Data Analyst vs Data Scientist: Must-Know Differences
Data Analyst:
- Role: Primarily focuses on interpreting data, identifying trends, and creating reports that inform business decisions.
- Best For: Individuals who enjoy working with existing data to uncover insights and support decision-making in business processes.
- Key Responsibilities:
- Collecting, cleaning, and organizing data from various sources.
- Performing descriptive analytics to summarize the data (trends, patterns, anomalies).
- Creating reports and dashboards using tools like Excel, SQL, Power BI, and Tableau.
- Collaborating with business stakeholders to provide data-driven insights and recommendations.
- Skills Required:
- Proficiency in data visualization tools (e.g., Power BI, Tableau).
- Strong analytical and statistical skills, along with expertise in SQL and Excel.
- Familiarity with business intelligence and basic programming (optional).
- Outcome: Data analysts provide actionable insights to help companies make informed decisions by analyzing and visualizing data, often focusing on current and historical trends.
Data Scientist:
- Role: Combines statistical methods, machine learning, and programming to build predictive models and derive deeper insights from data.
- Best For: Individuals who enjoy working with complex datasets, developing algorithms, and using advanced analytics to solve business problems.
- Key Responsibilities:
- Designing and developing machine learning models for predictive analytics.
- Collecting, processing, and analyzing large datasets (structured and unstructured).
- Using statistical methods, algorithms, and data mining to uncover hidden patterns.
- Writing and maintaining code in programming languages like Python, R, and SQL.
- Working with big data technologies and cloud platforms for scalable solutions.
- Skills Required:
- Proficiency in programming languages like Python, R, and SQL.
- Strong understanding of machine learning algorithms, statistics, and data modeling.
- Experience with big data tools (e.g., Hadoop, Spark) and cloud platforms (AWS, Azure).
- Outcome: Data scientists develop models that predict future outcomes and drive innovation through advanced analytics, going beyond what has happened to explain why it happened and what will happen next.
Data analysts focus on analyzing and visualizing existing data to provide insights for current business challenges, while data scientists apply advanced algorithms and machine learning to predict future outcomes and derive deeper insights. Data scientists typically handle more complex problems and require a stronger background in statistics, programming, and machine learning.
I have curated best 80+ top-notch Data Analytics Resources ๐๐
https://t.iss.one/DataSimplifier
Like this post for more content like this ๐โฅ๏ธ
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
Data Analyst:
- Role: Primarily focuses on interpreting data, identifying trends, and creating reports that inform business decisions.
- Best For: Individuals who enjoy working with existing data to uncover insights and support decision-making in business processes.
- Key Responsibilities:
- Collecting, cleaning, and organizing data from various sources.
- Performing descriptive analytics to summarize the data (trends, patterns, anomalies).
- Creating reports and dashboards using tools like Excel, SQL, Power BI, and Tableau.
- Collaborating with business stakeholders to provide data-driven insights and recommendations.
- Skills Required:
- Proficiency in data visualization tools (e.g., Power BI, Tableau).
- Strong analytical and statistical skills, along with expertise in SQL and Excel.
- Familiarity with business intelligence and basic programming (optional).
- Outcome: Data analysts provide actionable insights to help companies make informed decisions by analyzing and visualizing data, often focusing on current and historical trends.
Data Scientist:
- Role: Combines statistical methods, machine learning, and programming to build predictive models and derive deeper insights from data.
- Best For: Individuals who enjoy working with complex datasets, developing algorithms, and using advanced analytics to solve business problems.
- Key Responsibilities:
- Designing and developing machine learning models for predictive analytics.
- Collecting, processing, and analyzing large datasets (structured and unstructured).
- Using statistical methods, algorithms, and data mining to uncover hidden patterns.
- Writing and maintaining code in programming languages like Python, R, and SQL.
- Working with big data technologies and cloud platforms for scalable solutions.
- Skills Required:
- Proficiency in programming languages like Python, R, and SQL.
- Strong understanding of machine learning algorithms, statistics, and data modeling.
- Experience with big data tools (e.g., Hadoop, Spark) and cloud platforms (AWS, Azure).
- Outcome: Data scientists develop models that predict future outcomes and drive innovation through advanced analytics, going beyond what has happened to explain why it happened and what will happen next.
Data analysts focus on analyzing and visualizing existing data to provide insights for current business challenges, while data scientists apply advanced algorithms and machine learning to predict future outcomes and derive deeper insights. Data scientists typically handle more complex problems and require a stronger background in statistics, programming, and machine learning.
I have curated best 80+ top-notch Data Analytics Resources ๐๐
https://t.iss.one/DataSimplifier
Like this post for more content like this ๐โฅ๏ธ
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
โค2
AI vs ML vs DL ๐๐
โค1
1.What are the conditions for Overfitting and Underfitting?
Ans:
โข In Overfitting the model performs well for the training data, but for any new data it fails to provide output. For Underfitting the model is very simple and not able to identify the correct relationship. Following are the bias and variance conditions.
โข Overfitting โ Low bias and High Variance results in the overfitted model. The decision tree is more prone to Overfitting.
โข Underfitting โ High bias and Low Variance. Such a model doesnโt perform well on test data also. For example โ Linear Regression is more prone to Underfitting.
2. Which models are more prone to Overfitting?
Ans: Complex models, like the Random Forest, Neural Networks, and XGBoost are more prone to overfitting. Simpler models, like linear regression, can overfit too โ this typically happens when there are more features than the number of instances in the training data.
3. When does feature scaling should be done?
Ans: We need to perform Feature Scaling when we are dealing with Gradient Descent Based algorithms (Linear and Logistic Regression, Neural Network) and Distance-based algorithms (KNN, K-means, SVM) as these are very sensitive to the range of the data points.
4. What is a logistic function? What is the range of values of a logistic function?
Ans. f(z) = 1/(1+e -z )
The values of a logistic function will range from 0 to 1. The values of Z will vary from -infinity to +infinity.
5. What are the drawbacks of a linear model?
Ans. There are a couple of drawbacks of a linear model:
A linear model holds some strong assumptions that may not be true in application. It assumes a linear relationship, multivariate normality, no or little multicollinearity, no auto-correlation, and homoscedasticity
A linear model canโt be used for discrete or binary outcomes.
You canโt vary the model flexibility of a linear model.
Ans:
โข In Overfitting the model performs well for the training data, but for any new data it fails to provide output. For Underfitting the model is very simple and not able to identify the correct relationship. Following are the bias and variance conditions.
โข Overfitting โ Low bias and High Variance results in the overfitted model. The decision tree is more prone to Overfitting.
