Important Excel, Tableau, Statistics, SQL related Questions with answers
1. What are the common problems that data analysts encounter during analysis?
The common problems steps involved in any analytics project are:
Handling duplicate data
Collecting the meaningful right data at the right time
Handling data purging and storage problems
Making data secure and dealing with compliance issues
2. Explain the Type I and Type II errors in Statistics?
In Hypothesis testing, a Type I error occurs when the null hypothesis is rejected even if it is true. It is also known as a false positive.
A Type II error occurs when the null hypothesis is not rejected, even if it is false. It is also known as a false negative.
3. How do you make a dropdown list in MS Excel?
First, click on the Data tab that is present in the ribbon.
Under the Data Tools group, select Data Validation.
Then navigate to Settings > Allow > List.
Select the source you want to provide as a list array.
4. How do you subset or filter data in SQL?
To subset or filter data in SQL, we use WHERE and HAVING clauses which give us an option of including only the data matching certain conditions.
5. What is a Gantt Chart in Tableau?
A Gantt chart in Tableau depicts the progress of value over the period, i.e., it shows the duration of events. It consists of bars along with the time axis. The Gantt chart is mostly used as a project management tool where each bar is a measure of a task in the project
1. What are the common problems that data analysts encounter during analysis?
The common problems steps involved in any analytics project are:
Handling duplicate data
Collecting the meaningful right data at the right time
Handling data purging and storage problems
Making data secure and dealing with compliance issues
2. Explain the Type I and Type II errors in Statistics?
In Hypothesis testing, a Type I error occurs when the null hypothesis is rejected even if it is true. It is also known as a false positive.
A Type II error occurs when the null hypothesis is not rejected, even if it is false. It is also known as a false negative.
3. How do you make a dropdown list in MS Excel?
First, click on the Data tab that is present in the ribbon.
Under the Data Tools group, select Data Validation.
Then navigate to Settings > Allow > List.
Select the source you want to provide as a list array.
4. How do you subset or filter data in SQL?
To subset or filter data in SQL, we use WHERE and HAVING clauses which give us an option of including only the data matching certain conditions.
5. What is a Gantt Chart in Tableau?
A Gantt chart in Tableau depicts the progress of value over the period, i.e., it shows the duration of events. It consists of bars along with the time axis. The Gantt chart is mostly used as a project management tool where each bar is a measure of a task in the project
โค2
๐๐ฅ ๐๐ฒ๐ฐ๐ผ๐บ๐ฒ ๐ฎ๐ป ๐๐ด๐ฒ๐ป๐๐ถ๐ฐ ๐๐ ๐๐๐ถ๐น๐ฑ๐ฒ๐ฟ โ ๐๐ฟ๐ฒ๐ฒ ๐๐ฒ๐ฟ๐๐ถ๐ณ๐ถ๐ฐ๐ฎ๐๐ถ๐ผ๐ป ๐ฃ๐ฟ๐ผ๐ด๐ฟ๐ฎ๐บ
Master the most in-demand AI skill in todayโs job market: building autonomous AI systems.
In Ready Tensorโs free, project-first program, youโll create three portfolio-ready projects using ๐๐ฎ๐ป๐ด๐๐ต๐ฎ๐ถ๐ป, ๐๐ฎ๐ป๐ด๐๐ฟ๐ฎ๐ฝ๐ต, and vector databases โ and deploy production-ready agents that employers will notice.
Includes guided lectures, videos, and code.
๐๐ฟ๐ฒ๐ฒ. ๐ฆ๐ฒ๐น๐ณ-๐ฝ๐ฎ๐ฐ๐ฒ๐ฑ. ๐๐ฎ๐ฟ๐ฒ๐ฒ๐ฟ-๐ฐ๐ต๐ฎ๐ป๐ด๐ถ๐ป๐ด.
๐ Apply now: https://go.readytensor.ai/cert-553-agentic-ai-certification
Double Tap โฅ๏ธ for more free resources
Master the most in-demand AI skill in todayโs job market: building autonomous AI systems.
In Ready Tensorโs free, project-first program, youโll create three portfolio-ready projects using ๐๐ฎ๐ป๐ด๐๐ต๐ฎ๐ถ๐ป, ๐๐ฎ๐ป๐ด๐๐ฟ๐ฎ๐ฝ๐ต, and vector databases โ and deploy production-ready agents that employers will notice.
Includes guided lectures, videos, and code.
๐๐ฟ๐ฒ๐ฒ. ๐ฆ๐ฒ๐น๐ณ-๐ฝ๐ฎ๐ฐ๐ฒ๐ฑ. ๐๐ฎ๐ฟ๐ฒ๐ฒ๐ฟ-๐ฐ๐ต๐ฎ๐ป๐ด๐ถ๐ป๐ด.
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Agentic AI Developer Certification Program by Ready Tensor
Learn to build chatbots, AI assistants, and multi-agent systems with Ready Tensor's free, self-paced, and beginner-friendly Agentic AI Developer Certification. View the full program guide and how to get certified.
