stakeholders when starting a new project?”
Structure your answers using the STAR method (Situation, Task, Action, Result). This helps you provide clear and concise responses that highlight your skills.
By preparing for these soft skills questions, you’ll demonstrate that you’re not just technically fit, but also a well-rounded professional ready to make an impact on the business.
You can find useful tips to improve your soft skills here: 👇 https://news.1rj.ru/str/englishlearnerspro/
Here is what you should prepare for:
1. 𝗖𝗼𝗺𝗺𝘂𝗻𝗶𝗰𝗮𝘁𝗶𝗼𝗻: Be ready to discuss how you explain complex data insights to non-technical stakeholders.
𝘌𝘹𝘢𝘮𝘱𝘭𝘦 𝘲𝘶𝘦𝘴𝘵𝘪𝘰𝘯:
“How do you ensure that your data insights are understood and get used by non-technical stakeholders?”
2. 𝗧𝗲𝗮𝗺 𝗖𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗶𝗼𝗻: Show your ability to work well with others.
𝘌𝘹𝘢𝘮𝘱𝘭𝘦 𝘲𝘶𝘦𝘴𝘵𝘪𝘰𝘯:
“Can you talk about a time when you had to manage a conflict within a team? How did you resolve it?”
3. 𝗣𝗿𝗼𝗯𝗹𝗲𝗺-𝗦𝗼𝗹𝘃𝗶𝗻𝗴: Highlight your critical thinking and problem-solving skills.
𝘌𝘹𝘢𝘮𝘱𝘭𝘦 𝘲𝘶𝘦𝘴𝘵𝘪𝘰𝘯:
“Describe a situation where you had to make a quick decision based on incomplete data. What was the outcome?”
4. 𝗔𝗱𝗮𝗽𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆: Demonstrate your flexibility and openness to change.
𝘌𝘹𝘢𝘮𝘱𝘭𝘦 𝘲𝘶𝘦𝘴𝘵𝘪𝘰𝘯:
“How do you handle sudden changes in project priorities or scope?”
5. 𝗧𝗶𝗺𝗲 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁: Prove your ability to manage multiple tasks and deadlines.
𝘌𝘹𝘢𝘮𝘱𝘭𝘦 𝘲𝘶𝘦𝘴𝘵𝘪𝘰𝘯:
“Tell me about a time when you were under tight deadlines. How did you manage to meet them?”
6. 𝗘𝗺𝗽𝗮𝘁𝗵𝘆 𝗮𝗻𝗱 𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴: Show your ability to understand stakeholder needs.
𝘌𝘹𝘢𝘮𝘱𝘭𝘦 𝘲𝘶𝘦𝘴𝘵𝘪𝘰𝘯:
“How do you approach understanding the needs of different stakeholders when starting a new project?”
Structure your answers using the STAR method (Situation, Task, Action, Result). This helps you provide clear and concise responses that highlight your skills.
By preparing for these soft skills questions, you’ll demonstrate that you’re not just technically fit, but also a well-rounded professional ready to make an impact on the business.
You can find useful tips to improve your soft skills here: 👇 https://news.1rj.ru/str/englishlearnerspro/
👍2❤1
If you are targeting your first Data Analyst job then this is why you should avoid guided projects
The common thing nowadays is "Coffee Sales Analysis" and "Pizza Sales Analysis"
I don't see these projects as PROJECTS
But as big RED flags
We are showing our SKILLS through projects, RIGHT?
Then what's WRONG with these projects?
Don't think from YOUR side
Think from the HIRING team's side
These projects have more than a MILLION views on YouTube
Even if you consider 50% of this NUMBER
Then just IMAGINE how many aspiring Data Analysts would have created this same project
Hiring teams see hundreds of resumes and portfolios on a DAILY basis
Just imagine how many times they would have seen the SAME noscripts of projects again and again
They would know that these projects are PUBLICLY available for EVERYONE
You have simply copied pasted the ENTIRE project from YouTube
So now if I want to hire a Data Analyst then how would I JUDGE you or your technical skills?
What is the USE of Pizza or Coffee sales analysis projects for MY company?
By doing such guided projects, you are involving yourself in a big circle of COMPETITION
I repeat, there were more than a MILLION views
So please AVOID guided projects at all costs
Guided projects are good for your personal PRACTICE and LinkedIn CONTENT
But try not to involve them in your PORTFOLIO or RESUME
The common thing nowadays is "Coffee Sales Analysis" and "Pizza Sales Analysis"
I don't see these projects as PROJECTS
But as big RED flags
We are showing our SKILLS through projects, RIGHT?
Then what's WRONG with these projects?
Don't think from YOUR side
Think from the HIRING team's side
These projects have more than a MILLION views on YouTube
Even if you consider 50% of this NUMBER
Then just IMAGINE how many aspiring Data Analysts would have created this same project
Hiring teams see hundreds of resumes and portfolios on a DAILY basis
Just imagine how many times they would have seen the SAME noscripts of projects again and again
They would know that these projects are PUBLICLY available for EVERYONE
You have simply copied pasted the ENTIRE project from YouTube
So now if I want to hire a Data Analyst then how would I JUDGE you or your technical skills?
What is the USE of Pizza or Coffee sales analysis projects for MY company?
By doing such guided projects, you are involving yourself in a big circle of COMPETITION
I repeat, there were more than a MILLION views
So please AVOID guided projects at all costs
Guided projects are good for your personal PRACTICE and LinkedIn CONTENT
But try not to involve them in your PORTFOLIO or RESUME
👍15❤3👏1🎉1
Common Data Cleaning Techniques for Data Analysts
Remove Duplicates:
Purpose: Eliminate repeated rows to maintain unique data.
Example: SELECT DISTINCT column_name FROM table;
Handle Missing Values:
Purpose: Fill, remove, or impute missing data.
Example:
Remove: df.dropna() (in Python/Pandas)
Fill: df.fillna(0)
Standardize Data:
Purpose: Convert data to a consistent format (e.g., dates, numbers).
Example: Convert text to lowercase: df['column'] = df['column'].str.lower()
Remove Outliers:
Purpose: Identify and remove extreme values.
Example: df = df[df['column'] < threshold]
Correct Data Types:
Purpose: Ensure columns have the correct data type (e.g., dates as datetime, numeric values as integers).
Example: df['date'] = pd.to_datetime(df['date'])
Normalize Data:
Purpose: Scale numerical data to a standard range (0 to 1).
Example: from sklearn.preprocessing import MinMaxScaler; df['scaled'] = MinMaxScaler().fit_transform(df[['column']])
Data Transformation:
Purpose: Transform or aggregate data for better analysis (e.g., log transformations, aggregating columns).
Example: Apply log transformation: df['log_column'] = np.log(df['column'] + 1)
Handle Categorical Data:
Purpose: Convert categorical data into numerical data using encoding techniques.
Example: df['encoded_column'] = pd.get_dummies(df['category_column'])
Impute Missing Values:
Purpose: Fill missing values with a meaningful value (e.g., mean, median, or a specific value).
Example: df['column'] = df['column'].fillna(df['column'].mean())
Data Cleaning: https://whatsapp.com/channel/0029VarxgFqATRSpdUeHUA27
Like this post for more content like this 👍♥️
Share with credits: https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
Remove Duplicates:
Purpose: Eliminate repeated rows to maintain unique data.
Example: SELECT DISTINCT column_name FROM table;
Handle Missing Values:
Purpose: Fill, remove, or impute missing data.
