Data Analytics Interview Questions with Answers Part-1: 📱
1. What is the difference between data analysis and data analytics?
⦁ Data analysis involves inspecting, cleaning, and modeling data to discover useful information and patterns for decision-making.
⦁ Data analytics is a broader process that includes data collection, transformation, analysis, and interpretation, often involving predictive and prenoscriptive techniques to drive business strategies.
2. Explain the data cleaning process you follow.
⦁ Identify missing, inconsistent, or corrupt data.
⦁ Handle missing data by imputation (mean, median, mode) or removal if appropriate.
⦁ Standardize formats (dates, strings).
⦁ Remove duplicates.
⦁ Detect and treat outliers.
⦁ Validate cleaned data against known business rules.
3. How do you handle missing or duplicate data?
⦁ Missing data: Identify patterns; if random, impute using statistical methods or predictive modeling; else consider domain knowledge before removal.
⦁ Duplicate data: Detect with key fields; remove exact duplicates or merge fuzzy duplicates based on context.
4. What is a primary key in a database?
A primary key uniquely identifies each record in a table, ensuring entity integrity and enabling relationships between tables via foreign keys.
5. Write a SQL query to find the second highest salary in a table.
6. Explain INNER JOIN vs LEFT JOIN with examples.
⦁ INNER JOIN: Returns only matching rows between two tables.
⦁ LEFT JOIN: Returns all rows from the left table, plus matching rows from the right; if no match, right columns are NULL.
Example:
7. What are outliers? How do you detect and treat them?
⦁ Outliers are data points significantly different from others that can skew analysis.
⦁ Detect with boxplots, z-score (>3), or IQR method (values outside 1.5*IQR).
⦁ Treat by investigating causes, correcting errors, transforming data, or removing if they’re noise.
8. Describe what a pivot table is and how you use it.
A pivot table is a data summarization tool that groups, aggregates (sum, average), and displays data cross-categorically. Used in Excel and BI tools for quick insights and reporting.
9. How do you validate a data model’s performance?
⦁ Use relevant metrics (accuracy, precision, recall for classification; RMSE, MAE for regression).
⦁ Perform cross-validation to check generalizability.
⦁ Test on holdout or unseen data sets.
10. What is hypothesis testing? Explain t-test and z-test.
⦁ Hypothesis testing assesses if sample data supports a claim about a population.
⦁ t-test: Used when sample size is small and population variance is unknown, often comparing means.
⦁ z-test: Used for large samples with known variance to test population parameters.
React ♥️ for Part-2
1. What is the difference between data analysis and data analytics?
⦁ Data analysis involves inspecting, cleaning, and modeling data to discover useful information and patterns for decision-making.
⦁ Data analytics is a broader process that includes data collection, transformation, analysis, and interpretation, often involving predictive and prenoscriptive techniques to drive business strategies.
2. Explain the data cleaning process you follow.
⦁ Identify missing, inconsistent, or corrupt data.
⦁ Handle missing data by imputation (mean, median, mode) or removal if appropriate.
⦁ Standardize formats (dates, strings).
⦁ Remove duplicates.
⦁ Detect and treat outliers.
⦁ Validate cleaned data against known business rules.
3. How do you handle missing or duplicate data?
⦁ Missing data: Identify patterns; if random, impute using statistical methods or predictive modeling; else consider domain knowledge before removal.
⦁ Duplicate data: Detect with key fields; remove exact duplicates or merge fuzzy duplicates based on context.
4. What is a primary key in a database?
A primary key uniquely identifies each record in a table, ensuring entity integrity and enabling relationships between tables via foreign keys.
5. Write a SQL query to find the second highest salary in a table.
SELECT MAX(salary)
FROM employees
WHERE salary < (SELECT MAX(salary) FROM employees);
6. Explain INNER JOIN vs LEFT JOIN with examples.
⦁ INNER JOIN: Returns only matching rows between two tables.
⦁ LEFT JOIN: Returns all rows from the left table, plus matching rows from the right; if no match, right columns are NULL.
Example:
SELECT * FROM A INNER JOIN B ON A.id = B.id;
SELECT * FROM A LEFT JOIN B ON A.id = B.id;
7. What are outliers? How do you detect and treat them?
⦁ Outliers are data points significantly different from others that can skew analysis.
⦁ Detect with boxplots, z-score (>3), or IQR method (values outside 1.5*IQR).
⦁ Treat by investigating causes, correcting errors, transforming data, or removing if they’re noise.
8. Describe what a pivot table is and how you use it.
A pivot table is a data summarization tool that groups, aggregates (sum, average), and displays data cross-categorically. Used in Excel and BI tools for quick insights and reporting.
9. How do you validate a data model’s performance?
⦁ Use relevant metrics (accuracy, precision, recall for classification; RMSE, MAE for regression).
⦁ Perform cross-validation to check generalizability.
⦁ Test on holdout or unseen data sets.
10. What is hypothesis testing? Explain t-test and z-test.
⦁ Hypothesis testing assesses if sample data supports a claim about a population.
⦁ t-test: Used when sample size is small and population variance is unknown, often comparing means.
⦁ z-test: Used for large samples with known variance to test population parameters.
React ♥️ for Part-2
❤11
Data Analytics Interview Questions with Answers Part-2: ✅
11. How do you explain complex data insights to non-technical stakeholders?
Use simple, clear language; avoid jargon. Focus on key takeaways and business impact. Use visuals and storytelling to make insights relatable.
12. What tools do you use for data visualization?
Common tools include Tableau, Power BI, Excel, Python libraries like Matplotlib and Seaborn, and R’s ggplot2.
13. How do you optimize a slow SQL query?
Add indexes, avoid SELECT *, limit joins and subqueries, review execution plans, and rewrite queries for efficiency.
14. Describe a time when your analysis impacted a business decision.
Use the STAR approach: e.g., identified sales drop pattern, recommended marketing focus shift, which increased revenue by 10%.
15. What is the difference between clustered and non-clustered indexes?
Clustered indexes sort data physically in storage (one per table). Non-clustered indexes are separate pointers to data rows (multiple allowed).
16. Explain the bias-variance tradeoff.
Bias is error from oversimplified models (underfitting). Variance is error from models too sensitive to training data (overfitting). The tradeoff balances them to minimize total prediction error.
17. What is collaborative filtering?
A recommendation technique predicting user preferences based on similarities between users or items.
18. How do you handle large datasets?
Use distributed computing frameworks (Spark, Hadoop), sampling, optimized queries, efficient storage formats, and cloud resources.
19. What Python libraries do you use for data analysis?
Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn, Statsmodels are popular.
20. Describe data profiling and its importance.
Data profiling involves examining data for quality, consistency, and structure, helping detect issues early and ensuring reliability for analysis.
React ♥️ for Part-3
11. How do you explain complex data insights to non-technical stakeholders?
Use simple, clear language; avoid jargon. Focus on key takeaways and business impact. Use visuals and storytelling to make insights relatable.
12. What tools do you use for data visualization?
Common tools include Tableau, Power BI, Excel, Python libraries like Matplotlib and Seaborn, and R’s ggplot2.
13. How do you optimize a slow SQL query?
Add indexes, avoid SELECT *, limit joins and subqueries, review execution plans, and rewrite queries for efficiency.
14. Describe a time when your analysis impacted a business decision.
Use the STAR approach: e.g., identified sales drop pattern, recommended marketing focus shift, which increased revenue by 10%.
15. What is the difference between clustered and non-clustered indexes?
Clustered indexes sort data physically in storage (one per table). Non-clustered indexes are separate pointers to data rows (multiple allowed).