โค3
Java Roadmap
|
|-- Fundamentals
| |-- Basics of Programming
| | |-- Introduction to Java
| | |-- Java Development Kit (JDK) and Java Runtime Environment (JRE)
| | |-- Setting Up Development Environment (IDE: IntelliJ IDEA, Eclipse, etc.)
| |
| |-- Syntax and Structure
| | |-- Basic Syntax
| | |-- Variables and Data Types
| | |-- Operators and Expressions
|
|-- Control Structures
| |-- Conditional Statements
| | |-- If-Else Statements
| | |-- Switch Case
| |
| |-- Loops
| | |-- For Loop
| | |-- While Loop
| | |-- Do-While Loop
| |
| |-- Exception Handling
| | |-- Try-Catch Block
| | |-- Finally Block
| | |-- Throw and Throws Keywords
|
|-- Object-Oriented Programming (OOP)
| |-- Basics of OOP
| | |-- Classes and Objects
| | |-- Methods and Constructors
| |
| |-- Inheritance
| | |-- Single and Multiple Inheritance
| | |-- Method Overriding
| | |-- Super Keyword
| |
| |-- Polymorphism
| | |-- Method Overloading
| | |-- Runtime Polymorphism
| | |-- Dynamic Method Dispatch
| |
| |-- Encapsulation
| | |-- Access Modifiers (Public, Private, Protected)
| | |-- Getters and Setters
| | |-- Data Hiding
| |
| |-- Abstraction
| | |-- Abstract Classes
| | |-- Interfaces
|
|-- Advanced Java
| |-- Collections Framework
| | |-- List (ArrayList, LinkedList)
| | |-- Set (HashSet, TreeSet)
| | |-- Map (HashMap, TreeMap)
| | |-- Queue (PriorityQueue, LinkedList)
| |
| |-- Concurrency
| | |-- Multithreading (Creating Threads, Thread Lifecycle)
| | |-- Synchronization
| | |-- Concurrency Utilities (Executors Framework, Callable and Future, Locks and Semaphores)
|
|-- Java Standard Libraries
| |-- I/O Streams
| | |-- File Handling (File Class, Reading and Writing Files)
| | |-- Streams (Byte Streams, Character Streams, Buffered Streams)
| |
| |-- Networking
| | |-- Sockets (TCP and UDP, Socket and ServerSocket Classes)
| | |-- URL and HTTP (URL Class, HttpURLConnection)
| |
| |-- JDBC
| | |-- Database Connectivity (JDBC Drivers, Connection, Statement, and ResultSet)
| | |-- PreparedStatement and CallableStatement
|
|-- Java Frameworks
| |-- Spring Framework
| | |-- Spring Core (Dependency Injection, Inversion of Control)
| | |-- Spring MVC (Model-View-Controller Architecture)
| | |-- Spring Boot (Creating Spring Boot Applications, Starters and Auto-Configuration, Actuator)
| |
| |-- Hibernate
| | |-- ORM Basics (Introduction to ORM, Configuration and Mapping)
| | |-- Advanced Hibernate (Caching, Transactions and Concurrency, Criteria API)
|
|-- Web Development with Java
| |-- Java EE (Jakarta EE)
| | |-- Servlets (Lifecycle, Handling HTTP Requests and Responses, Session Management)
| | |-- JavaServer Pages (JSP) (Syntax, Directives, JSTL and Custom Tags, Expression Language)
| |
| |-- RESTful Web Services
| | |-- JAX-RS (Creating RESTful Services, Annotations and HTTP Methods, Consuming RESTful Services)
|
|-- Build Tools and Dependency Management
| |-- Maven
| | |-- Project Object Model (POM), Dependencies, Repositories, Build Lifecycle and Plugins
| |
| |-- Gradle
| | |-- Build Scripts, Dependency Management, Task Automation
|
|-- Testing in Java
| |-- Unit Testing
| | |-- JUnit (Annotations, Assertions, Test Suites and Runners)
| |
| |-- Mockito (Creating Mocks and Spies and Verification)
| |
| |-- Integration Testing
| | |-- Spring Test (Testing Spring Components and WebTestClient)
|
|-- Deployment and DevOps
| |-- Containers and Microservices
| | |-- Docker (Dockerfile, Image Creation, Container Management)
| | |-- Kubernetes (Pods, Services, Deployments, Managing Java Applications on Kubernetes)
Free books and courses to learn Java๐๐
https://imp.i115008.net/QOz50M
https://bit.ly/3hbu3Dg
https://imp.i115008.net/Jrjo1R
https://bit.ly/3BSHP5S
https://t.me/Java_Programming_Notes
Join @free4unow_backup for more free courses
ENJOY LEARNING๐๐
|
|-- Fundamentals
| |-- Basics of Programming
| | |-- Introduction to Java
| | |-- Java Development Kit (JDK) and Java Runtime Environment (JRE)
| | |-- Setting Up Development Environment (IDE: IntelliJ IDEA, Eclipse, etc.)