Example:
Remove: df.dropna() (in Python/Pandas)
Fill: df.fillna(0)
Standardize Data:
Purpose: Convert data to a consistent format (e.g., dates, numbers).
Example: Convert text to lowercase: df['column'] = df['column'].str.lower()
Remove Outliers:
Purpose: Identify and remove extreme values.
Example: df = df[df['column'] < threshold]
Correct Data Types:
Purpose: Ensure columns have the correct data type (e.g., dates as datetime, numeric values as integers).
Example: df['date'] = pd.to_datetime(df['date'])
Normalize Data:
Purpose: Scale numerical data to a standard range (0 to 1).
Example: from sklearn.preprocessing import MinMaxScaler; df['scaled'] = MinMaxScaler().fit_transform(df[['column']])
Data Transformation:
Purpose: Transform or aggregate data for better analysis (e.g., log transformations, aggregating columns).
Example: Apply log transformation: df['log_column'] = np.log(df['column'] + 1)
Handle Categorical Data:
Purpose: Convert categorical data into numerical data using encoding techniques.
Example: df['encoded_column'] = pd.get_dummies(df['category_column'])
Impute Missing Values:
Purpose: Fill missing values with a meaningful value (e.g., mean, median, or a specific value).
Example: df['column'] = df['column'].fillna(df['column'].mean())
Data Cleaning: https://whatsapp.com/channel/0029VarxgFqATRSpdUeHUA27
Like this post for more content like this 👍♥️
Share with credits: https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
👍12❤2
🧠 Technologies for Data Analysts!
📊 Data Manipulation & Analysis
▪️ Excel – Spreadsheet Data Analysis & Visualization
▪️ SQL – Structured Query Language for Data Extraction
▪️ Pandas (Python) – Data Analysis with DataFrames
▪️ NumPy (Python) – Numerical Computing for Large Datasets
▪️ Google Sheets – Online Collaboration for Data Analysis
📈 Data Visualization
▪️ Power BI – Business Intelligence & Dashboarding
▪️ Tableau – Interactive Data Visualization
▪️ Matplotlib (Python) – Plotting Graphs & Charts
▪️ Seaborn (Python) – Statistical Data Visualization
▪️ Google Data Studio – Free, Web-Based Visualization Tool
🔄 ETL (Extract, Transform, Load)
▪️ SQL Server Integration Services (SSIS) – Data Integration & ETL
▪️ Apache NiFi – Automating Data Flows
▪️ Talend – Data Integration for Cloud & On-premises
🧹 Data Cleaning & Preparation
▪️ OpenRefine – Clean & Transform Messy Data
▪️ Pandas Profiling (Python) – Data Profiling & Preprocessing
▪️ DataWrangler – Data Transformation Tool
📦 Data Storage & Databases
▪️ SQL – Relational Databases (MySQL, PostgreSQL, MS SQL)
▪️ NoSQL (MongoDB) – Flexible, Schema-less Data Storage
▪️ Google BigQuery – Scalable Cloud Data Warehousing
▪️ Redshift – Amazon’s Cloud Data Warehouse
⚙️ Data Automation
▪️ Alteryx – Data Blending & Advanced Analytics
▪️ Knime – Data Analytics & Reporting Automation
▪️ Zapier – Connect & Automate Data Workflows
📊 Advanced Analytics & Statistical Tools
▪️ R – Statistical Computing & Analysis
▪️ Python (SciPy, Statsmodels) – Statistical Modeling & Hypothesis Testing
▪️ SPSS – Statistical Software for Data Analysis
▪️ SAS – Advanced Analytics & Predictive Modeling
🌐 Collaboration & Reporting
▪️ Power BI Service – Online Sharing & Collaboration for Dashboards
▪️ Tableau Online – Cloud-Based Visualization & Sharing
▪️ Google Analytics – Web Traffic Data Insights
▪️ Trello / JIRA – Project & Task Management for Data Projects
Data-Driven Decisions with the Right Tools!
React ❤️ for more
📊 Data Manipulation & Analysis
▪️ Excel – Spreadsheet Data Analysis & Visualization
▪️ SQL – Structured Query Language for Data Extraction
▪️ Pandas (Python) – Data Analysis with DataFrames
▪️ NumPy (Python) – Numerical Computing for Large Datasets
▪️ Google Sheets – Online Collaboration for Data Analysis
📈 Data Visualization
▪️ Power BI – Business Intelligence & Dashboarding
▪️ Tableau – Interactive Data Visualization
▪️ Matplotlib (Python) – Plotting Graphs & Charts
▪️ Seaborn (Python) – Statistical Data Visualization
▪️ Google Data Studio – Free, Web-Based Visualization Tool
🔄 ETL (Extract, Transform, Load)
▪️ SQL Server Integration Services (SSIS) – Data Integration & ETL
▪️ Apache NiFi – Automating Data Flows
▪️ Talend – Data Integration for Cloud & On-premises
🧹 Data Cleaning & Preparation
▪️ OpenRefine – Clean & Transform Messy Data
▪️ Pandas Profiling (Python) – Data Profiling & Preprocessing
▪️ DataWrangler – Data Transformation Tool
📦 Data Storage & Databases
▪️ SQL – Relational Databases (MySQL, PostgreSQL, MS SQL)
▪️ NoSQL (MongoDB) – Flexible, Schema-less Data Storage
▪️ Google BigQuery – Scalable Cloud Data Warehousing
▪️ Redshift – Amazon’s Cloud Data Warehouse
⚙️ Data Automation
▪️ Alteryx – Data Blending & Advanced Analytics
▪️ Knime – Data Analytics & Reporting Automation
▪️ Zapier – Connect & Automate Data Workflows
📊 Advanced Analytics & Statistical Tools
▪️ R – Statistical Computing & Analysis
▪️ Python (SciPy, Statsmodels) – Statistical Modeling & Hypothesis Testing
▪️ SPSS – Statistical Software for Data Analysis
▪️ SAS – Advanced Analytics & Predictive Modeling
🌐 Collaboration & Reporting
▪️ Power BI Service – Online Sharing & Collaboration for Dashboards
▪️ Tableau Online – Cloud-Based Visualization & Sharing
▪️ Google Analytics – Web Traffic Data Insights
▪️ Trello / JIRA – Project & Task Management for Data Projects
Data-Driven Decisions with the Right Tools!
React ❤️ for more
❤13👍9🔥4
10 SQL Concepts Every Data Analyst Should Master 👇
✅ SELECT, WHERE, ORDER BY – Core of querying your data
✅ JOINs (INNER, LEFT, RIGHT, FULL) – Combine data from multiple tables
✅ GROUP BY & HAVING – Aggregate and filter grouped data
✅ Subqueries – Nest queries inside queries for complex logic
✅ CTEs (Common Table Expressions) – Write cleaner, reusable SQL logic
✅ Window Functions – Perform advanced analytics like rankings & running totals
✅ Indexes – Boost your query performance
✅ Normalization – Structure your database efficiently
✅ UNION vs UNION ALL – Combine result sets with or without duplicates
✅ Stored Procedures & Functions – Reusable logic inside your DB
React with ❤️ if you want me to cover each topic in detail
Share with credits: https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
✅ SELECT, WHERE, ORDER BY – Core of querying your data
✅ JOINs (INNER, LEFT, RIGHT, FULL) – Combine data from multiple tables
✅ GROUP BY & HAVING – Aggregate and filter grouped data
✅ Subqueries – Nest queries inside queries for complex logic
✅ CTEs (Common Table Expressions) – Write cleaner, reusable SQL logic
✅ Window Functions – Perform advanced analytics like rankings & running totals
✅ Indexes – Boost your query performance
✅ Normalization – Structure your database efficiently
✅ UNION vs UNION ALL – Combine result sets with or without duplicates
✅ Stored Procedures & Functions – Reusable logic inside your DB
React with ❤️ if you want me to cover each topic in detail
Share with credits: https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
❤11👍4
Excel Scenario-Based Questions Interview Questions and Answers :
Scenario 1) Imagine you have a dataset with missing values. How would you approach this problem in Excel?