16. Explain the bias-variance tradeoff.
Bias is error from oversimplified models (underfitting). Variance is error from models too sensitive to training data (overfitting). The tradeoff balances them to minimize total prediction error.
17. What is collaborative filtering?
A recommendation technique predicting user preferences based on similarities between users or items.
18. How do you handle large datasets?
Use distributed computing frameworks (Spark, Hadoop), sampling, optimized queries, efficient storage formats, and cloud resources.
19. What Python libraries do you use for data analysis?
Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn, Statsmodels are popular.
20. Describe data profiling and its importance.
Data profiling involves examining data for quality, consistency, and structure, helping detect issues early and ensuring reliability for analysis.
React ♥️ for Part-3
❤13
The program for the 10th AI Journey 2025 international conference has been unveiled: scientists, visionaries, and global AI practitioners will come together on one stage. Here, you will hear the voices of those who don't just believe in the future—they are creating it!
Speakers include visionaries Kai-Fu Lee and Chen Qufan, as well as dozens of global AI gurus from around the world!
On the first day of the conference, November 19, we will talk about how AI is already being used in various areas of life, helping to unlock human potential for the future and changing creative industries, and what impact it has on humans and on a sustainable future.
On November 20, we will focus on the role of AI in business and economic development and present technologies that will help businesses and developers be more effective by unlocking human potential.
On November 21, we will talk about how engineers and scientists are making scientific and technological breakthroughs and creating the future today!
Ride the wave with AI into the future!
Tune in to the AI Journey webcast on November 19-21.
Speakers include visionaries Kai-Fu Lee and Chen Qufan, as well as dozens of global AI gurus from around the world!
On the first day of the conference, November 19, we will talk about how AI is already being used in various areas of life, helping to unlock human potential for the future and changing creative industries, and what impact it has on humans and on a sustainable future.
On November 20, we will focus on the role of AI in business and economic development and present technologies that will help businesses and developers be more effective by unlocking human potential.
On November 21, we will talk about how engineers and scientists are making scientific and technological breakthroughs and creating the future today!
Ride the wave with AI into the future!
Tune in to the AI Journey webcast on November 19-21.
❤5
📊 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
❤5
✅ Data Analyst Scenario-Based Questions 🧠📊
1) You found inconsistent data entries across sources. What would you do?
Answer: I’d trace the origin of each source, identify mapping issues or schema mismatches, and apply transformation rules to standardize the data.
2) You're working with real-time data. What challenges might you face?
Answer: Latency, data freshness, system performance, and handling streaming data errors. I’d consider tools like Apache Kafka or real-time dashboards.
3) A KPI suddenly drops. What’s your first step?
Answer: I’d validate the data pipeline, check for recent changes, and perform root cause analysis by breaking down KPI components.
4) Your manager wants a one-click report. How would you deliver it?
Answer: I’d automate data refresh with tools like Power BI, Tableau, or Looker, and design an interactive dashboard with filters for custom views.
5) You’re given unstructured data. How do you approach it?
Answer: I’d use NLP techniques if it's text, apply parsing/regex, and structure it using Python or tools like pandas for analysis.
6) You’re collaborating with a data engineer. How do you ensure alignment?
Answer: I’d communicate data requirements clearly, define data formats, and agree on schemas, update schedules, and SLAs.
7) You’re asked to explain a complex model to business users. What’s your approach?
Answer: I’d focus on the impact, simplify terminology, use analogies, and visualize the model outputs instead of formulas.
8) Data shows opposite trend than expected. How do you react?
Answer: I’d double-check filters, time ranges, and assumptions. Then explore possible external or internal causes before reporting.
9) You’re asked to reduce report delivery time by 50%. Suggestions?
Answer: Optimize SQL queries, use data extracts, reduce dashboard complexity, and cache results where possible.
10) Stakeholders want daily insights, but data updates weekly. What do you say?
Answer: I’d explain the data refresh limitations and offer meaningful daily proxies or simulations until real-time data is available.
💬 Tap ❤️ for more!
1) You found inconsistent data entries across sources. What would you do?
Answer: I’d trace the origin of each source, identify mapping issues or schema mismatches, and apply transformation rules to standardize the data.
2) You're working with real-time data. What challenges might you face?
Answer: Latency, data freshness, system performance, and handling streaming data errors. I’d consider tools like Apache Kafka or real-time dashboards.
3) A KPI suddenly drops. What’s your first step?
Answer: I’d validate the data pipeline, check for recent changes, and perform root cause analysis by breaking down KPI components.
4) Your manager wants a one-click report. How would you deliver it?
Answer: I’d automate data refresh with tools like Power BI, Tableau, or Looker, and design an interactive dashboard with filters for custom views.
5) You’re given unstructured data. How do you approach it?
Answer: I’d use NLP techniques if it's text, apply parsing/regex, and structure it using Python or tools like pandas for analysis.
6) You’re collaborating with a data engineer. How do you ensure alignment?
Answer: I’d communicate data requirements clearly, define data formats, and agree on schemas, update schedules, and SLAs.
7) You’re asked to explain a complex model to business users. What’s your approach?
Answer: I’d focus on the impact, simplify terminology, use analogies, and visualize the model outputs instead of formulas.
8) Data shows opposite trend than expected. How do you react?
Answer: I’d double-check filters, time ranges, and assumptions. Then explore possible external or internal causes before reporting.
9) You’re asked to reduce report delivery time by 50%. Suggestions?
Answer: Optimize SQL queries, use data extracts, reduce dashboard complexity, and cache results where possible.
10) Stakeholders want daily insights, but data updates weekly. What do you say?
Answer: I’d explain the data refresh limitations and offer meaningful daily proxies or simulations until real-time data is available.
💬 Tap ❤️ for more!
❤6👏1
✅ Step-by-Step Guide to Create a Data Analyst Portfolio
✅ 1️⃣ Choose Your Tools & Skills
Decide what tools you want to showcase:
⦁ Excel, SQL, Python (Pandas, NumPy)
⦁ Data visualization (Tableau, Power BI, Matplotlib, Seaborn)
⦁ Basic statistics and data cleaning
✅ 2️⃣ Plan Your Portfolio Structure
Your portfolio should include:
⦁ Home Page – Brief intro about you
⦁ About Me – Skills, tools, background
⦁ Projects – Showcased with explanations and code
⦁ Contact – Email, LinkedIn, GitHub
⦁ Optional: Blog or case studies
✅ 3️⃣ Build Your Portfolio Website or Use Platforms
Options:
⦁ Build your own website with HTML/CSS or React
⦁ Use GitHub Pages, Tableau Public, or LinkedIn articles
⦁ Make sure it’s easy to navigate and mobile-friendly
✅ 4️⃣ Add 3–5 Detailed Projects
Projects should cover:
⦁ Data cleaning and preprocessing
⦁ Exploratory Data Analysis (EDA)
⦁ Data visualization dashboards or reports
⦁ SQL queries or Python noscripts for analysis
Each project should include:
⦁ Problem statement
⦁ Dataset source
⦁ Tools & techniques used
⦁ Key findings & visualizations
⦁ Link to code (GitHub) or live dashboard
✅ 5️⃣ Publish & Share Your Portfolio
Host your portfolio on:
⦁ GitHub Pages
⦁ Tableau Public
⦁ Personal website or blog
✅ 6️⃣ Keep It Updated
⦁ Add new projects regularly
⦁ Improve old ones based on feedback
⦁ Share insights on LinkedIn or data blogs
💡 Pro Tips
⦁ Focus on storytelling with data — explain what the numbers mean
⦁ Use clear visuals and dashboards
⦁ Highlight business impact or insights from your work
⦁ Include a downloadable resume and links to your profiles
🎯 Goal: Anyone visiting your portfolio should quickly understand your data skills, see your problem-solving ability, and know how to reach you.