| |
| |-- Syntax and Structure
| | |-- Basic Syntax
| | |-- Variables and Data Types
| | |-- Operators and Expressions
|
|-- Control Structures
| |-- Conditional Statements
| | |-- If-Else Statements
| | |-- Switch Case
| |
| |-- Loops
| | |-- For Loop
| | |-- While Loop
| | |-- Do-While Loop
| |
| |-- Exception Handling
| | |-- Try-Catch Block
| | |-- Finally Block
| | |-- Throw and Throws Keywords
|
|-- Object-Oriented Programming (OOP)
| |-- Basics of OOP
| | |-- Classes and Objects
| | |-- Methods and Constructors
| |
| |-- Inheritance
| | |-- Single and Multiple Inheritance
| | |-- Method Overriding
| | |-- Super Keyword
| |
| |-- Polymorphism
| | |-- Method Overloading
| | |-- Runtime Polymorphism
| | |-- Dynamic Method Dispatch
| |
| |-- Encapsulation
| | |-- Access Modifiers (Public, Private, Protected)
| | |-- Getters and Setters
| | |-- Data Hiding
| |
| |-- Abstraction
| | |-- Abstract Classes
| | |-- Interfaces
|
|-- Advanced Java
| |-- Collections Framework
| | |-- List (ArrayList, LinkedList)
| | |-- Set (HashSet, TreeSet)
| | |-- Map (HashMap, TreeMap)
| | |-- Queue (PriorityQueue, LinkedList)
| |
| |-- Concurrency
| | |-- Multithreading (Creating Threads, Thread Lifecycle)
| | |-- Synchronization
| | |-- Concurrency Utilities (Executors Framework, Callable and Future, Locks and Semaphores)
|
|-- Java Standard Libraries
| |-- I/O Streams
| | |-- File Handling (File Class, Reading and Writing Files)
| | |-- Streams (Byte Streams, Character Streams, Buffered Streams)
| |
| |-- Networking
| | |-- Sockets (TCP and UDP, Socket and ServerSocket Classes)
| | |-- URL and HTTP (URL Class, HttpURLConnection)
| |
| |-- JDBC
| | |-- Database Connectivity (JDBC Drivers, Connection, Statement, and ResultSet)
| | |-- PreparedStatement and CallableStatement
|
|-- Java Frameworks
| |-- Spring Framework
| | |-- Spring Core (Dependency Injection, Inversion of Control)
| | |-- Spring MVC (Model-View-Controller Architecture)
| | |-- Spring Boot (Creating Spring Boot Applications, Starters and Auto-Configuration, Actuator)
| |
| |-- Hibernate
| | |-- ORM Basics (Introduction to ORM, Configuration and Mapping)
| | |-- Advanced Hibernate (Caching, Transactions and Concurrency, Criteria API)
|
|-- Web Development with Java
| |-- Java EE (Jakarta EE)
| | |-- Servlets (Lifecycle, Handling HTTP Requests and Responses, Session Management)
| | |-- JavaServer Pages (JSP) (Syntax, Directives, JSTL and Custom Tags, Expression Language)
| |
| |-- RESTful Web Services
| | |-- JAX-RS (Creating RESTful Services, Annotations and HTTP Methods, Consuming RESTful Services)
|
|-- Build Tools and Dependency Management
| |-- Maven
| | |-- Project Object Model (POM), Dependencies, Repositories, Build Lifecycle and Plugins
| |
| |-- Gradle
| | |-- Build Scripts, Dependency Management, Task Automation
|
|-- Testing in Java
| |-- Unit Testing
| | |-- JUnit (Annotations, Assertions, Test Suites and Runners)
| |
| |-- Mockito (Creating Mocks and Spies and Verification)
| |
| |-- Integration Testing
| | |-- Spring Test (Testing Spring Components and WebTestClient)
|
|-- Deployment and DevOps
| |-- Containers and Microservices
| | |-- Docker (Dockerfile, Image Creation, Container Management)
| | |-- Kubernetes (Pods, Services, Deployments, Managing Java Applications on Kubernetes)
Free books and courses to learn Java๐๐
https://imp.i115008.net/QOz50M
https://bit.ly/3hbu3Dg
https://imp.i115008.net/Jrjo1R
https://bit.ly/3BSHP5S
https://t.me/Java_Programming_Notes
Join @free4unow_backup for more free courses
ENJOY LEARNING๐๐
โค5
DATA SCIENCE INTERVIEW QUESTIONS WITH ANSWERS
1. What are the assumptions required for linear regression? What if some of these assumptions are violated?
Ans: The assumptions are as follows:
The sample data used to fit the model is representative of the population
The relationship between X and the mean of Y is linear
The variance of the residual is the same for any value of X (homoscedasticity)
Observations are independent of each other
For any value of X, Y is normally distributed.
Extreme violations of these assumptions will make the results redundant. Small violations of these assumptions will result in a greater bias or variance of the estimate.
2.What is multicollinearity and how to remove it?
Ans: Multicollinearity exists when an independent variable is highly correlated with another independent variable in a multiple regression equation. This can be problematic because it undermines the statistical significance of an independent variable.
You could use the Variance Inflation Factors (VIF) to determine if there is any multicollinearity between independent variables โ a standard benchmark is that if the VIF is greater than 5 then multicollinearity exists.
3. What is overfitting and how to prevent it?
Ans: Overfitting is an error where the model โfitsโ the data too well, resulting in a model with high variance and low bias. As a consequence, an overfit model will inaccurately predict new data points even though it has a high accuracy on the training data.
Few approaches to prevent overfitting are:
- Cross-Validation:Cross-validation is a powerful preventative measure against overfitting. Here we use our initial training data to generate multiple mini train-test splits. Now we use these splits to tune our model.
- Train with more data: It wonโt work every time, but training with more data can help algorithms detect the signal better or it can help my model to understand general trends in particular.
- We can remove irrelevant information or the noise from our dataset.
- Early Stopping: When youโre training a learning algorithm iteratively, you can measure how well each iteration of the model performs.
Up until a certain number of iterations, new iterations improve the model. After that point, however, the modelโs ability to generalize can weaken as it begins to overfit the training data.
Early stopping refers stopping the training process before the learner passes that point.
- Regularization: It refers to a broad range of techniques for artificially forcing your model to be simpler. There are mainly 3 types of Regularization techniques:L1, L2,&,Elastic- net.
- Ensembling : Here we take number of learners and using these we get strong model. They are of two types : Bagging and Boosting.
