🚨Here is a comprehensive list of #interview questions that are commonly asked in job interviews for Data Scientist, Data Analyst, and Data Engineer positions:
➡️ Data Scientist Interview Questions
Technical Questions
1) What are your preferred programming languages for data science, and why?
2) Can you write a Python script to perform data cleaning on a given dataset?
3) Explain the Central Limit Theorem.
4) How do you handle missing data in a dataset?
5) Describe the difference between supervised and unsupervised learning.
6) How do you select the right algorithm for your model?
Questions Related To Problem-Solving and Projects
7) Walk me through a data science project you have worked on.
8) How did you handle data preprocessing in your project?
9) How do you evaluate the performance of a machine learning model?
10) What techniques do you use to prevent overfitting?
➡️Data Analyst Interview Questions
Technical Questions
1) Write a SQL query to find the second highest salary from the employee table.
2) How would you optimize a slow-running query?
3) How do you use pivot tables in Excel?
4) Explain the VLOOKUP function.
5) How do you handle outliers in your data?
6) Describe the steps you take to clean a dataset.
Analytical Questions
7) How do you interpret data to make business decisions?
8) Give an example of a time when your analysis directly influenced a business decision.
9) What are your preferred tools for data analysis and why?
10) How do you ensure the accuracy of your analysis?
➡️Data Engineer Interview Questions
Technical Questions
1) What is your experience with SQL and NoSQL databases?
2) How do you design a scalable database architecture?
3) Explain the ETL process you follow in your projects.
4) How do you handle data transformation and loading efficiently?
5) What is your experience with Hadoop/Spark?
6) How do you manage and process large datasets?
Questions Related To Problem-Solving and Optimization
7) Describe a data pipeline you have built.
8) What challenges did you face, and how did you overcome them?
9) How do you ensure your data processes run efficiently?
10) Describe a time when you had to optimize a slow data pipeline.
I have curated top-notch Data Analytics Resources 👇👇
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➡️ Data Scientist Interview Questions
Technical Questions
1) What are your preferred programming languages for data science, and why?
2) Can you write a Python script to perform data cleaning on a given dataset?
3) Explain the Central Limit Theorem.
4) How do you handle missing data in a dataset?
5) Describe the difference between supervised and unsupervised learning.
6) How do you select the right algorithm for your model?
Questions Related To Problem-Solving and Projects
7) Walk me through a data science project you have worked on.
8) How did you handle data preprocessing in your project?
9) How do you evaluate the performance of a machine learning model?
10) What techniques do you use to prevent overfitting?
➡️Data Analyst Interview Questions
Technical Questions
1) Write a SQL query to find the second highest salary from the employee table.
2) How would you optimize a slow-running query?
3) How do you use pivot tables in Excel?
4) Explain the VLOOKUP function.
5) How do you handle outliers in your data?
6) Describe the steps you take to clean a dataset.
Analytical Questions
7) How do you interpret data to make business decisions?
8) Give an example of a time when your analysis directly influenced a business decision.
9) What are your preferred tools for data analysis and why?
10) How do you ensure the accuracy of your analysis?
➡️Data Engineer Interview Questions
Technical Questions
1) What is your experience with SQL and NoSQL databases?
2) How do you design a scalable database architecture?
3) Explain the ETL process you follow in your projects.
4) How do you handle data transformation and loading efficiently?
5) What is your experience with Hadoop/Spark?
6) How do you manage and process large datasets?
Questions Related To Problem-Solving and Optimization
7) Describe a data pipeline you have built.
8) What challenges did you face, and how did you overcome them?
9) How do you ensure your data processes run efficiently?
10) Describe a time when you had to optimize a slow data pipeline.
I have curated top-notch Data Analytics Resources 👇👇
https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02
Hope this helps you 😊
❤4
Here is a list of 100 data science interview questions that can help you prepare for a data science job interview. These questions cover a wide range of topics and levels of difficulty, so be sure to review them thoroughly and practice your answers. 📱
Mathematics and Statistics:
1. What is the Central Limit Theorem, and why is it important in statistics?
2. Explain the difference between population and sample.
3. What is probability and how is it calculated?
4. What are the measures of central tendency, and when would you use each one?
5. Define variance and standard deviation.
6. What is the significance of hypothesis testing in data science?
7. Explain the p-value and its significance in hypothesis testing.
8. What is a normal distribution, and why is it important in statistics?
9. Describe the differences between a Z-score and a T-score.
10. What is correlation, and how is it measured?
11. What is the difference between covariance and correlation?
12. What is the law of large numbers?
Machine Learning:
13. What is machine learning, and how is it different from traditional programming?
14. Explain the bias-variance trade-off.
15. What are the different types of machine learning algorithms?
16. What is overfitting, and how can you prevent it?
17. Describe the k-fold cross-validation technique.
18. What is regularization, and why is it important in machine learning?
19. Explain the concept of feature engineering.
20. What is gradient descent, and how does it work in machine learning?
21. What is a decision tree, and how does it work?
22. What are ensemble methods in machine learning, and provide examples.
23. Explain the difference between supervised and unsupervised learning.
24. What is deep learning, and how does it differ from traditional neural networks?
25. What is a convolutional neural network (CNN), and where is it commonly used?
26. What is a recurrent neural network (RNN), and where is it commonly used?
27. What is the vanishing gradient problem in deep learning?
28. Describe the concept of transfer learning in deep learning.
Data Preprocessing:
29. What is data preprocessing, and why is it important in data science?
30. Explain missing data imputation techniques.
31. What is one-hot encoding, and when is it used?
32. How do you handle categorical data in machine learning?
33. Describe the process of data normalization and standardization.
34. What is feature scaling, and why is it necessary?
35. What is outlier detection, and how can you identify outliers in a dataset?
Data Exploration:
36. What is exploratory data analysis (EDA), and why is it important?
37. Explain the concept of data distribution.
38. What are box plots, and how are they used in EDA?
39. What is a histogram, and what insights can you gain from it?
40. Describe the concept of data skewness.
41. What are scatter plots, and how are they useful in data analysis?
42. What is a correlation matrix, and how is it used in EDA?
43. How do you handle imbalanced datasets in machine learning?
Model Evaluation:
44. What are the common metrics used for evaluating classification models?
45. Explain precision, recall, and F1-score.
46. What is ROC curve analysis, and what does it measure?
47. How do you choose the appropriate evaluation metric for a regression problem?
48. Describe the concept of confusion matrix.
49. What is cross-entropy loss, and how is it used in classification problems?
50. Explain the concept of AUC-ROC.
Python and Programming:
51. Describe the differences between Python 2 and Python 3.
52. What is the Global Interpreter Lock (GIL) in Python, and how does it affect multi-threading?
53. Explain the use of decorators in Python.
54. What are list comprehensions, and how do they work?
55. Describe the purpose of virtual environments in Python.
Mathematics and Statistics:
1. What is the Central Limit Theorem, and why is it important in statistics?
2. Explain the difference between population and sample.
3. What is probability and how is it calculated?
4. What are the measures of central tendency, and when would you use each one?
5. Define variance and standard deviation.
6. What is the significance of hypothesis testing in data science?
7. Explain the p-value and its significance in hypothesis testing.
8. What is a normal distribution, and why is it important in statistics?
9. Describe the differences between a Z-score and a T-score.
10. What is correlation, and how is it measured?
11. What is the difference between covariance and correlation?
12. What is the law of large numbers?
Machine Learning:
13. What is machine learning, and how is it different from traditional programming?
14. Explain the bias-variance trade-off.
15. What are the different types of machine learning algorithms?
16. What is overfitting, and how can you prevent it?
17. Describe the k-fold cross-validation technique.
18. What is regularization, and why is it important in machine learning?
19. Explain the concept of feature engineering.
20. What is gradient descent, and how does it work in machine learning?
21. What is a decision tree, and how does it work?
22. What are ensemble methods in machine learning, and provide examples.
23. Explain the difference between supervised and unsupervised learning.
24. What is deep learning, and how does it differ from traditional neural networks?
25. What is a convolutional neural network (CNN), and where is it commonly used?
26. What is a recurrent neural network (RNN), and where is it commonly used?
27. What is the vanishing gradient problem in deep learning?
28. Describe the concept of transfer learning in deep learning.
Data Preprocessing:
29. What is data preprocessing, and why is it important in data science?
30. Explain missing data imputation techniques.
31. What is one-hot encoding, and when is it used?
32. How do you handle categorical data in machine learning?
33. Describe the process of data normalization and standardization.
34. What is feature scaling, and why is it necessary?
35. What is outlier detection, and how can you identify outliers in a dataset?
Data Exploration:
36. What is exploratory data analysis (EDA), and why is it important?
37. Explain the concept of data distribution.
38. What are box plots, and how are they used in EDA?
39. What is a histogram, and what insights can you gain from it?
40. Describe the concept of data skewness.
41. What are scatter plots, and how are they useful in data analysis?
42. What is a correlation matrix, and how is it used in EDA?
43. How do you handle imbalanced datasets in machine learning?
Model Evaluation:
44. What are the common metrics used for evaluating classification models?
45. Explain precision, recall, and F1-score.
46. What is ROC curve analysis, and what does it measure?
47. How do you choose the appropriate evaluation metric for a regression problem?
48. Describe the concept of confusion matrix.
49. What is cross-entropy loss, and how is it used in classification problems?
50. Explain the concept of AUC-ROC.
Python and Programming:
51. Describe the differences between Python 2 and Python 3.
52. What is the Global Interpreter Lock (GIL) in Python, and how does it affect multi-threading?
53. Explain the use of decorators in Python.
54. What are list comprehensions, and how do they work?
55. Describe the purpose of virtual environments in Python.
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56. How can you handle exceptions in Python?