✅ 1️⃣ Choose Your Tools & Skills
Decide what tools you want to showcase:
⦁ Excel, SQL, Python (Pandas, NumPy)
⦁ Data visualization (Tableau, Power BI, Matplotlib, Seaborn)
⦁ Basic statistics and data cleaning
✅ 2️⃣ Plan Your Portfolio Structure
Your portfolio should include:
⦁ Home Page – Brief intro about you
⦁ About Me – Skills, tools, background
⦁ Projects – Showcased with explanations and code
⦁ Contact – Email, LinkedIn, GitHub
⦁ Optional: Blog or case studies
✅ 3️⃣ Build Your Portfolio Website or Use Platforms
Options:
⦁ Build your own website with HTML/CSS or React
⦁ Use GitHub Pages, Tableau Public, or LinkedIn articles
⦁ Make sure it’s easy to navigate and mobile-friendly
✅ 4️⃣ Add 3–5 Detailed Projects
Projects should cover:
⦁ Data cleaning and preprocessing
⦁ Exploratory Data Analysis (EDA)
⦁ Data visualization dashboards or reports
⦁ SQL queries or Python noscripts for analysis
Each project should include:
⦁ Problem statement
⦁ Dataset source
⦁ Tools & techniques used
⦁ Key findings & visualizations
⦁ Link to code (GitHub) or live dashboard
✅ 5️⃣ Publish & Share Your Portfolio
Host your portfolio on:
⦁ GitHub Pages
⦁ Tableau Public
⦁ Personal website or blog
✅ 6️⃣ Keep It Updated
⦁ Add new projects regularly
⦁ Improve old ones based on feedback
⦁ Share insights on LinkedIn or data blogs
💡 Pro Tips
⦁ Focus on storytelling with data — explain what the numbers mean
⦁ Use clear visuals and dashboards
⦁ Highlight business impact or insights from your work
⦁ Include a downloadable resume and links to your profiles
🎯 Goal: Anyone visiting your portfolio should quickly understand your data skills, see your problem-solving ability, and know how to reach you.
❤2
Tune in to the 10th AI Journey 2025 international conference: scientists, visionaries, and global AI practitioners will come together on one stage. Here, you will hear the voices of those who don't just believe in the future—they are creating it!
Speakers include visionaries Kai-Fu Lee and Chen Qufan, as well as dozens of global AI gurus! Do you agree with their predictions about AI?
On the first day of the conference, November 19, we will talk about how AI is already being used in various areas of life, helping to unlock human potential for the future and changing creative industries, and what impact it has on humans and on a sustainable future.
On November 20, we will focus on the role of AI in business and economic development and present technologies that will help businesses and developers be more effective by unlocking human potential.
On November 21, we will talk about how engineers and scientists are making scientific and technological breakthroughs and creating the future today! The day's program includes presentations by scientists from around the world:
- Ajit Abraham (Sai University, India) will present on “Generative AI in Healthcare”
- Nebojša Bačanin Džakula (Singidunum University, Serbia) will talk about the latest advances in bio-inspired metaheuristics
- AIexandre Ferreira Ramos (University of São Paulo, Brazil) will present his work on using thermodynamic models to study the regulatory logic of trannoscriptional control at the DNA level
- Anderson Rocha (University of Campinas, Brazil) will give a presentation ennoscriptd “AI in the New Era: From Basics to Trends, Opportunities, and Global Cooperation”.
And in the special AIJ Junior track, we will talk about how AI helps us learn, create and ride the wave with AI.
The day will conclude with an award ceremony for the winners of the AI Challenge for aspiring data scientists and the AIJ Contest for experienced AI specialists. The results of an open selection of AIJ Science research papers will be announced.
Ride the wave with AI into the future!
Tune in to the AI Journey webcast on November 19-21.
Speakers include visionaries Kai-Fu Lee and Chen Qufan, as well as dozens of global AI gurus! Do you agree with their predictions about AI?
On the first day of the conference, November 19, we will talk about how AI is already being used in various areas of life, helping to unlock human potential for the future and changing creative industries, and what impact it has on humans and on a sustainable future.
On November 20, we will focus on the role of AI in business and economic development and present technologies that will help businesses and developers be more effective by unlocking human potential.
On November 21, we will talk about how engineers and scientists are making scientific and technological breakthroughs and creating the future today! The day's program includes presentations by scientists from around the world:
- Ajit Abraham (Sai University, India) will present on “Generative AI in Healthcare”
- Nebojša Bačanin Džakula (Singidunum University, Serbia) will talk about the latest advances in bio-inspired metaheuristics
- AIexandre Ferreira Ramos (University of São Paulo, Brazil) will present his work on using thermodynamic models to study the regulatory logic of trannoscriptional control at the DNA level
- Anderson Rocha (University of Campinas, Brazil) will give a presentation ennoscriptd “AI in the New Era: From Basics to Trends, Opportunities, and Global Cooperation”.
And in the special AIJ Junior track, we will talk about how AI helps us learn, create and ride the wave with AI.
The day will conclude with an award ceremony for the winners of the AI Challenge for aspiring data scientists and the AIJ Contest for experienced AI specialists. The results of an open selection of AIJ Science research papers will be announced.
Ride the wave with AI into the future!
Tune in to the AI Journey webcast on November 19-21.
❤4👏1
✅ Data Science Mock Interview Questions with Answers 🤖🎯
1️⃣ Q: Explain the difference between Supervised and Unsupervised Learning.
A:
• Supervised Learning: Model learns from labeled data (input and desired output are provided). Examples: classification, regression.
• Unsupervised Learning: Model learns from unlabeled data (only input is provided). Examples: clustering, dimensionality reduction.
2️⃣ Q: What is the bias-variance tradeoff?
A:
• Bias: The error due to overly simplistic assumptions in the learning algorithm (underfitting).
• Variance: The error due to the model's sensitivity to small fluctuations in the training data (overfitting).
• Tradeoff: Aim for a model with low bias and low variance; reducing one often increases the other. Techniques like cross-validation and regularization help manage this tradeoff.
3️⃣ Q: Explain what a ROC curve is and how it is used.
A:
• ROC (Receiver Operating Characteristic) Curve: A graphical representation of the performance of a binary classification model at all classification thresholds.
• How it's used: Plots the True Positive Rate (TPR) against the False Positive Rate (FPR). It helps evaluate the model's ability to discriminate between positive and negative classes. The Area Under the Curve (AUC) quantifies the overall performance (AUC=1 is perfect, AUC=0.5 is random).
4️⃣ Q: What is the difference between precision and recall?
A:
• Precision: The proportion of true positives among the instances predicted as positive. (Out of all the predicted positives, how many were actually positive?)
• Recall: The proportion of true positives that were correctly identified by the model. (Out of all the actual positives, how many did the model correctly identify?)
5️⃣ Q: Explain how you would handle imbalanced datasets.
A: Techniques include:
• Resampling: Oversampling the minority class, undersampling the majority class.
• Synthetic Data Generation: Creating synthetic samples using techniques like SMOTE.
• Cost-Sensitive Learning: Assigning different costs to misclassifications based on class importance.
• Using Appropriate Evaluation Metrics: Precision, recall, F1-score, AUC-ROC.
6️⃣ Q: Describe how you would approach a data science project from start to finish.
A:
• Define the Problem: Understand the business objective and desired outcome.
• Gather Data: Collect relevant data from various sources.