โข Underfitting โ High bias and Low Variance. Such a model doesnโt perform well on test data also. For example โ Linear Regression is more prone to Underfitting.
2. Which models are more prone to Overfitting?
Ans: Complex models, like the Random Forest, Neural Networks, and XGBoost are more prone to overfitting. Simpler models, like linear regression, can overfit too โ this typically happens when there are more features than the number of instances in the training data.
3. When does feature scaling should be done?
Ans: We need to perform Feature Scaling when we are dealing with Gradient Descent Based algorithms (Linear and Logistic Regression, Neural Network) and Distance-based algorithms (KNN, K-means, SVM) as these are very sensitive to the range of the data points.
4. What is a logistic function? What is the range of values of a logistic function?
Ans. f(z) = 1/(1+e -z )
The values of a logistic function will range from 0 to 1. The values of Z will vary from -infinity to +infinity.
5. What are the drawbacks of a linear model?
Ans. There are a couple of drawbacks of a linear model:
A linear model holds some strong assumptions that may not be true in application. It assumes a linear relationship, multivariate normality, no or little multicollinearity, no auto-correlation, and homoscedasticity
A linear model canโt be used for discrete or binary outcomes.
You canโt vary the model flexibility of a linear model.
โค2
Excel Scenario-Based Questions Interview Questions and Answers :
Scenario 1) Imagine you have a dataset with missing values. How would you approach this problem in Excel?
Answer:
To handle missing values in Excel:
1. Identify Missing Data:
Use filters to quickly find blank cells.
Apply conditional formatting:
Home โ Conditional Formatting โ New Rule โ Format only cells that are blank.
2. Handle Missing Data:
Delete rows with missing critical data (if appropriate).
Fill missing values:
Use =IF(A2="", "N/A", A2) to replace blanks with โN/Aโ.
Use Fill Down (Ctrl + D) if the previous value applies.
Use functions like =AVERAGEIF(range, "<>", range) to fill with average.
3. Use Power Query (for large datasets):
Load data into Power Query and use โReplace Valuesโ or โRemove Emptyโ options.
Scenario 2) You are given a dataset with multiple sheets. How would you consolidate the data for analysis?
Answer:
Approach 1: Manual Consolidation
1. Use Copy-Paste from each sheet into a master sheet.
2. Add a new column to identify the source sheet (optional but useful).
3. Convert the master data into a table for analysis.
Approach 2: Use Power Query (Recommended for large datasets)
1. Go to Data โ Get & Transform โ Get Data โ From Workbook.
2. Load each sheet into Power Query.
3. Use the Append Queries option to merge all sheets.
4. Clean and transform as needed, then load it back to Excel.
Approach 3: Use VBA (Advanced Users)
Write a macro to loop through all sheets and append data to a master sheet.
Hope it helps :)
Scenario 1) Imagine you have a dataset with missing values. How would you approach this problem in Excel?
Answer:
To handle missing values in Excel:
1. Identify Missing Data:
Use filters to quickly find blank cells.
Apply conditional formatting:
Home โ Conditional Formatting โ New Rule โ Format only cells that are blank.
2. Handle Missing Data:
Delete rows with missing critical data (if appropriate).
Fill missing values:
Use =IF(A2="", "N/A", A2) to replace blanks with โN/Aโ.
Use Fill Down (Ctrl + D) if the previous value applies.
Use functions like =AVERAGEIF(range, "<>", range) to fill with average.
3. Use Power Query (for large datasets):
Load data into Power Query and use โReplace Valuesโ or โRemove Emptyโ options.
Scenario 2) You are given a dataset with multiple sheets. How would you consolidate the data for analysis?
Answer:
Approach 1: Manual Consolidation
1. Use Copy-Paste from each sheet into a master sheet.
2. Add a new column to identify the source sheet (optional but useful).
3. Convert the master data into a table for analysis.
Approach 2: Use Power Query (Recommended for large datasets)
1. Go to Data โ Get & Transform โ Get Data โ From Workbook.
2. Load each sheet into Power Query.
3. Use the Append Queries option to merge all sheets.
4. Clean and transform as needed, then load it back to Excel.
Approach 3: Use VBA (Advanced Users)
Write a macro to loop through all sheets and append data to a master sheet.
Hope it helps :)
โค2
If youโre a Data Analyst, chances are you use ๐๐๐ every single day. And if youโre preparing for interviews, youโve probably realized that it's not just about writing queries it's about writing smart, efficient, and scalable ones.
1. ๐๐ซ๐๐๐ค ๐๐ญ ๐๐จ๐ฐ๐ง ๐ฐ๐ข๐ญ๐ก ๐๐๐๐ฌ (๐๐จ๐ฆ๐ฆ๐จ๐ง ๐๐๐๐ฅ๐ ๐๐ฑ๐ฉ๐ซ๐๐ฌ๐ฌ๐ข๐จ๐ง๐ฌ)
Ever worked on a query that became an unreadable monster? CTEs let you break that down into logical steps. You can treat them like temporary views โ great for simplifying logic and improving collaboration across your team.
2. ๐๐ฌ๐ ๐๐ข๐ง๐๐จ๐ฐ ๐ ๐ฎ๐ง๐๐ญ๐ข๐จ๐ง๐ฌ
Forget the mess of subqueries. With functions like ROW_NUMBER(), RANK(), LEAD() and LAG(), you can compare rows, rank items, or calculate running totals โ all within the same query. Total
3. ๐๐ฎ๐๐ช๐ฎ๐๐ซ๐ข๐๐ฌ (๐๐๐ฌ๐ญ๐๐ ๐๐ฎ๐๐ซ๐ข๐๐ฌ)
Yes, they're old school, but nested subqueries are still powerful. Use them when you want to filter based on results of another query or isolate logic step-by-step before joining with the big picture.
4. ๐๐ง๐๐๐ฑ๐๐ฌ & ๐๐ฎ๐๐ซ๐ฒ ๐๐ฉ๐ญ๐ข๐ฆ๐ข๐ณ๐๐ญ๐ข๐จ๐ง
Query taking forever? Look at your indexes. Index the columns you use in JOINs, WHERE, and GROUP BY. Even basic knowledge of how the SQL engine reads data can take your skills up a notch.
5. ๐๐จ๐ข๐ง๐ฌ ๐ฏ๐ฌ. ๐๐ฎ๐๐ช๐ฎ๐๐ซ๐ข๐๐ฌ
Joins are usually faster and better for combining large datasets. Subqueries, on the other hand, are cleaner when doing one-off filters or smaller operations. Choose wisely based on the context.
