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.
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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
Like this post for more content like this 👍♥️
Share with credits: https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
❤2
Junior-level Data Analyst interview questions:
Introduction and Background
1. Can you tell me about your background and how you became interested in data analysis?
2. What do you know about our company/organization?
3. Why do you want to work as a data analyst?
Data Analysis and Interpretation
1. What is your experience with data analysis tools like Excel, SQL, or Tableau?
2. How would you approach analyzing a large dataset to identify trends and patterns?
3. Can you explain the concept of correlation versus causation?
4. How do you handle missing or incomplete data?
5. Can you walk me through a time when you had to interpret complex data results?
Technical Skills
1. Write a SQL query to extract data from a database.
2. How do you create a pivot table in Excel?
3. Can you explain the difference between a histogram and a box plot?
4. How do you perform data visualization using Tableau or Power BI?
5. Can you write a simple Python or R noscript to manipulate data?
Statistics and Math
1. What is the difference between mean, median, and mode?
2. Can you explain the concept of standard deviation and variance?
3. How do you calculate probability and confidence intervals?
4. Can you describe a time when you applied statistical concepts to a real-world problem?
5. How do you approach hypothesis testing?
Communication and Storytelling
1. Can you explain a complex data concept to a non-technical person?
2. How do you present data insights to stakeholders?
3. Can you walk me through a time when you had to communicate data results to a team?
4. How do you create effective data visualizations?
5. Can you tell a story using data?
Case Studies and Scenarios
1. You are given a dataset with customer purchase history. How would you analyze it to identify trends?
2. A company wants to increase sales. How would you use data to inform marketing strategies?
3. You notice a discrepancy in sales data. How would you investigate and resolve the issue?
4. Can you describe a time when you had to work with a stakeholder to understand their data needs?
5. How would you prioritize data projects with limited resources?
Behavioral Questions
1. Can you describe a time when you overcame a difficult data analysis challenge?
2. How do you handle tight deadlines and multiple projects?
3. Can you tell me about a project you worked on and your role in it?
4. How do you stay up-to-date with new data tools and technologies?
5. Can you describe a time when you received feedback on your data analysis work?
Final Questions
1. Do you have any questions about the company or role?
2. What do you think sets you apart from other candidates?
3. Can you summarize your experience and qualifications?
4. What are your long-term career goals?
Hope this helps you 😊
Introduction and Background
1. Can you tell me about your background and how you became interested in data analysis?
2. What do you know about our company/organization?
3. Why do you want to work as a data analyst?
Data Analysis and Interpretation
1. What is your experience with data analysis tools like Excel, SQL, or Tableau?
2. How would you approach analyzing a large dataset to identify trends and patterns?
3. Can you explain the concept of correlation versus causation?
4. How do you handle missing or incomplete data?
5. Can you walk me through a time when you had to interpret complex data results?
Technical Skills
1. Write a SQL query to extract data from a database.
2. How do you create a pivot table in Excel?
3. Can you explain the difference between a histogram and a box plot?
4. How do you perform data visualization using Tableau or Power BI?
5. Can you write a simple Python or R noscript to manipulate data?
Statistics and Math
1. What is the difference between mean, median, and mode?
2. Can you explain the concept of standard deviation and variance?
3. How do you calculate probability and confidence intervals?
4. Can you describe a time when you applied statistical concepts to a real-world problem?
5. How do you approach hypothesis testing?
Communication and Storytelling
1. Can you explain a complex data concept to a non-technical person?
2. How do you present data insights to stakeholders?
3. Can you walk me through a time when you had to communicate data results to a team?
4. How do you create effective data visualizations?
5. Can you tell a story using data?
Case Studies and Scenarios
1. You are given a dataset with customer purchase history. How would you analyze it to identify trends?
2. A company wants to increase sales. How would you use data to inform marketing strategies?
3. You notice a discrepancy in sales data. How would you investigate and resolve the issue?
4. Can you describe a time when you had to work with a stakeholder to understand their data needs?
5. How would you prioritize data projects with limited resources?
Behavioral Questions
1. Can you describe a time when you overcame a difficult data analysis challenge?
2. How do you handle tight deadlines and multiple projects?
3. Can you tell me about a project you worked on and your role in it?
4. How do you stay up-to-date with new data tools and technologies?
5. Can you describe a time when you received feedback on your data analysis work?
Final Questions
1. Do you have any questions about the company or role?
2. What do you think sets you apart from other candidates?
3. Can you summarize your experience and qualifications?
4. What are your long-term career goals?
Hope this helps you 😊
❤3
Excel Cheat Sheet 📔
This Excel cheatsheet is designed to be your quick reference guide for using Microsoft Excel efficiently.
1. Basic Functions
- SUM:
- AVERAGE:
- COUNT:
- MAX:
- MIN:
2. Text Functions
- CONCATENATE:
- LEFT:
- RIGHT:
- MID:
- TRIM:
3. Logical Functions
- IF:
- AND:
- OR:
- NOT:
4. Lookup Functions
- VLOOKUP:
- HLOOKUP:
- INDEX:
- MATCH:
5. Data Sorting & Filtering
- Sort: *Data > Sort*
- Filter: *Data > Filter*
- Advanced Filter: *Data > Advanced*
6. Conditional Formatting
- Apply Formatting: *Home > Conditional Formatting > New Rule*
- Highlight Cells: *Home > Conditional Formatting > Highlight Cells Rules*
7. Charts and Graphs
- Insert Chart: *Insert > Select Chart Type*
- Customize Chart: *Chart Tools > Design/Format*
8. PivotTables
- Create PivotTable: *Insert > PivotTable*
- Refresh PivotTable: *Right-click on PivotTable > Refresh*
9. Data Validation
- Set Validation: *Data > Data Validation*
- List: *Allow: List > Source: range or items*
10. Protecting Data
- Protect Sheet: *Review > Protect Sheet*
- Protect Workbook: *Review > Protect Workbook*
11. Shortcuts
- Copy:
- Paste:
- Undo:
- Redo:
- Save:
12. Printing Options
- Print Area: *Page Layout > Print Area > Set Print Area*
- Page Setup: *Page Layout > Page Setup*
Checklist for Data Analyst: https://dataanalytics.beehiiv.com/p/data
I have curated best 80+ top-notch Data Analytics Resources 👇👇
https://news.1rj.ru/str/DataSimplifier
Like for more Interview Resources ♥️
Share with credits: https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
This Excel cheatsheet is designed to be your quick reference guide for using Microsoft Excel efficiently.