4. Given two fair dices, what is the probability of getting scores that sum to 4 and 8?
Ans: There are 4 combinations of rolling a 4 (1+3, 3+1, 2+2):
P(rolling a 4) = 3/36 = 1/12
There are 5 combinations of rolling an 8 (2+6, 6+2, 3+5, 5+3, 4+4):
P(rolling an 8) = 5/36
ENJOY LEARNING ๐๐
1. What are the assumptions required for linear regression? What if some of these assumptions are violated?
Ans: The assumptions are as follows:
The sample data used to fit the model is representative of the population
The relationship between X and the mean of Y is linear
The variance of the residual is the same for any value of X (homoscedasticity)
Observations are independent of each other
For any value of X, Y is normally distributed.
Extreme violations of these assumptions will make the results redundant. Small violations of these assumptions will result in a greater bias or variance of the estimate.
2.What is multicollinearity and how to remove it?
Ans: Multicollinearity exists when an independent variable is highly correlated with another independent variable in a multiple regression equation. This can be problematic because it undermines the statistical significance of an independent variable.
You could use the Variance Inflation Factors (VIF) to determine if there is any multicollinearity between independent variables โ a standard benchmark is that if the VIF is greater than 5 then multicollinearity exists.
3. What is overfitting and how to prevent it?
Ans: Overfitting is an error where the model โfitsโ the data too well, resulting in a model with high variance and low bias. As a consequence, an overfit model will inaccurately predict new data points even though it has a high accuracy on the training data.
Few approaches to prevent overfitting are:
- Cross-Validation:Cross-validation is a powerful preventative measure against overfitting. Here we use our initial training data to generate multiple mini train-test splits. Now we use these splits to tune our model.
- Train with more data: It wonโt work every time, but training with more data can help algorithms detect the signal better or it can help my model to understand general trends in particular.
- We can remove irrelevant information or the noise from our dataset.
- Early Stopping: When youโre training a learning algorithm iteratively, you can measure how well each iteration of the model performs.
Up until a certain number of iterations, new iterations improve the model. After that point, however, the modelโs ability to generalize can weaken as it begins to overfit the training data.
Early stopping refers stopping the training process before the learner passes that point.
- Regularization: It refers to a broad range of techniques for artificially forcing your model to be simpler. There are mainly 3 types of Regularization techniques:L1, L2,&,Elastic- net.
- Ensembling : Here we take number of learners and using these we get strong model. They are of two types : Bagging and Boosting.
4. Given two fair dices, what is the probability of getting scores that sum to 4 and 8?
Ans: There are 4 combinations of rolling a 4 (1+3, 3+1, 2+2):
P(rolling a 4) = 3/36 = 1/12
There are 5 combinations of rolling an 8 (2+6, 6+2, 3+5, 5+3, 4+4):
P(rolling an 8) = 5/36
ENJOY LEARNING ๐๐
โค2
Hey guys,
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Do react with โฅ๏ธ if you need more content like this
ENJOY LEARNING ๐๐
Here is the list of best curated Telegram Channels for free education ๐๐
Free Courses with Certificate
Web Development Free Resources
Data Science & Machine Learning
Programming Free Books
Python Free Courses
Python Interview Resources
Ethical Hacking & Cyber Security
English Speaking & Communication
Stock Marketing & Investment Banking
Coding Projects
Jobs & Internship Opportunities
Learn Digital Marketing
Crack your coding Interviews
Udemy Free Courses with Certificate
Earn $10000 with ChatGPT
Google Jobs
Java Programming Free Resources
Learn Blockchain & Crypto
Data Analyst Jobs
Artificial Intelligence
Free access to all the Paid Channels
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https://t.me/addlist/4q2PYC0pH_VjZDk5
Do react with โฅ๏ธ if you need more content like this
ENJOY LEARNING ๐๐
โค5
If you want to Excel at Frontend Development and build stunning user interfaces, master these essential skills:
Core Technologies:
โข HTML5 & Semantic Tags โ Clean and accessible structure
โข CSS3 & Preprocessors (SASS, SCSS) โ Advanced styling
โข JavaScript ES6+ โ Arrow functions, Promises, Async/Await
CSS Frameworks & UI Libraries:
โข Bootstrap & Tailwind CSS โ Speed up styling
โข Flexbox & CSS Grid โ Modern layout techniques
โข Material UI, Ant Design, Chakra UI โ Prebuilt UI components
JavaScript Frameworks & Libraries:
โข React.js โ Component-based UI development
โข Vue.js / Angular โ Alternative frontend frameworks
โข Next.js & Nuxt.js โ Server-side rendering (SSR) & static site generation
State Management:
โข Redux / Context API (React) โ Manage complex state
โข Pinia / Vuex (Vue) โ Efficient state handling
API Integration & Data Handling:
โข Fetch API & Axios โ Consume RESTful APIs
โข GraphQL & Apollo Client โ Query APIs efficiently
Frontend Optimization & Performance:
โข Lazy Loading & Code Splitting โ Faster load times
โข Web Performance Optimization (Lighthouse, Core Web Vitals)
Version Control & Deployment:
โข Git & GitHub โ Track changes and collaborate
โข CI/CD & Hosting โ Deploy with Vercel, Netlify, Firebase
Like it if you need a complete tutorial on all these topics! ๐โค๏ธ