6. ๐๐๐๐ ๐๐ญ๐๐ญ๐๐ฆ๐๐ง๐ญ๐ฌ:
Want to categorize or bucket data without creating a separate table? Use CASE. Itโs ideal for conditional logic, custom labels, and grouping in a single query.
7. ๐๐ ๐ ๐ซ๐๐ ๐๐ญ๐ข๐จ๐ง๐ฌ & ๐๐๐๐๐ ๐๐
Most analytics questions start with "how many", "whatโs the average", or "which is the highest?". SUM(), COUNT(), AVG(), etc., and pair them with GROUP BY to drive insights that matter.
8. ๐๐๐ญ๐๐ฌ ๐๐ซ๐ ๐๐ฅ๐ฐ๐๐ฒ๐ฌ ๐๐ซ๐ข๐๐ค๐ฒ
Time-based analysis is everywhere: trends, cohorts, seasonality, etc. Get familiar with functions like DATEADD, DATEDIFF, DATE_TRUNC, and DATEPART to work confidently with time series data.
9. ๐๐๐ฅ๐-๐๐จ๐ข๐ง๐ฌ & ๐๐๐๐ฎ๐ซ๐ฌ๐ข๐ฏ๐ ๐๐ฎ๐๐ซ๐ข๐๐ฌ ๐๐จ๐ซ ๐๐ข๐๐ซ๐๐ซ๐๐ก๐ข๐๐ฌ
Whether it's org charts or product categories, not all data is flat. Learn how to join a table to itself or use recursive CTEs to navigate parent-child relationships effectively.
You donโt need to memorize 100 functions. You need to understand 10 really well and apply them smartly. These are the concepts I keep going back to not just in interviews, but in the real world where clarity, performance, and logic matter most.
1. ๐๐ซ๐๐๐ค ๐๐ญ ๐๐จ๐ฐ๐ง ๐ฐ๐ข๐ญ๐ก ๐๐๐๐ฌ (๐๐จ๐ฆ๐ฆ๐จ๐ง ๐๐๐๐ฅ๐ ๐๐ฑ๐ฉ๐ซ๐๐ฌ๐ฌ๐ข๐จ๐ง๐ฌ)
Ever worked on a query that became an unreadable monster? CTEs let you break that down into logical steps. You can treat them like temporary views โ great for simplifying logic and improving collaboration across your team.
2. ๐๐ฌ๐ ๐๐ข๐ง๐๐จ๐ฐ ๐ ๐ฎ๐ง๐๐ญ๐ข๐จ๐ง๐ฌ
Forget the mess of subqueries. With functions like ROW_NUMBER(), RANK(), LEAD() and LAG(), you can compare rows, rank items, or calculate running totals โ all within the same query. Total
3. ๐๐ฎ๐๐ช๐ฎ๐๐ซ๐ข๐๐ฌ (๐๐๐ฌ๐ญ๐๐ ๐๐ฎ๐๐ซ๐ข๐๐ฌ)
Yes, they're old school, but nested subqueries are still powerful. Use them when you want to filter based on results of another query or isolate logic step-by-step before joining with the big picture.
4. ๐๐ง๐๐๐ฑ๐๐ฌ & ๐๐ฎ๐๐ซ๐ฒ ๐๐ฉ๐ญ๐ข๐ฆ๐ข๐ณ๐๐ญ๐ข๐จ๐ง
Query taking forever? Look at your indexes. Index the columns you use in JOINs, WHERE, and GROUP BY. Even basic knowledge of how the SQL engine reads data can take your skills up a notch.
5. ๐๐จ๐ข๐ง๐ฌ ๐ฏ๐ฌ. ๐๐ฎ๐๐ช๐ฎ๐๐ซ๐ข๐๐ฌ
Joins are usually faster and better for combining large datasets. Subqueries, on the other hand, are cleaner when doing one-off filters or smaller operations. Choose wisely based on the context.
6. ๐๐๐๐ ๐๐ญ๐๐ญ๐๐ฆ๐๐ง๐ญ๐ฌ:
Want to categorize or bucket data without creating a separate table? Use CASE. Itโs ideal for conditional logic, custom labels, and grouping in a single query.
7. ๐๐ ๐ ๐ซ๐๐ ๐๐ญ๐ข๐จ๐ง๐ฌ & ๐๐๐๐๐ ๐๐
Most analytics questions start with "how many", "whatโs the average", or "which is the highest?". SUM(), COUNT(), AVG(), etc., and pair them with GROUP BY to drive insights that matter.
8. ๐๐๐ญ๐๐ฌ ๐๐ซ๐ ๐๐ฅ๐ฐ๐๐ฒ๐ฌ ๐๐ซ๐ข๐๐ค๐ฒ
Time-based analysis is everywhere: trends, cohorts, seasonality, etc. Get familiar with functions like DATEADD, DATEDIFF, DATE_TRUNC, and DATEPART to work confidently with time series data.
9. ๐๐๐ฅ๐-๐๐จ๐ข๐ง๐ฌ & ๐๐๐๐ฎ๐ซ๐ฌ๐ข๐ฏ๐ ๐๐ฎ๐๐ซ๐ข๐๐ฌ ๐๐จ๐ซ ๐๐ข๐๐ซ๐๐ซ๐๐ก๐ข๐๐ฌ
Whether it's org charts or product categories, not all data is flat. Learn how to join a table to itself or use recursive CTEs to navigate parent-child relationships effectively.
You donโt need to memorize 100 functions. You need to understand 10 really well and apply them smartly. These are the concepts I keep going back to not just in interviews, but in the real world where clarity, performance, and logic matter most.
โค2
Machine Learning โ Essential Concepts ๐
1๏ธโฃ Types of Machine Learning
Supervised Learning โ Uses labeled data to train models.
Examples: Linear Regression, Decision Trees, Random Forest, SVM
Unsupervised Learning โ Identifies patterns in unlabeled data.
Examples: Clustering (K-Means, DBSCAN), PCA
Reinforcement Learning โ Models learn through rewards and penalties.
Examples: Q-Learning, Deep Q Networks
2๏ธโฃ Key Algorithms
Regression โ Predicts continuous values (Linear Regression, Ridge, Lasso).
Classification โ Categorizes data into classes (Logistic Regression, Decision Tree, SVM, Naรฏve Bayes).
Clustering โ Groups similar data points (K-Means, Hierarchical Clustering, DBSCAN).
Dimensionality Reduction โ Reduces the number of features (PCA, t-SNE, LDA).
3๏ธโฃ Model Training & Evaluation
Train-Test Split โ Dividing data into training and testing sets.