1. Basic Functions
- SUM:
=SUM(range)- AVERAGE:
=AVERAGE(range)- COUNT:
=COUNT(range)- MAX:
=MAX(range)- MIN:
=MIN(range)2. Text Functions
- CONCATENATE:
=CONCATENATE(text1, text2, ...) or =TEXTJOIN(delimiter, ignore_empty, text1, text2, ...)- LEFT:
=LEFT(text, num_chars)- RIGHT:
=RIGHT(text, num_chars)- MID:
=MID(text, start_num, num_chars)- TRIM:
=TRIM(text)3. Logical Functions
- IF:
=IF(condition, true_value, false_value)- AND:
=AND(condition1, condition2, ...)- OR:
=OR(condition1, condition2, ...)- NOT:
=NOT(condition)4. Lookup Functions
- VLOOKUP:
=VLOOKUP(lookup_value, table_array, col_index_num, [range_lookup])- HLOOKUP:
=HLOOKUP(lookup_value, table_array, row_index_num, [range_lookup])- INDEX:
=INDEX(array, row_num, [column_num])- MATCH:
=MATCH(lookup_value, lookup_array, [match_type])5. Data Sorting & Filtering
- Sort: *Data > Sort*
- Filter: *Data > Filter*
- Advanced Filter: *Data > Advanced*
6. Conditional Formatting
- Apply Formatting: *Home > Conditional Formatting > New Rule*
- Highlight Cells: *Home > Conditional Formatting > Highlight Cells Rules*
7. Charts and Graphs
- Insert Chart: *Insert > Select Chart Type*
- Customize Chart: *Chart Tools > Design/Format*
8. PivotTables
- Create PivotTable: *Insert > PivotTable*
- Refresh PivotTable: *Right-click on PivotTable > Refresh*
9. Data Validation
- Set Validation: *Data > Data Validation*
- List: *Allow: List > Source: range or items*
10. Protecting Data
- Protect Sheet: *Review > Protect Sheet*
- Protect Workbook: *Review > Protect Workbook*
11. Shortcuts
- Copy:
Ctrl + C- Paste:
Ctrl + V- Undo:
Ctrl + Z- Redo:
Ctrl + Y- Save:
Ctrl + S12. Printing Options
- Print Area: *Page Layout > Print Area > Set Print Area*
- Page Setup: *Page Layout > Page Setup*
Checklist for Data Analyst: https://dataanalytics.beehiiv.com/p/data
I have curated best 80+ top-notch Data Analytics Resources 👇👇
https://news.1rj.ru/str/DataSimplifier
Like for more Interview Resources ♥️
Share with credits: https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
❤2
Machine Learning – Essential Concepts 🚀
1️⃣ Types of Machine Learning
Supervised Learning – Uses labeled data to train models.
Examples: Linear Regression, Decision Trees, Random Forest, SVM
Unsupervised Learning – Identifies patterns in unlabeled data.
Examples: Clustering (K-Means, DBSCAN), PCA
Reinforcement Learning – Models learn through rewards and penalties.
Examples: Q-Learning, Deep Q Networks
2️⃣ Key Algorithms
Regression – Predicts continuous values (Linear Regression, Ridge, Lasso).
Classification – Categorizes data into classes (Logistic Regression, Decision Tree, SVM, Naïve Bayes).
Clustering – Groups similar data points (K-Means, Hierarchical Clustering, DBSCAN).
Dimensionality Reduction – Reduces the number of features (PCA, t-SNE, LDA).
3️⃣ Model Training & Evaluation
Train-Test Split – Dividing data into training and testing sets.
Cross-Validation – Splitting data multiple times for better accuracy.
Metrics – Evaluating models with RMSE, Accuracy, Precision, Recall, F1-Score, ROC-AUC.
4️⃣ Feature Engineering
Handling missing data (mean imputation, dropna()).
Encoding categorical variables (One-Hot Encoding, Label Encoding).
Feature Scaling (Normalization, Standardization).
5️⃣ Overfitting & Underfitting
Overfitting – Model learns noise, performs well on training but poorly on test data.
Underfitting – Model is too simple and fails to capture patterns.
Solution: Regularization (L1, L2), Hyperparameter Tuning.
6️⃣ Ensemble Learning
Combining multiple models to improve performance.
Bagging (Random Forest)
Boosting (XGBoost, Gradient Boosting, AdaBoost)
7️⃣ Deep Learning Basics
Neural Networks (ANN, CNN, RNN).
Activation Functions (ReLU, Sigmoid, Tanh).
Backpropagation & Gradient Descent.
8️⃣ Model Deployment
Deploy models using Flask, FastAPI, or Streamlit.
Model versioning with MLflow.
Cloud deployment (AWS SageMaker, Google Vertex AI).
Join our WhatsApp channel: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
1️⃣ Types of Machine Learning
Supervised Learning – Uses labeled data to train models.
Examples: Linear Regression, Decision Trees, Random Forest, SVM
Unsupervised Learning – Identifies patterns in unlabeled data.
Examples: Clustering (K-Means, DBSCAN), PCA
Reinforcement Learning – Models learn through rewards and penalties.
Examples: Q-Learning, Deep Q Networks
2️⃣ Key Algorithms
Regression – Predicts continuous values (Linear Regression, Ridge, Lasso).
Classification – Categorizes data into classes (Logistic Regression, Decision Tree, SVM, Naïve Bayes).
Clustering – Groups similar data points (K-Means, Hierarchical Clustering, DBSCAN).
Dimensionality Reduction – Reduces the number of features (PCA, t-SNE, LDA).
3️⃣ Model Training & Evaluation
Train-Test Split – Dividing data into training and testing sets.
Cross-Validation – Splitting data multiple times for better accuracy.
Metrics – Evaluating models with RMSE, Accuracy, Precision, Recall, F1-Score, ROC-AUC.
4️⃣ Feature Engineering
Handling missing data (mean imputation, dropna()).
Encoding categorical variables (One-Hot Encoding, Label Encoding).
Feature Scaling (Normalization, Standardization).
5️⃣ Overfitting & Underfitting
Overfitting – Model learns noise, performs well on training but poorly on test data.
Underfitting – Model is too simple and fails to capture patterns.
Solution: Regularization (L1, L2), Hyperparameter Tuning.
6️⃣ Ensemble Learning
Combining multiple models to improve performance.
Bagging (Random Forest)
Boosting (XGBoost, Gradient Boosting, AdaBoost)
7️⃣ Deep Learning Basics
Neural Networks (ANN, CNN, RNN).
Activation Functions (ReLU, Sigmoid, Tanh).
Backpropagation & Gradient Descent.
8️⃣ Model Deployment
Deploy models using Flask, FastAPI, or Streamlit.
Model versioning with MLflow.
Cloud deployment (AWS SageMaker, Google Vertex AI).
Join our WhatsApp channel: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
❤3
Complete Roadmap to learn Excel in 2025 👇👇
1. Basic Excel Skills:
- Familiarize yourself with Excel's interface and navigation.
- Learn basic formulas (SUM, AVERAGE, COUNT, etc.).
- Understand cell referencing (absolute vs. relative).
2. Data Entry and Formatting:
- Practice entering and formatting data efficiently.
- Explore cell formatting options for a clean and organized dataset.
3. Advanced Formulas:
- Master more advanced formulas like VLOOKUP, HLOOKUP, INDEX-MATCH.
- Learn logical functions (IF, AND, OR).
- Understand array formulas for complex calculations.
4. Pivot Tables:
- Gain proficiency in creating Pivot Tables for data summarization.
- Learn to customize and format Pivot Tables effectively.
5. Data Cleaning:
- Acquire skills in cleaning and transforming data.
- Explore text-to-columns, remove duplicates, and data validation.
6. Charts and Graphs:
- Learn to create various charts (bar, line, pie) for data visualization.