• Explore and Clean Data: Perform EDA, handle missing values, and transform data.
• Feature Engineering: Create new features to improve model performance.
• Model Selection and Training: Choose appropriate machine learning algorithms and train the model.
• Model Evaluation: Assess model performance using appropriate metrics and techniques like cross-validation.
• Model Deployment: Deploy the model to a production environment.
• Monitoring and Maintenance: Continuously monitor model performance and retrain as needed.
7️⃣ Q: What are some common evaluation metrics for regression models?
A:
• Mean Squared Error (MSE): Average of the squared differences between predicted and actual values.
• Root Mean Squared Error (RMSE): Square root of the MSE.
• Mean Absolute Error (MAE): Average of the absolute differences between predicted and actual values.
• R-squared: Proportion of variance in the dependent variable that can be predicted from the independent variables.
8️⃣ Q: How do you prevent overfitting in a machine learning model?
A: Techniques include:
• Cross-Validation: Evaluating the model on multiple subsets of the data.
• Regularization: Adding a penalty term to the loss function (L1, L2 regularization).
• Early Stopping: Monitoring the model's performance on a validation set and stopping training when performance starts to degrade.
• Reducing Model Complexity: Using simpler models or reducing the number of features.
• Data Augmentation: Increasing the size of the training dataset by generating new, slightly modified samples.
👍 Tap ❤️ for more!
1️⃣ Q: Explain the difference between Supervised and Unsupervised Learning.
A:
• Supervised Learning: Model learns from labeled data (input and desired output are provided). Examples: classification, regression.
• Unsupervised Learning: Model learns from unlabeled data (only input is provided). Examples: clustering, dimensionality reduction.
2️⃣ Q: What is the bias-variance tradeoff?
A:
• Bias: The error due to overly simplistic assumptions in the learning algorithm (underfitting).
• Variance: The error due to the model's sensitivity to small fluctuations in the training data (overfitting).
• Tradeoff: Aim for a model with low bias and low variance; reducing one often increases the other. Techniques like cross-validation and regularization help manage this tradeoff.
3️⃣ Q: Explain what a ROC curve is and how it is used.
A:
• ROC (Receiver Operating Characteristic) Curve: A graphical representation of the performance of a binary classification model at all classification thresholds.
• How it's used: Plots the True Positive Rate (TPR) against the False Positive Rate (FPR). It helps evaluate the model's ability to discriminate between positive and negative classes. The Area Under the Curve (AUC) quantifies the overall performance (AUC=1 is perfect, AUC=0.5 is random).
4️⃣ Q: What is the difference between precision and recall?
A:
• Precision: The proportion of true positives among the instances predicted as positive. (Out of all the predicted positives, how many were actually positive?)
• Recall: The proportion of true positives that were correctly identified by the model. (Out of all the actual positives, how many did the model correctly identify?)
5️⃣ Q: Explain how you would handle imbalanced datasets.
A: Techniques include:
• Resampling: Oversampling the minority class, undersampling the majority class.
• Synthetic Data Generation: Creating synthetic samples using techniques like SMOTE.
• Cost-Sensitive Learning: Assigning different costs to misclassifications based on class importance.
• Using Appropriate Evaluation Metrics: Precision, recall, F1-score, AUC-ROC.
6️⃣ Q: Describe how you would approach a data science project from start to finish.
A:
• Define the Problem: Understand the business objective and desired outcome.
• Gather Data: Collect relevant data from various sources.
• Explore and Clean Data: Perform EDA, handle missing values, and transform data.
• Feature Engineering: Create new features to improve model performance.
• Model Selection and Training: Choose appropriate machine learning algorithms and train the model.
• Model Evaluation: Assess model performance using appropriate metrics and techniques like cross-validation.
• Model Deployment: Deploy the model to a production environment.
• Monitoring and Maintenance: Continuously monitor model performance and retrain as needed.
7️⃣ Q: What are some common evaluation metrics for regression models?
A:
• Mean Squared Error (MSE): Average of the squared differences between predicted and actual values.
• Root Mean Squared Error (RMSE): Square root of the MSE.
• Mean Absolute Error (MAE): Average of the absolute differences between predicted and actual values.
• R-squared: Proportion of variance in the dependent variable that can be predicted from the independent variables.
8️⃣ Q: How do you prevent overfitting in a machine learning model?
A: Techniques include:
• Cross-Validation: Evaluating the model on multiple subsets of the data.
• Regularization: Adding a penalty term to the loss function (L1, L2 regularization).
• Early Stopping: Monitoring the model's performance on a validation set and stopping training when performance starts to degrade.
• Reducing Model Complexity: Using simpler models or reducing the number of features.
• Data Augmentation: Increasing the size of the training dataset by generating new, slightly modified samples.
👍 Tap ❤️ for more!
❤3
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 💼
❤7
Top 50 Power BI Interview Questions (2025) ✅
1. What is Power BI?
2. Explain the key components of Power BI.
3. Differentiate between Power BI Desktop, Service, and Mobile.
4. What are the different types of data sources in Power BI?
5. Explain the Get Data process in Power BI.
6. What is Power Query Editor?
7. How do you clean and transform data in Power Query?
8. What are the different data transformations available in Power Query?
9. What is M language in Power BI?
10. Explain the concept of data modeling in Power BI.
11. What are relationships in Power BI?
12. What are the different types of relationships in Power BI?
13. What is cardinality in Power BI?
14. What is cross-filter direction in Power BI?
15. How do you create calculated columns and measures?
16. What is DAX?
17. Explain the difference between calculated columns and measures.
18. List some common DAX functions.
19. What is the CALCULATE function in DAX?
20. How do you use variables in DAX?
21. What are the different types of visuals in Power BI?
22. How do you create interactive dashboards in Power BI?
23. Explain the use of slicers in Power BI.
24. What are filters in Power BI?
25. How do you use bookmarks in Power BI?
26. What is the Power BI Service?
27. How do you publish reports to the Power BI Service?
28. How do you create dashboards in the Power BI Service?
29. How do you share reports and dashboards in Power BI?
30. What are workspaces in Power BI?
31. Explain the role of gateways in Power BI.
32. How do you schedule data refresh in Power BI?
33. What is Row-Level Security (RLS) in Power BI?
34. How do you implement RLS in Power BI?
35. What are Power BI apps?
36. What are dataflows in Power BI?
37. How do you use parameters in Power BI?
38. What are custom visuals in Power BI?
39. How do you import custom visuals into Power BI?
40. Explain performance optimization techniques in Power BI.
41. What is the difference between import and direct query mode?
42. When should you use direct query mode?
43. How do you connect to cloud data sources in Power BI?
44. What are the advantages of using Power BI?
45. How do you handle errors in Power BI?
46. What are the limitations of Power BI?
47. Explain Power BI Embedded.
48. What is Power BI Report Server?
49. How do you use Power BI with Azure?
50. What are the latest features of Power BI?
Double tap ❤️ for detailed answers!