Cross-Validation โ Splitting data multiple times for better accuracy.
Metrics โ Evaluating models with RMSE, Accuracy, Precision, Recall, F1-Score, ROC-AUC.
4๏ธโฃ Feature Engineering
Handling missing data (mean imputation, dropna()).
Encoding categorical variables (One-Hot Encoding, Label Encoding).
Feature Scaling (Normalization, Standardization).
5๏ธโฃ Overfitting & Underfitting
Overfitting โ Model learns noise, performs well on training but poorly on test data.
Underfitting โ Model is too simple and fails to capture patterns.
Solution: Regularization (L1, L2), Hyperparameter Tuning.
6๏ธโฃ Ensemble Learning
Combining multiple models to improve performance.
Bagging (Random Forest)
Boosting (XGBoost, Gradient Boosting, AdaBoost)
7๏ธโฃ Deep Learning Basics
Neural Networks (ANN, CNN, RNN).
Activation Functions (ReLU, Sigmoid, Tanh).
Backpropagation & Gradient Descent.
8๏ธโฃ Model Deployment
Deploy models using Flask, FastAPI, or Streamlit.
Model versioning with MLflow.
Cloud deployment (AWS SageMaker, Google Vertex AI).
Join our WhatsApp channel: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
1๏ธโฃ Types of Machine Learning
Supervised Learning โ Uses labeled data to train models.
Examples: Linear Regression, Decision Trees, Random Forest, SVM
Unsupervised Learning โ Identifies patterns in unlabeled data.
Examples: Clustering (K-Means, DBSCAN), PCA
Reinforcement Learning โ Models learn through rewards and penalties.
Examples: Q-Learning, Deep Q Networks
2๏ธโฃ Key Algorithms
Regression โ Predicts continuous values (Linear Regression, Ridge, Lasso).
Classification โ Categorizes data into classes (Logistic Regression, Decision Tree, SVM, Naรฏve Bayes).
Clustering โ Groups similar data points (K-Means, Hierarchical Clustering, DBSCAN).
Dimensionality Reduction โ Reduces the number of features (PCA, t-SNE, LDA).
3๏ธโฃ Model Training & Evaluation
Train-Test Split โ Dividing data into training and testing sets.
Cross-Validation โ Splitting data multiple times for better accuracy.
Metrics โ Evaluating models with RMSE, Accuracy, Precision, Recall, F1-Score, ROC-AUC.
4๏ธโฃ Feature Engineering
Handling missing data (mean imputation, dropna()).
Encoding categorical variables (One-Hot Encoding, Label Encoding).
Feature Scaling (Normalization, Standardization).
5๏ธโฃ Overfitting & Underfitting
Overfitting โ Model learns noise, performs well on training but poorly on test data.
Underfitting โ Model is too simple and fails to capture patterns.
Solution: Regularization (L1, L2), Hyperparameter Tuning.
6๏ธโฃ Ensemble Learning
Combining multiple models to improve performance.
Bagging (Random Forest)
Boosting (XGBoost, Gradient Boosting, AdaBoost)
7๏ธโฃ Deep Learning Basics
Neural Networks (ANN, CNN, RNN).
Activation Functions (ReLU, Sigmoid, Tanh).
Backpropagation & Gradient Descent.
8๏ธโฃ Model Deployment
Deploy models using Flask, FastAPI, or Streamlit.
Model versioning with MLflow.
Cloud deployment (AWS SageMaker, Google Vertex AI).
Join our WhatsApp channel: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
โค4
๐ Data Analyst Project Ideas for Beginners
1. Sales Analysis Dashboard: Use tools like Excel or Tableau to create a dashboard analyzing sales data. Visualize trends, top products, and seasonal patterns.
2. Customer Segmentation: Analyze customer data using clustering techniques (like K-means) to segment customers based on purchasing behavior and demographics.
3. Social Media Metrics Analysis: Gather data from social media platforms to analyze engagement metrics. Create visualizations to highlight trends and performance.
4. Survey Data Analysis: Conduct a survey and analyze the results using statistical techniques. Present findings with visualizations to showcase insights.
5. Exploratory Data Analysis (EDA): Choose a public dataset and perform EDA using Python (Pandas, Matplotlib) or R (tidyverse). Summarize key insights and visualizations.
6. Employee Performance Analysis: Analyze employee performance data to identify trends in productivity, turnover rates, and training effectiveness.
7. Public Health Data Analysis: Use datasets from public health sources (like CDC) to analyze trends in health metrics (e.g., vaccination rates, disease outbreaks) and visualize findings.
8. Real Estate Market Analysis: Analyze real estate listings to find trends in pricing, location, and features. Use data visualization to present your findings.
9. Weather Data Visualization: Collect weather data and analyze trends over time. Create visualizations to show changes in temperature, precipitation, or extreme weather events.
10. Financial Analysis: Analyze a companyโs financial statements to assess its performance over time. Create visualizations to highlight key financial ratios and trends.
Data Analytics Resources ๐๐
https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02
Hope it helps :)
1. Sales Analysis Dashboard: Use tools like Excel or Tableau to create a dashboard analyzing sales data. Visualize trends, top products, and seasonal patterns.
2. Customer Segmentation: Analyze customer data using clustering techniques (like K-means) to segment customers based on purchasing behavior and demographics.
3. Social Media Metrics Analysis: Gather data from social media platforms to analyze engagement metrics. Create visualizations to highlight trends and performance.
4. Survey Data Analysis: Conduct a survey and analyze the results using statistical techniques. Present findings with visualizations to showcase insights.
5. Exploratory Data Analysis (EDA): Choose a public dataset and perform EDA using Python (Pandas, Matplotlib) or R (tidyverse). Summarize key insights and visualizations.
6. Employee Performance Analysis: Analyze employee performance data to identify trends in productivity, turnover rates, and training effectiveness.
7. Public Health Data Analysis: Use datasets from public health sources (like CDC) to analyze trends in health metrics (e.g., vaccination rates, disease outbreaks) and visualize findings.
8. Real Estate Market Analysis: Analyze real estate listings to find trends in pricing, location, and features. Use data visualization to present your findings.
9. Weather Data Visualization: Collect weather data and analyze trends over time. Create visualizations to show changes in temperature, precipitation, or extreme weather events.
10. Financial Analysis: Analyze a companyโs financial statements to assess its performance over time. Create visualizations to highlight key financial ratios and trends.
Data Analytics Resources ๐๐
https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02
Hope it helps :)
โค1
๐ฉ๐ปโ๐ป Why should one study Linear Algebra for ML?