- Understand chart formatting and customization.
7. Dashboard Creation:
- Combine charts and tables to build basic dashboards.
- Explore dynamic dashboards using Excel features.
8. Macros and VBA:
- Dive into basic automation using Excel macros.
- Learn Visual Basic for Applications (VBA) for more advanced automation.
9. Power Query:
- Introduce yourself to Power Query for enhanced data manipulation.
- Learn to import, transform, and load data efficiently.
10. Advanced Excel Techniques:
- Explore advanced features like Goal Seek, Solver, and Scenario Manager.
- Master the use of data tables for sensitivity analysis.
11. Real-world Projects:
- Apply your skills to real-world projects or datasets.
- Practice solving analytical problems using Excel.
Remember to practice consistently, as hands-on experience is crucial for mastering Excel. This roadmap will provide a solid foundation for your journey into data analysis using Excel.
5️⃣ Free resources to practice Excel
https://www.w3schools.com/EXCEL/index.php
https://bit.ly/3PSorPT
http://learn.microsoft.com/en-gb/training/paths/modern-analytics/
https://news.1rj.ru/str/excel_analyst/52
https://excel-practice-online.com/
Join for more: https://news.1rj.ru/str/free4unow_backup
ENJOY LEARNING 👍👍
1. Basic Excel Skills:
- Familiarize yourself with Excel's interface and navigation.
- Learn basic formulas (SUM, AVERAGE, COUNT, etc.).
- Understand cell referencing (absolute vs. relative).
2. Data Entry and Formatting:
- Practice entering and formatting data efficiently.
- Explore cell formatting options for a clean and organized dataset.
3. Advanced Formulas:
- Master more advanced formulas like VLOOKUP, HLOOKUP, INDEX-MATCH.
- Learn logical functions (IF, AND, OR).
- Understand array formulas for complex calculations.
4. Pivot Tables:
- Gain proficiency in creating Pivot Tables for data summarization.
- Learn to customize and format Pivot Tables effectively.
5. Data Cleaning:
- Acquire skills in cleaning and transforming data.
- Explore text-to-columns, remove duplicates, and data validation.
6. Charts and Graphs:
- Learn to create various charts (bar, line, pie) for data visualization.
- Understand chart formatting and customization.
7. Dashboard Creation:
- Combine charts and tables to build basic dashboards.
- Explore dynamic dashboards using Excel features.
8. Macros and VBA:
- Dive into basic automation using Excel macros.
- Learn Visual Basic for Applications (VBA) for more advanced automation.
9. Power Query:
- Introduce yourself to Power Query for enhanced data manipulation.
- Learn to import, transform, and load data efficiently.
10. Advanced Excel Techniques:
- Explore advanced features like Goal Seek, Solver, and Scenario Manager.
- Master the use of data tables for sensitivity analysis.
11. Real-world Projects:
- Apply your skills to real-world projects or datasets.
- Practice solving analytical problems using Excel.
Remember to practice consistently, as hands-on experience is crucial for mastering Excel. This roadmap will provide a solid foundation for your journey into data analysis using Excel.
5️⃣ Free resources to practice Excel
https://www.w3schools.com/EXCEL/index.php
https://bit.ly/3PSorPT
http://learn.microsoft.com/en-gb/training/paths/modern-analytics/
https://news.1rj.ru/str/excel_analyst/52
https://excel-practice-online.com/
Join for more: https://news.1rj.ru/str/free4unow_backup
ENJOY LEARNING 👍👍
❤2
SQL From Basic to Advanced level
Basic SQL is ONLY 7 commands:
- SELECT
- FROM
- WHERE (also use SQL comparison operators such as =, <=, >=, <> etc.)
- ORDER BY
- Aggregate functions such as SUM, AVERAGE, COUNT etc.
- GROUP BY
- CREATE, INSERT, DELETE, etc.
You can do all this in just one morning.
Once you know these, take the next step and learn commands like:
- LEFT JOIN
- INNER JOIN
- LIKE
- IN
- CASE WHEN
- HAVING (undertstand how it's different from GROUP BY)
- UNION ALL
This should take another day.
Once both basic and intermediate are done, start learning more advanced SQL concepts such as:
- Subqueries (when to use subqueries vs CTE?)
- CTEs (WITH AS)
- Stored Procedures
- Triggers
- Window functions (LEAD, LAG, PARTITION BY, RANK, DENSE RANK)
These can be done in a couple of days.
Learning these concepts is NOT hard at all
- what takes time is practice and knowing what command to use when. How do you master that?
- First, create a basic SQL project
- Then, work on an intermediate SQL project (search online) -
Lastly, create something advanced on SQL with many CTEs, subqueries, stored procedures and triggers etc.
This is ALL you need to become a badass in SQL, and trust me when I say this, it is not rocket science. It's just logic.
Remember that practice is the key here. It will be more clear and perfect with the continous practice
Best telegram channel to learn SQL: https://news.1rj.ru/str/sqlanalyst
Data Analyst Jobs👇
https://news.1rj.ru/str/jobs_SQL
Join @free4unow_backup for more free resources.
Like this post if it helps 😄❤️
ENJOY LEARNING 👍👍
Basic SQL is ONLY 7 commands:
- SELECT
- FROM
- WHERE (also use SQL comparison operators such as =, <=, >=, <> etc.)
- ORDER BY
- Aggregate functions such as SUM, AVERAGE, COUNT etc.
- GROUP BY
- CREATE, INSERT, DELETE, etc.
You can do all this in just one morning.
Once you know these, take the next step and learn commands like:
- LEFT JOIN
- INNER JOIN
- LIKE
- IN
- CASE WHEN
- HAVING (undertstand how it's different from GROUP BY)
- UNION ALL
This should take another day.
Once both basic and intermediate are done, start learning more advanced SQL concepts such as:
- Subqueries (when to use subqueries vs CTE?)
- CTEs (WITH AS)
- Stored Procedures
- Triggers
- Window functions (LEAD, LAG, PARTITION BY, RANK, DENSE RANK)
These can be done in a couple of days.
Learning these concepts is NOT hard at all
- what takes time is practice and knowing what command to use when. How do you master that?
- First, create a basic SQL project
- Then, work on an intermediate SQL project (search online) -
Lastly, create something advanced on SQL with many CTEs, subqueries, stored procedures and triggers etc.
This is ALL you need to become a badass in SQL, and trust me when I say this, it is not rocket science. It's just logic.
Remember that practice is the key here. It will be more clear and perfect with the continous practice
Best telegram channel to learn SQL: https://news.1rj.ru/str/sqlanalyst
Data Analyst Jobs👇
https://news.1rj.ru/str/jobs_SQL
Join @free4unow_backup for more free resources.
Like this post if it helps 😄❤️
ENJOY LEARNING 👍👍
❤1
1. Define the term 'Data Wrangling.
Data Wrangling is the process wherein raw data is cleaned, structured, and enriched into a desired usable format for better decision making. It involves discovering, structuring, cleaning, enriching, validating, and analyzing data. This process can turn and map out large amounts of data extracted from various sources into a more useful format.