Answer:
To handle missing values in Excel:
1. Identify Missing Data:
Use filters to quickly find blank cells.
Apply conditional formatting:
Home → Conditional Formatting → New Rule → Format only cells that are blank.
2. Handle Missing Data:
Delete rows with missing critical data (if appropriate).
Fill missing values:
Use =IF(A2="", "N/A", A2) to replace blanks with “N/A”.
Use Fill Down (Ctrl + D) if the previous value applies.
Use functions like =AVERAGEIF(range, "<>", range) to fill with average.
3. Use Power Query (for large datasets):
Load data into Power Query and use “Replace Values” or “Remove Empty” options.
Scenario 2) You are given a dataset with multiple sheets. How would you consolidate the data for analysis?
Answer:
Approach 1: Manual Consolidation
1. Use Copy-Paste from each sheet into a master sheet.
2. Add a new column to identify the source sheet (optional but useful).
3. Convert the master data into a table for analysis.
Approach 2: Use Power Query (Recommended for large datasets)
1. Go to Data → Get & Transform → Get Data → From Workbook.
2. Load each sheet into Power Query.
3. Use the Append Queries option to merge all sheets.
4. Clean and transform as needed, then load it back to Excel.
Approach 3: Use VBA (Advanced Users)
Write a macro to loop through all sheets and append data to a master sheet.
Hope it helps :)
Scenario 1) Imagine you have a dataset with missing values. How would you approach this problem in Excel?
Answer:
To handle missing values in Excel:
1. Identify Missing Data:
Use filters to quickly find blank cells.
Apply conditional formatting:
Home → Conditional Formatting → New Rule → Format only cells that are blank.
2. Handle Missing Data:
Delete rows with missing critical data (if appropriate).
Fill missing values:
Use =IF(A2="", "N/A", A2) to replace blanks with “N/A”.
Use Fill Down (Ctrl + D) if the previous value applies.
Use functions like =AVERAGEIF(range, "<>", range) to fill with average.
3. Use Power Query (for large datasets):
Load data into Power Query and use “Replace Values” or “Remove Empty” options.
Scenario 2) You are given a dataset with multiple sheets. How would you consolidate the data for analysis?
Answer:
Approach 1: Manual Consolidation
1. Use Copy-Paste from each sheet into a master sheet.
2. Add a new column to identify the source sheet (optional but useful).
3. Convert the master data into a table for analysis.
Approach 2: Use Power Query (Recommended for large datasets)
1. Go to Data → Get & Transform → Get Data → From Workbook.
2. Load each sheet into Power Query.
3. Use the Append Queries option to merge all sheets.
4. Clean and transform as needed, then load it back to Excel.
Approach 3: Use VBA (Advanced Users)
Write a macro to loop through all sheets and append data to a master sheet.
Hope it helps :)
❤8👍4
🔟 Data Analyst Project Ideas for Beginners
1. Sales Analysis Dashboard: Use tools like Excel or Tableau to create a dashboard analyzing sales data. Visualize trends, top products, and seasonal patterns.
2. Customer Segmentation: Analyze customer data using clustering techniques (like K-means) to segment customers based on purchasing behavior and demographics.
3. Social Media Metrics Analysis: Gather data from social media platforms to analyze engagement metrics. Create visualizations to highlight trends and performance.
4. Survey Data Analysis: Conduct a survey and analyze the results using statistical techniques. Present findings with visualizations to showcase insights.
5. Exploratory Data Analysis (EDA): Choose a public dataset and perform EDA using Python (Pandas, Matplotlib) or R (tidyverse). Summarize key insights and visualizations.
6. Employee Performance Analysis: Analyze employee performance data to identify trends in productivity, turnover rates, and training effectiveness.
7. Public Health Data Analysis: Use datasets from public health sources (like CDC) to analyze trends in health metrics (e.g., vaccination rates, disease outbreaks) and visualize findings.
8. Real Estate Market Analysis: Analyze real estate listings to find trends in pricing, location, and features. Use data visualization to present your findings.
9. Weather Data Visualization: Collect weather data and analyze trends over time. Create visualizations to show changes in temperature, precipitation, or extreme weather events.
10. Financial Analysis: Analyze a company’s financial statements to assess its performance over time. Create visualizations to highlight key financial ratios and trends.
Data Analytics Resources 👇👇
https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02
Hope it helps :)
1. Sales Analysis Dashboard: Use tools like Excel or Tableau to create a dashboard analyzing sales data. Visualize trends, top products, and seasonal patterns.
2. Customer Segmentation: Analyze customer data using clustering techniques (like K-means) to segment customers based on purchasing behavior and demographics.
3. Social Media Metrics Analysis: Gather data from social media platforms to analyze engagement metrics. Create visualizations to highlight trends and performance.
4. Survey Data Analysis: Conduct a survey and analyze the results using statistical techniques. Present findings with visualizations to showcase insights.
5. Exploratory Data Analysis (EDA): Choose a public dataset and perform EDA using Python (Pandas, Matplotlib) or R (tidyverse). Summarize key insights and visualizations.
6. Employee Performance Analysis: Analyze employee performance data to identify trends in productivity, turnover rates, and training effectiveness.
7. Public Health Data Analysis: Use datasets from public health sources (like CDC) to analyze trends in health metrics (e.g., vaccination rates, disease outbreaks) and visualize findings.
8. Real Estate Market Analysis: Analyze real estate listings to find trends in pricing, location, and features. Use data visualization to present your findings.
9. Weather Data Visualization: Collect weather data and analyze trends over time. Create visualizations to show changes in temperature, precipitation, or extreme weather events.
10. Financial Analysis: Analyze a company’s financial statements to assess its performance over time. Create visualizations to highlight key financial ratios and trends.
Data Analytics Resources 👇👇
https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02
Hope it helps :)
❤4
If you are interested to learn SQL for data analytics purpose and clear the interviews, just cover the following topics
1)Install MYSQL workbench
2) Select
3) From
4) where
5) group by
6) having
7) limit
8) Joins (Left, right , inner, self, cross)
9) Aggregate function ( Sum, Max, Min , Avg)
9) windows function ( row num, rank, dense rank, lead, lag, Sum () over)
10)Case
11) Like
12) Sub queries
13) CTE
14) Replace CTE with temp tables
15) Methods to optimize Sql queries
16) Solve problems and case studies at Ankit Bansal youtube channel
Trick: Just copy each term and paste on youtube and watch any 10 to 15 minute on each topic and practise it while learning , By doing this , you get the basics understanding
17) Now time to go on youtube and search data analysis end to end project using sql
18) Watch them and practise them end to end.
17) learn integration with power bi
In this way , you will not only memorize the concepts but also learn how to implement them in your current working and projects and will be able to defend it in your interviews as well.
