𝐃𝐢𝐬𝐜𝐮𝐬𝐬𝐢𝐧𝐠 𝐏𝐨𝐰𝐞𝐫 𝐁𝐈 𝐬𝐜𝐞𝐧𝐚𝐫𝐢𝐨 𝐛𝐚𝐬𝐞𝐝 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧 💡
𝑺𝒄𝒆𝒏𝒂𝒓𝒊𝒐 👇
You are a data analyst for a global e-commerce company. You need to analyze the performance of your marketing campaigns across different regions and identify which campaigns have the highest return on investment (ROI). Additionally, you want to see how customer acquisition costs (CAC) vary by region and campaign.
𝑸𝒖𝒆𝒔𝒕𝒊𝒐𝒏 👇
How would you use Power BI to create a comprehensive report on marketing campaign performance and ROI analysis?
𝑨𝒏𝒔𝒘𝒆𝒓:
For this we are provided with three datasets:
𝐂𝐚𝐦𝐩𝐚𝐢𝐠𝐧𝐬: CampaignID, CampaignName, Region, StartDate, EndDate, Budget
𝐒𝐚𝐥𝐞𝐬: SaleID, CampaignID, SaleAmount, SaleDate
𝐄𝐱𝐩𝐞𝐧𝐬𝐞𝐬: ExpenseID, CampaignID, ExpenseAmount, ExpenseDate
▶ 𝑺𝒕𝒆𝒑 1: Analyze the dataset thoroughly and perform some data cleaning and transformation steps 📈
▶ 𝑺𝒕𝒆𝒑 2: Create Measures that are required in accordance with scenario given.
Total Sales = SUM(Sales[SaleAmount])
Total Expenses = SUM(Expenses[ExpenseAmount])
ROI = DIVIDE([Total Sales] - [Total Expenses], [Total Expenses])
Customer Acquisition Cost (CAC): CAC = DIVIDE([Total Expenses], DISTINCTCOUNT(Sales[SaleID]))
▶ 𝑺𝒕𝒆𝒑 3: Use appropriate filters and visuals according to your requirements. You may use clustered column chart for CAC by region, line chart for sales and expense trends, can add slicers for region, campaign name, and date range, etc.
▶ 𝑺𝒕𝒆𝒑 4: Analyze the project for some informative insights and trends.
I have curated the best interview resources to crack Power BI Interviews 👇👇
https://topmate.io/analyst/866125
Like this post if you need more resources like this 👍❤️
𝑺𝒄𝒆𝒏𝒂𝒓𝒊𝒐 👇
You are a data analyst for a global e-commerce company. You need to analyze the performance of your marketing campaigns across different regions and identify which campaigns have the highest return on investment (ROI). Additionally, you want to see how customer acquisition costs (CAC) vary by region and campaign.
𝑸𝒖𝒆𝒔𝒕𝒊𝒐𝒏 👇
How would you use Power BI to create a comprehensive report on marketing campaign performance and ROI analysis?
𝑨𝒏𝒔𝒘𝒆𝒓:
For this we are provided with three datasets:
𝐂𝐚𝐦𝐩𝐚𝐢𝐠𝐧𝐬: CampaignID, CampaignName, Region, StartDate, EndDate, Budget
𝐒𝐚𝐥𝐞𝐬: SaleID, CampaignID, SaleAmount, SaleDate
𝐄𝐱𝐩𝐞𝐧𝐬𝐞𝐬: ExpenseID, CampaignID, ExpenseAmount, ExpenseDate
▶ 𝑺𝒕𝒆𝒑 1: Analyze the dataset thoroughly and perform some data cleaning and transformation steps 📈
▶ 𝑺𝒕𝒆𝒑 2: Create Measures that are required in accordance with scenario given.
Total Sales = SUM(Sales[SaleAmount])
Total Expenses = SUM(Expenses[ExpenseAmount])
ROI = DIVIDE([Total Sales] - [Total Expenses], [Total Expenses])
Customer Acquisition Cost (CAC): CAC = DIVIDE([Total Expenses], DISTINCTCOUNT(Sales[SaleID]))
▶ 𝑺𝒕𝒆𝒑 3: Use appropriate filters and visuals according to your requirements. You may use clustered column chart for CAC by region, line chart for sales and expense trends, can add slicers for region, campaign name, and date range, etc.
▶ 𝑺𝒕𝒆𝒑 4: Analyze the project for some informative insights and trends.
I have curated the best interview resources to crack Power BI Interviews 👇👇
https://topmate.io/analyst/866125
Like this post if you need more resources like this 👍❤️
❤3
Technical Questions Wipro may ask on their interviews
1. Data Structures and Algorithms:
- "Can you explain the difference between an array and a linked list? When would you use one over the other in a real-world application?"
- "Write code to implement a binary search algorithm."
2. Programming Languages:
- "What is the difference between Java and C++? Can you provide an example of a situation where you would prefer one language over the other?"
- "Write a program in your preferred programming language to reverse a string."
3. Database and SQL:
- "Explain the ACID properties in the context of database transactions."
- "Write an SQL query to retrieve all records from a 'customers' table where the 'country' column is 'India'."
4. Networking:
- "What is the difference between TCP and UDP? When would you choose one over the other for a specific application?"
- "Explain the concept of DNS (Domain Name System) and how it works."
5. System Design:
- "Design a simple online messaging system. What components would you include, and how would they interact?"
- "How would you ensure the scalability and fault tolerance of a web service or application?"
1. Data Structures and Algorithms:
- "Can you explain the difference between an array and a linked list? When would you use one over the other in a real-world application?"
- "Write code to implement a binary search algorithm."
2. Programming Languages:
- "What is the difference between Java and C++? Can you provide an example of a situation where you would prefer one language over the other?"
- "Write a program in your preferred programming language to reverse a string."
3. Database and SQL:
- "Explain the ACID properties in the context of database transactions."
- "Write an SQL query to retrieve all records from a 'customers' table where the 'country' column is 'India'."
