📊 Complete Roadmap to Become a Power BI Expert
📂 1. Understand Basics of Data & BI
– What is Business Intelligence?
– Importance of data visualization
📂 2. Learn Power BI Interface
– Power BI Desktop overview
– Power Query Editor basics
📂 3. Connect to Data Sources
– Excel, SQL Server, SharePoint, APIs, CSV, etc.
📂 4. Data Transformation & Cleaning
– Use Power Query to shape, clean, and prepare data
📂 5. Learn Data Modeling
– Create relationships between tables
– Understand star schema & normalization basics
📂 6. Master DAX (Data Analysis Expressions)
– Calculated columns, measures, time intelligence functions
📂 7. Create Interactive Visualizations
– Charts, slicers, maps, tables, and custom visuals
📂 8. Build Dashboards & Reports
– Combine visuals for insightful dashboards
– Use bookmarks, drill-throughs, tooltips
📂 9. Publish & Share Reports
– Power BI Service basics
– Sharing, workspaces, and app creation
📂 10. Learn Power BI Administration
– Row-level security (RLS)
– Gateway setup & scheduled refresh
📂 11. Practice Real-World Projects
– Sales dashboards, financial reports, customer insights
👍 Like for more!
📂 1. Understand Basics of Data & BI
– What is Business Intelligence?
– Importance of data visualization
📂 2. Learn Power BI Interface
– Power BI Desktop overview
– Power Query Editor basics
📂 3. Connect to Data Sources
– Excel, SQL Server, SharePoint, APIs, CSV, etc.
📂 4. Data Transformation & Cleaning
– Use Power Query to shape, clean, and prepare data
📂 5. Learn Data Modeling
– Create relationships between tables
– Understand star schema & normalization basics
📂 6. Master DAX (Data Analysis Expressions)
– Calculated columns, measures, time intelligence functions
📂 7. Create Interactive Visualizations
– Charts, slicers, maps, tables, and custom visuals
📂 8. Build Dashboards & Reports
– Combine visuals for insightful dashboards
– Use bookmarks, drill-throughs, tooltips
📂 9. Publish & Share Reports
– Power BI Service basics
– Sharing, workspaces, and app creation
📂 10. Learn Power BI Administration
– Row-level security (RLS)
– Gateway setup & scheduled refresh
📂 11. Practice Real-World Projects
– Sales dashboards, financial reports, customer insights
👍 Like for more!
❤3
✅ Top Deep Learning Interview Questions & Answers 🤖🧠
📍 1. What is Deep Learning?
Answer: A subset of Machine Learning that uses multi-layered neural networks to learn patterns from large datasets. It excels in image recognition, speech processing, and NLP.
📍 2. What is a Neural Network?
Answer: A system of interconnected nodes (neurons) organized in layers — input, hidden, and output — that process data using weights and activation functions.
📍 3. What are Activation Functions?
Answer: They introduce non-linearity into the network. Common types:
⦁ ReLU: max(0, x) — fast and widely used
⦁ Sigmoid: outputs between 0 and 1
⦁ Tanh: outputs between -1 and 1
📍 4. What is Backpropagation?
Answer: The process of updating weights in a neural network by calculating the gradient of the loss function and propagating it backward using chain rule.
📍 5. What is Dropout?
Answer: A regularization technique that randomly disables neurons during training to prevent overfitting.
📍 6. What is Transfer Learning?
Answer: Using a pre-trained model on a new, related task. Example: fine-tuning ResNet for medical image classification.
📍 7. What are CNNs used for?
Answer: Convolutional Neural Networks are ideal for image and video data. They use filters to detect spatial hierarchies like edges, shapes, and textures.
📍 8. What are RNNs and LSTMs?
Answer:
⦁ RNNs handle sequential data but suffer from vanishing gradients.
⦁ LSTMs solve this using memory cells and gates to retain long-term dependencies.
📍 9. What are Autoencoders?
Answer: Unsupervised neural networks that compress data into a lower-dimensional form and then reconstruct it. Used in anomaly detection and denoising.
📍 10. What are GANs?