๐๐ผ Clearly, to develop a better intuition for machine learning and deep learning algorithms and not treat them as black boxes. This would allow you to choose proper hyper-parameters and develop a better model. You would also be able to code algorithms from scratch and make your own variations to them as well.
๐๐ผ Learn Linear Algebra for Machine Learning with:
Khan Academy: https://www.khanacademy.org/math/linear-algebra
Udacity: https://www.udacity.com/course/linear-algebra-refresher-course--ud953
Coursera: https://www.coursera.org/learn/linear-algebra-machine-learning
Here are some amazing freely available ebooks on the same topic:
Mathematics for Machine Learning: https://mml-book.github.io/book/mml-book.pdf
An Introduction to Statistical Learning: https://faculty.marshall.usc.edu/gareth-james/ISL/
Happy machine learning! ๐
๐๐ผ Clearly, to develop a better intuition for machine learning and deep learning algorithms and not treat them as black boxes. This would allow you to choose proper hyper-parameters and develop a better model. You would also be able to code algorithms from scratch and make your own variations to them as well.
๐๐ผ Learn Linear Algebra for Machine Learning with:
Khan Academy: https://www.khanacademy.org/math/linear-algebra
Udacity: https://www.udacity.com/course/linear-algebra-refresher-course--ud953
Coursera: https://www.coursera.org/learn/linear-algebra-machine-learning
Here are some amazing freely available ebooks on the same topic:
Mathematics for Machine Learning: https://mml-book.github.io/book/mml-book.pdf
An Introduction to Statistical Learning: https://faculty.marshall.usc.edu/gareth-james/ISL/
Happy machine learning! ๐
โค2
10 AI Trends to Watch in 2025
โ Open-Source LLM Boom โ Models like Mistral, LLaMA, and Mixtral rivaling proprietary giants
โ Multi-Agent AI Systems โ AIs collaborating with each other to complete complex tasks
โ Edge AI โ Smarter AI running directly on mobile & IoT devices, no cloud needed
โ AI Legislation & Ethics โ Governments setting global AI rules and ethical frameworks
โ Personalized AI Companions โ Customizable chatbots for productivity, learning, and therapy
โ AI in Robotics โ Real-world actions powered by vision-language models
โ AI-Powered Search โ Tools like Perplexity and You.com reshaping how we explore the web
โ Generative Video & 3D โ Text-to-video and image-to-3D tools going mainstream
โ AI-Native Programming โ Entire codebases generated and managed by AI agents
โ Sustainable AI โ Focus on reducing model training energy & creating green AI systems
React if you're following any of these trends closely!
#genai
โ Open-Source LLM Boom โ Models like Mistral, LLaMA, and Mixtral rivaling proprietary giants
โ Multi-Agent AI Systems โ AIs collaborating with each other to complete complex tasks
โ Edge AI โ Smarter AI running directly on mobile & IoT devices, no cloud needed
โ AI Legislation & Ethics โ Governments setting global AI rules and ethical frameworks
โ Personalized AI Companions โ Customizable chatbots for productivity, learning, and therapy
โ AI in Robotics โ Real-world actions powered by vision-language models
โ AI-Powered Search โ Tools like Perplexity and You.com reshaping how we explore the web
โ Generative Video & 3D โ Text-to-video and image-to-3D tools going mainstream
โ AI-Native Programming โ Entire codebases generated and managed by AI agents
โ Sustainable AI โ Focus on reducing model training energy & creating green AI systems
React if you're following any of these trends closely!
#genai
โค3
I recently saw a radar chart (shared below) that maps out the skill sets across these rolesโand it got me thinkingโฆ
Hereโs a quick breakdown:
๐ง ๐๐ฎ๐๐ฎ ๐๐ป๐ด๐ถ๐ป๐ฒ๐ฒ๐ฟ โ The pipeline architect. Loves building scalable systems. Tools like Kafka, Spark, and Airflow are your playground.
๐ค ๐ ๐ ๐๐ป๐ด๐ถ๐ป๐ฒ๐ฒ๐ฟ โ The deployment expert. Knows how to take a model and make it work in the real world. Think automation, DevOps, and system design.
๐ง ๐๐ฎ๐๐ฎ ๐ฆ๐ฐ๐ถ๐ฒ๐ป๐๐ถ๐๐ โ The experimenter. Focused on digging deep, modeling, and delivering insights. Python, stats, and Jupyter notebooks all day.
๐ ๐๐ฎ๐๐ฎ ๐๐ป๐ฎ๐น๐๐๐ โ The storyteller. Turns raw numbers into meaningful business insights. If you live in Excel, Tableau, or Power BIโyou know what I mean.
๐ก ๐ฅ๐ฒ๐ฎ๐น ๐๐ฎ๐น๐ธ: You donโt need to be all of them. But knowing where you shine helps you aim your learning and job search in the right direction.
Whatโs your current roleโand whatโs one skill you're working on this year? ๐
Hereโs a quick breakdown:
๐ง ๐๐ฎ๐๐ฎ ๐๐ป๐ด๐ถ๐ป๐ฒ๐ฒ๐ฟ โ The pipeline architect. Loves building scalable systems. Tools like Kafka, Spark, and Airflow are your playground.
๐ค ๐ ๐ ๐๐ป๐ด๐ถ๐ป๐ฒ๐ฒ๐ฟ โ The deployment expert. Knows how to take a model and make it work in the real world. Think automation, DevOps, and system design.
๐ง ๐๐ฎ๐๐ฎ ๐ฆ๐ฐ๐ถ๐ฒ๐ป๐๐ถ๐๐ โ The experimenter. Focused on digging deep, modeling, and delivering insights. Python, stats, and Jupyter notebooks all day.
๐ ๐๐ฎ๐๐ฎ ๐๐ป๐ฎ๐น๐๐๐ โ The storyteller. Turns raw numbers into meaningful business insights. If you live in Excel, Tableau, or Power BIโyou know what I mean.
๐ก ๐ฅ๐ฒ๐ฎ๐น ๐๐ฎ๐น๐ธ: You donโt need to be all of them. But knowing where you shine helps you aim your learning and job search in the right direction.
Whatโs your current roleโand whatโs one skill you're working on this year? ๐
โค2
Hey guys!
Iโve been getting a lot of requests from you all asking for solid Data Analytics projects that can help you boost resume and build real skills.
So here you go โ
These arenโt just โfor practice,โ theyโre portfolio-worthy projects that show recruiters youโre ready for real-world work.
1. Sales Performance Dashboard
Tools: Excel / Power BI / Tableau
Youโll take raw sales data and turn it into a clean, interactive dashboard. Show key metrics like revenue, profit, top products, and regional trends.