4. Networking:
- "What is the difference between TCP and UDP? When would you choose one over the other for a specific application?"
- "Explain the concept of DNS (Domain Name System) and how it works."
5. System Design:
- "Design a simple online messaging system. What components would you include, and how would they interact?"
- "How would you ensure the scalability and fault tolerance of a web service or application?"
❤3
Machine Learning Roadmap
|
|-- Fundamentals
| |-- Mathematics
| | |-- Linear Algebra
| | |-- Calculus (Gradients, Optimization)
| | |-- Probability and Statistics
| | |-- Matrix Operations
| |
| |-- Programming
| | |-- Python (NumPy, Pandas, Scikit-learn)
| | |-- R (Optional for Statistical Modeling)
| | |-- SQL (For Data Extraction)
|
|-- Data Preprocessing
| |-- Data Cleaning
| |-- Feature Engineering
| | |-- Encoding Categorical Data
| | |-- Feature Scaling (Standardization, Normalization)
| | |-- Handling Missing Values
| |-- Dimensionality Reduction (PCA, LDA)
|
|-- Supervised Learning
| |-- Regression
| | |-- Linear Regression
| | |-- Polynomial Regression
| | |-- Ridge and Lasso Regression
| |-- Classification
| | |-- Logistic Regression
| | |-- Decision Trees
| | |-- Support Vector Machines (SVM)
| | |-- Ensemble Methods (Random Forest, Gradient Boosting, XGBoost)
|
|-- Unsupervised Learning
| |-- Clustering
| | |-- K-Means
| | |-- Hierarchical Clustering
| | |-- DBSCAN
| |-- Dimensionality Reduction
| | |-- Principal Component Analysis (PCA)
| | |-- t-SNE
| |-- Association Rules (Apriori, FP-Growth)
|
|-- Reinforcement Learning
| |-- Markov Decision Processes
| |-- Q-Learning
| |-- Deep Q-Learning
| |-- Policy Gradient Methods
|
|-- Model Evaluation and Optimization
| |-- Train-Test Split and Cross-Validation
| |-- Performance Metrics
| | |-- Accuracy, Precision, Recall, F1-Score
| | |-- ROC-AUC
| | |-- Mean Squared Error (MSE), R-squared
| |-- Hyperparameter Tuning
| | |-- Grid Search
| | |-- Random Search
| | |-- Bayesian Optimization
|
|-- Deep Learning
| |-- Neural Networks
| | |-- Perceptrons
| | |-- Backpropagation
| |-- Convolutional Neural Networks (CNN)
| | |-- Image Classification
| | |-- Object Detection (YOLO, SSD)
| |-- Recurrent Neural Networks (RNN)
| | |-- LSTM
| | |-- GRU
| |-- Transformers (Attention Mechanisms, BERT, GPT)
| |-- Tools and Frameworks (TensorFlow, PyTorch)
|
|-- Advanced Topics
| |-- Transfer Learning
| |-- Generative Adversarial Networks (GANs)
| |-- Reinforcement Learning with Neural Networks
| |-- Explainable AI (SHAP, LIME)
|
|-- Applications of Machine Learning
| |-- Recommender Systems (Collaborative Filtering, Content-Based)
| |-- Fraud Detection
| |-- Sentiment Analysis
| |-- Predictive Maintenance
| |-- Autonomous Vehicles
|
|-- Deployment of Models
| |-- Flask, FastAPI
| |-- Cloud Deployment (AWS SageMaker, Azure ML)
| |-- Containerization (Docker, Kubernetes)
| |-- Model Monitoring and Retraining
Best Resources to learn Machine Learning 👇👇
Learn Python for Free
Prompt Engineering Course
Prompt Engineering Guide
Data Science Course
Google Cloud Generative AI Path
Machine Learning with Python Free Course
Machine Learning Free Book
Deep Learning Nanodegree Program with Real-world Projects
AI, Machine Learning and Deep Learning
Join @free4unow_backup for more free courses
ENJOY LEARNING👍👍
|
|-- Fundamentals
| |-- Mathematics
| | |-- Linear Algebra
| | |-- Calculus (Gradients, Optimization)
| | |-- Probability and Statistics
| | |-- Matrix Operations
| |
| |-- Programming
| | |-- Python (NumPy, Pandas, Scikit-learn)
| | |-- R (Optional for Statistical Modeling)
| | |-- SQL (For Data Extraction)
|
|-- Data Preprocessing
| |-- Data Cleaning
| |-- Feature Engineering
| | |-- Encoding Categorical Data
| | |-- Feature Scaling (Standardization, Normalization)
| | |-- Handling Missing Values
| |-- Dimensionality Reduction (PCA, LDA)
|
|-- Supervised Learning
| |-- Regression
| | |-- Linear Regression
| | |-- Polynomial Regression
| | |-- Ridge and Lasso Regression
| |-- Classification
| | |-- Logistic Regression
| | |-- Decision Trees
| | |-- Support Vector Machines (SVM)
| | |-- Ensemble Methods (Random Forest, Gradient Boosting, XGBoost)
|
|-- Unsupervised Learning
| |-- Clustering
| | |-- K-Means
| | |-- Hierarchical Clustering
| | |-- DBSCAN
| |-- Dimensionality Reduction
| | |-- Principal Component Analysis (PCA)
| | |-- t-SNE
| |-- Association Rules (Apriori, FP-Growth)
|
|-- Reinforcement Learning
| |-- Markov Decision Processes
| |-- Q-Learning
| |-- Deep Q-Learning
| |-- Policy Gradient Methods
|
|-- Model Evaluation and Optimization
| |-- Train-Test Split and Cross-Validation
| |-- Performance Metrics
| | |-- Accuracy, Precision, Recall, F1-Score
| | |-- ROC-AUC
| | |-- Mean Squared Error (MSE), R-squared
| |-- Hyperparameter Tuning
| | |-- Grid Search
| | |-- Random Search
| | |-- Bayesian Optimization
|
|-- Deep Learning
| |-- Neural Networks
| | |-- Perceptrons
| | |-- Backpropagation
| |-- Convolutional Neural Networks (CNN)
| | |-- Image Classification
| | |-- Object Detection (YOLO, SSD)
| |-- Recurrent Neural Networks (RNN)
| | |-- LSTM
| | |-- GRU
| |-- Transformers (Attention Mechanisms, BERT, GPT)
| |-- Tools and Frameworks (TensorFlow, PyTorch)
|
|-- Advanced Topics
| |-- Transfer Learning
| |-- Generative Adversarial Networks (GANs)
| |-- Reinforcement Learning with Neural Networks
| |-- Explainable AI (SHAP, LIME)
|
|-- Applications of Machine Learning
| |-- Recommender Systems (Collaborative Filtering, Content-Based)
| |-- Fraud Detection
| |-- Sentiment Analysis
| |-- Predictive Maintenance
| |-- Autonomous Vehicles
|
|-- Deployment of Models
| |-- Flask, FastAPI
| |-- Cloud Deployment (AWS SageMaker, Azure ML)
| |-- Containerization (Docker, Kubernetes)
| |-- Model Monitoring and Retraining
Best Resources to learn Machine Learning 👇👇
Learn Python for Free
Prompt Engineering Course
Prompt Engineering Guide
Data Science Course
Google Cloud Generative AI Path
Machine Learning with Python Free Course
Machine Learning Free Book
Deep Learning Nanodegree Program with Real-world Projects
AI, Machine Learning and Deep Learning
Join @free4unow_backup for more free courses
ENJOY LEARNING👍👍
❤6
Master SQL step-by-step! From basics to advanced, here are the key topics you need for a solid SQL foundation. 🚀
1. Foundations:
- Learn basic SQL syntax, including SELECT, FROM, WHERE clauses.