Answer: Generative Adversarial Networks consist of a Generator (creates fake data) and a Discriminator (detects fakes). Used in image synthesis, deepfakes, and art generation.
📍 11. What is Regularization in Deep Learning?
Answer: Techniques like L1/L2 penalties, Dropout, and Early Stopping help reduce overfitting by constraining model complexity.
📍 12. What is the Vanishing Gradient Problem?
Answer: In deep networks, gradients can become too small during backpropagation, making it hard to update weights. Solutions include using ReLU and batch normalization.
📍 13. What is Batch Normalization?
Answer: It normalizes inputs to each layer, stabilizing learning and speeding up training.
📍 14. What is the role of Epochs, Batches, and Iterations?
Answer:
⦁ Epoch: One full pass through the dataset
⦁ Batch: Subset of data used in one forward/backward pass
⦁ Iteration: One update of weights per batch
📍 15. What is the difference between Training and Inference?
Answer:
⦁ Training: Model learns from data
⦁ Inference: Model makes predictions using learned weights
💡 Pro Tip: Always explain concepts with examples or analogies in interviews. For instance, compare CNN filters to human vision detecting edges and shapes.
❤️ Tap for more AI/ML interview prep!
📍 1. What is Deep Learning?
Answer: A subset of Machine Learning that uses multi-layered neural networks to learn patterns from large datasets. It excels in image recognition, speech processing, and NLP.
📍 2. What is a Neural Network?
Answer: A system of interconnected nodes (neurons) organized in layers — input, hidden, and output — that process data using weights and activation functions.
📍 3. What are Activation Functions?
Answer: They introduce non-linearity into the network. Common types:
⦁ ReLU: max(0, x) — fast and widely used
⦁ Sigmoid: outputs between 0 and 1
⦁ Tanh: outputs between -1 and 1
📍 4. What is Backpropagation?
Answer: The process of updating weights in a neural network by calculating the gradient of the loss function and propagating it backward using chain rule.
📍 5. What is Dropout?
Answer: A regularization technique that randomly disables neurons during training to prevent overfitting.
📍 6. What is Transfer Learning?
Answer: Using a pre-trained model on a new, related task. Example: fine-tuning ResNet for medical image classification.
📍 7. What are CNNs used for?
Answer: Convolutional Neural Networks are ideal for image and video data. They use filters to detect spatial hierarchies like edges, shapes, and textures.
📍 8. What are RNNs and LSTMs?
Answer:
⦁ RNNs handle sequential data but suffer from vanishing gradients.
⦁ LSTMs solve this using memory cells and gates to retain long-term dependencies.
📍 9. What are Autoencoders?
Answer: Unsupervised neural networks that compress data into a lower-dimensional form and then reconstruct it. Used in anomaly detection and denoising.
📍 10. What are GANs?
Answer: Generative Adversarial Networks consist of a Generator (creates fake data) and a Discriminator (detects fakes). Used in image synthesis, deepfakes, and art generation.
📍 11. What is Regularization in Deep Learning?
Answer: Techniques like L1/L2 penalties, Dropout, and Early Stopping help reduce overfitting by constraining model complexity.
📍 12. What is the Vanishing Gradient Problem?
Answer: In deep networks, gradients can become too small during backpropagation, making it hard to update weights. Solutions include using ReLU and batch normalization.
📍 13. What is Batch Normalization?
Answer: It normalizes inputs to each layer, stabilizing learning and speeding up training.
📍 14. What is the role of Epochs, Batches, and Iterations?
Answer:
⦁ Epoch: One full pass through the dataset
⦁ Batch: Subset of data used in one forward/backward pass
⦁ Iteration: One update of weights per batch
📍 15. What is the difference between Training and Inference?
Answer:
⦁ Training: Model learns from data
⦁ Inference: Model makes predictions using learned weights
💡 Pro Tip: Always explain concepts with examples or analogies in interviews. For instance, compare CNN filters to human vision detecting edges and shapes.
❤️ Tap for more AI/ML interview prep!
❤5
Difference between linear regression and logistic regression 👇👇
Linear regression and logistic regression are both types of statistical models used for prediction and modeling, but they have different purposes and applications.