Skills you build: Data cleaning, slicing & filtering, dashboard creation, business storytelling.
2. Customer Churn Analysis
Tools: Python (Pandas, Seaborn)
Work with a telecom or SaaS dataset to identify which customers are likely to leave and why.
Skills you build: Exploratory data analysis, visualization, correlation, and basic machine learning.
3. E-commerce Product Insights using SQL
Tools: SQL + Power BI
Analyze product categories, top-selling items, and revenue trends from a sample e-commerce dataset.
Skills you build: Joins, GROUP BY, aggregation, data modeling, and visual storytelling.
4. HR Analytics Dashboard
Tools: Excel / Power BI
Dive into employee data to find patterns in attrition, hiring trends, average salaries by department, etc.
Skills you build: Data summarization, calculated fields, visual formatting, DAX basics.
5. Movie Trends Analysis (Netflix or IMDb Dataset)
Tools: Python (Pandas, Matplotlib)
Explore trends across genres, ratings, and release years. Great for people who love entertainment and want to show creativity.
Skills you build: Data wrangling, time-series plots, filtering techniques.
6. Marketing Campaign Analysis
Tools: Excel / Power BI / SQL
Analyze data from a marketing campaign to measure ROI, conversion rates, and customer engagement. Identify which channels or strategies worked best and suggest improvements.
Skills you build: Data blending, KPI calculation, segmentation, and actionable insights.
7. Financial Expense Analysis & Budget Forecasting
Tools: Excel / Power BI / Python
Work on a companyโs expense data to analyze spending patterns, categorize expenses, and create a forecasting model to predict future budgets.
Skills you build: Time series analysis, forecasting, budgeting, and financial storytelling.
Pick 2โ3 projects. Donโt just show the final visuals โ explain your process on LinkedIn or GitHub. Thatโs what sets you apart.
Like for more useful content โค๏ธ
Iโve been getting a lot of requests from you all asking for solid Data Analytics projects that can help you boost resume and build real skills.
So here you go โ
These arenโt just โfor practice,โ theyโre portfolio-worthy projects that show recruiters youโre ready for real-world work.
1. Sales Performance Dashboard
Tools: Excel / Power BI / Tableau
Youโll take raw sales data and turn it into a clean, interactive dashboard. Show key metrics like revenue, profit, top products, and regional trends.
Skills you build: Data cleaning, slicing & filtering, dashboard creation, business storytelling.
2. Customer Churn Analysis
Tools: Python (Pandas, Seaborn)
Work with a telecom or SaaS dataset to identify which customers are likely to leave and why.
Skills you build: Exploratory data analysis, visualization, correlation, and basic machine learning.
3. E-commerce Product Insights using SQL
Tools: SQL + Power BI
Analyze product categories, top-selling items, and revenue trends from a sample e-commerce dataset.
Skills you build: Joins, GROUP BY, aggregation, data modeling, and visual storytelling.
4. HR Analytics Dashboard
Tools: Excel / Power BI
Dive into employee data to find patterns in attrition, hiring trends, average salaries by department, etc.
Skills you build: Data summarization, calculated fields, visual formatting, DAX basics.
5. Movie Trends Analysis (Netflix or IMDb Dataset)
Tools: Python (Pandas, Matplotlib)
Explore trends across genres, ratings, and release years. Great for people who love entertainment and want to show creativity.
Skills you build: Data wrangling, time-series plots, filtering techniques.
6. Marketing Campaign Analysis
Tools: Excel / Power BI / SQL
Analyze data from a marketing campaign to measure ROI, conversion rates, and customer engagement. Identify which channels or strategies worked best and suggest improvements.
Skills you build: Data blending, KPI calculation, segmentation, and actionable insights.
7. Financial Expense Analysis & Budget Forecasting
Tools: Excel / Power BI / Python
Work on a companyโs expense data to analyze spending patterns, categorize expenses, and create a forecasting model to predict future budgets.
Skills you build: Time series analysis, forecasting, budgeting, and financial storytelling.
Pick 2โ3 projects. Donโt just show the final visuals โ explain your process on LinkedIn or GitHub. Thatโs what sets you apart.
Like for more useful content โค๏ธ
โค4
For those of you who are new to Data Science and Machine learning algorithms, let me try to give you a brief overview. ML Algorithms can be categorized into three types: supervised learning, unsupervised learning, and reinforcement learning.
1. Supervised Learning:
- Definition: Algorithms learn from labeled training data, making predictions or decisions based on input-output pairs.
- Examples: Linear regression, decision trees, support vector machines (SVM), and neural networks.
- Applications: Email spam detection, image recognition, and medical diagnosis.
2. Unsupervised Learning:
- Definition: Algorithms analyze and group unlabeled data, identifying patterns and structures without prior knowledge of the outcomes.
- Examples: K-means clustering, hierarchical clustering, and principal component analysis (PCA).
- Applications: Customer segmentation, market basket analysis, and anomaly detection.
3. Reinforcement Learning:
- Definition: Algorithms learn by interacting with an environment, receiving rewards or penalties based on their actions, and optimizing for long-term goals.
- Examples: Q-learning, deep Q-networks (DQN), and policy gradient methods.
- Applications: Robotics, game playing (like AlphaGo), and self-driving cars.
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
Credits: https://t.iss.one/datasciencefun
Like if you need similar content
ENJOY LEARNING ๐๐
1. Supervised Learning:
- Definition: Algorithms learn from labeled training data, making predictions or decisions based on input-output pairs.
- Examples: Linear regression, decision trees, support vector machines (SVM), and neural networks.
- Applications: Email spam detection, image recognition, and medical diagnosis.
2. Unsupervised Learning:
- Definition: Algorithms analyze and group unlabeled data, identifying patterns and structures without prior knowledge of the outcomes.
- Examples: K-means clustering, hierarchical clustering, and principal component analysis (PCA).
- Applications: Customer segmentation, market basket analysis, and anomaly detection.
3. Reinforcement Learning:
- Definition: Algorithms learn by interacting with an environment, receiving rewards or penalties based on their actions, and optimizing for long-term goals.
- Examples: Q-learning, deep Q-networks (DQN), and policy gradient methods.
- Applications: Robotics, game playing (like AlphaGo), and self-driving cars.