Like for more
Here you can find essential SQL Interview Resources👇
https://news.1rj.ru/str/DataSimplifier
Hope it helps :)
1)Install MYSQL workbench
2) Select
3) From
4) where
5) group by
6) having
7) limit
8) Joins (Left, right , inner, self, cross)
9) Aggregate function ( Sum, Max, Min , Avg)
9) windows function ( row num, rank, dense rank, lead, lag, Sum () over)
10)Case
11) Like
12) Sub queries
13) CTE
14) Replace CTE with temp tables
15) Methods to optimize Sql queries
16) Solve problems and case studies at Ankit Bansal youtube channel
Trick: Just copy each term and paste on youtube and watch any 10 to 15 minute on each topic and practise it while learning , By doing this , you get the basics understanding
17) Now time to go on youtube and search data analysis end to end project using sql
18) Watch them and practise them end to end.
17) learn integration with power bi
In this way , you will not only memorize the concepts but also learn how to implement them in your current working and projects and will be able to defend it in your interviews as well.
Like for more
Here you can find essential SQL Interview Resources👇
https://news.1rj.ru/str/DataSimplifier
Hope it helps :)
❤11🔥4👍1
Step-by-step guide to become a Data Analyst in 2025—📊
1. Learn the Fundamentals:
Start with Excel, basic statistics, and data visualization concepts.
2. Pick Up Key Tools & Languages:
Master SQL, Python (or R), and data visualization tools like Tableau or Power BI.
3. Get Formal Education or Certification:
A bachelor’s degree in a relevant field (like Computer Science, Math, or Economics) helps, but you can also do online courses or certifications in data analytics.
4. Build Hands-on Experience:
Work on real-world projects—use Kaggle datasets, internships, or freelance gigs to practice data cleaning, analysis, and visualization.
5. Create a Portfolio:
Showcase your projects on GitHub or a personal website. Include dashboards, reports, and code samples.
6. Develop Soft Skills:
Focus on communication, problem-solving, teamwork, and attention to detail—these are just as important as technical skills.
7. Apply for Entry-Level Jobs:
Look for roles like “Junior Data Analyst” or “Business Analyst.” Tailor your resume to highlight your skills and portfolio.
8. Keep Learning:
Stay updated with new tools (like AI-driven analytics), trends, and advanced topics such as machine learning or domain-specific analytics.
React ❤️ for more
1. Learn the Fundamentals:
Start with Excel, basic statistics, and data visualization concepts.
2. Pick Up Key Tools & Languages:
Master SQL, Python (or R), and data visualization tools like Tableau or Power BI.
3. Get Formal Education or Certification:
A bachelor’s degree in a relevant field (like Computer Science, Math, or Economics) helps, but you can also do online courses or certifications in data analytics.
4. Build Hands-on Experience:
Work on real-world projects—use Kaggle datasets, internships, or freelance gigs to practice data cleaning, analysis, and visualization.
5. Create a Portfolio:
Showcase your projects on GitHub or a personal website. Include dashboards, reports, and code samples.
6. Develop Soft Skills:
Focus on communication, problem-solving, teamwork, and attention to detail—these are just as important as technical skills.
7. Apply for Entry-Level Jobs:
Look for roles like “Junior Data Analyst” or “Business Analyst.” Tailor your resume to highlight your skills and portfolio.
8. Keep Learning:
Stay updated with new tools (like AI-driven analytics), trends, and advanced topics such as machine learning or domain-specific analytics.
React ❤️ for more
❤12👍4
Excel Hack of the Week—super simple and super useful! 😎
🧹 Remove Duplicates in Seconds!
1️⃣ Select your data range.
2️⃣ Go to Data > Remove Duplicates.
3️⃣ Pick the columns to check for duplicates and hit OK—done!
🔍 Example:
✅ Got a list of emails with repeats? Remove Duplicates keeps only unique ones!
✅ Cleaning up sales data? Instantly get rid of double entries!
📌 Bonus: Use this trick to tidy up contact lists, inventory records, or survey responses—no formulas needed!
Like this post if you want more Excel and data hacks every week! 👍✨
Credits: https://whatsapp.com/channel/0029VaifY548qIzv0u1AHz3i
🧹 Remove Duplicates in Seconds!
1️⃣ Select your data range.
2️⃣ Go to Data > Remove Duplicates.
3️⃣ Pick the columns to check for duplicates and hit OK—done!
🔍 Example:
✅ Got a list of emails with repeats? Remove Duplicates keeps only unique ones!
✅ Cleaning up sales data? Instantly get rid of double entries!
📌 Bonus: Use this trick to tidy up contact lists, inventory records, or survey responses—no formulas needed!
Like this post if you want more Excel and data hacks every week! 👍✨
Credits: https://whatsapp.com/channel/0029VaifY548qIzv0u1AHz3i
❤5👍4
Roadmap to Become a Data Analyst:
📊 Learn Excel & Google Sheets (Formulas, Pivot Tables)
∟📊 Master SQL (SELECT, JOINs, CTEs, Window Functions)
∟📊 Learn Data Visualization (Power BI / Tableau)
∟📊 Understand Statistics & Probability
∟📊 Learn Python (Pandas, NumPy, Matplotlib, Seaborn)
∟📊 Work with Real Datasets (Kaggle / Public APIs)
∟📊 Learn Data Cleaning & Preprocessing Techniques
∟📊 Build Case Studies & Projects
∟📊 Create Portfolio & Resume
∟✅ Apply for Internships / Jobs
React ❤️ for More 💼
📊 Learn Excel & Google Sheets (Formulas, Pivot Tables)
∟📊 Master SQL (SELECT, JOINs, CTEs, Window Functions)
∟📊 Learn Data Visualization (Power BI / Tableau)
∟📊 Understand Statistics & Probability
∟📊 Learn Python (Pandas, NumPy, Matplotlib, Seaborn)
∟📊 Work with Real Datasets (Kaggle / Public APIs)
∟📊 Learn Data Cleaning & Preprocessing Techniques
∟📊 Build Case Studies & Projects
∟📊 Create Portfolio & Resume
∟✅ Apply for Internships / Jobs
React ❤️ for More 💼
❤27👍2
🔥 Top SQL Projects for Data Analytics 🚀
If you're preparing for a Data Analyst role or looking to level up your SQL skills, working on real-world projects is the best way to learn!
Here are some must-do SQL projects to strengthen your portfolio. 👇
🟢 Beginner-Friendly SQL Projects (Great for Learning Basics)
✅ Employee Database Management – Build and query HR data 📊
✅ Library Book Tracking – Create a database for book loans and returns
✅ Student Grading System – Analyze student performance data
✅ Retail Point-of-Sale System – Work with sales and transactions 💰
✅ Hotel Booking System – Manage customer bookings and check-ins 🏨
🟡 Intermediate SQL Projects (For Stronger Querying & Analysis)
⚡ E-commerce Order Management – Analyze order trends & customer data 🛒
⚡ Sales Performance Analysis – Work with revenue, profit margins & KPIs 📈
⚡ Inventory Control System – Optimize stock tracking 📦
⚡ Real Estate Listings – Manage and analyze property data 🏡
⚡ Movie Rating System – Analyze user reviews & trends 🎬
🔵 Advanced SQL Projects (For Business-Level Analytics)
🔹 Social Media Analytics – Track user engagement & content trends
🔹 Insurance Claim Management – Fraud detection & risk assessment
🔹 Customer Feedback Analysis – Perform sentiment analysis on reviews ⭐
🔹 Freelance Job Platform – Match freelancers with project opportunities
🔹 Pharmacy Inventory System – Optimize stock levels & prenoscriptions
🔴 Expert-Level SQL Projects (For Data-Driven Decision Making)
🔥 Music Streaming Analysis – Study user behavior & song trends 🎶
🔥 Healthcare Prenoscription Tracking – Identify patterns in medicine usage
🔥 Employee Shift Scheduling – Optimize workforce efficiency ⏳
🔥 Warehouse Stock Control – Manage supply chain data efficiently
🔥 Online Auction System – Analyze bidding patterns & sales performance 🛍️
🔗 Pro Tip: If you're applying for Data Analyst roles, pick 3-4 projects, clean the data, and create interactive dashboards using Power BI/Tableau to showcase insights!