Linear regression is used to model the relationship between a dependent variable and one or more independent variables. It is used when the dependent variable is continuous and can take any value within a range. The goal of linear regression is to find the best-fitting line that describes the relationship between the independent and dependent variables.
Logistic regression, on the other hand, is used when the dependent variable is binary or categorical. It is used to model the probability of a certain event occurring based on one or more independent variables. The output of logistic regression is a probability value between 0 and 1, which can be interpreted as the likelihood of the event happening.
Data Science Interview Resources
👇👇
https://topmate.io/coding/914624
Like for more 😄
Linear regression and logistic regression are both types of statistical models used for prediction and modeling, but they have different purposes and applications.
Linear regression is used to model the relationship between a dependent variable and one or more independent variables. It is used when the dependent variable is continuous and can take any value within a range. The goal of linear regression is to find the best-fitting line that describes the relationship between the independent and dependent variables.
Logistic regression, on the other hand, is used when the dependent variable is binary or categorical. It is used to model the probability of a certain event occurring based on one or more independent variables. The output of logistic regression is a probability value between 0 and 1, which can be interpreted as the likelihood of the event happening.
Data Science Interview Resources
👇👇
https://topmate.io/coding/914624
Like for more 😄
❤2
10 commonly asked data science interview questions along with their answers
1️⃣ What is the difference between supervised and unsupervised learning?
Supervised learning involves learning from labeled data to predict outcomes while unsupervised learning involves finding patterns in unlabeled data.
2️⃣ Explain the bias-variance tradeoff in machine learning.
The bias-variance tradeoff is a key concept in machine learning. Models with high bias have low complexity and over-simplify, while models with high variance are more complex and over-fit to the training data. The goal is to find the right balance between bias and variance.
3️⃣ What is the Central Limit Theorem and why is it important in statistics?
The Central Limit Theorem (CLT) states that the sampling distribution of the sample means will be approximately normally distributed regardless of the underlying population distribution, as long as the sample size is sufficiently large. It is important because it justifies the use of statistics, such as hypothesis testing and confidence intervals, on small sample sizes.
4️⃣ Describe the process of feature selection and why it is important in machine learning.
Feature selection is the process of selecting the most relevant features (variables) from a dataset. This is important because unnecessary features can lead to over-fitting, slower training times, and reduced accuracy.
5️⃣ What is the difference between overfitting and underfitting in machine learning? How do you address them?
Overfitting occurs when a model is too complex and fits the training data too well, resulting in poor performance on unseen data. Underfitting occurs when a model is too simple and cannot fit the training data well enough, resulting in poor performance on both training and unseen data. Techniques to address overfitting include regularization and early stopping, while techniques to address underfitting include using more complex models or increasing the amount of input data.
6️⃣ What is regularization and why is it used in machine learning?
Regularization is a technique used to prevent overfitting in machine learning. It involves adding a penalty term to the loss function to limit the complexity of the model, effectively reducing the impact of certain features.
7️⃣ How do you handle missing data in a dataset?
Handling missing data can be done by either deleting the missing samples, imputing the missing values, or using models that can handle missing data directly.
8️⃣ What is the difference between classification and regression in machine learning?
Classification is a type of supervised learning where the goal is to predict a categorical or discrete outcome, while regression is a type of supervised learning where the goal is to predict a continuous or numerical outcome.
9️⃣ Explain the concept of cross-validation and why it is used.
Cross-validation is a technique used to evaluate the performance of a machine learning model. It involves spliting the data into training and validation sets, and then training and evaluating the model on multiple such splits. Cross-validation gives a better idea of the model's generalization ability and helps prevent over-fitting.
🔟 What evaluation metrics would you use to evaluate a binary classification model?
Some commonly used evaluation metrics for binary classification models are accuracy, precision, recall, F1 score, and ROC-AUC. The choice of metric depends on the specific requirements of the problem.
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
Credits: https://t.me/datasciencefun
Like if you need similar content 😄👍
Hope this helps you 😊
1️⃣ What is the difference between supervised and unsupervised learning?
Supervised learning involves learning from labeled data to predict outcomes while unsupervised learning involves finding patterns in unlabeled data.