57. What is a lambda function, and where is it typically used?
58. Explain the difference between shallow and deep copy in Python.
59. What is the purpose of the map() and filter() functions in Python?
60. Describe the difference between append() and extend() methods for lists.
SQL and Database Knowledge:
61. What is SQL, and how is it used in data science?
62. Explain the difference between SQL's INNER JOIN and LEFT JOIN.
63. What is a primary key and a foreign key in a relational database?
64. How do you write a SQL query to retrieve data from a database table?
65. What is the purpose of the GROUP BY clause in SQL?
66. Explain the concept of indexing in databases.
67. What are NoSQL databases, and how are they different from SQL databases?
Big Data and Distributed Computing:
68. What is Hadoop, and how does it handle big data?
69. Explain the MapReduce programming model.
70. What is Apache Spark, and why is it popular in big data processing?
71. Describe the concept of distributed computing.
72. What are the advantages and disadvantages of distributed databases?
Data Visualization:
73. Why is data visualization important in data science?
74. Describe the types of charts and graphs commonly used in data visualization.
75. What is the purpose of a heatmap in data visualization?
76. Explain the concept of storytelling through data visualization.
77. How can you create interactive data visualizations in Python?
Natural Language Processing (NLP):
78. What is natural language processing, and what are its applications?
79. Describe the steps involved in text preprocessing for NLP.
80. What is tokenization, and why is it necessary in NLP?
81. Explain the concept of stop words in NLP.
82. What are n-grams, and how are they used in text analysis?
83. What is sentiment analysis, and how is it performed using NLP techniques?
84. What is named entity recognition (NER) in NLP?
Time Series Analysis:
85. What is a time series, and give examples of time series data.
86. Explain the components of a time series (trend, seasonality, and noise).
87. What is autocorrelation in time series analysis?
88. How do you perform time series forecasting?
89. What are ARIMA models, and how are they used in time series forecasting?
90. Describe exponential smoothing methods in time series analysis.
Dimensionality Reduction:
91. Why is dimensionality reduction important in machine learning?
92. Explain the concept of Principal Component Analysis (PCA).
93. What is t-SNE, and how is it used for dimensionality reduction?
94. Describe the curse of dimensionality.
95. When would you use feature selection versus feature extraction for dimensionality reduction?
Ethical and Business Considerations:
96. What are the ethical considerations in data science?
97. How can bias be introduced into machine learning models, and how can it be mitigated?
98. Explain the concept of data privacy and GDPR compliance.
99. How can data science provide value to a business?
100. Describe a real-world project where data science had a significant impact.
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57. What is a lambda function, and where is it typically used?
58. Explain the difference between shallow and deep copy in Python.
59. What is the purpose of the map() and filter() functions in Python?
60. Describe the difference between append() and extend() methods for lists.
SQL and Database Knowledge:
61. What is SQL, and how is it used in data science?
62. Explain the difference between SQL's INNER JOIN and LEFT JOIN.
63. What is a primary key and a foreign key in a relational database?
64. How do you write a SQL query to retrieve data from a database table?
65. What is the purpose of the GROUP BY clause in SQL?
66. Explain the concept of indexing in databases.
67. What are NoSQL databases, and how are they different from SQL databases?
Big Data and Distributed Computing:
68. What is Hadoop, and how does it handle big data?
69. Explain the MapReduce programming model.
70. What is Apache Spark, and why is it popular in big data processing?
71. Describe the concept of distributed computing.
72. What are the advantages and disadvantages of distributed databases?
Data Visualization:
73. Why is data visualization important in data science?
74. Describe the types of charts and graphs commonly used in data visualization.
75. What is the purpose of a heatmap in data visualization?
76. Explain the concept of storytelling through data visualization.
77. How can you create interactive data visualizations in Python?
Natural Language Processing (NLP):
78. What is natural language processing, and what are its applications?
79. Describe the steps involved in text preprocessing for NLP.
80. What is tokenization, and why is it necessary in NLP?
81. Explain the concept of stop words in NLP.
82. What are n-grams, and how are they used in text analysis?
83. What is sentiment analysis, and how is it performed using NLP techniques?
84. What is named entity recognition (NER) in NLP?
Time Series Analysis:
85. What is a time series, and give examples of time series data.
86. Explain the components of a time series (trend, seasonality, and noise).
87. What is autocorrelation in time series analysis?
88. How do you perform time series forecasting?
89. What are ARIMA models, and how are they used in time series forecasting?
90. Describe exponential smoothing methods in time series analysis.
Dimensionality Reduction:
91. Why is dimensionality reduction important in machine learning?
92. Explain the concept of Principal Component Analysis (PCA).
93. What is t-SNE, and how is it used for dimensionality reduction?
94. Describe the curse of dimensionality.
95. When would you use feature selection versus feature extraction for dimensionality reduction?
Ethical and Business Considerations:
96. What are the ethical considerations in data science?
97. How can bias be introduced into machine learning models, and how can it be mitigated?
98. Explain the concept of data privacy and GDPR compliance.
99. How can data science provide value to a business?
100. Describe a real-world project where data science had a significant impact.
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If you're serious about getting into Data Science with Python, follow this 5-step roadmap.