2. What are the best methods for data cleaning?
Create a data cleaning plan by understanding where the common errors take place and keep all the communications open. Before working with the data, identify and remove the duplicates. This will lead to an easy and effective data analysis process.Focus on the accuracy of the data. Set cross-field validation, maintain the value types of data, and provide mandatory constraints.Normalize the data at the entry point so that it is less chaotic. You will be able to ensure that all information is standardized, leading to fewer errors on entry.
3. Explain 4 steps to use CTE in sql.
All CTE starts with "with" clause.
After with you need to define CTE name and the field names. For instance in the below code snippet I have 3 fields Count,Column and Id. The name of CTE is "MyTemp".
Once you have defined CTE we need to specify the SQL which will give the result for the CTE.
Finally you can use the CTE in your SQL query.
Data Wrangling is the process wherein raw data is cleaned, structured, and enriched into a desired usable format for better decision making. It involves discovering, structuring, cleaning, enriching, validating, and analyzing data. This process can turn and map out large amounts of data extracted from various sources into a more useful format.
2. What are the best methods for data cleaning?
Create a data cleaning plan by understanding where the common errors take place and keep all the communications open. Before working with the data, identify and remove the duplicates. This will lead to an easy and effective data analysis process.Focus on the accuracy of the data. Set cross-field validation, maintain the value types of data, and provide mandatory constraints.Normalize the data at the entry point so that it is less chaotic. You will be able to ensure that all information is standardized, leading to fewer errors on entry.
3. Explain 4 steps to use CTE in sql.
All CTE starts with "with" clause.
After with you need to define CTE name and the field names. For instance in the below code snippet I have 3 fields Count,Column and Id. The name of CTE is "MyTemp".
Once you have defined CTE we need to specify the SQL which will give the result for the CTE.
Finally you can use the CTE in your SQL query.
❤2
Dreaming of a perfect day as a data analyst?
Here is the reality check:
• You arrive at the office, grab a coffee, and dive deep into solving complex problems.
𝗕𝘂𝘁, you spend the first hour trying to figure out why one of your dashboards shows outdated data.
• You present impactful insights to a room full of executives, who trust your recommendations and are eager to execute your ideas.
𝗕𝘂𝘁, you will explain for the 10th time why Excel isn’t the best tool for running the complex analysis they are requesting.
• You use the latest machine learning models to accurately predict future trends.
𝗕𝘂𝘁, you will spend whole days wrangling messy, incomplete datasets.
• You collaborate with a team of data scientists to create innovative solutions.
𝗕𝘂𝘁, you will have to send a dozen Slack messages to IT just to get access to the data you need.
• You spend the afternoon writing elegant, and efficient Python code.
𝗕𝘂𝘁, you will google basic pandas function more times than you’d like to admit.
Manage your expectations and find humor in your daily work. It’s all part of the journey to those moments where you will drive real business impact as a data analyst!
Here is the reality check:
• You arrive at the office, grab a coffee, and dive deep into solving complex problems.
𝗕𝘂𝘁, you spend the first hour trying to figure out why one of your dashboards shows outdated data.
• You present impactful insights to a room full of executives, who trust your recommendations and are eager to execute your ideas.
𝗕𝘂𝘁, you will explain for the 10th time why Excel isn’t the best tool for running the complex analysis they are requesting.
• You use the latest machine learning models to accurately predict future trends.
𝗕𝘂𝘁, you will spend whole days wrangling messy, incomplete datasets.
• You collaborate with a team of data scientists to create innovative solutions.
𝗕𝘂𝘁, you will have to send a dozen Slack messages to IT just to get access to the data you need.
• You spend the afternoon writing elegant, and efficient Python code.
𝗕𝘂𝘁, you will google basic pandas function more times than you’d like to admit.
Manage your expectations and find humor in your daily work. It’s all part of the journey to those moments where you will drive real business impact as a data analyst!
❤1
Junior-level Data Analyst interview questions:
Introduction and Background
1. Can you tell me about your background and how you became interested in data analysis?
2. What do you know about our company/organization?
3. Why do you want to work as a data analyst?
Data Analysis and Interpretation
1. What is your experience with data analysis tools like Excel, SQL, or Tableau?
2. How would you approach analyzing a large dataset to identify trends and patterns?
3. Can you explain the concept of correlation versus causation?
4. How do you handle missing or incomplete data?
5. Can you walk me through a time when you had to interpret complex data results?
Technical Skills
1. Write a SQL query to extract data from a database.
2. How do you create a pivot table in Excel?
3. Can you explain the difference between a histogram and a box plot?
4. How do you perform data visualization using Tableau or Power BI?
5. Can you write a simple Python or R noscript to manipulate data?
Statistics and Math
1. What is the difference between mean, median, and mode?
2. Can you explain the concept of standard deviation and variance?
3. How do you calculate probability and confidence intervals?
4. Can you describe a time when you applied statistical concepts to a real-world problem?
5. How do you approach hypothesis testing?
Communication and Storytelling
1. Can you explain a complex data concept to a non-technical person?
2. How do you present data insights to stakeholders?
3. Can you walk me through a time when you had to communicate data results to a team?
4. How do you create effective data visualizations?
5. Can you tell a story using data?
Case Studies and Scenarios
1. You are given a dataset with customer purchase history. How would you analyze it to identify trends?
2. A company wants to increase sales. How would you use data to inform marketing strategies?
3. You notice a discrepancy in sales data. How would you investigate and resolve the issue?
4. Can you describe a time when you had to work with a stakeholder to understand their data needs?
5. How would you prioritize data projects with limited resources?
Behavioral Questions
1. Can you describe a time when you overcame a difficult data analysis challenge?
2. How do you handle tight deadlines and multiple projects?
3. Can you tell me about a project you worked on and your role in it?
4. How do you stay up-to-date with new data tools and technologies?
5. Can you describe a time when you received feedback on your data analysis work?
Final Questions
1. Do you have any questions about the company or role?
2. What do you think sets you apart from other candidates?
3. Can you summarize your experience and qualifications?
4. What are your long-term career goals?
Hope this helps you 😊
Introduction and Background
1. Can you tell me about your background and how you became interested in data analysis?
2. What do you know about our company/organization?
3. Why do you want to work as a data analyst?
Data Analysis and Interpretation
1. What is your experience with data analysis tools like Excel, SQL, or Tableau?
2. How would you approach analyzing a large dataset to identify trends and patterns?
3. Can you explain the concept of correlation versus causation?
4. How do you handle missing or incomplete data?
5. Can you walk me through a time when you had to interpret complex data results?
Technical Skills
1. Write a SQL query to extract data from a database.
2. How do you create a pivot table in Excel?
3. Can you explain the difference between a histogram and a box plot?
4. How do you perform data visualization using Tableau or Power BI?
5. Can you write a simple Python or R noscript to manipulate data?
Statistics and Math
1. What is the difference between mean, median, and mode?
2. Can you explain the concept of standard deviation and variance?
3. How do you calculate probability and confidence intervals?
4. Can you describe a time when you applied statistical concepts to a real-world problem?
5. How do you approach hypothesis testing?
Communication and Storytelling
1. Can you explain a complex data concept to a non-technical person?
2. How do you present data insights to stakeholders?
3. Can you walk me through a time when you had to communicate data results to a team?
4. How do you create effective data visualizations?
5. Can you tell a story using data?
Case Studies and Scenarios
1. You are given a dataset with customer purchase history. How would you analyze it to identify trends?
2. A company wants to increase sales. How would you use data to inform marketing strategies?
3. You notice a discrepancy in sales data. How would you investigate and resolve the issue?
4. Can you describe a time when you had to work with a stakeholder to understand their data needs?
5. How would you prioritize data projects with limited resources?
Behavioral Questions
1. Can you describe a time when you overcame a difficult data analysis challenge?
2. How do you handle tight deadlines and multiple projects?
3. Can you tell me about a project you worked on and your role in it?
4. How do you stay up-to-date with new data tools and technologies?
5. Can you describe a time when you received feedback on your data analysis work?
Final Questions
1. Do you have any questions about the company or role?
2. What do you think sets you apart from other candidates?
3. Can you summarize your experience and qualifications?
4. What are your long-term career goals?
Hope this helps you 😊
❤2🤔1
Essential Skills Excel for Data Analysts 🚀
1️⃣ Data Cleaning & Transformation
Remove Duplicates – Ensure unique records.
Find & Replace – Quick data modifications.
Text Functions – TRIM, LEN, LEFT, RIGHT, MID, PROPER.
Data Validation – Restrict input values.
2️⃣ Data Analysis & Manipulation
Sorting & Filtering – Organize and extract key insights.
Conditional Formatting – Highlight trends, outliers.
Pivot Tables – Summarize large datasets efficiently.
Power Query – Automate data transformation.
3️⃣ Essential Formulas & Functions
Lookup Functions – VLOOKUP, HLOOKUP, XLOOKUP, INDEX-MATCH.
Logical Functions – IF, AND, OR, IFERROR, IFS.
Aggregation Functions – SUM, AVERAGE, MIN, MAX, COUNT, COUNTA.
Text Functions – CONCATENATE, TEXTJOIN, SUBSTITUTE.
4️⃣ Data Visualization
Charts & Graphs – Bar, Line, Pie, Scatter, Histogram.
Sparklines – Miniature charts inside cells.
Conditional Formatting – Color scales, data bars.
Dashboard Creation – Interactive and dynamic reports.
5️⃣ Advanced Excel Techniques
Array Formulas – Dynamic calculations with multiple values.
Power Pivot & DAX – Advanced data modeling.
What-If Analysis – Goal Seek, Scenario Manager.
Macros & VBA – Automate repetitive tasks.
6️⃣ Data Import & Export
CSV & TXT Files – Import and clean raw data.
Power Query – Connect to databases, web sources.
Exporting Reports – PDF, CSV, Excel formats.
Here you can find some free Excel books & useful resources: https://news.1rj.ru/str/excel_data
Hope it helps :)
#dataanalyst
1️⃣ Data Cleaning & Transformation
Remove Duplicates – Ensure unique records.
Find & Replace – Quick data modifications.
Text Functions – TRIM, LEN, LEFT, RIGHT, MID, PROPER.
Data Validation – Restrict input values.
2️⃣ Data Analysis & Manipulation
Sorting & Filtering – Organize and extract key insights.
Conditional Formatting – Highlight trends, outliers.
Pivot Tables – Summarize large datasets efficiently.
Power Query – Automate data transformation.
3️⃣ Essential Formulas & Functions
Lookup Functions – VLOOKUP, HLOOKUP, XLOOKUP, INDEX-MATCH.
Logical Functions – IF, AND, OR, IFERROR, IFS.
Aggregation Functions – SUM, AVERAGE, MIN, MAX, COUNT, COUNTA.
Text Functions – CONCATENATE, TEXTJOIN, SUBSTITUTE.
4️⃣ Data Visualization
Charts & Graphs – Bar, Line, Pie, Scatter, Histogram.
Sparklines – Miniature charts inside cells.
Conditional Formatting – Color scales, data bars.
Dashboard Creation – Interactive and dynamic reports.
5️⃣ Advanced Excel Techniques
Array Formulas – Dynamic calculations with multiple values.
Power Pivot & DAX – Advanced data modeling.
What-If Analysis – Goal Seek, Scenario Manager.
Macros & VBA – Automate repetitive tasks.
6️⃣ Data Import & Export
CSV & TXT Files – Import and clean raw data.
Power Query – Connect to databases, web sources.
Exporting Reports – PDF, CSV, Excel formats.
Here you can find some free Excel books & useful resources: https://news.1rj.ru/str/excel_data
Hope it helps :)
#dataanalyst
❤2
Hey guys,
Today, I curated a list of essential Power BI interview questions that every aspiring data analyst should be prepared to answer 👇👇
1. What is Power BI?
Power BI is a business analytics service developed by Microsoft. It provides tools for aggregating, analyzing, visualizing, and sharing data. With Power BI, users can create dynamic dashboards and interactive reports from multiple data sources.
Key Features:
- Data transformation using Power Query
- Powerful visualizations and reporting tools
- DAX (Data Analysis Expressions) for complex calculations
2. What are the building blocks of Power BI?
The main building blocks of Power BI include:
- Visualizations: Graphical representations of data (charts, graphs, etc.).
- Datasets: A collection of data used to create visualizations.
- Reports: A collection of visualizations on one or more pages.
- Dashboards: A single page that combines multiple visualizations from reports.
- Tiles: Single visualization found on a report or dashboard.
3. What is DAX, and why is it important in Power BI?
DAX (Data Analysis Expressions) is a formula language used in Power BI for creating custom calculations and aggregations. DAX is similar to Excel formulas but offers much more powerful data manipulation capabilities.
Tip: Be ready to explain not just the syntax, but scenarios where DAX is essential, such as calculating year-over-year growth or creating dynamic measures.
4. How does Power BI differ from Excel in data visualization?
While Excel is great for individual analysis and data manipulation, Power BI excels in handling large datasets, creating interactive dashboards, and sharing insights across the organization. Power BI also integrates better and allows for real-time data streaming.
5. What are the types of filters in Power BI, and how are they used?
Power BI offers several types of filters to refine data and display only what’s relevant:
- Visual-level filters: Apply filters to individual visuals.
- Page-level filters: Apply filters to all the visuals on a report page.
- Report-level filters: Apply filters to all pages in the report.
Filters help to create more customized and targeted reports by narrowing down the data view based on specific conditions.
6. What are Power BI Desktop, Power BI Service, and Power BI Mobile? How do they interact?
- Power BI Desktop: A desktop-based application used for data modeling, creating reports, and building dashboards.
- Power BI Service: A cloud-based platform that allows users to publish and share reports created in Power BI Desktop.
- Power BI Mobile: Allows users to view reports and dashboards on mobile devices for on-the-go access.
These components work together in a typical workflow:
1. Build reports and dashboards in Power BI Desktop.
2. Publish them to the Power BI Service for sharing and collaboration.
3. View and interact with reports on Power BI Mobile for easy access anywhere.
7. Explain the difference between calculated columns and measures.
- Calculated columns are added to a table using DAX and are calculated row by row.