2️⃣ Explain the bias-variance tradeoff in machine learning.
The bias-variance tradeoff is a key concept in machine learning. Models with high bias have low complexity and over-simplify, while models with high variance are more complex and over-fit to the training data. The goal is to find the right balance between bias and variance.
3️⃣ What is the Central Limit Theorem and why is it important in statistics?
The Central Limit Theorem (CLT) states that the sampling distribution of the sample means will be approximately normally distributed regardless of the underlying population distribution, as long as the sample size is sufficiently large. It is important because it justifies the use of statistics, such as hypothesis testing and confidence intervals, on small sample sizes.
4️⃣ Describe the process of feature selection and why it is important in machine learning.
Feature selection is the process of selecting the most relevant features (variables) from a dataset. This is important because unnecessary features can lead to over-fitting, slower training times, and reduced accuracy.
5️⃣ What is the difference between overfitting and underfitting in machine learning? How do you address them?
Overfitting occurs when a model is too complex and fits the training data too well, resulting in poor performance on unseen data. Underfitting occurs when a model is too simple and cannot fit the training data well enough, resulting in poor performance on both training and unseen data. Techniques to address overfitting include regularization and early stopping, while techniques to address underfitting include using more complex models or increasing the amount of input data.
6️⃣ What is regularization and why is it used in machine learning?
Regularization is a technique used to prevent overfitting in machine learning. It involves adding a penalty term to the loss function to limit the complexity of the model, effectively reducing the impact of certain features.
7️⃣ How do you handle missing data in a dataset?
Handling missing data can be done by either deleting the missing samples, imputing the missing values, or using models that can handle missing data directly.
8️⃣ What is the difference between classification and regression in machine learning?
Classification is a type of supervised learning where the goal is to predict a categorical or discrete outcome, while regression is a type of supervised learning where the goal is to predict a continuous or numerical outcome.
9️⃣ Explain the concept of cross-validation and why it is used.
Cross-validation is a technique used to evaluate the performance of a machine learning model. It involves spliting the data into training and validation sets, and then training and evaluating the model on multiple such splits. Cross-validation gives a better idea of the model's generalization ability and helps prevent over-fitting.
🔟 What evaluation metrics would you use to evaluate a binary classification model?
Some commonly used evaluation metrics for binary classification models are accuracy, precision, recall, F1 score, and ROC-AUC. The choice of metric depends on the specific requirements of the problem.
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
Credits: https://t.me/datasciencefun
Like if you need similar content 😄👍
Hope this helps you 😊
❤5
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🔖 Cheat sheets on Transformers and LLMs from Stanford
We found an excellent set of materials from the Stanford CME-295 course on large language models.
They cover tokenization, the self-attention mechanism, prompting, fine-tuning, LLM-as-a-judge, RAG, AI agents, and reasoning models.
⛓️ Download here
https://github.com/afshinea/stanford-cme-295-transformers-large-language-models
We found an excellent set of materials from the Stanford CME-295 course on large language models.
They cover tokenization, the self-attention mechanism, prompting, fine-tuning, LLM-as-a-judge, RAG, AI agents, and reasoning models.
⛓️ Download here
https://github.com/afshinea/stanford-cme-295-transformers-large-language-models
❤2
Understanding Popular ML Algorithms:
1️⃣ Linear Regression: Think of it as drawing a straight line through data points to predict future outcomes.
2️⃣ Logistic Regression: Like a yes/no machine - it predicts the likelihood of something happening or not.
3️⃣ Decision Trees: Imagine making decisions by answering yes/no questions, leading to a conclusion.
4️⃣ Random Forest: It's like a group of decision trees working together, making more accurate predictions.
5️⃣ Support Vector Machines (SVM): Visualize drawing lines to separate different types of things, like cats and dogs.
6️⃣ K-Nearest Neighbors (KNN): Friends sticking together - if most of your friends like something, chances are you'll like it too!
7️⃣ Neural Networks: Inspired by the brain, they learn patterns from examples - perfect for recognizing faces or understanding speech.
8️⃣ K-Means Clustering: Imagine sorting your socks by color without knowing how many colors there are - it groups similar things.