Each phase builds on the previous one, so don’t rush.
Take your time, build projects, and keep moving forward.
Step 1: Python Fundamentals
Before anything else, get your hands dirty with core Python.
This is the language that powers everything else.
✅ What to learn:
type(), int(), float(), str(), list(), dict()
if, elif, else, for, while, range()
def, return, function arguments
List comprehensions: [x for x in list if condition]
– Mini Checkpoint:
Build a mini console-based data calculator (inputs, basic operations, conditionals, loops).
Step 2: Data Cleaning with Pandas
Pandas is the tool you'll use to clean, reshape, and explore data in real-world scenarios.
✅ What to learn:
Cleaning: df.dropna(), df.fillna(), df.replace(), df.drop_duplicates()
Merging & reshaping: pd.merge(), df.pivot(), df.melt()
Grouping & aggregation: df.groupby(), df.agg()
– Mini Checkpoint:
Build a data cleaning script for a messy CSV file. Add comments to explain every step.
Step 3: Data Visualization with Matplotlib
Nobody wants raw tables.
Learn to tell stories through charts.
✅ What to learn:
Basic charts: plt.plot(), plt.scatter()
Advanced plots: plt.hist(), plt.kde(), plt.boxplot()
Subplots & customizations: plt.subplots(), fig.add_subplot(), plt.title(), plt.legend(), plt.xlabel()
– Mini Checkpoint:
Create a dashboard-style notebook visualizing a dataset, include at least 4 types of plots.
Step 4: Exploratory Data Analysis (EDA)
This is where your analytical skills kick in.
You’ll draw insights, detect trends, and prepare for modeling.
✅ What to learn:
Descriptive stats: df.mean(), df.median(), df.mode(), df.std(), df.var(), df.min(), df.max(), df.quantile()
Correlation analysis: df.corr(), plt.imshow(), scipy.stats.pearsonr()
— Mini Checkpoint:
Write an EDA report (Markdown or PDF) based on your findings from a public dataset.
Step 5: Intro to Machine Learning with Scikit-Learn
Now that your data skills are sharp, it's time to model and predict.
✅ What to learn:
Training & evaluation: train_test_split(), .fit(), .predict(), cross_val_score()
Regression: LinearRegression(), mean_squared_error(), r2_score()
Classification: LogisticRegression(), accuracy_score(), confusion_matrix()
Clustering: KMeans(), silhouette_score()
– Final Checkpoint:
Build your first ML project end-to-end
✅ Load data
✅ Clean it
✅ Visualize it
✅ Run EDA
✅ Train & test a model
✅ Share the project with visuals and explanations on GitHub
Don’t just complete tutorialsm create things.
Explain your work.
Build your GitHub.
Write a blog.
That’s how you go from “learning” to “landing a job
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
All the best 👍👍
Each phase builds on the previous one, so don’t rush.
Take your time, build projects, and keep moving forward.
Step 1: Python Fundamentals
Before anything else, get your hands dirty with core Python.
This is the language that powers everything else.
✅ What to learn:
type(), int(), float(), str(), list(), dict()
if, elif, else, for, while, range()
def, return, function arguments
List comprehensions: [x for x in list if condition]
– Mini Checkpoint:
Build a mini console-based data calculator (inputs, basic operations, conditionals, loops).
Step 2: Data Cleaning with Pandas
Pandas is the tool you'll use to clean, reshape, and explore data in real-world scenarios.
✅ What to learn:
Cleaning: df.dropna(), df.fillna(), df.replace(), df.drop_duplicates()
Merging & reshaping: pd.merge(), df.pivot(), df.melt()
Grouping & aggregation: df.groupby(), df.agg()
– Mini Checkpoint:
Build a data cleaning script for a messy CSV file. Add comments to explain every step.
Step 3: Data Visualization with Matplotlib
Nobody wants raw tables.
Learn to tell stories through charts.
✅ What to learn:
Basic charts: plt.plot(), plt.scatter()
Advanced plots: plt.hist(), plt.kde(), plt.boxplot()
Subplots & customizations: plt.subplots(), fig.add_subplot(), plt.title(), plt.legend(), plt.xlabel()
– Mini Checkpoint:
Create a dashboard-style notebook visualizing a dataset, include at least 4 types of plots.
Step 4: Exploratory Data Analysis (EDA)
This is where your analytical skills kick in.
You’ll draw insights, detect trends, and prepare for modeling.