- Understand data types, constraints, and the basic structure of a database.
2. Database Design:
- Study database normalization to ensure efficient data organization.
- Learn about primary keys, foreign keys, and relationships between tables.
3. Queries and Joins:
- Practice writing simple to complex SELECT queries.
- Master different types of joins (INNER, LEFT, RIGHT, FULL) to combine data from multiple tables.
4. Aggregation and Grouping:
- Explore aggregate functions like COUNT, SUM, AVG, MAX, and MIN.
- Understand GROUP BY clause for summarizing data based on specific criteria.
5. Subqueries and Nested Queries:
- Learn how to use subqueries to perform operations within another query.
- Understand the concept of nested queries and their practical applications.
6. Indexing and Optimization:
- Study indexing for enhancing query performance.
- Learn optimization techniques, such as avoiding SELECT * and using appropriate indexes.
7. Transactions and ACID Properties:
- Understand the basics of transactions and their role in maintaining data integrity.
- Explore ACID properties (Atomicity, Consistency, Isolation, Durability) in database management.
8. Views and Stored Procedures:
- Create and use views to simplify complex queries.
- Learn about stored procedures for reusable and efficient query execution.
9. Security and Permissions:
- Understand SQL injection risks and how to prevent them.
- Learn how to manage user permissions and access control.
10. Advanced Topics:
- Explore advanced SQL concepts like window functions, CTEs (Common Table Expressions), and recursive queries.
- Familiarize yourself with database-specific features (e.g., PostgreSQL's JSON functions, MySQL's spatial data types).
11. Real-world Projects:
- Apply your knowledge to real-world scenarios by working on projects.
- Practice with sample databases or create your own to reinforce your skills.
12. Continuous Learning:
- Stay updated on SQL advancements and industry best practices.
- Engage with online communities, forums, and resources for ongoing learning and problem-solving.
Here are some free resources to learn & practice SQL 👇👇
Udacity free course- https://imp.i115008.net/AoAg7K
SQL For Data Analysis: https://news.1rj.ru/str/sqlanalyst
For Practice- https://stratascratch.com/?via=free
SQL Learning Series: https://news.1rj.ru/str/sqlspecialist/567
Top 10 SQL Projects with Datasets: https://news.1rj.ru/str/DataPortfolio/16
Join for more free resources: https://news.1rj.ru/str/free4unow_backup
ENJOY LEARNING 👍👍
1. Foundations:
- Learn basic SQL syntax, including SELECT, FROM, WHERE clauses.
- Understand data types, constraints, and the basic structure of a database.
2. Database Design:
- Study database normalization to ensure efficient data organization.
- Learn about primary keys, foreign keys, and relationships between tables.
3. Queries and Joins:
- Practice writing simple to complex SELECT queries.
- Master different types of joins (INNER, LEFT, RIGHT, FULL) to combine data from multiple tables.
4. Aggregation and Grouping:
- Explore aggregate functions like COUNT, SUM, AVG, MAX, and MIN.
- Understand GROUP BY clause for summarizing data based on specific criteria.
5. Subqueries and Nested Queries:
- Learn how to use subqueries to perform operations within another query.
- Understand the concept of nested queries and their practical applications.
6. Indexing and Optimization:
- Study indexing for enhancing query performance.
- Learn optimization techniques, such as avoiding SELECT * and using appropriate indexes.
7. Transactions and ACID Properties:
- Understand the basics of transactions and their role in maintaining data integrity.
- Explore ACID properties (Atomicity, Consistency, Isolation, Durability) in database management.
8. Views and Stored Procedures:
- Create and use views to simplify complex queries.
- Learn about stored procedures for reusable and efficient query execution.
9. Security and Permissions:
- Understand SQL injection risks and how to prevent them.
- Learn how to manage user permissions and access control.
10. Advanced Topics:
- Explore advanced SQL concepts like window functions, CTEs (Common Table Expressions), and recursive queries.
- Familiarize yourself with database-specific features (e.g., PostgreSQL's JSON functions, MySQL's spatial data types).
11. Real-world Projects:
- Apply your knowledge to real-world scenarios by working on projects.
- Practice with sample databases or create your own to reinforce your skills.
12. Continuous Learning:
- Stay updated on SQL advancements and industry best practices.
- Engage with online communities, forums, and resources for ongoing learning and problem-solving.