React with ♥️ if you want detailed explanation of each project
Share with credits: 👇 https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
If you're preparing for a Data Analyst role or looking to level up your SQL skills, working on real-world projects is the best way to learn!
Here are some must-do SQL projects to strengthen your portfolio. 👇
🟢 Beginner-Friendly SQL Projects (Great for Learning Basics)
✅ Employee Database Management – Build and query HR data 📊
✅ Library Book Tracking – Create a database for book loans and returns
✅ Student Grading System – Analyze student performance data
✅ Retail Point-of-Sale System – Work with sales and transactions 💰
✅ Hotel Booking System – Manage customer bookings and check-ins 🏨
🟡 Intermediate SQL Projects (For Stronger Querying & Analysis)
⚡ E-commerce Order Management – Analyze order trends & customer data 🛒
⚡ Sales Performance Analysis – Work with revenue, profit margins & KPIs 📈
⚡ Inventory Control System – Optimize stock tracking 📦
⚡ Real Estate Listings – Manage and analyze property data 🏡
⚡ Movie Rating System – Analyze user reviews & trends 🎬
🔵 Advanced SQL Projects (For Business-Level Analytics)
🔹 Social Media Analytics – Track user engagement & content trends
🔹 Insurance Claim Management – Fraud detection & risk assessment
🔹 Customer Feedback Analysis – Perform sentiment analysis on reviews ⭐
🔹 Freelance Job Platform – Match freelancers with project opportunities
🔹 Pharmacy Inventory System – Optimize stock levels & prenoscriptions
🔴 Expert-Level SQL Projects (For Data-Driven Decision Making)
🔥 Music Streaming Analysis – Study user behavior & song trends 🎶
🔥 Healthcare Prenoscription Tracking – Identify patterns in medicine usage
🔥 Employee Shift Scheduling – Optimize workforce efficiency ⏳
🔥 Warehouse Stock Control – Manage supply chain data efficiently
🔥 Online Auction System – Analyze bidding patterns & sales performance 🛍️
🔗 Pro Tip: If you're applying for Data Analyst roles, pick 3-4 projects, clean the data, and create interactive dashboards using Power BI/Tableau to showcase insights!
React with ♥️ if you want detailed explanation of each project
Share with credits: 👇 https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
❤14
10 Data Analyst Project Ideas to Boost Your Portfolio
✅ Sales Dashboard (Power BI/Tableau) – Analyze revenue, region-wise trends, and KPIs
✅ HR Analytics – Employee attrition, retention trends using Excel/SQL/Power BI
✅ Customer Segmentation (SQL + Excel) – Analyze buying patterns and group customers
✅ Survey Data Analysis – Clean, visualize, and interpret survey insights
✅ E-commerce Data Analysis – Funnel analysis, product trends, and revenue mapping
✅ Superstore Sales Analysis – Use public datasets to show time series and cohort trends
✅ Marketing Campaign Effectiveness – SQL + A/B test analysis with statistical methods
✅ Financial Dashboard – Visualize profit, loss, and KPIs using Power BI
✅ YouTube/Instagram Analytics – Use social media data to find audience behavior insights
✅ SQL Reporting Automation – Build and schedule automated SQL reports and visualizations
React ❤️ for more
✅ Sales Dashboard (Power BI/Tableau) – Analyze revenue, region-wise trends, and KPIs
✅ HR Analytics – Employee attrition, retention trends using Excel/SQL/Power BI
✅ Customer Segmentation (SQL + Excel) – Analyze buying patterns and group customers
✅ Survey Data Analysis – Clean, visualize, and interpret survey insights
✅ E-commerce Data Analysis – Funnel analysis, product trends, and revenue mapping
✅ Superstore Sales Analysis – Use public datasets to show time series and cohort trends
✅ Marketing Campaign Effectiveness – SQL + A/B test analysis with statistical methods
✅ Financial Dashboard – Visualize profit, loss, and KPIs using Power BI
✅ YouTube/Instagram Analytics – Use social media data to find audience behavior insights
✅ SQL Reporting Automation – Build and schedule automated SQL reports and visualizations
React ❤️ for more
❤18
1. What is the difference between the RANK() and DENSE_RANK() functions?
The RANK() function in the result set defines the rank of each row within your ordered partition. If both rows have the same rank, the next number in the ranking will be the previous rank plus a number of duplicates. If we have three records at rank 4, for example, the next level indicated is 7. The DENSE_RANK() function assigns a distinct rank to each row within a partition based on the provided column value, with no gaps. If we have three records at rank 4, for example, the next level indicated is 5.
2. Explain One-hot encoding and Label Encoding. How do they affect the dimensionality of the given dataset?
One-hot encoding is the representation of categorical variables as binary vectors. Label Encoding is converting labels/words into numeric form. Using one-hot encoding increases the dimensionality of the data set. Label encoding doesn’t affect the dimensionality of the data set. One-hot encoding creates a new variable for each level in the variable whereas, in Label encoding, the levels of a variable get encoded as 1 and 0.
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.
5. Define shelves and sets in Tableau?
Shelves: Every worksheet in Tableau will have shelves such as columns, rows, marks, filters, pages, and more. By placing filters on shelves we can build our own visualization structure. We can control the marks by including or excluding data.
Sets: The sets are used to compute a condition on which the dataset will be prepared. Data will be grouped together based on a condition. Fields which is responsible for grouping are known assets. For example – students having grades of more than 70%.
The RANK() function in the result set defines the rank of each row within your ordered partition. If both rows have the same rank, the next number in the ranking will be the previous rank plus a number of duplicates. If we have three records at rank 4, for example, the next level indicated is 7. The DENSE_RANK() function assigns a distinct rank to each row within a partition based on the provided column value, with no gaps. If we have three records at rank 4, for example, the next level indicated is 5.
2. Explain One-hot encoding and Label Encoding. How do they affect the dimensionality of the given dataset?
One-hot encoding is the representation of categorical variables as binary vectors. Label Encoding is converting labels/words into numeric form. Using one-hot encoding increases the dimensionality of the data set. Label encoding doesn’t affect the dimensionality of the data set. One-hot encoding creates a new variable for each level in the variable whereas, in Label encoding, the levels of a variable get encoded as 1 and 0.
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.
5. Define shelves and sets in Tableau?
Shelves: Every worksheet in Tableau will have shelves such as columns, rows, marks, filters, pages, and more. By placing filters on shelves we can build our own visualization structure. We can control the marks by including or excluding data.
Sets: The sets are used to compute a condition on which the dataset will be prepared. Data will be grouped together based on a condition. Fields which is responsible for grouping are known assets. For example – students having grades of more than 70%.