✅ What to learn:
Descriptive stats: df.mean(), df.median(), df.mode(), df.std(), df.var(), df.min(), df.max(), df.quantile()
Correlation analysis: df.corr(), plt.imshow(), scipy.stats.pearsonr()
— Mini Checkpoint:
Write an EDA report (Markdown or PDF) based on your findings from a public dataset.
Step 5: Intro to Machine Learning with Scikit-Learn
Now that your data skills are sharp, it's time to model and predict.
✅ What to learn:
Training & evaluation: train_test_split(), .fit(), .predict(), cross_val_score()
Regression: LinearRegression(), mean_squared_error(), r2_score()
Classification: LogisticRegression(), accuracy_score(), confusion_matrix()
Clustering: KMeans(), silhouette_score()
– Final Checkpoint:
Build your first ML project end-to-end
✅ Load data
✅ Clean it
✅ Visualize it
✅ Run EDA
✅ Train & test a model
✅ Share the project with visuals and explanations on GitHub
Don’t just complete tutorialsm create things.
Explain your work.
Build your GitHub.
Write a blog.
That’s how you go from “learning” to “landing a job
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
All the best 👍👍
❤14👍1
🚀 PowerBI Interview Questions Recently Asked at an MNC:
1️⃣ What are the limitations of using Direct Query connection mode reports?
Direct Query connects your Power BI report directly to the live data source, but it comes with some limitations. Here’s a simplified explanation:
➡️ Slower Performance
Every report interaction sends a query to the data source, causing delays.
Example: Imagine asking a librarian for every book you need, instead of having the books already with you.
➡️ Limited Features
Some advanced Power BI features aren’t supported in Direct Query mode.
Example: A basic calculator can’t perform complex scientific functions like specialized software.
➡️ Dependent on Source
Report performance depends entirely on the data source’s speed and availability.
Example: If the library (data source) is slow or closed, you can’t access your books (data).
➡️ Complex Queries
Handling complex calculations can be difficult or slow.
Example: Solving advanced math on a basic calculator takes time and effort.
➡️ Security and Access Issues
Direct Query relies on the data source’s security settings, which may limit access.
Example: If the library restricts access to rare books, you’ll face similar limitations.
💡 Key Takeaway: Direct Query ensures real-time data but can be slower, less flexible, and depends heavily on the data source’s performance and security.
#PowerBIInterview
1️⃣ What are the limitations of using Direct Query connection mode reports?
Direct Query connects your Power BI report directly to the live data source, but it comes with some limitations. Here’s a simplified explanation:
➡️ Slower Performance
Every report interaction sends a query to the data source, causing delays.
Example: Imagine asking a librarian for every book you need, instead of having the books already with you.
➡️ Limited Features
Some advanced Power BI features aren’t supported in Direct Query mode.
Example: A basic calculator can’t perform complex scientific functions like specialized software.
➡️ Dependent on Source
Report performance depends entirely on the data source’s speed and availability.
Example: If the library (data source) is slow or closed, you can’t access your books (data).
➡️ Complex Queries
Handling complex calculations can be difficult or slow.
Example: Solving advanced math on a basic calculator takes time and effort.
➡️ Security and Access Issues
Direct Query relies on the data source’s security settings, which may limit access.
Example: If the library restricts access to rare books, you’ll face similar limitations.
💡 Key Takeaway: Direct Query ensures real-time data but can be slower, less flexible, and depends heavily on the data source’s performance and security.
#PowerBIInterview
❤8
10 Simple Habits to Boost Your Data Science Skills 🧠📊
1) Practice data wrangling daily (Pandas, dplyr)
2) Work on small end-to-end projects (ETL, analysis, visualization)
3) Revisit and improve previous notebooks or scripts
4) Share findings in a clear, story-driven way
5) Follow data science blogs, newsletters, and researchers
6) Tackle weekly datasets or Kaggle competitions
7) Maintain a notebooks/journal with experiments and results
8) Version control your work (Git + GitHub)
9) Learn to communicate uncertainty (confidence intervals, p-values)
10) Stay curious about new tools (SQL, Python libs, ML basics)
💬 React "❤️" for more! 😊
1) Practice data wrangling daily (Pandas, dplyr)
2) Work on small end-to-end projects (ETL, analysis, visualization)
3) Revisit and improve previous notebooks or scripts
4) Share findings in a clear, story-driven way
5) Follow data science blogs, newsletters, and researchers
6) Tackle weekly datasets or Kaggle competitions
7) Maintain a notebooks/journal with experiments and results
8) Version control your work (Git + GitHub)
9) Learn to communicate uncertainty (confidence intervals, p-values)
10) Stay curious about new tools (SQL, Python libs, ML basics)
💬 React "❤️" for more! 😊
❤12
🗄️ SQL Developer Roadmap
📂 SQL Basics (SELECT, WHERE, ORDER BY)
∟📂 Joins (INNER, LEFT, RIGHT, FULL)
∟📂 Aggregate Functions (COUNT, SUM, AVG)
∟📂 Grouping Data (GROUP BY, HAVING)
∟📂 Subqueries & Nested Queries
∟📂 Data Modification (INSERT, UPDATE, DELETE)
∟📂 Database Design (Normalization, Keys)
∟📂 Indexing & Query Optimization
∟📂 Stored Procedures & Functions
∟📂 Transactions & Locks
∟📂 Views & Triggers
∟📂 Backup & Restore
∟📂 Working with NoSQL basics (optional)
∟📂 Real Projects & Practice
∟✅ Apply for SQL Dev Roles
❤️ React for More!