Web Development Best Resources
Share with credits: https://t.me/webdevcoursefree
ENJOY LEARNING ๐๐
Core Technologies:
โข HTML5 & Semantic Tags โ Clean and accessible structure
โข CSS3 & Preprocessors (SASS, SCSS) โ Advanced styling
โข JavaScript ES6+ โ Arrow functions, Promises, Async/Await
CSS Frameworks & UI Libraries:
โข Bootstrap & Tailwind CSS โ Speed up styling
โข Flexbox & CSS Grid โ Modern layout techniques
โข Material UI, Ant Design, Chakra UI โ Prebuilt UI components
JavaScript Frameworks & Libraries:
โข React.js โ Component-based UI development
โข Vue.js / Angular โ Alternative frontend frameworks
โข Next.js & Nuxt.js โ Server-side rendering (SSR) & static site generation
State Management:
โข Redux / Context API (React) โ Manage complex state
โข Pinia / Vuex (Vue) โ Efficient state handling
API Integration & Data Handling:
โข Fetch API & Axios โ Consume RESTful APIs
โข GraphQL & Apollo Client โ Query APIs efficiently
Frontend Optimization & Performance:
โข Lazy Loading & Code Splitting โ Faster load times
โข Web Performance Optimization (Lighthouse, Core Web Vitals)
Version Control & Deployment:
โข Git & GitHub โ Track changes and collaborate
โข CI/CD & Hosting โ Deploy with Vercel, Netlify, Firebase
Like it if you need a complete tutorial on all these topics! ๐โค๏ธ
Web Development Best Resources
Share with credits: https://t.me/webdevcoursefree
ENJOY LEARNING ๐๐
โค2
AI Engineering has levels to it:
โ Level 1: Using AI
Start by mastering the fundamentals:
-- Prompt engineering (zero-shot, few-shot, chain-of-thought)
-- Calling APIs (OpenAI, Anthropic, Cohere, Hugging Face)
-- Understanding tokens, context windows, and parameters (temperature, top-p)
With just these basics, you can already solve real problems.
โ Level 2: Integrating AI
Move from using AI to building with it:
-- Retrieval Augmented Generation (RAG) with vector databases (Pinecone, FAISS, Weaviate, Milvus)
-- Embeddings and similarity search (cosine, Euclidean, dot product)
-- Caching and batching for cost and latency improvements
-- Agents and tool use (safe function calling, API orchestration)
This is the foundation of most modern AI products.
โ Level 3: Engineering AI Systems
Level up from prototypes to production-ready systems:
-- Fine-tuning vs instruction-tuning vs RLHF (know when each applies)
-- Guardrails for safety and compliance (filters, validators, adversarial testing)
-- Multi-model architectures (LLMs + smaller specialized models)
-- Evaluation frameworks (BLEU, ROUGE, perplexity, win-rates, human evals)
Hereโs where you shift from โit worksโ to โit works reliably.โ
โ Level 4: Optimizing AI at Scale
Finally, learn how to run AI systems efficiently and responsibly:
-- Distributed inference (vLLM, Ray Serve, Hugging Face TGI)
-- Managing context length and memory (chunking, summarization, attention strategies)
-- Balancing cost vs performance (open-source vs proprietary tradeoffs)
-- Privacy, compliance, and governance (PII redaction, SOC2, HIPAA, GDPR)
At this stage, youโre not just building AIโyouโre designing systems that scale in the real world.
โ Level 1: Using AI
Start by mastering the fundamentals:
-- Prompt engineering (zero-shot, few-shot, chain-of-thought)
-- Calling APIs (OpenAI, Anthropic, Cohere, Hugging Face)
-- Understanding tokens, context windows, and parameters (temperature, top-p)
With just these basics, you can already solve real problems.
โ Level 2: Integrating AI
Move from using AI to building with it:
-- Retrieval Augmented Generation (RAG) with vector databases (Pinecone, FAISS, Weaviate, Milvus)
-- Embeddings and similarity search (cosine, Euclidean, dot product)
-- Caching and batching for cost and latency improvements
-- Agents and tool use (safe function calling, API orchestration)
This is the foundation of most modern AI products.
โ Level 3: Engineering AI Systems
Level up from prototypes to production-ready systems:
-- Fine-tuning vs instruction-tuning vs RLHF (know when each applies)
-- Guardrails for safety and compliance (filters, validators, adversarial testing)
-- Multi-model architectures (LLMs + smaller specialized models)
-- Evaluation frameworks (BLEU, ROUGE, perplexity, win-rates, human evals)
Hereโs where you shift from โit worksโ to โit works reliably.โ
โ Level 4: Optimizing AI at Scale
Finally, learn how to run AI systems efficiently and responsibly:
-- Distributed inference (vLLM, Ray Serve, Hugging Face TGI)
-- Managing context length and memory (chunking, summarization, attention strategies)
-- Balancing cost vs performance (open-source vs proprietary tradeoffs)
-- Privacy, compliance, and governance (PII redaction, SOC2, HIPAA, GDPR)
At this stage, youโre not just building AIโyouโre designing systems that scale in the real world.
โค1
Tableau Cheat Sheet โ
This Tableau cheatsheet is designed to be your quick reference guide for data visualization and analysis using Tableau. Whether youโre a beginner learning the basics or an experienced user looking for a handy resource, this cheatsheet covers essential topics.
1. Connecting to Data
- Use *Connect* pane to connect to various data sources (Excel, SQL Server, Text files, etc.).
2. Data Preparation
- Data Interpreter: Clean data automatically using the Data Interpreter.
- Join Data: Combine data from multiple tables using joins (Inner, Left, Right, Outer).
- Union Data: Stack data from multiple tables with the same structure.
3. Creating Views
- Drag & Drop: Drag fields from the Data pane onto Rows, Columns, or Marks to create visualizations.