Here are some free resources to learn & practice SQL 👇👇
Udacity free course- https://imp.i115008.net/AoAg7K
SQL For Data Analysis: https://news.1rj.ru/str/sqlanalyst
For Practice- https://stratascratch.com/?via=free
SQL Learning Series: https://news.1rj.ru/str/sqlspecialist/567
Top 10 SQL Projects with Datasets: https://news.1rj.ru/str/DataPortfolio/16
Join for more free resources: https://news.1rj.ru/str/free4unow_backup
ENJOY LEARNING 👍👍
❤3
20 Must-Know Statistics Questions for Data Analyst and Business Analyst Roles (With Detailed Answers)
1. What is the difference between denoscriptive and inferential statistics?
Denoscriptive statistics summarize and organize data (e.g., mean, median, mode).
Inferential statistics make predictions or inferences about a population based on a sample (e.g., hypothesis testing, confidence intervals).
2. Explain mean, median, and mode and when to use each.
Mean is the average; use when data is symmetrically distributed.
Median is the middle value; best when data has outliers.
Mode is the most frequent value; useful for categorical data.
3. What is standard deviation, and why is it important?
It measures data spread around the mean. A low value = less variability; high value = more spread. Important for understanding consistency and risk.
4. Define correlation vs. causation with examples.
Correlation: Two variables move together but don't cause each other (e.g., ice cream sales and drowning).
Causation: One variable directly affects another (e.g., smoking causes lung cancer).
5. What is a p-value, and how do you interpret it?
P-value measures the probability of observing results given that the null hypothesis is true. A small p-value (typically < 0.05) suggests rejecting the null.
6. Explain the concept of confidence intervals.
A range of values used to estimate a population parameter. A 95% CI means there's a 95% chance the true value falls within the range.
7. What are outliers, and how can you handle them?
Outliers are extreme values differing significantly from others. Handle using:
Removal (if due to error)
Transformation
Capping (e.g., winsorizing)
8. When would you use a t-test vs. a z-test?
T-test: Small samples (n < 30) and unknown population standard deviation.
Z-test: Large samples and known standard deviation.
9. What is the Central Limit Theorem (CLT), and why is it important?
CLT states that the sampling distribution of the sample mean approaches a normal distribution as sample size grows, regardless of population distribution. Essential for inference.
10. Explain the difference between population and sample.
Population: Entire group of interest.
Sample: Subset used for analysis. Inference is made from the sample to the population.
11. What is regression analysis, and what are its key assumptions?
Predicts a dependent variable using one or more independent variables.
Assumptions: Linearity, independence, homoscedasticity, no multicollinearity, normality of residuals.
12. How do you calculate probability, and why does it matter in analytics?
Probability = (Favorable outcomes) / (Total outcomes).
Critical for risk estimation, decision-making, and predictions.
13. Explain the concept of Bayes’ Theorem with a practical example.
Bayes’ updates the probability of an event based on new evidence:
P(A|B) = [P(B|A) * P(A)] / P(B)
Example: Calculating disease probability given a positive test result.
14. What is an ANOVA test, and when should it be used?
ANOVA (Analysis of Variance) compares means across 3+ groups to see if at least one differs.
Use when comparing more than two groups.
15. Define skewness and kurtosis in a dataset.
Skewness: Measure of asymmetry (positive = right-skewed, negative = left).
Kurtosis: Measure of tail thickness (high kurtosis = heavy tails, outliers).
16. What is the difference between parametric and non-parametric tests?
Parametric: Assumes data follows a distribution (e.g., t-test).
Non-parametric: No assumptions; use with skewed or ordinal data (e.g., Mann-Whitney U).
17. What are Type I and Type II errors in hypothesis testing?
Type I error: False positive (rejecting a true null).
Type II error: False negative (failing to reject a false null).
18. How do you handle missing data in a dataset?
Methods:
Deletion (listwise or pairwise)
Imputation (mean, median, mode, regression)
Advanced: KNN, MICE
1. What is the difference between denoscriptive and inferential statistics?
Denoscriptive statistics summarize and organize data (e.g., mean, median, mode).
Inferential statistics make predictions or inferences about a population based on a sample (e.g., hypothesis testing, confidence intervals).
2. Explain mean, median, and mode and when to use each.
Mean is the average; use when data is symmetrically distributed.
Median is the middle value; best when data has outliers.
Mode is the most frequent value; useful for categorical data.
3. What is standard deviation, and why is it important?
It measures data spread around the mean. A low value = less variability; high value = more spread. Important for understanding consistency and risk.
4. Define correlation vs. causation with examples.
Correlation: Two variables move together but don't cause each other (e.g., ice cream sales and drowning).
Causation: One variable directly affects another (e.g., smoking causes lung cancer).
5. What is a p-value, and how do you interpret it?
P-value measures the probability of observing results given that the null hypothesis is true. A small p-value (typically < 0.05) suggests rejecting the null.
6. Explain the concept of confidence intervals.
A range of values used to estimate a population parameter. A 95% CI means there's a 95% chance the true value falls within the range.
7. What are outliers, and how can you handle them?
Outliers are extreme values differing significantly from others. Handle using:
Removal (if due to error)
Transformation
Capping (e.g., winsorizing)
8. When would you use a t-test vs. a z-test?
T-test: Small samples (n < 30) and unknown population standard deviation.
Z-test: Large samples and known standard deviation.
9. What is the Central Limit Theorem (CLT), and why is it important?
CLT states that the sampling distribution of the sample mean approaches a normal distribution as sample size grows, regardless of population distribution. Essential for inference.
10. Explain the difference between population and sample.
Population: Entire group of interest.
Sample: Subset used for analysis. Inference is made from the sample to the population.
11. What is regression analysis, and what are its key assumptions?
Predicts a dependent variable using one or more independent variables.
Assumptions: Linearity, independence, homoscedasticity, no multicollinearity, normality of residuals.
12. How do you calculate probability, and why does it matter in analytics?
Probability = (Favorable outcomes) / (Total outcomes).