📂 SQL Basics (SELECT, WHERE, ORDER BY)
∟📂 Joins (INNER, LEFT, RIGHT, FULL)
∟📂 Aggregate Functions (COUNT, SUM, AVG)
∟📂 Grouping Data (GROUP BY, HAVING)
∟📂 Subqueries & Nested Queries
∟📂 Data Modification (INSERT, UPDATE, DELETE)
∟📂 Database Design (Normalization, Keys)
∟📂 Indexing & Query Optimization
∟📂 Stored Procedures & Functions
∟📂 Transactions & Locks
∟📂 Views & Triggers
∟📂 Backup & Restore
∟📂 Working with NoSQL basics (optional)
∟📂 Real Projects & Practice
∟✅ Apply for SQL Dev Roles
❤️ React for More!
❤8👏1
Follow this to optimise your linkedin profile 👇👇
Step 1: Upload a professional (looking) photo as this is your first impression
Step 2: Add your Industry and Location. Location is one of the top 5 fields that LinkedIn prioritizes when doing a key-word search. The other 4 fields are: Name, Headline, Summary and Experience.
Step 3: Customize your LinkedIn URL. To do this click on “Edit your public profile”
Step 4: Write a summary. This is a great opportunity to communicate your brand, as well as, use your key words. As a starting point you can use summary from your resume.
Step 5: Describe your experience with relevant keywords.
Step 6: Add 5 or more relevant skills.
Step 7: List your education with specialization.
Step 8: Connect with 500+ contacts in your industry to expand your network.
Step 9: Turn ON “Let recruiters know you’re open”
Step 1: Upload a professional (looking) photo as this is your first impression
Step 2: Add your Industry and Location. Location is one of the top 5 fields that LinkedIn prioritizes when doing a key-word search. The other 4 fields are: Name, Headline, Summary and Experience.
Step 3: Customize your LinkedIn URL. To do this click on “Edit your public profile”
Step 4: Write a summary. This is a great opportunity to communicate your brand, as well as, use your key words. As a starting point you can use summary from your resume.
Step 5: Describe your experience with relevant keywords.
Step 6: Add 5 or more relevant skills.
Step 7: List your education with specialization.
Step 8: Connect with 500+ contacts in your industry to expand your network.
Step 9: Turn ON “Let recruiters know you’re open”
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In a disease detection model, a patient has the disease, but the model predicts they don’t.
Which cell of the confusion matrix does this case fall into?
Which cell of the confusion matrix does this case fall into?
Anonymous Quiz
15%
a) True Positive
26%
b) False Positive
33%
c) True Negative
26%
d) False Negative
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Since many of you got the last question incorrect, let's understand Confusion Matrix in detail
A Confusion Matrix is used to evaluate how well a classification model performs by comparing actual vs predicted outcomes.
🔍 Structure:
• Actual Positive, Predicted Positive → ✅ True Positive (TP)
• Actual Positive, Predicted Negative → ❌ False Negative (FN)
• Actual Negative, Predicted Positive → ❌ False Positive (FP)
• Actual Negative, Predicted Negative → ✅ True Negative (TN)
📘 Key Terms:
• TP: Predicted Positive & Actually Positive
• TN: Predicted Negative & Actually Negative
• FP: Predicted Positive but Actually Negative
• FN: Predicted Negative but Actually Positive
🧮 Formulas:
• ×Accuracy× = (TP + TN) / Total
• ×Precision× = TP / (TP + FP)
• ×Recall× = TP / (TP + FN)
• ×F1 Score× = 2 × (Precision × Recall) / (Precision + Recall)
💡 Analogy: Spam Email Detector
• TP: Spam email marked as spam
• TN: Real email marked as not spam
• FP: Real email marked as spam
• FN: Spam email marked as real
💬 React with ❤️ for more such tutorials!
A Confusion Matrix is used to evaluate how well a classification model performs by comparing actual vs predicted outcomes.