1. What is Power BI?
2. Explain the key components of Power BI.
3. Differentiate between Power BI Desktop, Service, and Mobile.
4. What are the different types of data sources in Power BI?
5. Explain the Get Data process in Power BI.
6. What is Power Query Editor?
7. How do you clean and transform data in Power Query?
8. What are the different data transformations available in Power Query?
9. What is M language in Power BI?
10. Explain the concept of data modeling in Power BI.
11. What are relationships in Power BI?
12. What are the different types of relationships in Power BI?
13. What is cardinality in Power BI?
14. What is cross-filter direction in Power BI?
15. How do you create calculated columns and measures?
16. What is DAX?
17. Explain the difference between calculated columns and measures.
18. List some common DAX functions.
19. What is the CALCULATE function in DAX?
20. How do you use variables in DAX?
21. What are the different types of visuals in Power BI?
22. How do you create interactive dashboards in Power BI?
23. Explain the use of slicers in Power BI.
24. What are filters in Power BI?
25. How do you use bookmarks in Power BI?
26. What is the Power BI Service?
27. How do you publish reports to the Power BI Service?
28. How do you create dashboards in the Power BI Service?
29. How do you share reports and dashboards in Power BI?
30. What are workspaces in Power BI?
31. Explain the role of gateways in Power BI.
32. How do you schedule data refresh in Power BI?
33. What is Row-Level Security (RLS) in Power BI?
34. How do you implement RLS in Power BI?
35. What are Power BI apps?
36. What are dataflows in Power BI?
37. How do you use parameters in Power BI?
38. What are custom visuals in Power BI?
39. How do you import custom visuals into Power BI?
40. Explain performance optimization techniques in Power BI.
41. What is the difference between import and direct query mode?
42. When should you use direct query mode?
43. How do you connect to cloud data sources in Power BI?
44. What are the advantages of using Power BI?
45. How do you handle errors in Power BI?
46. What are the limitations of Power BI?
47. Explain Power BI Embedded.
48. What is Power BI Report Server?
49. How do you use Power BI with Azure?
50. What are the latest features of Power BI?
Double tap ❤️ for detailed answers!
❤15
Power BI Interview Questions with Answers Part-1 ✅
1. What is Power BI?
Power BI is a Microsoft business analytics tool that enables users to connect to multiple data sources, transform and model data, and create interactive reports and dashboards for data-driven decision making.
2. Explain the key components of Power BI.
The main components are:
⦁ Power Query for data extraction and transformation.
⦁ Power Pivot for data modeling and relationships.
⦁ Power View for interactive visualizations.
⦁ Power BI Service for publishing and sharing reports.
⦁ Power BI Mobile for accessing reports on mobile devices.
3. Differentiate between Power BI Desktop, Service, and Mobile.
⦁ Desktop: The primary application for building reports and models.
⦁ Service: Cloud-based platform for publishing, sharing, and collaboration.
⦁ Mobile: Apps for viewing reports and dashboards on mobile devices.
4. What are the different types of data sources in Power BI?
Power BI connects to a wide range of sources: files (Excel, CSV), databases (SQL Server, Oracle), cloud sources (Azure, Salesforce), online services, and web APIs.
5. Explain the Get Data process in Power BI.
“Get Data” is the process to connect and import data into Power BI from various sources using connectors, enabling users to load and prepare data for analysis.
6. What is Power Query Editor?
Power Query Editor is a graphical interface in Power BI for data transformation and cleansing, allowing users to filter, merge, pivot, and shape data before loading it into the model.
7. How do you clean and transform data in Power Query?
By applying transformations like removing duplicates, filtering rows, changing data types, splitting columns, merging queries, and adding calculated columns using the intuitive UI or M language.
8. What are the different data transformations available in Power Query?
Common transformations include filtering rows, sorting, pivot/unpivot columns, splitting columns, replacing values, aggregations, and adding custom columns.
9. What is M language in Power BI?
M is the functional programming language behind Power Query, used for building advanced data transformation noscripts beyond the UI capabilities.
10. Explain the concept of data modeling in Power BI.
Data modeling is organizing data tables, defining relationships, setting cardinality and cross-filter directions, and creating calculated columns and measures to enable efficient and accurate data analysis.
Double Tap ❤️ for Part-2
1. What is Power BI?
Power BI is a Microsoft business analytics tool that enables users to connect to multiple data sources, transform and model data, and create interactive reports and dashboards for data-driven decision making.
2. Explain the key components of Power BI.
The main components are:
⦁ Power Query for data extraction and transformation.
⦁ Power Pivot for data modeling and relationships.
⦁ Power View for interactive visualizations.
⦁ Power BI Service for publishing and sharing reports.
⦁ Power BI Mobile for accessing reports on mobile devices.
3. Differentiate between Power BI Desktop, Service, and Mobile.
⦁ Desktop: The primary application for building reports and models.
⦁ Service: Cloud-based platform for publishing, sharing, and collaboration.
⦁ Mobile: Apps for viewing reports and dashboards on mobile devices.
4. What are the different types of data sources in Power BI?
Power BI connects to a wide range of sources: files (Excel, CSV), databases (SQL Server, Oracle), cloud sources (Azure, Salesforce), online services, and web APIs.
5. Explain the Get Data process in Power BI.
“Get Data” is the process to connect and import data into Power BI from various sources using connectors, enabling users to load and prepare data for analysis.
6. What is Power Query Editor?
Power Query Editor is a graphical interface in Power BI for data transformation and cleansing, allowing users to filter, merge, pivot, and shape data before loading it into the model.
7. How do you clean and transform data in Power Query?
By applying transformations like removing duplicates, filtering rows, changing data types, splitting columns, merging queries, and adding calculated columns using the intuitive UI or M language.
8. What are the different data transformations available in Power Query?
Common transformations include filtering rows, sorting, pivot/unpivot columns, splitting columns, replacing values, aggregations, and adding custom columns.
9. What is M language in Power BI?
M is the functional programming language behind Power Query, used for building advanced data transformation noscripts beyond the UI capabilities.
10. Explain the concept of data modeling in Power BI.
Data modeling is organizing data tables, defining relationships, setting cardinality and cross-filter directions, and creating calculated columns and measures to enable efficient and accurate data analysis.
Double Tap ❤️ for Part-2
❤10
How to apply for Tech companies.pdf
83.7 KB
👉🏻 DO REACT IF YOU WANT MORE RESOURCES LIKE THIS FOR 🆓
❤9
Sometimes reality outpaces expectations in the most unexpected ways.
While global AI development seems increasingly fragmented, Sber just released Europe's largest open-source AI collection—full weights, code, and commercial rights included.
✅ No API paywalls.
✅ No usage restrictions.
✅ Just four complete model families ready to run in your private infrastructure, fine-tuned on your data, serving your specific needs.
What makes this release remarkable isn't merely the technical prowess, but the quiet confidence behind sharing it openly when others are building walls. Find out more in the article from the developers.
GigaChat Ultra Preview: 702B-parameter MoE model (36B active per token) with 128K context window. Trained from scratch, it outperforms DeepSeek V3.1 on specialized benchmarks while maintaining faster inference than previous flagships. Enterprise-ready with offline fine-tuning for secure environments.
GitHub | HuggingFace | GitVerse
GigaChat Lightning offers the opposite balance: compact yet powerful MoE architecture running on your laptop. It competes with Qwen3-4B in quality, matches the speed of Qwen3-1.7B, yet is significantly smarter and larger in parameter count.
Lightning holds its own against the best open-source models in its class, outperforms comparable models on different tasks, and delivers ultra-fast inference—making it ideal for scenarios where Ultra would be overkill and speed is critical. Plus, it features stable expert routing and a welcome bonus: 256K context support.
GitHub | Hugging Face | GitVerse
Kandinsky 5.0 brings a significant step forward in open generative models. The flagship Video Pro matches Veo 3 in visual quality and outperforms Wan 2.2-A14B, while Video Lite and Image Lite offer fast, lightweight alternatives for real-time use cases. The suite is powered by K-VAE 1.0, a high-efficiency open-source visual encoder that enables strong compression and serves as a solid base for training generative models. This stack balances performance, scalability, and practicality—whether you're building video pipelines or experimenting with multimodal generation.