9️⃣ Principal Component Analysis (PCA): Simplifies complex data by focusing on what's important, like summarizing a long story with just a few key points.
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
ENJOY LEARNING 👍👍
1️⃣ Linear Regression: Think of it as drawing a straight line through data points to predict future outcomes.
2️⃣ Logistic Regression: Like a yes/no machine - it predicts the likelihood of something happening or not.
3️⃣ Decision Trees: Imagine making decisions by answering yes/no questions, leading to a conclusion.
4️⃣ Random Forest: It's like a group of decision trees working together, making more accurate predictions.
5️⃣ Support Vector Machines (SVM): Visualize drawing lines to separate different types of things, like cats and dogs.
6️⃣ K-Nearest Neighbors (KNN): Friends sticking together - if most of your friends like something, chances are you'll like it too!
7️⃣ Neural Networks: Inspired by the brain, they learn patterns from examples - perfect for recognizing faces or understanding speech.
8️⃣ K-Means Clustering: Imagine sorting your socks by color without knowing how many colors there are - it groups similar things.
9️⃣ Principal Component Analysis (PCA): Simplifies complex data by focusing on what's important, like summarizing a long story with just a few key points.
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
ENJOY LEARNING 👍👍
❤5
Stop buying AI courses.
Top companies are teaching AI for free 👇
1/ Google:
https://grow.google/ai
2/ NVIDIA:
https://www.nvidia.com/en-in/learn/ai-learning-essentials/
3/ Anthropic:
https://anthropic.skilljar.com/
4/ OpenAI:
https://academy.openai.com/
5/ IBM:
https://skillsbuild.org/
Top companies are teaching AI for free 👇
1/ Google:
https://grow.google/ai
2/ NVIDIA:
https://www.nvidia.com/en-in/learn/ai-learning-essentials/
3/ Anthropic:
https://anthropic.skilljar.com/
4/ OpenAI:
https://academy.openai.com/
5/ IBM:
https://skillsbuild.org/
❤2
Websites I wish I knew about before graduating 🥲
1. DORA : This website lets you create animated apps and sites in seconds
https://www.dora.run/
2. Chatgpt study : Turns your ChatGPT into an actual tutor that can test you in real time
https://openai.com/index/chatgpt-study-mode/
3. docsity : Gives you free access to past papers, exams just by entering your university
https://www.docsity.com/en/
4. chatpdf : Lets you upload your documents so you can chat and question the different sections
https://www.chatpdf.com/
5. paper panda : This website can legit gives you access to any paper or article you can’t access
https://paperpanda.app/
6. Paperpal: You can paste your entire essay in here and it edits it like a top students
https://paperpal.com
7. flow: This website lets you create an entire animated movie using AI
https://labs.google/flow/about
1. DORA : This website lets you create animated apps and sites in seconds
https://www.dora.run/
2. Chatgpt study : Turns your ChatGPT into an actual tutor that can test you in real time
https://openai.com/index/chatgpt-study-mode/
3. docsity : Gives you free access to past papers, exams just by entering your university
https://www.docsity.com/en/
4. chatpdf : Lets you upload your documents so you can chat and question the different sections
https://www.chatpdf.com/
5. paper panda : This website can legit gives you access to any paper or article you can’t access
https://paperpanda.app/
6. Paperpal: You can paste your entire essay in here and it edits it like a top students
https://paperpal.com
7. flow: This website lets you create an entire animated movie using AI
https://labs.google/flow/about
❤2
Quick Power BI Functions Cheat Sheet for Beginners 📊✍️
Aggregation Functions (DAX)
• SUM(column): Returns the total of a column.
• AVERAGE(column): Calculates the average value.
• COUNT(column): Counts non-blank rows in a column.
• DISTINCTCOUNT(column): Counts unique values.
• MAX(column): Returns the highest value.
• MIN(column): Returns the lowest value.
Filter Functions
• FILTER(table, condition): Returns a filtered table based on a condition.
• ALL(table/column): Removes filters from a table or column.
• ALLEXCEPT(table, column): Removes all filters except specified columns.
• VALUES(column): Returns unique values from a column.