🔍 Structure:
• Actual Positive, Predicted Positive → ✅ True Positive (TP)
• Actual Positive, Predicted Negative → ❌ False Negative (FN)
• Actual Negative, Predicted Positive → ❌ False Positive (FP)
• Actual Negative, Predicted Negative → ✅ True Negative (TN)
📘 Key Terms:
• TP: Predicted Positive & Actually Positive
• TN: Predicted Negative & Actually Negative
• FP: Predicted Positive but Actually Negative
• FN: Predicted Negative but Actually Positive
🧮 Formulas:
• ×Accuracy× = (TP + TN) / Total
• ×Precision× = TP / (TP + FP)
• ×Recall× = TP / (TP + FN)
• ×F1 Score× = 2 × (Precision × Recall) / (Precision + Recall)
💡 Analogy: Spam Email Detector
• TP: Spam email marked as spam
• TN: Real email marked as not spam
• FP: Real email marked as spam
• FN: Spam email marked as real
💬 React with ❤️ for more such tutorials!
❤8👍1🔥1
Advanced Questions Asked by Big 4
📊 Excel Questions
1. How do you use Excel to forecast future trends based on historical data? Describe a scenario where you built a forecasting model.
2. Can you explain how you would automate repetitive tasks in Excel using VBA (Visual Basic for Applications)? Provide an example of a complex macro you created.
3. Describe a time when you had to merge and analyze data from multiple Excel workbooks. How did you ensure data integrity and accuracy?
🗄 SQL Questions
1. How would you design a database schema for a new e-commerce platform to efficiently handle large volumes of transactions and user data?
2. Describe a complex SQL query you wrote to solve a business problem. What was the problem, and how did your query help resolve it?
3. How do you ensure data integrity and consistency in a multi-user database environment? Explain the techniques and tools you use.
🐍 Python Questions
1. How would you use Python to automate data extraction from various APIs and combine the data for analysis? Provide an example.
2. Describe a machine learning project you worked on using Python. What was the objective, and how did you approach the data preprocessing, model selection, and evaluation?
3. Explain how you would use Python to detect and handle anomalies in a dataset. What techniques and libraries would you employ?
📈 Power BI Questions
1. How do you create interactive dashboards in Power BI that can dynamically update based on user inputs? Provide an example of a dashboard you built.
2. Describe a scenario where you used Power BI to integrate data from non-traditional sources (e.g., web scraping, APIs). How did you handle the data transformation and visualization?
3. How do you ensure the performance and scalability of Power BI reports when dealing with large datasets? Describe the techniques and best practices you follow.
💡 Tips for Success:
Understand the business context: Tailor your answers to show how your technical skills solve real business problems.
Provide specific examples: Highlight your past experiences with concrete examples.
Stay updated: Continuously learn and adapt to new tools and methodologies.
Hope it helps :)
📊 Excel Questions
1. How do you use Excel to forecast future trends based on historical data? Describe a scenario where you built a forecasting model.
2. Can you explain how you would automate repetitive tasks in Excel using VBA (Visual Basic for Applications)? Provide an example of a complex macro you created.
3. Describe a time when you had to merge and analyze data from multiple Excel workbooks. How did you ensure data integrity and accuracy?
🗄 SQL Questions
1. How would you design a database schema for a new e-commerce platform to efficiently handle large volumes of transactions and user data?
2. Describe a complex SQL query you wrote to solve a business problem. What was the problem, and how did your query help resolve it?
3. How do you ensure data integrity and consistency in a multi-user database environment? Explain the techniques and tools you use.
🐍 Python Questions
1. How would you use Python to automate data extraction from various APIs and combine the data for analysis? Provide an example.
2. Describe a machine learning project you worked on using Python. What was the objective, and how did you approach the data preprocessing, model selection, and evaluation?
3. Explain how you would use Python to detect and handle anomalies in a dataset. What techniques and libraries would you employ?
📈 Power BI Questions
1. How do you create interactive dashboards in Power BI that can dynamically update based on user inputs? Provide an example of a dashboard you built.
2. Describe a scenario where you used Power BI to integrate data from non-traditional sources (e.g., web scraping, APIs). How did you handle the data transformation and visualization?
3. How do you ensure the performance and scalability of Power BI reports when dealing with large datasets? Describe the techniques and best practices you follow.
💡 Tips for Success:
Understand the business context: Tailor your answers to show how your technical skills solve real business problems.
Provide specific examples: Highlight your past experiences with concrete examples.
Stay updated: Continuously learn and adapt to new tools and methodologies.
Hope it helps :)
❤4👍1
20 essential Python libraries for data science:
🔹 pandas: Data manipulation and analysis. Essential for handling DataFrames.
🔹 numpy: Numerical computing. Perfect for working with arrays and mathematical functions.
🔹 scikit-learn: Machine learning. Comprehensive tools for predictive data analysis.
🔹 matplotlib: Data visualization. Great for creating static, animated, and interactive plots.
🔹 seaborn: Statistical data visualization. Makes complex plots easy and beautiful.
Data Science
🔹 scipy: Scientific computing. Provides algorithms for optimization, integration, and more.
🔹 statsmodels: Statistical modeling. Ideal for conducting statistical tests and data exploration.
🔹 tensorflow: Deep learning. End-to-end open-source platform for machine learning.