GitHub | GitVerse | Hugging Face | Technical report
Audio gets its upgrade too: GigaAM-v3 delivers speech recognition model with 50% lower WER than Whisper-large-v3, trained on 700k hours of audio with punctuation/normalization for spontaneous speech.
GitHub | HuggingFace | GitVerse
Every model can be deployed on-premises, fine-tuned on your data, and used commercially. It's not just about catching up – it's about building sovereign AI infrastructure that belongs to everyone who needs it.
While global AI development seems increasingly fragmented, Sber just released Europe's largest open-source AI collection—full weights, code, and commercial rights included.
✅ No API paywalls.
✅ No usage restrictions.
✅ Just four complete model families ready to run in your private infrastructure, fine-tuned on your data, serving your specific needs.
What makes this release remarkable isn't merely the technical prowess, but the quiet confidence behind sharing it openly when others are building walls. Find out more in the article from the developers.
GigaChat Ultra Preview: 702B-parameter MoE model (36B active per token) with 128K context window. Trained from scratch, it outperforms DeepSeek V3.1 on specialized benchmarks while maintaining faster inference than previous flagships. Enterprise-ready with offline fine-tuning for secure environments.
GitHub | HuggingFace | GitVerse
GigaChat Lightning offers the opposite balance: compact yet powerful MoE architecture running on your laptop. It competes with Qwen3-4B in quality, matches the speed of Qwen3-1.7B, yet is significantly smarter and larger in parameter count.
Lightning holds its own against the best open-source models in its class, outperforms comparable models on different tasks, and delivers ultra-fast inference—making it ideal for scenarios where Ultra would be overkill and speed is critical. Plus, it features stable expert routing and a welcome bonus: 256K context support.
GitHub | Hugging Face | GitVerse
Kandinsky 5.0 brings a significant step forward in open generative models. The flagship Video Pro matches Veo 3 in visual quality and outperforms Wan 2.2-A14B, while Video Lite and Image Lite offer fast, lightweight alternatives for real-time use cases. The suite is powered by K-VAE 1.0, a high-efficiency open-source visual encoder that enables strong compression and serves as a solid base for training generative models. This stack balances performance, scalability, and practicality—whether you're building video pipelines or experimenting with multimodal generation.
GitHub | GitVerse | Hugging Face | Technical report
Audio gets its upgrade too: GigaAM-v3 delivers speech recognition model with 50% lower WER than Whisper-large-v3, trained on 700k hours of audio with punctuation/normalization for spontaneous speech.
GitHub | HuggingFace | GitVerse
Every model can be deployed on-premises, fine-tuned on your data, and used commercially. It's not just about catching up – it's about building sovereign AI infrastructure that belongs to everyone who needs it.
❤1👏1
✅ Data Analytics Roadmap for Beginners (2025) 📊🧠
1. Understand What Data Analytics Is
⦁ Extracting insights from data to support decisions
⦁ Types: Denoscriptive, Diagnostic, Predictive, Prenoscriptive
2. Learn Excel or Google Sheets
⦁ Functions: VLOOKUP, INDEX-MATCH, IF, SUMIFS
⦁ Pivot tables, charts, data cleaning
3. Learn SQL
⦁ SELECT, WHERE, JOIN, GROUP BY
⦁ Analyze real-world datasets (sales, users, etc.)
4. Learn Python for Data
⦁ Libraries:
⦁ Pandas (data manipulation)
⦁ NumPy (arrays, math)
⦁ Matplotlib/Seaborn (visualization)
5. Learn Data Visualization Tools
⦁ Power BI or Tableau
⦁ Dashboards, filters, KPIs, storyboards
6. Practice with Real Datasets
⦁ Kaggle
⦁ Google Dataset Search
⦁ Government portals
7. Understand Basic Statistics
⦁ Mean, Median, Mode
⦁ Correlation vs. Causation
⦁ Hypothesis testing & p-values
8. Work on Projects
⦁ Sales performance dashboard
⦁ Customer segmentation
⦁ Product usage trends
9. Learn Basics of Reporting & Storytelling
⦁ Turn numbers into clear insights
⦁ Focus on key metrics and visuals
10. Bonus Skills
⦁ Git & GitHub
⦁ Data cleaning techniques
⦁ Intro to machine learning (optional)
💬 Double Tap ♥️ For More
1. Understand What Data Analytics Is
⦁ Extracting insights from data to support decisions
⦁ Types: Denoscriptive, Diagnostic, Predictive, Prenoscriptive
2. Learn Excel or Google Sheets
⦁ Functions: VLOOKUP, INDEX-MATCH, IF, SUMIFS
⦁ Pivot tables, charts, data cleaning
3. Learn SQL
⦁ SELECT, WHERE, JOIN, GROUP BY
⦁ Analyze real-world datasets (sales, users, etc.)
4. Learn Python for Data
⦁ Libraries:
⦁ Pandas (data manipulation)
⦁ NumPy (arrays, math)
⦁ Matplotlib/Seaborn (visualization)
5. Learn Data Visualization Tools
⦁ Power BI or Tableau
⦁ Dashboards, filters, KPIs, storyboards
6. Practice with Real Datasets
⦁ Kaggle
⦁ Google Dataset Search
⦁ Government portals
7. Understand Basic Statistics
⦁ Mean, Median, Mode
⦁ Correlation vs. Causation
⦁ Hypothesis testing & p-values
8. Work on Projects
⦁ Sales performance dashboard
⦁ Customer segmentation
⦁ Product usage trends
9. Learn Basics of Reporting & Storytelling
⦁ Turn numbers into clear insights
⦁ Focus on key metrics and visuals
10. Bonus Skills
⦁ Git & GitHub
⦁ Data cleaning techniques
⦁ Intro to machine learning (optional)
💬 Double Tap ♥️ For More
❤9
✅ Power BI Roadmap for Beginners 📊
1️⃣ Understand What Power BI Is
⦁ Business Intelligence tool by Microsoft
⦁ Turns raw data into interactive dashboards and reports
2️⃣ Setup Power BI
⦁ Install Power BI Desktop (free)
⦁ Learn interface: Report, Data, Model views
3️⃣ Import & Connect Data
⦁ Connect to Excel, CSV, SQL, SharePoint, APIs
⦁ Use Power Query for data transformation
⦁ Clean and shape data (remove nulls, split columns)
4️⃣ Data Modeling
⦁ Create relationships between tables
⦁ Understand star/snowflake schema
⦁ Use Primary and Foreign keys correctly
⦁ Mark date table
5️⃣ DAX Basics (Data Analysis Expressions)
⦁ Learn functions like:
⦁ SUM(), AVERAGE(), CALCULATE()
⦁ FILTER(), IF(), SWITCH(), ALL()
⦁ Use Measures vs Calculated Columns
6️⃣ Visualizations
⦁ Use bar, line, pie, table, matrix, card, slicer
⦁ Apply filters, hierarchies, and drilldowns
⦁ Use bookmarks and tooltips for interactivity
7️⃣ Reports & Dashboards
⦁ Build multi-page reports