Logical Functions
• IF(condition, true_result, false_result): Performs conditional logic.
• SWITCH(expression, value1, result1, …): Evaluates multiple conditions.
• AND(condition1, condition2): Returns TRUE if both conditions are true.
• OR(condition1, condition2): Returns TRUE if any condition is true.
Date Time Functions
• TODAY(): Returns the current date.
• NOW(): Returns the current date and time.
• YEAR(date): Extracts the year from a date.
• MONTH(date): Extracts the month from a date.
• DATEDIFF(date1, date2, interval): Returns the difference between two dates.
Text Functions
• CONCATENATE(text1, text2): Joins two text values.
• LEFT(text, num_chars): Extracts characters from the left.
• RIGHT(text, num_chars): Extracts characters from the right.
• LEN(text): Returns the length of text.
• FORMAT(value, text, format_string): Converts value to formatted text.
Relationship Functions
• RELATED(column): Fetches data from a related table.
• RELATEDTABLE(table): Returns related rows from another table.
• USERELATIONSHIP(column1, column2): Activates an inactive relationship.
Power BI Resources: https://whatsapp.com/channel/0029Vai1xKf1dAvuk6s1v22c
Double Tap ♥️ For More
Aggregation Functions (DAX)
• SUM(column): Returns the total of a column.
• AVERAGE(column): Calculates the average value.
• COUNT(column): Counts non-blank rows in a column.
• DISTINCTCOUNT(column): Counts unique values.
• MAX(column): Returns the highest value.
• MIN(column): Returns the lowest value.
Filter Functions
• FILTER(table, condition): Returns a filtered table based on a condition.
• ALL(table/column): Removes filters from a table or column.
• ALLEXCEPT(table, column): Removes all filters except specified columns.
• VALUES(column): Returns unique values from a column.
Logical Functions
• IF(condition, true_result, false_result): Performs conditional logic.
• SWITCH(expression, value1, result1, …): Evaluates multiple conditions.
• AND(condition1, condition2): Returns TRUE if both conditions are true.
• OR(condition1, condition2): Returns TRUE if any condition is true.
Date Time Functions
• TODAY(): Returns the current date.
• NOW(): Returns the current date and time.
• YEAR(date): Extracts the year from a date.
• MONTH(date): Extracts the month from a date.
• DATEDIFF(date1, date2, interval): Returns the difference between two dates.
Text Functions
• CONCATENATE(text1, text2): Joins two text values.
• LEFT(text, num_chars): Extracts characters from the left.
• RIGHT(text, num_chars): Extracts characters from the right.
• LEN(text): Returns the length of text.
• FORMAT(value, text, format_string): Converts value to formatted text.
Relationship Functions
• RELATED(column): Fetches data from a related table.
• RELATEDTABLE(table): Returns related rows from another table.
• USERELATIONSHIP(column1, column2): Activates an inactive relationship.
Power BI Resources: https://whatsapp.com/channel/0029Vai1xKf1dAvuk6s1v22c
Double Tap ♥️ For More
❤1
Top Free AI Courses from Google: ⬇️
1. Intro to the Cloud
Understand the cloud and why it's critical in AI.
Link: https://lnkd.in/eU6icWR3
2. Prompt Design in Vertex AI
Master better prompts & use Gemini effectively.
Link: https://lnkd.in/eAaG_iHb
3. Google AI Essentials (via Coursera)
Use AI to brainstorm, write faster, and be more productive.
Link: https://lnkd.in/eJYGt2Xa
4. What is Generative AI?
Google’s free certified course explains it in simple terms.
Perfect for beginners — no coding needed.
Link: https://lnkd.in/eqqa37dV
5. Introduction to Machine Learning
Make predictions from data and explore ethical AI.
Link: https://lnkd.in/eHpGkmrx
6. Intro to Large Language Models (LLMs)
Learn how LLMs (like ChatGPT) work and why they matter.
Google’s FREE certified course is live now:
Link: https://lnkd.in/enNnyxwU
7. Applying AI Principles with Google Cloud
Learn how Google applies responsible AI in practice.