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
Credits: https://t.iss.one/datasciencefun
Like if you need similar content
ENJOY LEARNING ๐๐
โค2
SQL CHEAT SHEET๐ฉโ๐ป
Here is a quick cheat sheet of some of the most essential SQL commands:
SELECT - Retrieves data from a database
UPDATE - Updates existing data in a database
DELETE - Removes data from a database
INSERT - Adds data to a database
CREATE - Creates an object such as a database or table
ALTER - Modifies an existing object in a database
DROP -Deletes an entire table or database
ORDER BY - Sorts the selected data in an ascending or descending order
WHERE โ Condition used to filter a specific set of records from the database
GROUP BY - Groups a set of data by a common parameter
HAVING - Allows the use of aggregate functions within the query
JOIN - Joins two or more tables together to retrieve data
INDEX - Creates an index on a table, to speed up search times.
Here is a quick cheat sheet of some of the most essential SQL commands:
SELECT - Retrieves data from a database
UPDATE - Updates existing data in a database
DELETE - Removes data from a database
INSERT - Adds data to a database
CREATE - Creates an object such as a database or table
ALTER - Modifies an existing object in a database
DROP -Deletes an entire table or database
ORDER BY - Sorts the selected data in an ascending or descending order
WHERE โ Condition used to filter a specific set of records from the database
GROUP BY - Groups a set of data by a common parameter
HAVING - Allows the use of aggregate functions within the query
JOIN - Joins two or more tables together to retrieve data
INDEX - Creates an index on a table, to speed up search times.
โค4๐1
5 Essential Skills Every Data Analyst Must Master in 2025
Data analytics continues to evolve rapidly, and as a data analyst, it's crucial to stay ahead of the curve. In 2025, the skills that were once optional are now essential to stand out in this competitive field. Here are five must-have skills for every data analyst this year.
1. Data Wrangling & Cleaning:
The ability to clean, organize, and prepare data for analysis is critical. No matter how sophisticated your tools are, they can't work with messy, inconsistent data. Mastering data wranglingโremoving duplicates, handling missing values, and standardizing formatsโwill help you deliver accurate and actionable insights.
Tools to master: Python (Pandas), R, SQL
2. Advanced Excel Skills:
Excel remains one of the most widely used tools in the data analysis world. Beyond the basics, you should master advanced formulas, pivot tables, and Power Query. Excel continues to be indispensable for quick analyses and prototype dashboards.
Key skills to learn: VLOOKUP, INDEX/MATCH, Power Pivot, advanced charting
3. Data Visualization:
The ability to convey your findings through compelling data visuals is what sets top analysts apart. Learn how to use tools like Tableau, Power BI, or even D3.js for web-based visualization. Your visuals should tell a story thatโs easy for stakeholders to understand at a glance.
Focus areas: Interactive dashboards, storytelling with data, advanced chart types (heat maps, scatter plots)
4. Statistical Analysis & Hypothesis Testing:
Understanding statistics is fundamental for any data analyst. Master concepts like regression analysis, probability theory, and hypothesis testing. This skill will help you not only describe trends but also make data-driven predictions and assess the significance of your findings.
Skills to focus on: T-tests, ANOVA, correlation, regression models
5. Machine Learning Basics:
While you donโt need to be a data scientist, having a basic understanding of machine learning algorithms is increasingly important. Knowledge of supervised vs unsupervised learning, decision trees, and clustering techniques will allow you to push your analysis to the next level.
Begin with: Linear regression, K-means clustering, decision trees (using Python libraries like Scikit-learn)
In 2025, data analysts must embrace a multi-faceted skill set that combines technical expertise, statistical knowledge, and the ability to communicate findings effectively.
Keep learning and adapting to these emerging trends to ensure you're ready for the challenges of tomorrow.
I have curated best 80+ top-notch Data Analytics Resources ๐๐
https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02
Like this post for more content like this ๐โฅ๏ธ
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
Data analytics continues to evolve rapidly, and as a data analyst, it's crucial to stay ahead of the curve. In 2025, the skills that were once optional are now essential to stand out in this competitive field. Here are five must-have skills for every data analyst this year.
1. Data Wrangling & Cleaning:
The ability to clean, organize, and prepare data for analysis is critical. No matter how sophisticated your tools are, they can't work with messy, inconsistent data. Mastering data wranglingโremoving duplicates, handling missing values, and standardizing formatsโwill help you deliver accurate and actionable insights.
Tools to master: Python (Pandas), R, SQL
2. Advanced Excel Skills:
Excel remains one of the most widely used tools in the data analysis world. Beyond the basics, you should master advanced formulas, pivot tables, and Power Query. Excel continues to be indispensable for quick analyses and prototype dashboards.
Key skills to learn: VLOOKUP, INDEX/MATCH, Power Pivot, advanced charting
3. Data Visualization:
The ability to convey your findings through compelling data visuals is what sets top analysts apart. Learn how to use tools like Tableau, Power BI, or even D3.js for web-based visualization. Your visuals should tell a story thatโs easy for stakeholders to understand at a glance.
Focus areas: Interactive dashboards, storytelling with data, advanced chart types (heat maps, scatter plots)
4. Statistical Analysis & Hypothesis Testing:
Understanding statistics is fundamental for any data analyst. Master concepts like regression analysis, probability theory, and hypothesis testing. This skill will help you not only describe trends but also make data-driven predictions and assess the significance of your findings.
Skills to focus on: T-tests, ANOVA, correlation, regression models
5. Machine Learning Basics:
While you donโt need to be a data scientist, having a basic understanding of machine learning algorithms is increasingly important. Knowledge of supervised vs unsupervised learning, decision trees, and clustering techniques will allow you to push your analysis to the next level.
Begin with: Linear regression, K-means clustering, decision trees (using Python libraries like Scikit-learn)
In 2025, data analysts must embrace a multi-faceted skill set that combines technical expertise, statistical knowledge, and the ability to communicate findings effectively.
Keep learning and adapting to these emerging trends to ensure you're ready for the challenges of tomorrow.