❤11👍1
7 Must-Have Tools for Data Analysts in 2025:
✅ SQL – Still the #1 skill for querying and managing structured data
✅ Excel / Google Sheets – Quick analysis, pivot tables, and essential calculations
✅ Python (Pandas, NumPy) – For deep data manipulation and automation
✅ Power BI – Transform data into interactive dashboards
✅ Tableau – Visualize data patterns and trends with ease
✅ Jupyter Notebook – Document, code, and visualize all in one place
✅ Looker Studio – A free and sleek way to create shareable reports with live data.
Perfect blend of code, visuals, and storytelling.
React with ❤️ for free tutorials on each tool
Share with credits: https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
✅ SQL – Still the #1 skill for querying and managing structured data
✅ Excel / Google Sheets – Quick analysis, pivot tables, and essential calculations
✅ Python (Pandas, NumPy) – For deep data manipulation and automation
✅ Power BI – Transform data into interactive dashboards
✅ Tableau – Visualize data patterns and trends with ease
✅ Jupyter Notebook – Document, code, and visualize all in one place
✅ Looker Studio – A free and sleek way to create shareable reports with live data.
Perfect blend of code, visuals, and storytelling.
React with ❤️ for free tutorials on each tool
Share with credits: https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
❤18
📊 Data Analyst Roadmap (2025)
Master the Skills That Top Companies Are Hiring For!
📍 1. Learn Excel / Google Sheets
Basic formulas & formatting
VLOOKUP, Pivot Tables, Charts
Data cleaning & conditional formatting
📍 2. Master SQL
SELECT, WHERE, ORDER BY
JOINs (INNER, LEFT, RIGHT)
GROUP BY, HAVING, LIMIT
Subqueries, CTEs, Window Functions
📍 3. Learn Data Visualization Tools
Power BI / Tableau (choose one)
Charts, filters, slicers
Dashboards & storytelling
📍 4. Get Comfortable with Statistics
Mean, Median, Mode, Std Dev
Probability basics
A/B Testing, Hypothesis Testing
Correlation & Regression
📍 5. Learn Python for Data Analysis (Optional but Powerful)
Pandas & NumPy for data handling
Seaborn, Matplotlib for visuals
Jupyter Notebooks for analysis
📍 6. Data Cleaning & Wrangling
Handle missing values
Fix data types, remove duplicates
Text processing & date formatting
📍 7. Understand Business Metrics
KPIs: Revenue, Churn, CAC, LTV
Think like a business analyst
Deliver actionable insights
📍 8. Communication & Storytelling
Present insights with clarity
Simplify complex data
Speak the language of stakeholders
📍 9. Version Control (Git & GitHub)
Track your projects
Build a data portfolio
Collaborate with the community
📍 10. Interview & Resume Preparation
Excel, SQL, case-based questions
Mock interviews + real projects
Resume with measurable achievements
✨ React ❤️ for more
Master the Skills That Top Companies Are Hiring For!
📍 1. Learn Excel / Google Sheets
Basic formulas & formatting
VLOOKUP, Pivot Tables, Charts
Data cleaning & conditional formatting
📍 2. Master SQL
SELECT, WHERE, ORDER BY
JOINs (INNER, LEFT, RIGHT)
GROUP BY, HAVING, LIMIT
Subqueries, CTEs, Window Functions
📍 3. Learn Data Visualization Tools
Power BI / Tableau (choose one)
Charts, filters, slicers
Dashboards & storytelling
📍 4. Get Comfortable with Statistics
Mean, Median, Mode, Std Dev
Probability basics
A/B Testing, Hypothesis Testing
Correlation & Regression
📍 5. Learn Python for Data Analysis (Optional but Powerful)
Pandas & NumPy for data handling
Seaborn, Matplotlib for visuals
Jupyter Notebooks for analysis
📍 6. Data Cleaning & Wrangling
Handle missing values
Fix data types, remove duplicates
Text processing & date formatting
📍 7. Understand Business Metrics
KPIs: Revenue, Churn, CAC, LTV
Think like a business analyst
Deliver actionable insights
📍 8. Communication & Storytelling
Present insights with clarity
Simplify complex data
Speak the language of stakeholders
📍 9. Version Control (Git & GitHub)
Track your projects
Build a data portfolio
Collaborate with the community
📍 10. Interview & Resume Preparation
Excel, SQL, case-based questions
Mock interviews + real projects
Resume with measurable achievements
✨ React ❤️ for more
❤26👍3
SQL Basics for Beginners: Must-Know Concepts
1. What is SQL?
SQL (Structured Query Language) is a standard language used to communicate with databases. It allows you to query, update, and manage relational databases by writing simple or complex queries.
2. SQL Syntax
SQL is written using statements, which consist of keywords like
- SQL keywords are not case-sensitive, but it's common to write them in uppercase (e.g.,
3. SQL Data Types
Databases store data in different formats. The most common data types are:
-
-
-
-
4. Basic SQL Queries
Here are some fundamental SQL operations:
- SELECT Statement: Used to retrieve data from a database.
- WHERE Clause: Filters data based on conditions.
- ORDER BY: Sorts data in ascending (
- LIMIT: Limits the number of rows returned.
5. Filtering Data with WHERE Clause
The
You can use comparison operators like:
-
-
-
-
6. Aggregating Data
SQL provides functions to summarize or aggregate data:
- COUNT(): Counts the number of rows.
- SUM(): Adds up values in a column.
- AVG(): Calculates the average value.
- GROUP BY: Groups rows that have the same values into summary rows.
7. Joins in SQL
Joins combine data from two or more tables:
- INNER JOIN: Retrieves records with matching values in both tables.
- LEFT JOIN: Retrieves all records from the left table and matched records from the right table.
8. Inserting Data
To add new data to a table, you use the
9. Updating Data
You can update existing data in a table using the
10. Deleting Data
To remove data from a table, use the
Here you can find essential SQL Interview Resources👇
https://news.1rj.ru/str/DataSimplifier
Like this post if you need more 👍❤️
Hope it helps :)
1. What is SQL?
SQL (Structured Query Language) is a standard language used to communicate with databases. It allows you to query, update, and manage relational databases by writing simple or complex queries.
2. SQL Syntax
SQL is written using statements, which consist of keywords like
SELECT, FROM, WHERE, etc., to perform operations on the data.- SQL keywords are not case-sensitive, but it's common to write them in uppercase (e.g.,
SELECT, FROM).3. SQL Data Types
Databases store data in different formats. The most common data types are:
-
INT (Integer): For whole numbers.-
VARCHAR(n) or TEXT: For storing text data.-
DATE: For dates.-
DECIMAL: For precise decimal values, often used in financial calculations.4. Basic SQL Queries
Here are some fundamental SQL operations:
- SELECT Statement: Used to retrieve data from a database.
SELECT column1, column2 FROM table_name;
- WHERE Clause: Filters data based on conditions.
SELECT * FROM table_name WHERE condition;
- ORDER BY: Sorts data in ascending (
ASC) or descending (DESC) order.SELECT column1, column2 FROM table_name ORDER BY column1 ASC;
- LIMIT: Limits the number of rows returned.
SELECT * FROM table_name LIMIT 5;
5. Filtering Data with WHERE Clause
The
WHERE clause helps you filter data based on a condition:SELECT * FROM employees WHERE salary > 50000;
You can use comparison operators like:
-
=: Equal to-
>: Greater than-
<: Less than-
LIKE: For pattern matching6. Aggregating Data
SQL provides functions to summarize or aggregate data:
- COUNT(): Counts the number of rows.