- Show Me: Use the *Show Me* panel to select different visualization types.
4. Types of Visualizations
- Bar Chart: Compare values across categories.
- Line Chart: Display trends over time.
- Pie Chart: Show proportions of a whole (use sparingly).
- Map: Visualize geographic data.
- Scatter Plot: Show relationships between two variables.
5. Filters
- Dimension Filters: Filter data based on categorical values.
- Measure Filters: Filter data based on numerical values.
- Context Filters: Set a context for other filters to improve performance.
6. Calculated Fields
- Create calculated fields to derive new data:
- Example:
7. Parameters
- Use parameters to allow user input and control measures dynamically.
8. Formatting
- Format fonts, colors, borders, and lines using the Format pane for better visual appeal.
9. Dashboards
- Combine multiple sheets into a dashboard using the *Dashboard* tab.
- Use dashboard actions (filter, highlight, URL) to create interactivity.
10. Story Points
- Create a story to guide users through insights with narrative and visualizations.
11. Publishing & Sharing
- Publish dashboards to Tableau Server or Tableau Online for sharing and collaboration.
12. Export Options
- Export to PDF or image for offline use.
13. Keyboard Shortcuts
- Show/Hide Sidebar:
- Duplicate Sheet:
- Undo:
- Redo:
14. Performance Optimization
- Use extracts instead of live connections for faster performance.
- Optimize calculations and filters to improve dashboard loading times.
Best Resources to learn Tableau: https://t.me/PowerBI_analyst
Hope you'll like it
Share with credits: https://t.me/sqlspecialist
Hope it helps :)
This Tableau cheatsheet is designed to be your quick reference guide for data visualization and analysis using Tableau. Whether youโre a beginner learning the basics or an experienced user looking for a handy resource, this cheatsheet covers essential topics.
1. Connecting to Data
- Use *Connect* pane to connect to various data sources (Excel, SQL Server, Text files, etc.).
2. Data Preparation
- Data Interpreter: Clean data automatically using the Data Interpreter.
- Join Data: Combine data from multiple tables using joins (Inner, Left, Right, Outer).
- Union Data: Stack data from multiple tables with the same structure.
3. Creating Views
- Drag & Drop: Drag fields from the Data pane onto Rows, Columns, or Marks to create visualizations.
- Show Me: Use the *Show Me* panel to select different visualization types.
4. Types of Visualizations
- Bar Chart: Compare values across categories.
- Line Chart: Display trends over time.
- Pie Chart: Show proportions of a whole (use sparingly).
- Map: Visualize geographic data.
- Scatter Plot: Show relationships between two variables.
5. Filters
- Dimension Filters: Filter data based on categorical values.
- Measure Filters: Filter data based on numerical values.
- Context Filters: Set a context for other filters to improve performance.
6. Calculated Fields
- Create calculated fields to derive new data:
- Example:
Sales Growth = SUM([Sales]) - SUM([Previous Sales])7. Parameters
- Use parameters to allow user input and control measures dynamically.
8. Formatting
- Format fonts, colors, borders, and lines using the Format pane for better visual appeal.
9. Dashboards
- Combine multiple sheets into a dashboard using the *Dashboard* tab.
- Use dashboard actions (filter, highlight, URL) to create interactivity.
10. Story Points
- Create a story to guide users through insights with narrative and visualizations.
11. Publishing & Sharing
- Publish dashboards to Tableau Server or Tableau Online for sharing and collaboration.
12. Export Options
- Export to PDF or image for offline use.
13. Keyboard Shortcuts
- Show/Hide Sidebar:
Ctrl+Alt+T- Duplicate Sheet:
Ctrl + D- Undo:
Ctrl + Z- Redo:
Ctrl + Y14. Performance Optimization
- Use extracts instead of live connections for faster performance.
- Optimize calculations and filters to improve dashboard loading times.
Best Resources to learn Tableau: https://t.me/PowerBI_analyst
Hope you'll like it
Share with credits: https://t.me/sqlspecialist
Hope it helps :)
โค3
Important Excel, Tableau, Statistics, SQL related Questions with answers
1. What are the common problems that data analysts encounter during analysis?
The common problems steps involved in any analytics project are:
Handling duplicate data
Collecting the meaningful right data at the right time
Handling data purging and storage problems
Making data secure and dealing with compliance issues
2. Explain the Type I and Type II errors in Statistics?
In Hypothesis testing, a Type I error occurs when the null hypothesis is rejected even if it is true. It is also known as a false positive.
A Type II error occurs when the null hypothesis is not rejected, even if it is false. It is also known as a false negative.
3. How do you make a dropdown list in MS Excel?
First, click on the Data tab that is present in the ribbon.
Under the Data Tools group, select Data Validation.
Then navigate to Settings > Allow > List.
Select the source you want to provide as a list array.
4. How do you subset or filter data in SQL?
To subset or filter data in SQL, we use WHERE and HAVING clauses which give us an option of including only the data matching certain conditions.
5. What is a Gantt Chart in Tableau?
A Gantt chart in Tableau depicts the progress of value over the period, i.e., it shows the duration of events. It consists of bars along with the time axis. The Gantt chart is mostly used as a project management tool where each bar is a measure of a task in the project
1. What are the common problems that data analysts encounter during analysis?
The common problems steps involved in any analytics project are:
Handling duplicate data
Collecting the meaningful right data at the right time
Handling data purging and storage problems
Making data secure and dealing with compliance issues
2. Explain the Type I and Type II errors in Statistics?
In Hypothesis testing, a Type I error occurs when the null hypothesis is rejected even if it is true. It is also known as a false positive.