Link: https://lnkd.in/eRU7xB_t
8. Basics of Code
A beginner-friendly intro to programming and logic.
👉 https://lnkd.in/eUd_XEcf
9. Cloud Computing Foundations
Dive into cloud, big data, and ML — in one course.
Link: https://lnkd.in/ebVfPdrD
10. Intro to Responsible AI
Learn how to build safer and more inclusive AI systems.
Link: https://lnkd.in/e4EC8xNJ
Double Tap ❤️ For More
1. Intro to the Cloud
Understand the cloud and why it's critical in AI.
Link: https://lnkd.in/eU6icWR3
2. Prompt Design in Vertex AI
Master better prompts & use Gemini effectively.
Link: https://lnkd.in/eAaG_iHb
3. Google AI Essentials (via Coursera)
Use AI to brainstorm, write faster, and be more productive.
Link: https://lnkd.in/eJYGt2Xa
4. What is Generative AI?
Google’s free certified course explains it in simple terms.
Perfect for beginners — no coding needed.
Link: https://lnkd.in/eqqa37dV
5. Introduction to Machine Learning
Make predictions from data and explore ethical AI.
Link: https://lnkd.in/eHpGkmrx
6. Intro to Large Language Models (LLMs)
Learn how LLMs (like ChatGPT) work and why they matter.
Google’s FREE certified course is live now:
Link: https://lnkd.in/enNnyxwU
7. Applying AI Principles with Google Cloud
Learn how Google applies responsible AI in practice.
Link: https://lnkd.in/eRU7xB_t
8. Basics of Code
A beginner-friendly intro to programming and logic.
👉 https://lnkd.in/eUd_XEcf
9. Cloud Computing Foundations
Dive into cloud, big data, and ML — in one course.
Link: https://lnkd.in/ebVfPdrD
10. Intro to Responsible AI
Learn how to build safer and more inclusive AI systems.
Link: https://lnkd.in/e4EC8xNJ
Double Tap ❤️ For More
❤1
Python has a built-in topological dependency sorter!🚀
If you're working with tasks that have dependencies — for example, in build systems, CI/CD pipelines, or workflow orchestration — the order of execution often has to be determined manually.
Usually through graphs, DFS,, or custom execution order logic.
But Python's standard library already has graphlib.TopologicalSorter.
After preparation, the sorter returns the correct execution order.
Result:
Especially useful for workflow management systems, dependency resolution, orchestration systems, and any tasks with a dependency graph.
🔥 TopologicalSorter allows you to solve dependency problems using Python's built-in tools without having to implement graph algorithms manually.
If you're working with tasks that have dependencies — for example, in build systems, CI/CD pipelines, or workflow orchestration — the order of execution often has to be determined manually.
Usually through graphs, DFS,, or custom execution order logic.
But Python's standard library already has graphlib.TopologicalSorter.
ts = TopologicalSorter()
ts.add("deploy", "test")
ts.add("test", "build")
After preparation, the sorter returns the correct execution order.
tuple(ts.static_order())
Result:
("build", "test", "deploy")Especially useful for workflow management systems, dependency resolution, orchestration systems, and any tasks with a dependency graph.
🔥 TopologicalSorter allows you to solve dependency problems using Python's built-in tools without having to implement graph algorithms manually.
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Data Analyst Interview Questions & Preparation Tips
Be prepared with a mix of technical, analytical, and business-oriented interview questions.
1. Technical Questions (Data Analysis & Reporting)
SQL Questions:
How do you write a query to fetch the top 5 highest revenue-generating customers?
Explain the difference between INNER JOIN, LEFT JOIN, and FULL OUTER JOIN.
How would you optimize a slow-running query?
What are CTEs and when would you use them?
Data Visualization (Power BI / Tableau / Excel)
How would you create a dashboard to track key performance metrics?
Explain the difference between measures and calculated columns in Power BI.
How do you handle missing data in Tableau?
What are DAX functions, and can you give an example?
ETL & Data Processing (Alteryx, Power BI, Excel)
What is ETL, and how does it relate to BI?
Have you used Alteryx for data transformation? Explain a complex workflow you built.
How do you automate reporting using Power Query in Excel?