SELECT COUNT(*) FROM table_name;
- SUM(): Adds up values in a column.
SELECT SUM(salary) FROM employees;
- AVG(): Calculates the average value.
SELECT AVG(salary) FROM employees;
- GROUP BY: Groups rows that have the same values into summary rows.
SELECT department, AVG(salary) FROM employees GROUP BY department;
7. Joins in SQL
Joins combine data from two or more tables:
- INNER JOIN: Retrieves records with matching values in both tables.
SELECT employees.name, departments.department
FROM employees
INNER JOIN departments
ON employees.department_id = departments.id;
- LEFT JOIN: Retrieves all records from the left table and matched records from the right table.
SELECT employees.name, departments.department
FROM employees
LEFT JOIN departments
ON employees.department_id = departments.id;
8. Inserting Data
To add new data to a table, you use the
INSERT INTO statement: INSERT INTO employees (name, position, salary) VALUES ('John Doe', 'Analyst', 60000);
9. Updating Data
You can update existing data in a table using the
UPDATE statement:UPDATE employees SET salary = 65000 WHERE name = 'John Doe';
10. Deleting Data
To remove data from a table, use the
DELETE statement:DELETE FROM employees WHERE name = 'John Doe';
Here you can find essential SQL Interview Resources👇
https://news.1rj.ru/str/DataSimplifier
Like this post if you need more 👍❤️
Hope it helps :)
❤11
If you’re a Data Analyst, chances are you use 𝐒𝐐𝐋 every single day. And if you’re preparing for interviews, you’ve probably realized that it's not just about writing queries it's about writing smart, efficient, and scalable ones.
1. 𝐁𝐫𝐞𝐚𝐤 𝐈𝐭 𝐃𝐨𝐰𝐧 𝐰𝐢𝐭𝐡 𝐂𝐓𝐄𝐬 (𝐂𝐨𝐦𝐦𝐨𝐧 𝐓𝐚𝐛𝐥𝐞 𝐄𝐱𝐩𝐫𝐞𝐬𝐬𝐢𝐨𝐧𝐬)
Ever worked on a query that became an unreadable monster? CTEs let you break that down into logical steps. You can treat them like temporary views — great for simplifying logic and improving collaboration across your team.
2. 𝐔𝐬𝐞 𝐖𝐢𝐧𝐝𝐨𝐰 𝐅𝐮𝐧𝐜𝐭𝐢𝐨𝐧𝐬
Forget the mess of subqueries. With functions like ROW_NUMBER(), RANK(), LEAD() and LAG(), you can compare rows, rank items, or calculate running totals — all within the same query. Total
3. 𝐒𝐮𝐛𝐪𝐮𝐞𝐫𝐢𝐞𝐬 (𝐍𝐞𝐬𝐭𝐞𝐝 𝐐𝐮𝐞𝐫𝐢𝐞𝐬)
Yes, they're old school, but nested subqueries are still powerful. Use them when you want to filter based on results of another query or isolate logic step-by-step before joining with the big picture.
4. 𝐈𝐧𝐝𝐞𝐱𝐞𝐬 & 𝐐𝐮𝐞𝐫𝐲 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧
Query taking forever? Look at your indexes. Index the columns you use in JOINs, WHERE, and GROUP BY. Even basic knowledge of how the SQL engine reads data can take your skills up a notch.
5. 𝐉𝐨𝐢𝐧𝐬 𝐯𝐬. 𝐒𝐮𝐛𝐪𝐮𝐞𝐫𝐢𝐞𝐬
Joins are usually faster and better for combining large datasets. Subqueries, on the other hand, are cleaner when doing one-off filters or smaller operations. Choose wisely based on the context.
6. 𝐂𝐀𝐒𝐄 𝐒𝐭𝐚𝐭𝐞𝐦𝐞𝐧𝐭𝐬:
Want to categorize or bucket data without creating a separate table? Use CASE. It’s ideal for conditional logic, custom labels, and grouping in a single query.
7. 𝐀𝐠𝐠𝐫𝐞𝐠𝐚𝐭𝐢𝐨𝐧𝐬 & 𝐆𝐑𝐎𝐔𝐏 𝐁𝐘
Most analytics questions start with "how many", "what’s the average", or "which is the highest?". SUM(), COUNT(), AVG(), etc., and pair them with GROUP BY to drive insights that matter.
8. 𝐃𝐚𝐭𝐞𝐬 𝐀𝐫𝐞 𝐀𝐥𝐰𝐚𝐲𝐬 𝐓𝐫𝐢𝐜𝐤𝐲
Time-based analysis is everywhere: trends, cohorts, seasonality, etc. Get familiar with functions like DATEADD, DATEDIFF, DATE_TRUNC, and DATEPART to work confidently with time series data.
9. 𝐒𝐞𝐥𝐟-𝐉𝐨𝐢𝐧𝐬 & 𝐑𝐞𝐜𝐮𝐫𝐬𝐢𝐯𝐞 𝐐𝐮𝐞𝐫𝐢𝐞𝐬 𝐟𝐨𝐫 𝐇𝐢𝐞𝐫𝐚𝐫𝐜𝐡𝐢𝐞𝐬
Whether it's org charts or product categories, not all data is flat. Learn how to join a table to itself or use recursive CTEs to navigate parent-child relationships effectively.
You don’t need to memorize 100 functions. You need to understand 10 really well and apply them smartly. These are the concepts I keep going back to not just in interviews, but in the real world where clarity, performance, and logic matter most.
1. 𝐁𝐫𝐞𝐚𝐤 𝐈𝐭 𝐃𝐨𝐰𝐧 𝐰𝐢𝐭𝐡 𝐂𝐓𝐄𝐬 (𝐂𝐨𝐦𝐦𝐨𝐧 𝐓𝐚𝐛𝐥𝐞 𝐄𝐱𝐩𝐫𝐞𝐬𝐬𝐢𝐨𝐧𝐬)
Ever worked on a query that became an unreadable monster? CTEs let you break that down into logical steps. You can treat them like temporary views — great for simplifying logic and improving collaboration across your team.
2. 𝐔𝐬𝐞 𝐖𝐢𝐧𝐝𝐨𝐰 𝐅𝐮𝐧𝐜𝐭𝐢𝐨𝐧𝐬
Forget the mess of subqueries. With functions like ROW_NUMBER(), RANK(), LEAD() and LAG(), you can compare rows, rank items, or calculate running totals — all within the same query. Total
3. 𝐒𝐮𝐛𝐪𝐮𝐞𝐫𝐢𝐞𝐬 (𝐍𝐞𝐬𝐭𝐞𝐝 𝐐𝐮𝐞𝐫𝐢𝐞𝐬)
Yes, they're old school, but nested subqueries are still powerful. Use them when you want to filter based on results of another query or isolate logic step-by-step before joining with the big picture.
4. 𝐈𝐧𝐝𝐞𝐱𝐞𝐬 & 𝐐𝐮𝐞𝐫𝐲 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧
Query taking forever? Look at your indexes. Index the columns you use in JOINs, WHERE, and GROUP BY. Even basic knowledge of how the SQL engine reads data can take your skills up a notch.
5. 𝐉𝐨𝐢𝐧𝐬 𝐯𝐬. 𝐒𝐮𝐛𝐪𝐮𝐞𝐫𝐢𝐞𝐬
Joins are usually faster and better for combining large datasets. Subqueries, on the other hand, are cleaner when doing one-off filters or smaller operations. Choose wisely based on the context.