🔹 keras: High-level neural networks API. Simplifies building and training deep learning models.
🔹 pytorch: Deep learning. A flexible and easy-to-use deep learning library.
🔹 mlflow: Machine learning lifecycle. Manages the machine learning lifecycle, including experimentation, reproducibility, and deployment.
🔹 pydantic: Data validation. Provides data validation and settings management using Python type annotations.
🔹 xgboost: Gradient boosting. An optimized distributed gradient boosting library.
🔹 lightgbm: Gradient boosting. A fast, distributed, high-performance gradient boosting framework.
🔹 pandas: Data manipulation and analysis. Essential for handling DataFrames.
🔹 numpy: Numerical computing. Perfect for working with arrays and mathematical functions.
🔹 scikit-learn: Machine learning. Comprehensive tools for predictive data analysis.
🔹 matplotlib: Data visualization. Great for creating static, animated, and interactive plots.
🔹 seaborn: Statistical data visualization. Makes complex plots easy and beautiful.
Data Science
🔹 scipy: Scientific computing. Provides algorithms for optimization, integration, and more.
🔹 statsmodels: Statistical modeling. Ideal for conducting statistical tests and data exploration.
🔹 tensorflow: Deep learning. End-to-end open-source platform for machine learning.
🔹 keras: High-level neural networks API. Simplifies building and training deep learning models.
🔹 pytorch: Deep learning. A flexible and easy-to-use deep learning library.
🔹 mlflow: Machine learning lifecycle. Manages the machine learning lifecycle, including experimentation, reproducibility, and deployment.
🔹 pydantic: Data validation. Provides data validation and settings management using Python type annotations.
🔹 xgboost: Gradient boosting. An optimized distributed gradient boosting library.
🔹 lightgbm: Gradient boosting. A fast, distributed, high-performance gradient boosting framework.
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🔍 Best Data Analytics Roles Based on Your Graduation Background!
Thinking about a career in Data Analytics but unsure which role fits your background? Check out these top job roles based on your degree:
🚀 For Mathematics/Statistics Graduates:
🔹 Data Analyst
🔹 Statistical Analyst
🔹 Quantitative Analyst
🔹 Risk Analyst
🚀 For Computer Science/IT Graduates:
🔹 Data Scientist
🔹 Business Intelligence Developer
🔹 Data Engineer
🔹 Data Architect
🚀 For Economics/Finance Graduates:
🔹 Financial Analyst
🔹 Market Research Analyst
🔹 Economic Consultant
🔹 Data Journalist
🚀 For Business/Management Graduates:
🔹 Business Analyst
🔹 Operations Research Analyst
🔹 Marketing Analytics Manager
🔹 Supply Chain Analyst
🚀 For Engineering Graduates:
🔹 Data Scientist
🔹 Industrial Engineer
🔹 Operations Research Analyst
🔹 Quality Engineer
🚀 For Social Science Graduates:
🔹 Data Analyst
🔹 Research Assistant
🔹 Social Media Analyst
🔹 Public Health Analyst
🚀 For Biology/Healthcare Graduates:
🔹 Clinical Data Analyst
🔹 Biostatistician
🔹 Research Coordinator
🔹 Healthcare Consultant
✅ Pro Tip:
Some of these roles may require additional certifications or upskilling in SQL, Python, Power BI, Tableau, or Machine Learning to stand out in the job market.
Like if it helps ❤️
Thinking about a career in Data Analytics but unsure which role fits your background? Check out these top job roles based on your degree:
🚀 For Mathematics/Statistics Graduates:
🔹 Data Analyst
🔹 Statistical Analyst
🔹 Quantitative Analyst
🔹 Risk Analyst
🚀 For Computer Science/IT Graduates:
🔹 Data Scientist
🔹 Business Intelligence Developer
🔹 Data Engineer
🔹 Data Architect
🚀 For Economics/Finance Graduates:
🔹 Financial Analyst
🔹 Market Research Analyst
🔹 Economic Consultant
🔹 Data Journalist
🚀 For Business/Management Graduates:
🔹 Business Analyst
🔹 Operations Research Analyst
🔹 Marketing Analytics Manager
🔹 Supply Chain Analyst
🚀 For Engineering Graduates:
🔹 Data Scientist
🔹 Industrial Engineer
🔹 Operations Research Analyst
🔹 Quality Engineer
🚀 For Social Science Graduates:
🔹 Data Analyst
🔹 Research Assistant
🔹 Social Media Analyst
🔹 Public Health Analyst
🚀 For Biology/Healthcare Graduates:
🔹 Clinical Data Analyst
🔹 Biostatistician
🔹 Research Coordinator
🔹 Healthcare Consultant
✅ Pro Tip:
Some of these roles may require additional certifications or upskilling in SQL, Python, Power BI, Tableau, or Machine Learning to stand out in the job market.
Like if it helps ❤️
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