Critical for risk estimation, decision-making, and predictions.
13. Explain the concept of Bayes’ Theorem with a practical example.
Bayes’ updates the probability of an event based on new evidence:
P(A|B) = [P(B|A) * P(A)] / P(B)
Example: Calculating disease probability given a positive test result.
14. What is an ANOVA test, and when should it be used?
ANOVA (Analysis of Variance) compares means across 3+ groups to see if at least one differs.
Use when comparing more than two groups.
15. Define skewness and kurtosis in a dataset.
Skewness: Measure of asymmetry (positive = right-skewed, negative = left).
Kurtosis: Measure of tail thickness (high kurtosis = heavy tails, outliers).
16. What is the difference between parametric and non-parametric tests?
Parametric: Assumes data follows a distribution (e.g., t-test).
Non-parametric: No assumptions; use with skewed or ordinal data (e.g., Mann-Whitney U).
17. What are Type I and Type II errors in hypothesis testing?
Type I error: False positive (rejecting a true null).
Type II error: False negative (failing to reject a false null).
18. How do you handle missing data in a dataset?
Methods:
Deletion (listwise or pairwise)
Imputation (mean, median, mode, regression)
Advanced: KNN, MICE
❤7
19. What is A/B testing, and how do you analyze the results?
Comparing two versions (A & B) to see which performs better.
Use t-tests or proportions test, check for statistical significance.
20. What is a Chi-square test, and when is it used?
Tests independence between categorical variables.
Used in contingency tables (e.g., is gender associated with purchase behavior?).
Credits: https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02
Hope it helps :)
Comparing two versions (A & B) to see which performs better.
Use t-tests or proportions test, check for statistical significance.
20. What is a Chi-square test, and when is it used?
Tests independence between categorical variables.
Used in contingency tables (e.g., is gender associated with purchase behavior?).
Credits: https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02
Hope it helps :)
❤5
Everything you need to learn Python for FREE ✅
Python Resources: https://lnkd.in/gQk8siKn
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Python Projects: https://lnkd.in/dbbReX7H
Web Development: https://lnkd.in/gj3dmvgQ
Data Analysts: https://lnkd.in/ds3J-w4b
Data Science: https://lnkd.in/g2Fjzbma
Machine Learning: https://lnkd.in/ddhUzMGC
Python for Data Science: https://lnkd.in/dNSst9s7
Artificial Intelligence: https://lnkd.in/dyEZQwXv
FREE Courses: https://lnkd.in/gMGmeB-2
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❤2
🔥 Top SQL Projects for Data Analytics 🚀
If you're preparing for a Data Analyst role or looking to level up your SQL skills, working on real-world projects is the best way to learn!
Here are some must-do SQL projects to strengthen your portfolio. 👇
🟢 Beginner-Friendly SQL Projects (Great for Learning Basics)
✅ Employee Database Management – Build and query HR data 📊
✅ Library Book Tracking – Create a database for book loans and returns
✅ Student Grading System – Analyze student performance data
✅ Retail Point-of-Sale System – Work with sales and transactions 💰
✅ Hotel Booking System – Manage customer bookings and check-ins 🏨
🟡 Intermediate SQL Projects (For Stronger Querying & Analysis)
⚡ E-commerce Order Management – Analyze order trends & customer data 🛒
⚡ Sales Performance Analysis – Work with revenue, profit margins & KPIs 📈
⚡ Inventory Control System – Optimize stock tracking 📦
⚡ Real Estate Listings – Manage and analyze property data 🏡
⚡ Movie Rating System – Analyze user reviews & trends 🎬
🔵 Advanced SQL Projects (For Business-Level Analytics)
🔹 Social Media Analytics – Track user engagement & content trends
🔹 Insurance Claim Management – Fraud detection & risk assessment
🔹 Customer Feedback Analysis – Perform sentiment analysis on reviews ⭐
🔹 Freelance Job Platform – Match freelancers with project opportunities
🔹 Pharmacy Inventory System – Optimize stock levels & prenoscriptions
🔴 Expert-Level SQL Projects (For Data-Driven Decision Making)
🔥 Music Streaming Analysis – Study user behavior & song trends 🎶
🔥 Healthcare Prenoscription Tracking – Identify patterns in medicine usage
🔥 Employee Shift Scheduling – Optimize workforce efficiency ⏳
🔥 Warehouse Stock Control – Manage supply chain data efficiently
🔥 Online Auction System – Analyze bidding patterns & sales performance 🛍️
🔗 Pro Tip: If you're applying for Data Analyst roles, pick 3-4 projects, clean the data, and create interactive dashboards using Power BI/Tableau to showcase insights!
React with ♥️ if you want detailed explanation of each project
Share with credits: 👇 https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
If you're preparing for a Data Analyst role or looking to level up your SQL skills, working on real-world projects is the best way to learn!
Here are some must-do SQL projects to strengthen your portfolio. 👇
🟢 Beginner-Friendly SQL Projects (Great for Learning Basics)
✅ Employee Database Management – Build and query HR data 📊
✅ Library Book Tracking – Create a database for book loans and returns
✅ Student Grading System – Analyze student performance data
✅ Retail Point-of-Sale System – Work with sales and transactions 💰
✅ Hotel Booking System – Manage customer bookings and check-ins 🏨
🟡 Intermediate SQL Projects (For Stronger Querying & Analysis)
⚡ E-commerce Order Management – Analyze order trends & customer data 🛒
⚡ Sales Performance Analysis – Work with revenue, profit margins & KPIs 📈
⚡ Inventory Control System – Optimize stock tracking 📦
⚡ Real Estate Listings – Manage and analyze property data 🏡
⚡ Movie Rating System – Analyze user reviews & trends 🎬
🔵 Advanced SQL Projects (For Business-Level Analytics)
🔹 Social Media Analytics – Track user engagement & content trends
🔹 Insurance Claim Management – Fraud detection & risk assessment
🔹 Customer Feedback Analysis – Perform sentiment analysis on reviews ⭐
🔹 Freelance Job Platform – Match freelancers with project opportunities
🔹 Pharmacy Inventory System – Optimize stock levels & prenoscriptions
🔴 Expert-Level SQL Projects (For Data-Driven Decision Making)
🔥 Music Streaming Analysis – Study user behavior & song trends 🎶
🔥 Healthcare Prenoscription Tracking – Identify patterns in medicine usage
🔥 Employee Shift Scheduling – Optimize workforce efficiency ⏳
🔥 Warehouse Stock Control – Manage supply chain data efficiently
🔥 Online Auction System – Analyze bidding patterns & sales performance 🛍️
🔗 Pro Tip: If you're applying for Data Analyst roles, pick 3-4 projects, clean the data, and create interactive dashboards using Power BI/Tableau to showcase insights!