⦁ Use themes and consistent formatting
⦁ Add slicers for dynamic filtering
⦁ Create mobile-friendly layouts
8️⃣ Publishing & Sharing
⦁ Publish to Power BI Service
⦁ Set refresh schedules
⦁ Share reports via workspace, link, or Teams
9️⃣ Real-World Projects
⦁ Sales Dashboard
⦁ HR Analytics
⦁ Financial KPIs
⦁ Customer Segmentation
🔟 Tips to Learn Faster
⦁ Use sample datasets (like AdventureWorks)
⦁ Join Power BI Community & Microsoft Docs
⦁ Watch tutorials on YouTube (Guy in a Cube, LearnPowerBI)
💬 Tap ❤️ for more
1️⃣ Understand What Power BI Is
⦁ Business Intelligence tool by Microsoft
⦁ Turns raw data into interactive dashboards and reports
2️⃣ Setup Power BI
⦁ Install Power BI Desktop (free)
⦁ Learn interface: Report, Data, Model views
3️⃣ Import & Connect Data
⦁ Connect to Excel, CSV, SQL, SharePoint, APIs
⦁ Use Power Query for data transformation
⦁ Clean and shape data (remove nulls, split columns)
4️⃣ Data Modeling
⦁ Create relationships between tables
⦁ Understand star/snowflake schema
⦁ Use Primary and Foreign keys correctly
⦁ Mark date table
5️⃣ DAX Basics (Data Analysis Expressions)
⦁ Learn functions like:
⦁ SUM(), AVERAGE(), CALCULATE()
⦁ FILTER(), IF(), SWITCH(), ALL()
⦁ Use Measures vs Calculated Columns
6️⃣ Visualizations
⦁ Use bar, line, pie, table, matrix, card, slicer
⦁ Apply filters, hierarchies, and drilldowns
⦁ Use bookmarks and tooltips for interactivity
7️⃣ Reports & Dashboards
⦁ Build multi-page reports
⦁ Use themes and consistent formatting
⦁ Add slicers for dynamic filtering
⦁ Create mobile-friendly layouts
8️⃣ Publishing & Sharing
⦁ Publish to Power BI Service
⦁ Set refresh schedules
⦁ Share reports via workspace, link, or Teams
9️⃣ Real-World Projects
⦁ Sales Dashboard
⦁ HR Analytics
⦁ Financial KPIs
⦁ Customer Segmentation
🔟 Tips to Learn Faster
⦁ Use sample datasets (like AdventureWorks)
⦁ Join Power BI Community & Microsoft Docs
⦁ Watch tutorials on YouTube (Guy in a Cube, LearnPowerBI)
💬 Tap ❤️ for more
❤6
✅ Top Skills Every Data Analyst Should Master 📊🧠
1️⃣ Excel
⦁ Formulas (VLOOKUP, INDEX-MATCH)
⦁ Pivot Tables, Charts, Conditional Formatting
⦁ Data Cleaning & Analysis
2️⃣ SQL
⦁ SELECT, JOINs, GROUP BY, HAVING
⦁ Subqueries, CTEs, Window Functions
⦁ Extracting and analyzing relational data
3️⃣ Data Visualization
⦁ Tools: Power BI, Tableau, Excel
⦁ Dashboards, filters, slicers, KPIs
⦁ Clear, insightful visuals
4️⃣ Python
⦁ Libraries: Pandas, NumPy, Matplotlib, Seaborn
⦁ Data cleaning, wrangling, EDA
⦁ Basic automation and noscripting
5️⃣ Statistics
⦁ Mean, median, mode, standard deviation
⦁ Probability, distributions
⦁ Hypothesis testing, A/B Testing
6️⃣ Business Understanding
⦁ Know key metrics: revenue, churn, CAC, CLV
⦁ Interpret data in business context
⦁ Communicate insights clearly
7️⃣ Critical Thinking
⦁ Ask the right questions
⦁ Validate findings
⦁ Avoid assumptions
8️⃣ Communication Skills
⦁ Report writing
⦁ Presenting insights to non-technical teams
⦁ Storytelling with data
💬 React ❤️ for more!
1️⃣ Excel
⦁ Formulas (VLOOKUP, INDEX-MATCH)
⦁ Pivot Tables, Charts, Conditional Formatting
⦁ Data Cleaning & Analysis
2️⃣ SQL
⦁ SELECT, JOINs, GROUP BY, HAVING
⦁ Subqueries, CTEs, Window Functions
⦁ Extracting and analyzing relational data
3️⃣ Data Visualization
⦁ Tools: Power BI, Tableau, Excel
⦁ Dashboards, filters, slicers, KPIs
⦁ Clear, insightful visuals
4️⃣ Python
⦁ Libraries: Pandas, NumPy, Matplotlib, Seaborn
⦁ Data cleaning, wrangling, EDA
⦁ Basic automation and noscripting
5️⃣ Statistics
⦁ Mean, median, mode, standard deviation
⦁ Probability, distributions
⦁ Hypothesis testing, A/B Testing
6️⃣ Business Understanding
⦁ Know key metrics: revenue, churn, CAC, CLV
⦁ Interpret data in business context
⦁ Communicate insights clearly
7️⃣ Critical Thinking
⦁ Ask the right questions
⦁ Validate findings
⦁ Avoid assumptions
8️⃣ Communication Skills
⦁ Report writing
⦁ Presenting insights to non-technical teams
⦁ Storytelling with data
💬 React ❤️ for more!
❤4
🤖 Artificial Intelligence Roadmap 🧠
|-- Fundamentals
| |-- Mathematics
| | |-- Linear Algebra
| | |-- Calculus
| | |-- Probability & Statistics
| | └─ Discrete Mathematics
| |
| |-- Programming
| | |-- Python
| | |-- R (Optional)
| | └─ Data Structures & Algorithms
| |
| └─ Machine Learning Basics
| |-- Supervised Learning
| |-- Unsupervised Learning
| |-- Reinforcement Learning
| └─ Model Evaluation & Selection
|-- Supervised_Learning
| |-- Regression
| | |-- Linear Regression
| | |-- Polynomial Regression
| | └─ Regularization Techniques
| |
| |-- Classification
| | |-- Logistic Regression
| | |-- Support Vector Machines (SVM)
| | |-- Decision Trees
| | |-- Random Forests
| | └─ Naive Bayes
| |
| └─ Model Evaluation
| |-- Metrics (Accuracy, Precision, Recall, F1-Score)
| |-- Cross-Validation
| └─ Hyperparameter Tuning
|-- Unsupervised_Learning
| |-- Clustering
| | |-- K-Means Clustering
| | |-- Hierarchical Clustering
| | └─ DBSCAN
| |
| └─ Dimensionality Reduction
| |-- Principal Component Analysis (PCA)
| └─ t-distributed Stochastic Neighbor Embedding (t-SNE)
|-- Deep_Learning
| |-- Neural Networks Basics
| | |-- Activation Functions
| | |-- Loss Functions
| | └─ Optimization Algorithms
| |
| |-- Convolutional Neural Networks (CNNs)
| | |-- Image Classification
| | └─ Object Detection
| |
| |-- Recurrent Neural Networks (RNNs)
| | |-- Sequence Modeling
| | └─ Natural Language Processing (NLP)
| |
| └─ Transformers
| |-- Attention Mechanisms
| |-- BERT
| |-- GPT
|-- Reinforcement_Learning
| |-- Markov Decision Processes (MDPs)
| |-- Q-Learning
| |-- Deep Q-Networks (DQN)
| └─ Policy Gradient Methods
|-- Natural_Language_Processing (NLP)
| |-- Text Processing Techniques
| |-- Sentiment Analysis
| |-- Topic Modeling
| |-- Machine Translation
| └─ Language Modeling
|-- Computer_Vision
| |-- Image Processing Fundamentals
| |-- Image Classification
| |-- Object Detection
| |-- Image Segmentation
| └─ Image Generation
|-- Ethical AI & Responsible AI
| |-- Bias Detection and Mitigation
| |-- Fairness in AI
| |-- Privacy Concerns
| └─ Explainable AI (XAI)
|-- Deployment & Production
| |-- Model Deployment Strategies
| |-- Cloud Platforms (AWS, Azure, GCP)
| |-- Model Monitoring
| └─ Version Control
|-- Online_Resources
| |-- Coursera
| |-- Udacity
| |-- fast.ai
| |-- Kaggle
| └─ TensorFlow, PyTorch Documentation
React ❤️ if this helped you!