I have curated best 80+ top-notch Data Analytics Resources ๐๐
https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02
Like this post for more content like this ๐โฅ๏ธ
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
โค4
๐ Key Skills for Aspiring Tech Specialists
๐ Data Analyst:
- Proficiency in SQL for database querying
- Advanced Excel for data manipulation
- Programming with Python or R for data analysis
- Statistical analysis to understand data trends
- Data visualization tools like Tableau or PowerBI
- Data preprocessing to clean and structure data
- Exploratory data analysis techniques
๐ง Data Scientist:
- Strong knowledge of Python and R for statistical analysis
- Machine learning for predictive modeling
- Deep understanding of mathematics and statistics
- Data wrangling to prepare data for analysis
- Big data platforms like Hadoop or Spark
- Data visualization and communication skills
- Experience with A/B testing frameworks
๐ Data Engineer:
- Expertise in SQL and NoSQL databases
- Experience with data warehousing solutions
- ETL (Extract, Transform, Load) process knowledge
- Familiarity with big data tools (e.g., Apache Spark)
- Proficient in Python, Java, or Scala
- Knowledge of cloud services like AWS, GCP, or Azure
- Understanding of data pipeline and workflow management tools
๐ค Machine Learning Engineer:
- Proficiency in Python and libraries like scikit-learn, TensorFlow
- Solid understanding of machine learning algorithms
- Experience with neural networks and deep learning frameworks
- Ability to implement models and fine-tune their parameters
- Knowledge of software engineering best practices
- Data modeling and evaluation strategies
- Strong mathematical skills, particularly in linear algebra and calculus
๐ง Deep Learning Engineer:
- Expertise in deep learning frameworks like TensorFlow or PyTorch
- Understanding of Convolutional and Recurrent Neural Networks
- Experience with GPU computing and parallel processing
- Familiarity with computer vision and natural language processing
- Ability to handle large datasets and train complex models
- Research mindset to keep up with the latest developments in deep learning
๐คฏ AI Engineer:
- Solid foundation in algorithms, logic, and mathematics
- Proficiency in programming languages like Python or C++
- Experience with AI technologies including ML, neural networks, and cognitive computing
- Understanding of AI model deployment and scaling
- Knowledge of AI ethics and responsible AI practices
- Strong problem-solving and analytical skills
๐ NLP Engineer:
- Background in linguistics and language models
- Proficiency with NLP libraries (e.g., NLTK, spaCy)
- Experience with text preprocessing and tokenization
- Understanding of sentiment analysis, text classification, and named entity recognition
- Familiarity with transformer models like BERT and GPT
- Ability to work with large text datasets and sequential data
๐ Embrace the world of data and AI, and become the architect of tomorrow's technology!
๐ Data Analyst:
- Proficiency in SQL for database querying
- Advanced Excel for data manipulation
- Programming with Python or R for data analysis
- Statistical analysis to understand data trends
- Data visualization tools like Tableau or PowerBI
- Data preprocessing to clean and structure data
- Exploratory data analysis techniques
๐ง Data Scientist:
- Strong knowledge of Python and R for statistical analysis
- Machine learning for predictive modeling
- Deep understanding of mathematics and statistics
- Data wrangling to prepare data for analysis
- Big data platforms like Hadoop or Spark
- Data visualization and communication skills
- Experience with A/B testing frameworks
๐ Data Engineer:
- Expertise in SQL and NoSQL databases
- Experience with data warehousing solutions
- ETL (Extract, Transform, Load) process knowledge
- Familiarity with big data tools (e.g., Apache Spark)
- Proficient in Python, Java, or Scala
- Knowledge of cloud services like AWS, GCP, or Azure
- Understanding of data pipeline and workflow management tools
๐ค Machine Learning Engineer:
- Proficiency in Python and libraries like scikit-learn, TensorFlow
- Solid understanding of machine learning algorithms
- Experience with neural networks and deep learning frameworks
- Ability to implement models and fine-tune their parameters
- Knowledge of software engineering best practices
- Data modeling and evaluation strategies
- Strong mathematical skills, particularly in linear algebra and calculus
๐ง Deep Learning Engineer:
- Expertise in deep learning frameworks like TensorFlow or PyTorch
- Understanding of Convolutional and Recurrent Neural Networks
- Experience with GPU computing and parallel processing
- Familiarity with computer vision and natural language processing
- Ability to handle large datasets and train complex models
- Research mindset to keep up with the latest developments in deep learning
๐คฏ AI Engineer:
- Solid foundation in algorithms, logic, and mathematics
- Proficiency in programming languages like Python or C++
- Experience with AI technologies including ML, neural networks, and cognitive computing
- Understanding of AI model deployment and scaling
- Knowledge of AI ethics and responsible AI practices
- Strong problem-solving and analytical skills
๐ NLP Engineer:
- Background in linguistics and language models
- Proficiency with NLP libraries (e.g., NLTK, spaCy)
- Experience with text preprocessing and tokenization
- Understanding of sentiment analysis, text classification, and named entity recognition
- Familiarity with transformer models like BERT and GPT
- Ability to work with large text datasets and sequential data
๐ Embrace the world of data and AI, and become the architect of tomorrow's technology!
โค3
SQL Interview Questions with Answers
1. How to change a table name in SQL?
This is the command to change a table name in SQL:
ALTER TABLE table_name
RENAME TO new_table_name;
We will start off by giving the keywords ALTER TABLE, then we will follow it up by giving the original name of the table, after that, we will give in the keywords RENAME TO and finally, we will give the new table name.
2. How to use LIKE in SQL?
The LIKE operator checks if an attribute value matches a given string pattern. Here is an example of LIKE operator
SELECT * FROM employees WHERE first_name like โStevenโ;
With this command, we will be able to extract all the records where the first name is like โStevenโ.
3. If we drop a table, does it also drop related objects like constraints, indexes, columns, default, views and sorted procedures?
Yes, SQL server drops all related objects, which exists inside a table like constraints, indexes, columns, defaults etc. But dropping a table will not drop views and sorted procedures as they exist outside the table.
4. Explain SQL Constraints.
SQL Constraints are used to specify the rules of data type in a table. They can be specified while creating and altering the table. The following are the constraints in SQL: NOT NULL CHECK DEFAULT UNIQUE PRIMARY KEY FOREIGN KEY
React โค๏ธ for more
1. How to change a table name in SQL?
This is the command to change a table name in SQL:
ALTER TABLE table_name
RENAME TO new_table_name;
We will start off by giving the keywords ALTER TABLE, then we will follow it up by giving the original name of the table, after that, we will give in the keywords RENAME TO and finally, we will give the new table name.
2. How to use LIKE in SQL?
The LIKE operator checks if an attribute value matches a given string pattern. Here is an example of LIKE operator
SELECT * FROM employees WHERE first_name like โStevenโ;
With this command, we will be able to extract all the records where the first name is like โStevenโ.
3. If we drop a table, does it also drop related objects like constraints, indexes, columns, default, views and sorted procedures?
Yes, SQL server drops all related objects, which exists inside a table like constraints, indexes, columns, defaults etc. But dropping a table will not drop views and sorted procedures as they exist outside the table.
4. Explain SQL Constraints.
SQL Constraints are used to specify the rules of data type in a table. They can be specified while creating and altering the table. The following are the constraints in SQL: NOT NULL CHECK DEFAULT UNIQUE PRIMARY KEY FOREIGN KEY
React โค๏ธ for more
โค4