React with ♥️ if you want detailed explanation of each project
Share with credits: 👇 https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
❤11
Top 10 Python Libraries for Data Science & Machine Learning
1. NumPy: NumPy is a fundamental package for scientific computing in Python. It provides support for large, multi-dimensional arrays and matrices, along with a collection of mathematical functions to operate on these arrays.
2. Pandas: Pandas is a powerful data manipulation library that provides data structures like DataFrame and Series, which make it easy to work with structured data. It offers tools for data cleaning, reshaping, merging, and slicing data.
3. Matplotlib: Matplotlib is a plotting library for creating static, interactive, and animated visualizations in Python. It allows you to generate various types of plots, including line plots, bar charts, histograms, scatter plots, and more.
4. Scikit-learn: Scikit-learn is a machine learning library that provides simple and efficient tools for data mining and data analysis. It includes a wide range of algorithms for classification, regression, clustering, dimensionality reduction, and model selection.
5. TensorFlow: TensorFlow is an open-source machine learning framework developed by Google. It enables you to build and train deep learning models using high-level APIs and tools for neural networks, natural language processing, computer vision, and more.
6. Keras: Keras is a high-level neural networks API that runs on top of TensorFlow, Theano, or Microsoft Cognitive Toolkit. It allows you to quickly prototype deep learning models with minimal code and easily experiment with different architectures.
7. Seaborn: Seaborn is a data visualization library based on Matplotlib that provides a high-level interface for creating attractive and informative statistical graphics. It simplifies the process of creating complex visualizations like heatmaps, violin plots, and pair plots.
8. Statsmodels: Statsmodels is a library that focuses on statistical modeling and hypothesis testing in Python. It offers a wide range of statistical models, including linear regression, logistic regression, time series analysis, and more.
9. XGBoost: XGBoost is an optimized gradient boosting library that provides an efficient implementation of the gradient boosting algorithm. It is widely used in machine learning competitions and has become a popular choice for building accurate predictive models.
10. NLTK (Natural Language Toolkit): NLTK is a library for natural language processing (NLP) that provides tools for text processing, tokenization, part-of-speech tagging, named entity recognition, sentiment analysis, and more. It is a valuable resource for working with textual data in data science projects.
Data Science Resources for Beginners
👇👇
https://drive.google.com/drive/folders/1uCShXgmol-fGMqeF2hf9xA5XPKVSxeTo
Share with credits: https://news.1rj.ru/str/datasciencefun
ENJOY LEARNING 👍👍
1. NumPy: NumPy is a fundamental package for scientific computing in Python. It provides support for large, multi-dimensional arrays and matrices, along with a collection of mathematical functions to operate on these arrays.
2. Pandas: Pandas is a powerful data manipulation library that provides data structures like DataFrame and Series, which make it easy to work with structured data. It offers tools for data cleaning, reshaping, merging, and slicing data.
3. Matplotlib: Matplotlib is a plotting library for creating static, interactive, and animated visualizations in Python. It allows you to generate various types of plots, including line plots, bar charts, histograms, scatter plots, and more.
4. Scikit-learn: Scikit-learn is a machine learning library that provides simple and efficient tools for data mining and data analysis. It includes a wide range of algorithms for classification, regression, clustering, dimensionality reduction, and model selection.
5. TensorFlow: TensorFlow is an open-source machine learning framework developed by Google. It enables you to build and train deep learning models using high-level APIs and tools for neural networks, natural language processing, computer vision, and more.
6. Keras: Keras is a high-level neural networks API that runs on top of TensorFlow, Theano, or Microsoft Cognitive Toolkit. It allows you to quickly prototype deep learning models with minimal code and easily experiment with different architectures.
7. Seaborn: Seaborn is a data visualization library based on Matplotlib that provides a high-level interface for creating attractive and informative statistical graphics. It simplifies the process of creating complex visualizations like heatmaps, violin plots, and pair plots.
8. Statsmodels: Statsmodels is a library that focuses on statistical modeling and hypothesis testing in Python. It offers a wide range of statistical models, including linear regression, logistic regression, time series analysis, and more.
9. XGBoost: XGBoost is an optimized gradient boosting library that provides an efficient implementation of the gradient boosting algorithm. It is widely used in machine learning competitions and has become a popular choice for building accurate predictive models.
10. NLTK (Natural Language Toolkit): NLTK is a library for natural language processing (NLP) that provides tools for text processing, tokenization, part-of-speech tagging, named entity recognition, sentiment analysis, and more. It is a valuable resource for working with textual data in data science projects.
Data Science Resources for Beginners
👇👇
https://drive.google.com/drive/folders/1uCShXgmol-fGMqeF2hf9xA5XPKVSxeTo
Share with credits: https://news.1rj.ru/str/datasciencefun
ENJOY LEARNING 👍👍
❤5
Essential Programming Languages to Learn Data Science 👇👇
1. Python: Python is one of the most popular programming languages for data science due to its simplicity, versatility, and extensive library support (such as NumPy, Pandas, and Scikit-learn).
2. R: R is another popular language for data science, particularly in academia and research settings. It has powerful statistical analysis capabilities and a wide range of packages for data manipulation and visualization.