6. 𝐂𝐀𝐒𝐄 𝐒𝐭𝐚𝐭𝐞𝐦𝐞𝐧𝐭𝐬:
Want to categorize or bucket data without creating a separate table? Use CASE. It’s ideal for conditional logic, custom labels, and grouping in a single query.
7. 𝐀𝐠𝐠𝐫𝐞𝐠𝐚𝐭𝐢𝐨𝐧𝐬 & 𝐆𝐑𝐎𝐔𝐏 𝐁𝐘
Most analytics questions start with "how many", "what’s the average", or "which is the highest?". SUM(), COUNT(), AVG(), etc., and pair them with GROUP BY to drive insights that matter.
8. 𝐃𝐚𝐭𝐞𝐬 𝐀𝐫𝐞 𝐀𝐥𝐰𝐚𝐲𝐬 𝐓𝐫𝐢𝐜𝐤𝐲
Time-based analysis is everywhere: trends, cohorts, seasonality, etc. Get familiar with functions like DATEADD, DATEDIFF, DATE_TRUNC, and DATEPART to work confidently with time series data.
9. 𝐒𝐞𝐥𝐟-𝐉𝐨𝐢𝐧𝐬 & 𝐑𝐞𝐜𝐮𝐫𝐬𝐢𝐯𝐞 𝐐𝐮𝐞𝐫𝐢𝐞𝐬 𝐟𝐨𝐫 𝐇𝐢𝐞𝐫𝐚𝐫𝐜𝐡𝐢𝐞𝐬
Whether it's org charts or product categories, not all data is flat. Learn how to join a table to itself or use recursive CTEs to navigate parent-child relationships effectively.
You don’t need to memorize 100 functions. You need to understand 10 really well and apply them smartly. These are the concepts I keep going back to not just in interviews, but in the real world where clarity, performance, and logic matter most.
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SQL Basics for Beginners: Must-Know Concepts
1. What is SQL?
SQL (Structured Query Language) is a standard language used to communicate with databases. It allows you to query, update, and manage relational databases by writing simple or complex queries.
2. SQL Syntax
SQL is written using statements, which consist of keywords like
- SQL keywords are not case-sensitive, but it's common to write them in uppercase (e.g.,
3. SQL Data Types
Databases store data in different formats. The most common data types are:
-
-
-
-
4. Basic SQL Queries
Here are some fundamental SQL operations:
- SELECT Statement: Used to retrieve data from a database.
- WHERE Clause: Filters data based on conditions.
- ORDER BY: Sorts data in ascending (
- LIMIT: Limits the number of rows returned.
5. Filtering Data with WHERE Clause
The
You can use comparison operators like:
-
-
-
-
6. Aggregating Data
SQL provides functions to summarize or aggregate data:
- COUNT(): Counts the number of rows.
- SUM(): Adds up values in a column.
- AVG(): Calculates the average value.
- GROUP BY: Groups rows that have the same values into summary rows.
7. Joins in SQL
Joins combine data from two or more tables:
- INNER JOIN: Retrieves records with matching values in both tables.
- LEFT JOIN: Retrieves all records from the left table and matched records from the right table.
8. Inserting Data
To add new data to a table, you use the
9. Updating Data
You can update existing data in a table using the
10. Deleting Data
To remove data from a table, use the
Here you can find essential SQL Interview Resources👇
https://news.1rj.ru/str/DataSimplifier
Like this post if you need more 👍❤️
Hope it helps :)
1. What is SQL?
SQL (Structured Query Language) is a standard language used to communicate with databases. It allows you to query, update, and manage relational databases by writing simple or complex queries.
2. SQL Syntax
SQL is written using statements, which consist of keywords like
SELECT, FROM, WHERE, etc., to perform operations on the data.- SQL keywords are not case-sensitive, but it's common to write them in uppercase (e.g.,
SELECT, FROM).3. SQL Data Types
Databases store data in different formats. The most common data types are:
-
INT (Integer): For whole numbers.-
VARCHAR(n) or TEXT: For storing text data.-
DATE: For dates.-
DECIMAL: For precise decimal values, often used in financial calculations.4. Basic SQL Queries
Here are some fundamental SQL operations:
- SELECT Statement: Used to retrieve data from a database.
SELECT column1, column2 FROM table_name;
- WHERE Clause: Filters data based on conditions.
SELECT * FROM table_name WHERE condition;
- ORDER BY: Sorts data in ascending (
ASC) or descending (DESC) order.SELECT column1, column2 FROM table_name ORDER BY column1 ASC;
- LIMIT: Limits the number of rows returned.
SELECT * FROM table_name LIMIT 5;
5. Filtering Data with WHERE Clause
The
WHERE clause helps you filter data based on a condition:SELECT * FROM employees WHERE salary > 50000;
You can use comparison operators like:
-
=: Equal to-
>: Greater than-
<: Less than-
LIKE: For pattern matching6. Aggregating Data
SQL provides functions to summarize or aggregate data:
- COUNT(): Counts the number of rows.
SELECT COUNT(*) FROM table_name;
- SUM(): Adds up values in a column.
SELECT SUM(salary) FROM employees;
- AVG(): Calculates the average value.
SELECT AVG(salary) FROM employees;
- GROUP BY: Groups rows that have the same values into summary rows.
SELECT department, AVG(salary) FROM employees GROUP BY department;
7. Joins in SQL
Joins combine data from two or more tables:
- INNER JOIN: Retrieves records with matching values in both tables.
SELECT employees.name, departments.department
FROM employees
INNER JOIN departments
ON employees.department_id = departments.id;
- LEFT JOIN: Retrieves all records from the left table and matched records from the right table.
SELECT employees.name, departments.department
FROM employees
LEFT JOIN departments
ON employees.department_id = departments.id;
8. Inserting Data
To add new data to a table, you use the
INSERT INTO statement: INSERT INTO employees (name, position, salary) VALUES ('John Doe', 'Analyst', 60000);
9. Updating Data
You can update existing data in a table using the
UPDATE statement:UPDATE employees SET salary = 65000 WHERE name = 'John Doe';
10. Deleting Data
To remove data from a table, use the
DELETE statement:DELETE FROM employees WHERE name = 'John Doe';
Here you can find essential SQL Interview Resources👇
https://news.1rj.ru/str/DataSimplifier
Like this post if you need more 👍❤️
Hope it helps :)
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Top Python Libraries for Data Analysis
Pandas: For data manipulation and analysis.
NumPy: For numerical computations and array operations.
Matplotlib: For creating static visualizations.
Seaborn: For statistical data visualization.
SciPy: For advanced mathematical and scientific computations.
Scikit-learn: For machine learning tasks.
Statsmodels: For statistical modeling and hypothesis testing.
Plotly: For interactive visualizations.
OpenPyXL: For working with Excel files.
PySpark: For big data processing.
Here you can find essential Python Interview Resources👇
https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02
Like this post for more resources like this 👍♥️
Share with credits: https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
Pandas: For data manipulation and analysis.
NumPy: For numerical computations and array operations.
Matplotlib: For creating static visualizations.
Seaborn: For statistical data visualization.
SciPy: For advanced mathematical and scientific computations.
Scikit-learn: For machine learning tasks.
Statsmodels: For statistical modeling and hypothesis testing.
Plotly: For interactive visualizations.
OpenPyXL: For working with Excel files.
PySpark: For big data processing.
Here you can find essential Python Interview Resources👇
https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02
Like this post for more resources like this 👍♥️
Share with credits: https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
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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 😊
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