- Measures are calculations used in aggregations, such as sums, averages, and ratios. Unlike calculated columns, measures are dynamic and evaluated based on the filter context of a report.
8. How would you perform data cleaning and transformation in Power BI?
Data cleaning and transformation in Power BI are mainly done using Power Query Editor. Here, you can:
- Remove duplicates or empty rows
- Split columns (e.g., text into multiple parts)
- Change data types (e.g., text to numbers)
- Merge and append queries from different data sources
Power BI isn’t just about visuals; it’s about turning raw data into actionable insights. So, keep honing your skills, try building dashboards, and soon enough, you’ll be impressing your interviewers too!
I have curated best 80+ top-notch Data Analytics Resources 👇👇
https://news.1rj.ru/str/DataSimplifier
Share with credits: https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
Today, I curated a list of essential Power BI interview questions that every aspiring data analyst should be prepared to answer 👇👇
1. What is Power BI?
Power BI is a business analytics service developed by Microsoft. It provides tools for aggregating, analyzing, visualizing, and sharing data. With Power BI, users can create dynamic dashboards and interactive reports from multiple data sources.
Key Features:
- Data transformation using Power Query
- Powerful visualizations and reporting tools
- DAX (Data Analysis Expressions) for complex calculations
2. What are the building blocks of Power BI?
The main building blocks of Power BI include:
- Visualizations: Graphical representations of data (charts, graphs, etc.).
- Datasets: A collection of data used to create visualizations.
- Reports: A collection of visualizations on one or more pages.
- Dashboards: A single page that combines multiple visualizations from reports.
- Tiles: Single visualization found on a report or dashboard.
3. What is DAX, and why is it important in Power BI?
DAX (Data Analysis Expressions) is a formula language used in Power BI for creating custom calculations and aggregations. DAX is similar to Excel formulas but offers much more powerful data manipulation capabilities.
Tip: Be ready to explain not just the syntax, but scenarios where DAX is essential, such as calculating year-over-year growth or creating dynamic measures.
4. How does Power BI differ from Excel in data visualization?
While Excel is great for individual analysis and data manipulation, Power BI excels in handling large datasets, creating interactive dashboards, and sharing insights across the organization. Power BI also integrates better and allows for real-time data streaming.
5. What are the types of filters in Power BI, and how are they used?
Power BI offers several types of filters to refine data and display only what’s relevant:
- Visual-level filters: Apply filters to individual visuals.
- Page-level filters: Apply filters to all the visuals on a report page.
- Report-level filters: Apply filters to all pages in the report.
Filters help to create more customized and targeted reports by narrowing down the data view based on specific conditions.
6. What are Power BI Desktop, Power BI Service, and Power BI Mobile? How do they interact?
- Power BI Desktop: A desktop-based application used for data modeling, creating reports, and building dashboards.
- Power BI Service: A cloud-based platform that allows users to publish and share reports created in Power BI Desktop.
- Power BI Mobile: Allows users to view reports and dashboards on mobile devices for on-the-go access.
These components work together in a typical workflow:
1. Build reports and dashboards in Power BI Desktop.
2. Publish them to the Power BI Service for sharing and collaboration.
3. View and interact with reports on Power BI Mobile for easy access anywhere.
7. Explain the difference between calculated columns and measures.
- Calculated columns are added to a table using DAX and are calculated row by row.
- Measures are calculations used in aggregations, such as sums, averages, and ratios. Unlike calculated columns, measures are dynamic and evaluated based on the filter context of a report.
8. How would you perform data cleaning and transformation in Power BI?
Data cleaning and transformation in Power BI are mainly done using Power Query Editor. Here, you can:
- Remove duplicates or empty rows
- Split columns (e.g., text into multiple parts)
- Change data types (e.g., text to numbers)
- Merge and append queries from different data sources
Power BI isn’t just about visuals; it’s about turning raw data into actionable insights. So, keep honing your skills, try building dashboards, and soon enough, you’ll be impressing your interviewers too!
I have curated best 80+ top-notch Data Analytics Resources 👇👇
https://news.1rj.ru/str/DataSimplifier
Share with credits: https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
Q1: How do you ensure data consistency and integrity in a data warehousing environment?
Ans:
I implement data validation checks, use constraints like primary and foreign keys, and ensure that ETL processes have error-handling mechanisms. Regular audits and data reconciliation processes are also set up to ensure data accuracy and consistency.
Q2: Describe a situation where you had to design a star schema for a data warehousing project.
Ans:
For a retail sales data warehousing project, I designed a star schema with a central fact table containing sales transactions. Surrounding this were dimension tables like Products, Stores, Time, and Customers. This structure allowed for efficient querying and reporting of sales metrics across various dimensions.
Q3: How would you use data analytics to assess credit risk for loan applicants?
Ans:
I'd analyze the applicant's financial history, including credit score, income, employment stability, and existing debts. Using predictive modeling, I'd assess the probability of default based on historical data of similar applicants. This would help in making informed lending decisions.
Q4: Describe a situation where you had to ensure data security for sensitive financial data.
Ans:
While working on a project involving customer transaction data, I ensured that all data was encrypted both at rest and in transit. I also implemented role-based access controls, ensuring that only authorized personnel could access specific data sets. Regular audits and penetration tests were conducted to identify and rectify potential vulnerabilities.
Ans:
I implement data validation checks, use constraints like primary and foreign keys, and ensure that ETL processes have error-handling mechanisms. Regular audits and data reconciliation processes are also set up to ensure data accuracy and consistency.
Q2: Describe a situation where you had to design a star schema for a data warehousing project.
Ans:
For a retail sales data warehousing project, I designed a star schema with a central fact table containing sales transactions. Surrounding this were dimension tables like Products, Stores, Time, and Customers. This structure allowed for efficient querying and reporting of sales metrics across various dimensions.
Q3: How would you use data analytics to assess credit risk for loan applicants?
Ans:
I'd analyze the applicant's financial history, including credit score, income, employment stability, and existing debts. Using predictive modeling, I'd assess the probability of default based on historical data of similar applicants. This would help in making informed lending decisions.
Q4: Describe a situation where you had to ensure data security for sensitive financial data.
Ans:
While working on a project involving customer transaction data, I ensured that all data was encrypted both at rest and in transit. I also implemented role-based access controls, ensuring that only authorized personnel could access specific data sets. Regular audits and penetration tests were conducted to identify and rectify potential vulnerabilities.
❤1
Data Analyst Interview Questions with Answers
Q1: How do you ensure data consistency and integrity in a data warehousing environment?
Ans: I implement data validation checks, use constraints like primary and foreign keys, and ensure that ETL processes have error-handling mechanisms. Regular audits and data reconciliation processes are also set up to ensure data accuracy and consistency.
Q2: Describe a situation where you had to design a star schema for a data warehousing project.
Ans: For a retail sales data warehousing project, I designed a star schema with a central fact table containing sales transactions. Surrounding this were dimension tables like Products, Stores, Time, and Customers. This structure allowed for efficient querying and reporting of sales metrics across various dimensions.
Q3: How would you use data analytics to assess credit risk for loan applicants?