3. SQL: SQL (Structured Query Language) is essential for working with databases, which are a critical component of data science projects. Knowledge of SQL is necessary for querying and manipulating data stored in relational databases.
4. Java: Java is a versatile language that is widely used in enterprise applications and big data processing frameworks like Apache Hadoop and Apache Spark. Knowledge of Java can be beneficial for working with large-scale data processing systems.
5. Scala: Scala is a functional programming language that is often used in conjunction with Apache Spark for distributed data processing. Knowledge of Scala can be valuable for building high-performance data processing applications.
6. Julia: Julia is a high-performance language specifically designed for scientific computing and data analysis. It is gaining popularity in the data science community due to its speed and ease of use for numerical computations.
7. MATLAB: MATLAB is a proprietary programming language commonly used in engineering and scientific research for data analysis, visualization, and modeling. It is particularly useful for signal processing and image analysis tasks.
Free Resources to master data analytics concepts 👇👇
Data Analysis with R
Intro to Data Science
Practical Python Programming
SQL for Data Analysis
Java Essential Concepts
Machine Learning with Python
Data Science Project Ideas
Learning SQL FREE Book
Join @free4unow_backup for more free resources.
ENJOY LEARNING👍👍
1. Python: Python is one of the most popular programming languages for data science due to its simplicity, versatility, and extensive library support (such as NumPy, Pandas, and Scikit-learn).
2. R: R is another popular language for data science, particularly in academia and research settings. It has powerful statistical analysis capabilities and a wide range of packages for data manipulation and visualization.
3. SQL: SQL (Structured Query Language) is essential for working with databases, which are a critical component of data science projects. Knowledge of SQL is necessary for querying and manipulating data stored in relational databases.
4. Java: Java is a versatile language that is widely used in enterprise applications and big data processing frameworks like Apache Hadoop and Apache Spark. Knowledge of Java can be beneficial for working with large-scale data processing systems.
5. Scala: Scala is a functional programming language that is often used in conjunction with Apache Spark for distributed data processing. Knowledge of Scala can be valuable for building high-performance data processing applications.
6. Julia: Julia is a high-performance language specifically designed for scientific computing and data analysis. It is gaining popularity in the data science community due to its speed and ease of use for numerical computations.
7. MATLAB: MATLAB is a proprietary programming language commonly used in engineering and scientific research for data analysis, visualization, and modeling. It is particularly useful for signal processing and image analysis tasks.
Free Resources to master data analytics concepts 👇👇
Data Analysis with R
Intro to Data Science
Practical Python Programming
SQL for Data Analysis
Java Essential Concepts
Machine Learning with Python
Data Science Project Ideas
Learning SQL FREE Book
Join @free4unow_backup for more free resources.
ENJOY LEARNING👍👍
❤3🎉1
Complete Roadmap to learn Machine Learning and Artificial Intelligence
👇👇
Week 1-2: Introduction to Machine Learning
- Learn the basics of Python programming language (if you are not already familiar with it)
- Understand the fundamentals of Machine Learning concepts such as supervised learning, unsupervised learning, and reinforcement learning
- Study linear algebra and calculus basics
- Complete online courses like Andrew Ng's Machine Learning course on Coursera
Week 3-4: Deep Learning Fundamentals
- Dive into neural networks and deep learning
- Learn about different types of neural networks like Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs)
- Implement deep learning models using frameworks like TensorFlow or PyTorch
- Complete online courses like Deep Learning Specialization on Coursera
Week 5-6: Natural Language Processing (NLP) and Computer Vision
- Explore NLP techniques such as tokenization, word embeddings, and sentiment analysis
- Dive into computer vision concepts like image classification, object detection, and image segmentation
- Work on projects involving NLP and Computer Vision applications
Week 7-8: Reinforcement Learning and AI Applications
- Learn about Reinforcement Learning algorithms like Q-learning and Deep Q Networks
- Explore AI applications in fields like healthcare, finance, and autonomous vehicles
- Work on a final project that combines different aspects of Machine Learning and AI
Additional Tips:
- Practice coding regularly to strengthen your programming skills
- Join online communities like Kaggle or GitHub to collaborate with other learners
- Read research papers and articles to stay updated on the latest advancements in the field
Pro Tip: Roadmap won't help unless you start working on it consistently. Start working on projects as early as possible.
2 months are good as a starting point to get grasp the basics of ML & AI but mastering it is very difficult as AI keeps evolving every day.
Best Resources to learn ML & AI 👇
Learn Python for Free
Prompt Engineering Course
Prompt Engineering Guide
Data Science Course
Google Cloud Generative AI Path
Unlock the power of Generative AI Models
Machine Learning with Python Free Course
Machine Learning Free Book
Deep Learning Nanodegree Program with Real-world Projects
AI, Machine Learning and Deep Learning
Join @free4unow_backup for more free courses
ENJOY LEARNING👍👍
👇👇
Week 1-2: Introduction to Machine Learning
- Learn the basics of Python programming language (if you are not already familiar with it)
- Understand the fundamentals of Machine Learning concepts such as supervised learning, unsupervised learning, and reinforcement learning
- Study linear algebra and calculus basics
- Complete online courses like Andrew Ng's Machine Learning course on Coursera
Week 3-4: Deep Learning Fundamentals
- Dive into neural networks and deep learning
- Learn about different types of neural networks like Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs)
- Implement deep learning models using frameworks like TensorFlow or PyTorch
- Complete online courses like Deep Learning Specialization on Coursera
Week 5-6: Natural Language Processing (NLP) and Computer Vision
- Explore NLP techniques such as tokenization, word embeddings, and sentiment analysis
- Dive into computer vision concepts like image classification, object detection, and image segmentation
- Work on projects involving NLP and Computer Vision applications
Week 7-8: Reinforcement Learning and AI Applications
- Learn about Reinforcement Learning algorithms like Q-learning and Deep Q Networks
- Explore AI applications in fields like healthcare, finance, and autonomous vehicles
- Work on a final project that combines different aspects of Machine Learning and AI
Additional Tips:
- Practice coding regularly to strengthen your programming skills
- Join online communities like Kaggle or GitHub to collaborate with other learners
- Read research papers and articles to stay updated on the latest advancements in the field
Pro Tip: Roadmap won't help unless you start working on it consistently. Start working on projects as early as possible.