A Type II error occurs when the null hypothesis is not rejected, even if it is false. It is also known as a false negative.
3. How do you make a dropdown list in MS Excel?
First, click on the Data tab that is present in the ribbon.
Under the Data Tools group, select Data Validation.
Then navigate to Settings > Allow > List.
Select the source you want to provide as a list array.
4. How do you subset or filter data in SQL?
To subset or filter data in SQL, we use WHERE and HAVING clauses which give us an option of including only the data matching certain conditions.
5. What is a Gantt Chart in Tableau?
A Gantt chart in Tableau depicts the progress of value over the period, i.e., it shows the duration of events. It consists of bars along with the time axis. The Gantt chart is mostly used as a project management tool where each bar is a measure of a task in the project
โค3
5 Easy Projects to Build as a Beginner
(No AI degree needed. Just curiosity & coffee.)
โฏ 1. Calculator App
โโข Learn logic building
โโข Try it in Python, JavaScript or C++
โโข Bonus: Add GUI using Tkinter or HTML/CSS
โฏ 2. Quiz App (with Score Tracker)
โโข Build a fun MCQ quiz
โโข Use basic conditions, loops, and arrays
โโข Add a timer for extra challenge!
โฏ 3. Rock, Paper, Scissors Game
โโข Classic game using random choice
โโข Great to practice conditions and user input
โโข Optional: Add a scoreboard
โฏ 4. Currency Converter
โโข Convert from USD to INR, EUR, etc.
โโข Use basic math or try fetching live rates via API
โโข Build a mini web app for it!
โฏ 5. To-Do List App
โโข Create, read, update, delete tasks
โโข Perfect for learning arrays and functions
โโข Bonus: Add local storage (in JS) or file saving (in Python)
React with โค๏ธ for the source code
Python Projects: https://whatsapp.com/channel/0029Vau5fZECsU9HJFLacm2a
Coding Projects: https://whatsapp.com/channel/0029VazkxJ62UPB7OQhBE502
ENJOY LEARNING ๐๐
(No AI degree needed. Just curiosity & coffee.)
โฏ 1. Calculator App
โโข Learn logic building
โโข Try it in Python, JavaScript or C++
โโข Bonus: Add GUI using Tkinter or HTML/CSS
โฏ 2. Quiz App (with Score Tracker)
โโข Build a fun MCQ quiz
โโข Use basic conditions, loops, and arrays
โโข Add a timer for extra challenge!
โฏ 3. Rock, Paper, Scissors Game
โโข Classic game using random choice
โโข Great to practice conditions and user input
โโข Optional: Add a scoreboard
โฏ 4. Currency Converter
โโข Convert from USD to INR, EUR, etc.
โโข Use basic math or try fetching live rates via API
โโข Build a mini web app for it!
โฏ 5. To-Do List App
โโข Create, read, update, delete tasks
โโข Perfect for learning arrays and functions
โโข Bonus: Add local storage (in JS) or file saving (in Python)
React with โค๏ธ for the source code
Python Projects: https://whatsapp.com/channel/0029Vau5fZECsU9HJFLacm2a
Coding Projects: https://whatsapp.com/channel/0029VazkxJ62UPB7OQhBE502
ENJOY LEARNING ๐๐
โค4๐1
ยฉHow fresher can get a job as a data scientist?ยฉ
India as a job market is highly resistant to hire data scientist as a fresher. Everyone out there asks for at least 2 years of experience, but then the question is where will we get the two years experience from?
The important thing here to build a portfolio. As you are a fresher I would assume you had learnt data science through online courses. They only teach you the basics, the analytical skills required to clean the data and apply machine learning algorithms to them comes only from practice.
Do some real-world data science projects, participate in Kaggle competition. kaggle provides data sets for practice as well. Whatever projects you do, create a GitHub repository for it. Place all your projects there so when a recruiter is looking at your profile they know you have hands-on practice and do know the basics. This will take you a long way.
All the major data science jobs for freshers will only be available through off-campus interviews.
Some companies that hires data scientists are:
Siemens
Accenture
IBM
Cerner
Creating a technical portfolio will showcase the knowledge you have already gained and that is essential while you got out there as a fresher and try to find a data scientist job.
India as a job market is highly resistant to hire data scientist as a fresher. Everyone out there asks for at least 2 years of experience, but then the question is where will we get the two years experience from?
The important thing here to build a portfolio. As you are a fresher I would assume you had learnt data science through online courses. They only teach you the basics, the analytical skills required to clean the data and apply machine learning algorithms to them comes only from practice.
Do some real-world data science projects, participate in Kaggle competition. kaggle provides data sets for practice as well. Whatever projects you do, create a GitHub repository for it. Place all your projects there so when a recruiter is looking at your profile they know you have hands-on practice and do know the basics. This will take you a long way.
All the major data science jobs for freshers will only be available through off-campus interviews.
Some companies that hires data scientists are:
Siemens
Accenture
IBM
Cerner
Creating a technical portfolio will showcase the knowledge you have already gained and that is essential while you got out there as a fresher and try to find a data scientist job.