|-- Fundamentals
| |-- Mathematics
| | |-- Linear Algebra
| | |-- Calculus
| | |-- Probability & Statistics
| | └─ Discrete Mathematics
| |
| |-- Programming
| | |-- Python
| | |-- R (Optional)
| | └─ Data Structures & Algorithms
| |
| └─ Machine Learning Basics
| |-- Supervised Learning
| |-- Unsupervised Learning
| |-- Reinforcement Learning
| └─ Model Evaluation & Selection
|-- Supervised_Learning
| |-- Regression
| | |-- Linear Regression
| | |-- Polynomial Regression
| | └─ Regularization Techniques
| |
| |-- Classification
| | |-- Logistic Regression
| | |-- Support Vector Machines (SVM)
| | |-- Decision Trees
| | |-- Random Forests
| | └─ Naive Bayes
| |
| └─ Model Evaluation
| |-- Metrics (Accuracy, Precision, Recall, F1-Score)
| |-- Cross-Validation
| └─ Hyperparameter Tuning
|-- Unsupervised_Learning
| |-- Clustering
| | |-- K-Means Clustering
| | |-- Hierarchical Clustering
| | └─ DBSCAN
| |
| └─ Dimensionality Reduction
| |-- Principal Component Analysis (PCA)
| └─ t-distributed Stochastic Neighbor Embedding (t-SNE)
|-- Deep_Learning
| |-- Neural Networks Basics
| | |-- Activation Functions
| | |-- Loss Functions
| | └─ Optimization Algorithms
| |
| |-- Convolutional Neural Networks (CNNs)
| | |-- Image Classification
| | └─ Object Detection
| |
| |-- Recurrent Neural Networks (RNNs)
| | |-- Sequence Modeling
| | └─ Natural Language Processing (NLP)
| |
| └─ Transformers
| |-- Attention Mechanisms
| |-- BERT
| |-- GPT
|-- Reinforcement_Learning
| |-- Markov Decision Processes (MDPs)
| |-- Q-Learning
| |-- Deep Q-Networks (DQN)
| └─ Policy Gradient Methods
|-- Natural_Language_Processing (NLP)
| |-- Text Processing Techniques
| |-- Sentiment Analysis
| |-- Topic Modeling
| |-- Machine Translation
| └─ Language Modeling
|-- Computer_Vision
| |-- Image Processing Fundamentals
| |-- Image Classification
| |-- Object Detection
| |-- Image Segmentation
| └─ Image Generation
|-- Ethical AI & Responsible AI
| |-- Bias Detection and Mitigation
| |-- Fairness in AI
| |-- Privacy Concerns
| └─ Explainable AI (XAI)
|-- Deployment & Production
| |-- Model Deployment Strategies
| |-- Cloud Platforms (AWS, Azure, GCP)
| |-- Model Monitoring
| └─ Version Control
|-- Online_Resources
| |-- Coursera
| |-- Udacity
| |-- fast.ai
| |-- Kaggle
| └─ TensorFlow, PyTorch Documentation
React ❤️ if this helped you!
❤6
Data Analytics Interview Questions
Q1: Describe a situation where you had to clean a messy dataset. What steps did you take?
Ans: I encountered a dataset with missing values, duplicates, and inconsistent formats. I used Python's Pandas library to identify and handle missing values, standardized data formats using regular expressions, and removed duplicates. I also validated the cleaned data against known benchmarks to ensure accuracy.
Q2: How do you handle outliers in a dataset?
Ans: I start by visualizing the data using box plots or scatter plots to identify potential outliers. Then, depending on the nature of the data and the problem context, I might cap the outliers, transform the data, or even remove them if they're due to errors.
Q3: How would you use data to suggest optimal pricing strategies to Airbnb hosts?
Ans: I'd analyze factors like location, property type, amenities, local events, and historical booking rates. Using regression analysis, I'd model the relationship between these factors and pricing to suggest an optimal price range. Additionally, analyzing competitor pricing in the area can provide insights into market rates.
Q4: Describe a situation where you used data to improve the user experience on the Airbnb platform.
Ans: While analyzing user feedback and platform interaction data, I noticed that users often had difficulty navigating the booking process. Based on this, I suggested streamlining the booking steps and providing clearer instructions. A/B testing confirmed that these changes led to a higher conversion rate and improved user feedback.
Q1: Describe a situation where you had to clean a messy dataset. What steps did you take?
Ans: I encountered a dataset with missing values, duplicates, and inconsistent formats. I used Python's Pandas library to identify and handle missing values, standardized data formats using regular expressions, and removed duplicates. I also validated the cleaned data against known benchmarks to ensure accuracy.
Q2: How do you handle outliers in a dataset?
Ans: I start by visualizing the data using box plots or scatter plots to identify potential outliers. Then, depending on the nature of the data and the problem context, I might cap the outliers, transform the data, or even remove them if they're due to errors.
Q3: How would you use data to suggest optimal pricing strategies to Airbnb hosts?
Ans: I'd analyze factors like location, property type, amenities, local events, and historical booking rates. Using regression analysis, I'd model the relationship between these factors and pricing to suggest an optimal price range. Additionally, analyzing competitor pricing in the area can provide insights into market rates.
Q4: Describe a situation where you used data to improve the user experience on the Airbnb platform.
Ans: While analyzing user feedback and platform interaction data, I noticed that users often had difficulty navigating the booking process. Based on this, I suggested streamlining the booking steps and providing clearer instructions. A/B testing confirmed that these changes led to a higher conversion rate and improved user feedback.
❤3
Useful websites to practice and enhance your Data Analytics skills
👇👇
1. SQL
https://mode.com/sql-tutorial/introduction-to-sql
https://news.1rj.ru/str/sqlspecialist/232
2. Python
https://www.learnpython.org/
https://bit.ly/3T7y4ta
https://www.geeksforgeeks.org/python-programming-language/learn-python-tutorial
3. R
https://www.datacamp.com/courses/free-introduction-to-r
4. Data Structures
https://leetcode.com/study-plan/data-structure/
https://www.udacity.com/course/data-structures-and-algorithms-in-python--ud513
5. Data Visualization
https://www.freecodecamp.org/learn/data-visualization/
https://www.tableau.com/learn/training/20223
https://www.workout-wednesday.com/power-bi-challenges/
6. Excel
https://excel-practice-online.com/
https://www.w3schools.com/EXCEL/index.php
Join @free4unow_backup for more free courses
ENJOY LEARNING 👍👍
👇👇
1. SQL
https://mode.com/sql-tutorial/introduction-to-sql
https://news.1rj.ru/str/sqlspecialist/232
2. Python
https://www.learnpython.org/
https://bit.ly/3T7y4ta
https://www.geeksforgeeks.org/python-programming-language/learn-python-tutorial
3. R
https://www.datacamp.com/courses/free-introduction-to-r
4. Data Structures
https://leetcode.com/study-plan/data-structure/
https://www.udacity.com/course/data-structures-and-algorithms-in-python--ud513
5. Data Visualization
https://www.freecodecamp.org/learn/data-visualization/
https://www.tableau.com/learn/training/20223
https://www.workout-wednesday.com/power-bi-challenges/
6. Excel
https://excel-practice-online.com/
https://www.w3schools.com/EXCEL/index.php
Join @free4unow_backup for more free courses
ENJOY LEARNING 👍👍
❤4👌1