Ans: I'd analyze the applicant's financial history, including credit score, income, employment stability, and existing debts. Using predictive modeling, I'd assess the probability of default based on historical data of similar applicants. This would help in making informed lending decisions.
Q4: Describe a situation where you had to ensure data security for sensitive financial data.
Ans: While working on a project involving customer transaction data, I ensured that all data was encrypted both at rest and in transit. I also implemented role-based access controls, ensuring that only authorized personnel could access specific data sets. Regular audits and penetration tests were conducted to identify and rectify potential vulnerabilities.
React ❤️ for more
Q1: How do you ensure data consistency and integrity in a data warehousing environment?
Ans: I implement data validation checks, use constraints like primary and foreign keys, and ensure that ETL processes have error-handling mechanisms. Regular audits and data reconciliation processes are also set up to ensure data accuracy and consistency.
Q2: Describe a situation where you had to design a star schema for a data warehousing project.
Ans: For a retail sales data warehousing project, I designed a star schema with a central fact table containing sales transactions. Surrounding this were dimension tables like Products, Stores, Time, and Customers. This structure allowed for efficient querying and reporting of sales metrics across various dimensions.
Q3: How would you use data analytics to assess credit risk for loan applicants?
Ans: I'd analyze the applicant's financial history, including credit score, income, employment stability, and existing debts. Using predictive modeling, I'd assess the probability of default based on historical data of similar applicants. This would help in making informed lending decisions.
Q4: Describe a situation where you had to ensure data security for sensitive financial data.
Ans: While working on a project involving customer transaction data, I ensured that all data was encrypted both at rest and in transit. I also implemented role-based access controls, ensuring that only authorized personnel could access specific data sets. Regular audits and penetration tests were conducted to identify and rectify potential vulnerabilities.
React ❤️ for more
❤5
📘 SQL Challenges for Data Analytics – With Explanation 🧠
(Beginner ➡️ Advanced)
1️⃣ Select Specific Columns
This fetches only the
✔️ Used when you don’t want all columns from a table.
2️⃣ Filter Records with WHERE
The
✔️ Used for applying conditions on data.
3️⃣ ORDER BY Clause
Sorts all users based on
✔️ Helpful to get latest data first.
4️⃣ Aggregate Functions (COUNT, AVG)
Explanation:
-
-
✔️ Used for quick stats from tables.
5️⃣ GROUP BY Usage
Groups data by
✔️ Use when you want grouped summaries.
6️⃣ JOIN Tables
Fetches user names along with order amounts by joining
✔️ Essential when combining data from multiple tables.
7️⃣ Use of HAVING
Like
✔️ **Use
8️⃣ Subqueries
Finds users whose salary is above the average. The subquery calculates the average salary first.
✔️ Nested queries for dynamic filtering9️⃣ CASE Statementnt**
Adds a new column that classifies users into categories based on age.
✔️ Powerful for conditional logic.
🔟 Window Functions (Advanced)
Ranks users by score *within each city*.
SQL Learning Series: https://whatsapp.com/channel/0029VanC5rODzgT6TiTGoa1v/1075
(Beginner ➡️ Advanced)
1️⃣ Select Specific Columns
SELECT name, email FROM users;
This fetches only the
name and email columns from the users table. ✔️ Used when you don’t want all columns from a table.
2️⃣ Filter Records with WHERE
SELECT * FROM users WHERE age > 30;
The
WHERE clause filters rows where age is greater than 30. ✔️ Used for applying conditions on data.
3️⃣ ORDER BY Clause
SELECT * FROM users ORDER BY registered_at DESC;
Sorts all users based on
registered_at in descending order. ✔️ Helpful to get latest data first.
4️⃣ Aggregate Functions (COUNT, AVG)
SELECT COUNT(*) AS total_users, AVG(age) AS avg_age FROM users;
Explanation:
-
COUNT(*) counts total rows (users). -
AVG(age) calculates the average age. ✔️ Used for quick stats from tables.
5️⃣ GROUP BY Usage
SELECT city, COUNT(*) AS user_count FROM users GROUP BY city;
Groups data by
city and counts users in each group. ✔️ Use when you want grouped summaries.
6️⃣ JOIN Tables
SELECT users.name, orders.amount
FROM users
JOIN orders ON users.id = orders.user_id;
Fetches user names along with order amounts by joining
users and orders on matching IDs. ✔️ Essential when combining data from multiple tables.
7️⃣ Use of HAVING
SELECT city, COUNT(*) AS total
FROM users
GROUP BY city
HAVING COUNT(*) > 5;
Like
WHERE, but used with aggregates. This filters cities with more than 5 users. ✔️ **Use
HAVING after GROUP BY.**8️⃣ Subqueries
SELECT * FROM users
WHERE salary > (SELECT AVG(salary) FROM users);
Finds users whose salary is above the average. The subquery calculates the average salary first.
✔️ Nested queries for dynamic filtering9️⃣ CASE Statementnt**
SELECT name,
CASE
WHEN age < 18 THEN 'Teen'
WHEN age <= 40 THEN 'Adult'
ELSE 'Senior'
END AS age_group
FROM users;
Adds a new column that classifies users into categories based on age.
✔️ Powerful for conditional logic.
🔟 Window Functions (Advanced)
SELECT name, city, score,
RANK() OVER (PARTITION BY city ORDER BY score DESC) AS rank
FROM users;
Ranks users by score *within each city*.
SQL Learning Series: https://whatsapp.com/channel/0029VanC5rODzgT6TiTGoa1v/1075
❤4🥰1
SQL Interview Questions with Answers
1. How to change a table name in SQL?
This is the command to change a table name in SQL:
ALTER TABLE table_name
RENAME TO new_table_name;
We will start off by giving the keywords ALTER TABLE, then we will follow it up by giving the original name of the table, after that, we will give in the keywords RENAME TO and finally, we will give the new table name.
2. How to use LIKE in SQL?
The LIKE operator checks if an attribute value matches a given string pattern. Here is an example of LIKE operator
SELECT * FROM employees WHERE first_name like ‘Steven’;
With this command, we will be able to extract all the records where the first name is like “Steven”.
3. If we drop a table, does it also drop related objects like constraints, indexes, columns, default, views and sorted procedures?
Yes, SQL server drops all related objects, which exists inside a table like constraints, indexes, columns, defaults etc. But dropping a table will not drop views and sorted procedures as they exist outside the table.
4. Explain SQL Constraints.
SQL Constraints are used to specify the rules of data type in a table. They can be specified while creating and altering the table. The following are the constraints in SQL: NOT NULL CHECK DEFAULT UNIQUE PRIMARY KEY FOREIGN KEY
React ❤️ for more
1. How to change a table name in SQL?
This is the command to change a table name in SQL:
ALTER TABLE table_name
RENAME TO new_table_name;
We will start off by giving the keywords ALTER TABLE, then we will follow it up by giving the original name of the table, after that, we will give in the keywords RENAME TO and finally, we will give the new table name.
2. How to use LIKE in SQL?
The LIKE operator checks if an attribute value matches a given string pattern. Here is an example of LIKE operator
SELECT * FROM employees WHERE first_name like ‘Steven’;
With this command, we will be able to extract all the records where the first name is like “Steven”.