2 months are good as a starting point to get grasp the basics of ML & AI but mastering it is very difficult as AI keeps evolving every day.
Best Resources to learn ML & AI 👇
Learn Python for Free
Prompt Engineering Course
Prompt Engineering Guide
Data Science Course
Google Cloud Generative AI Path
Unlock the power of Generative AI Models
Machine Learning with Python Free Course
Machine Learning Free Book
Deep Learning Nanodegree Program with Real-world Projects
AI, Machine Learning and Deep Learning
Join @free4unow_backup for more free courses
ENJOY LEARNING👍👍
❤10
SQL can be simple—if you learn it the smart way..
If you’re aiming to become a data analyst, mastering SQL is non-negotiable.
Here’s a smart roadmap to ace it:
1. Basics First: Understand data types, simple queries (SELECT, FROM, WHERE). Master basic filtering.
2. Joins & Relationships: Dive into INNER, LEFT, RIGHT joins. Practice combining tables to extract meaningful insights.
3. Aggregations & Functions: Get comfortable with COUNT, SUM, AVG, MAX, GROUP BY, and HAVING clauses. These are essential for summarizing data.
4. Subqueries & Nested Queries: Learn how to query within queries. This is powerful for handling complex datasets.
5. Window Functions: Explore ranking, cumulative sums, and sliding windows to work with running totals and moving averages.
6. Optimization: Study indexing and query optimization for faster, more efficient queries.
7. Real-World Scenarios: Apply your SQL knowledge to solve real-world business problems.
The journey may seem tough, but each step sharpens your skills and brings you closer to data analysis excellence. Stay consistent, practice regularly, and let SQL become your superpower! 💪
Here you can find essential SQL Interview Resources👇
https://whatsapp.com/channel/0029VanC5rODzgT6TiTGoa1v
Like this post if you need more 👍❤️
Hope it helps :)
If you’re aiming to become a data analyst, mastering SQL is non-negotiable.
Here’s a smart roadmap to ace it:
1. Basics First: Understand data types, simple queries (SELECT, FROM, WHERE). Master basic filtering.
2. Joins & Relationships: Dive into INNER, LEFT, RIGHT joins. Practice combining tables to extract meaningful insights.
3. Aggregations & Functions: Get comfortable with COUNT, SUM, AVG, MAX, GROUP BY, and HAVING clauses. These are essential for summarizing data.
4. Subqueries & Nested Queries: Learn how to query within queries. This is powerful for handling complex datasets.
5. Window Functions: Explore ranking, cumulative sums, and sliding windows to work with running totals and moving averages.
6. Optimization: Study indexing and query optimization for faster, more efficient queries.
7. Real-World Scenarios: Apply your SQL knowledge to solve real-world business problems.
The journey may seem tough, but each step sharpens your skills and brings you closer to data analysis excellence. Stay consistent, practice regularly, and let SQL become your superpower! 💪
Here you can find essential SQL Interview Resources👇
https://whatsapp.com/channel/0029VanC5rODzgT6TiTGoa1v
Like this post if you need more 👍❤️
Hope it helps :)
❤4
Math Topics every Data Scientist should know
❤5🥰1
List of Top 12 Coding Channels on WhatsApp:
1. Python Programming:
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2. Coding Resources:
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3. Coding Projects:
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5. Java Programming:
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6. Javanoscript:
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8. Artificial Intelligence:
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9. Data Science:
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10. Machine Learning:
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11. SQL:
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12. GitHub:
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1. Python Programming:
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2. Coding Resources:
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3. Coding Projects:
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4. Coding Interviews:
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5. Java Programming:
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6. Javanoscript:
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7. Web Development:
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8. Artificial Intelligence:
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10. Machine Learning:
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11. SQL:
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12. GitHub:
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❤6🥰1
Preparing for a machine learning interview as a data analyst is a great step.
Here are some common machine learning interview questions :-
1. Explain the steps involved in a machine learning project lifecycle.
2. What is the difference between supervised and unsupervised learning? Give examples of each.
3. What evaluation metrics would you use to assess the performance of a regression model?
4. What is overfitting and how can you prevent it?
5. Describe the bias-variance tradeoff.
6. What is cross-validation, and why is it important in machine learning?
7. What are some feature selection techniques you are familiar with?
8.What are the assumptions of linear regression?
9. How does regularization help in linear models?
10. Explain the difference between classification and regression.
11. What are some common algorithms used for dimensionality reduction?
12. Describe how a decision tree works.
13. What are ensemble methods, and why are they useful?
14. How do you handle missing or corrupted data in a dataset?
15. What are the different kernels used in Support Vector Machines (SVM)?
These questions cover a range of fundamental concepts and techniques in machine learning that are important for a data scientist role.
Good luck with your interview preparation!
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
Like if you need similar content 😄👍
Here are some common machine learning interview questions :-
1. Explain the steps involved in a machine learning project lifecycle.
2. What is the difference between supervised and unsupervised learning? Give examples of each.
3. What evaluation metrics would you use to assess the performance of a regression model?
4. What is overfitting and how can you prevent it?
5. Describe the bias-variance tradeoff.
6. What is cross-validation, and why is it important in machine learning?
7. What are some feature selection techniques you are familiar with?
8.What are the assumptions of linear regression?
9. How does regularization help in linear models?
10. Explain the difference between classification and regression.
11. What are some common algorithms used for dimensionality reduction?
12. Describe how a decision tree works.
13. What are ensemble methods, and why are they useful?
14. How do you handle missing or corrupted data in a dataset?
15. What are the different kernels used in Support Vector Machines (SVM)?
These questions cover a range of fundamental concepts and techniques in machine learning that are important for a data scientist role.
Good luck with your interview preparation!
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
Like if you need similar content 😄👍
❤2