2. Business and Analytical Questions
How do you define KPIs for a business process?
Give an example of how you used data to drive a business decision.
How would you identify cost-saving opportunities in a reporting process?
Explain a time when your report uncovered a hidden business insight.
3. Scenario-Based & Behavioral Questions
Stakeholder Management:
How do you handle a situation where different business units have conflicting reporting requirements?
How do you explain complex data insights to non-technical stakeholders?
Problem-Solving & Debugging:
What would you do if your report is showing incorrect numbers?
How do you ensure the accuracy of a new KPI you introduced?
Project Management & Process Improvement:
Have you led a project to automate or improve a reporting process?
What steps do you take to ensure the timely delivery of reports?
4. Industry-Specific Questions (Credit Reporting & Financial Services)
What are some key credit risk metrics used in financial services?
How would you analyze trends in customer credit behavior?
How do you ensure compliance and data security in reporting?
5. General HR Questions
Why do you want to work at this company?
Tell me about a challenging project and how you handled it.
What are your strengths and weaknesses?
Where do you see yourself in five years?
How to Prepare?
Brush up on SQL, Power BI, and ETL tools (especially Alteryx).
Learn about key financial and credit reporting metrics.(varies company to company)
Practice explaining data-driven insights in a business-friendly manner.
Be ready to showcase problem-solving skills with real-world examples.
React with ❤️ if you want me to also post sample answer for the above questions
Share with credits: https://t.me/sqlspecialist
Hope it helps :)
Be prepared with a mix of technical, analytical, and business-oriented interview questions.
1. Technical Questions (Data Analysis & Reporting)
SQL Questions:
How do you write a query to fetch the top 5 highest revenue-generating customers?
Explain the difference between INNER JOIN, LEFT JOIN, and FULL OUTER JOIN.
How would you optimize a slow-running query?
What are CTEs and when would you use them?
Data Visualization (Power BI / Tableau / Excel)
How would you create a dashboard to track key performance metrics?
Explain the difference between measures and calculated columns in Power BI.
How do you handle missing data in Tableau?
What are DAX functions, and can you give an example?
ETL & Data Processing (Alteryx, Power BI, Excel)
What is ETL, and how does it relate to BI?
Have you used Alteryx for data transformation? Explain a complex workflow you built.
How do you automate reporting using Power Query in Excel?
2. Business and Analytical Questions
How do you define KPIs for a business process?
Give an example of how you used data to drive a business decision.
How would you identify cost-saving opportunities in a reporting process?
Explain a time when your report uncovered a hidden business insight.
3. Scenario-Based & Behavioral Questions
Stakeholder Management:
How do you handle a situation where different business units have conflicting reporting requirements?
How do you explain complex data insights to non-technical stakeholders?
Problem-Solving & Debugging:
What would you do if your report is showing incorrect numbers?
How do you ensure the accuracy of a new KPI you introduced?
Project Management & Process Improvement:
Have you led a project to automate or improve a reporting process?
What steps do you take to ensure the timely delivery of reports?
4. Industry-Specific Questions (Credit Reporting & Financial Services)
What are some key credit risk metrics used in financial services?
How would you analyze trends in customer credit behavior?
How do you ensure compliance and data security in reporting?
5. General HR Questions
Why do you want to work at this company?
Tell me about a challenging project and how you handled it.
What are your strengths and weaknesses?
Where do you see yourself in five years?
How to Prepare?
Brush up on SQL, Power BI, and ETL tools (especially Alteryx).
Learn about key financial and credit reporting metrics.(varies company to company)
Practice explaining data-driven insights in a business-friendly manner.
Be ready to showcase problem-solving skills with real-world examples.
React with ❤️ if you want me to also post sample answer for the above questions
Share with credits: https://t.me/sqlspecialist
Hope it helps :)
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Most people spend months watching random YouTube videos and still don’t become job-ready.
Instead, learn in a structured offline classroom.
📌 Data Analytics with GenAI
📌 Python + SQL + Power BI
📌 6-Month Program
📌 1:1 Mentorship
📌 Job Assistance
📍Now available in your city.
Seats are limited.
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