3. If we drop a table, does it also drop related objects like constraints, indexes, columns, default, views and sorted procedures?
Yes, SQL server drops all related objects, which exists inside a table like constraints, indexes, columns, defaults etc. But dropping a table will not drop views and sorted procedures as they exist outside the table.
4. Explain SQL Constraints.
SQL Constraints are used to specify the rules of data type in a table. They can be specified while creating and altering the table. The following are the constraints in SQL: NOT NULL CHECK DEFAULT UNIQUE PRIMARY KEY FOREIGN KEY
React ❤️ for more
❤4
Data Analytics project ideas to build your portfolio in 2025:
1. Sales Data Analysis Dashboard
Analyze sales trends, seasonal patterns, and product performance.
Use Power BI, Tableau, or Python (Dash/Plotly) for visualization.
2. Customer Segmentation
Use clustering (K-means, hierarchical) on customer data to identify groups.
Provide actionable marketing insights.
3. Social Media Sentiment Analysis
Analyze tweets or reviews using NLP to gauge public sentiment.
Visualize positive, negative, and neutral trends over time.
4. Churn Prediction Model
Analyze customer data to predict who might leave a service.
Use logistic regression, decision trees, or random forest.
5. Financial Data Analysis
Study stock prices, moving averages, and volatility.
Create an interactive dashboard with key metrics.
6. Healthcare Analytics
Analyze patient data for disease trends or hospital resource usage.
Use visualization to highlight key findings.
7. Website Traffic Analysis
Use Google Analytics data to identify user behavior patterns.
Suggest improvements for user engagement and conversion.
8. Employee Attrition Analysis
Analyze HR data to find factors leading to employee turnover.
Use statistical tests and visualization.
React ❤️ for more
1. Sales Data Analysis Dashboard
Analyze sales trends, seasonal patterns, and product performance.
Use Power BI, Tableau, or Python (Dash/Plotly) for visualization.
2. Customer Segmentation
Use clustering (K-means, hierarchical) on customer data to identify groups.
Provide actionable marketing insights.
3. Social Media Sentiment Analysis
Analyze tweets or reviews using NLP to gauge public sentiment.
Visualize positive, negative, and neutral trends over time.
4. Churn Prediction Model
Analyze customer data to predict who might leave a service.
Use logistic regression, decision trees, or random forest.
5. Financial Data Analysis
Study stock prices, moving averages, and volatility.
Create an interactive dashboard with key metrics.
6. Healthcare Analytics
Analyze patient data for disease trends or hospital resource usage.
Use visualization to highlight key findings.
7. Website Traffic Analysis
Use Google Analytics data to identify user behavior patterns.
Suggest improvements for user engagement and conversion.
8. Employee Attrition Analysis
Analyze HR data to find factors leading to employee turnover.
Use statistical tests and visualization.
React ❤️ for more
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Here are some commonly asked SQL interview questions along with brief answers:
1. What is SQL?
- SQL stands for Structured Query Language, used for managing and manipulating relational databases.
2. What are the types of SQL commands?
- SQL commands can be broadly categorized into four types: Data Definition Language (DDL), Data Manipulation Language (DML), Data Control Language (DCL), and Transaction Control Language (TCL).
3. What is the difference between CHAR and VARCHAR data types?
- CHAR is a fixed-length character data type, while VARCHAR is a variable-length character data type. CHAR will always occupy the same amount of storage space, while VARCHAR will only use the necessary space to store the actual data.
4. What is a primary key?
- A primary key is a column or a set of columns that uniquely identifies each row in a table. It ensures data integrity by enforcing uniqueness and can be used to establish relationships between tables.
5. What is a foreign key?
- A foreign key is a column or a set of columns in one table that refers to the primary key in another table. It establishes a relationship between two tables and ensures referential integrity.
6. What is a JOIN in SQL?
- JOIN is used to combine rows from two or more tables based on a related column between them. There are different types of JOINs, including INNER JOIN, LEFT JOIN, RIGHT JOIN, and FULL JOIN.
7. What is the difference between INNER JOIN and OUTER JOIN?
- INNER JOIN returns only the rows that have matching values in both tables, while OUTER JOIN (LEFT, RIGHT, FULL) returns all rows from one or both tables, with NULL values in columns where there is no match.
8. What is the difference between GROUP BY and ORDER BY?
- GROUP BY is used to group rows that have the same values into summary rows, typically used with aggregate functions like SUM, COUNT, AVG, etc., while ORDER BY is used to sort the result set based on one or more columns.
9. What is a subquery?
- A subquery is a query nested within another query, used to return data that will be used in the main query. Subqueries can be used in SELECT, INSERT, UPDATE, and DELETE statements.
10. What is normalization in SQL?
- Normalization is the process of organizing data in a database to reduce redundancy and dependency. It involves dividing large tables into smaller tables and defining relationships between them to improve data integrity and efficiency.
Around 90% questions will be asked from sql in data analytics interview, so please make sure to practice SQL skills using websites like stratascratch. ☺️💪
1. What is SQL?
- SQL stands for Structured Query Language, used for managing and manipulating relational databases.
2. What are the types of SQL commands?
- SQL commands can be broadly categorized into four types: Data Definition Language (DDL), Data Manipulation Language (DML), Data Control Language (DCL), and Transaction Control Language (TCL).
3. What is the difference between CHAR and VARCHAR data types?
- CHAR is a fixed-length character data type, while VARCHAR is a variable-length character data type. CHAR will always occupy the same amount of storage space, while VARCHAR will only use the necessary space to store the actual data.
4. What is a primary key?
- A primary key is a column or a set of columns that uniquely identifies each row in a table. It ensures data integrity by enforcing uniqueness and can be used to establish relationships between tables.
5. What is a foreign key?
- A foreign key is a column or a set of columns in one table that refers to the primary key in another table. It establishes a relationship between two tables and ensures referential integrity.
6. What is a JOIN in SQL?
- JOIN is used to combine rows from two or more tables based on a related column between them. There are different types of JOINs, including INNER JOIN, LEFT JOIN, RIGHT JOIN, and FULL JOIN.
7. What is the difference between INNER JOIN and OUTER JOIN?
- INNER JOIN returns only the rows that have matching values in both tables, while OUTER JOIN (LEFT, RIGHT, FULL) returns all rows from one or both tables, with NULL values in columns where there is no match.
8. What is the difference between GROUP BY and ORDER BY?
- GROUP BY is used to group rows that have the same values into summary rows, typically used with aggregate functions like SUM, COUNT, AVG, etc., while ORDER BY is used to sort the result set based on one or more columns.
9. What is a subquery?
- A subquery is a query nested within another query, used to return data that will be used in the main query. Subqueries can be used in SELECT, INSERT, UPDATE, and DELETE statements.
10. What is normalization in SQL?
- Normalization is the process of organizing data in a database to reduce redundancy and dependency. It involves dividing large tables into smaller tables and defining relationships between them to improve data integrity and efficiency.
Around 90% questions will be asked from sql in data analytics interview, so please make sure to practice SQL skills using websites like stratascratch. ☺️💪
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