โค3
๐๐ฅ๐๐ ๐ข๐ป๐น๐ถ๐ป๐ฒ ๐๐ผ๐๐ฟ๐๐ฒ๐ ๐ง๐ผ ๐๐ป๐ฟ๐ผ๐น๐น ๐๐ป ๐ฎ๐ฌ๐ฎ๐ฑ ๐
Learn Fundamental Skills with Free Online Courses & Earn Certificates
- AI
- GenAI
- Data Science,
- BigData
- Python
- Cloud Computing
- Machine Learning
- Cyber Security
๐๐ข๐ง๐ค ๐:-
https://linkpd.in/freecourses
Enroll for FREE & Get Certified ๐
Learn Fundamental Skills with Free Online Courses & Earn Certificates
- AI
- GenAI
- Data Science,
- BigData
- Python
- Cloud Computing
- Machine Learning
- Cyber Security
๐๐ข๐ง๐ค ๐:-
https://linkpd.in/freecourses
Enroll for FREE & Get Certified ๐
โค1
A-Z of essential data science concepts
A: Algorithm - A set of rules or instructions for solving a problem or completing a task.
B: Big Data - Large and complex datasets that traditional data processing applications are unable to handle efficiently.
C: Classification - A type of machine learning task that involves assigning labels to instances based on their characteristics.
D: Data Mining - The process of discovering patterns and extracting useful information from large datasets.
E: Ensemble Learning - A machine learning technique that combines multiple models to improve predictive performance.
F: Feature Engineering - The process of selecting, extracting, and transforming features from raw data to improve model performance.
G: Gradient Descent - An optimization algorithm used to minimize the error of a model by adjusting its parameters iteratively.
H: Hypothesis Testing - A statistical method used to make inferences about a population based on sample data.
I: Imputation - The process of replacing missing values in a dataset with estimated values.
J: Joint Probability - The probability of the intersection of two or more events occurring simultaneously.
K: K-Means Clustering - A popular unsupervised machine learning algorithm used for clustering data points into groups.
L: Logistic Regression - A statistical model used for binary classification tasks.
M: Machine Learning - A subset of artificial intelligence that enables systems to learn from data and improve performance over time.
N: Neural Network - A computer system inspired by the structure of the human brain, used for various machine learning tasks.
O: Outlier Detection - The process of identifying observations in a dataset that significantly deviate from the rest of the data points.
P: Precision and Recall - Evaluation metrics used to assess the performance of classification models.
Q: Quantitative Analysis - The process of using mathematical and statistical methods to analyze and interpret data.
R: Regression Analysis - A statistical technique used to model the relationship between a dependent variable and one or more independent variables.
S: Support Vector Machine - A supervised machine learning algorithm used for classification and regression tasks.
T: Time Series Analysis - The study of data collected over time to detect patterns, trends, and seasonal variations.
U: Unsupervised Learning - Machine learning techniques used to identify patterns and relationships in data without labeled outcomes.
V: Validation - The process of assessing the performance and generalization of a machine learning model using independent datasets.
W: Weka - A popular open-source software tool used for data mining and machine learning tasks.
X: XGBoost - An optimized implementation of gradient boosting that is widely used for classification and regression tasks.
Y: Yarn - A resource manager used in Apache Hadoop for managing resources across distributed clusters.
Z: Zero-Inflated Model - A statistical model used to analyze data with excess zeros, commonly found in count data.
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
Credits: https://t.me/datasciencefun
Like if you need similar content ๐๐
Hope this helps you ๐
A: Algorithm - A set of rules or instructions for solving a problem or completing a task.
B: Big Data - Large and complex datasets that traditional data processing applications are unable to handle efficiently.
C: Classification - A type of machine learning task that involves assigning labels to instances based on their characteristics.
D: Data Mining - The process of discovering patterns and extracting useful information from large datasets.
E: Ensemble Learning - A machine learning technique that combines multiple models to improve predictive performance.
F: Feature Engineering - The process of selecting, extracting, and transforming features from raw data to improve model performance.
G: Gradient Descent - An optimization algorithm used to minimize the error of a model by adjusting its parameters iteratively.
H: Hypothesis Testing - A statistical method used to make inferences about a population based on sample data.
I: Imputation - The process of replacing missing values in a dataset with estimated values.
J: Joint Probability - The probability of the intersection of two or more events occurring simultaneously.
K: K-Means Clustering - A popular unsupervised machine learning algorithm used for clustering data points into groups.
L: Logistic Regression - A statistical model used for binary classification tasks.
M: Machine Learning - A subset of artificial intelligence that enables systems to learn from data and improve performance over time.
N: Neural Network - A computer system inspired by the structure of the human brain, used for various machine learning tasks.
O: Outlier Detection - The process of identifying observations in a dataset that significantly deviate from the rest of the data points.
P: Precision and Recall - Evaluation metrics used to assess the performance of classification models.
Q: Quantitative Analysis - The process of using mathematical and statistical methods to analyze and interpret data.
R: Regression Analysis - A statistical technique used to model the relationship between a dependent variable and one or more independent variables.
S: Support Vector Machine - A supervised machine learning algorithm used for classification and regression tasks.
T: Time Series Analysis - The study of data collected over time to detect patterns, trends, and seasonal variations.
U: Unsupervised Learning - Machine learning techniques used to identify patterns and relationships in data without labeled outcomes.
V: Validation - The process of assessing the performance and generalization of a machine learning model using independent datasets.
W: Weka - A popular open-source software tool used for data mining and machine learning tasks.
X: XGBoost - An optimized implementation of gradient boosting that is widely used for classification and regression tasks.
Y: Yarn - A resource manager used in Apache Hadoop for managing resources across distributed clusters.
Z: Zero-Inflated Model - A statistical model used to analyze data with excess zeros, commonly found in count data.
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
Credits: https://t.me/datasciencefun
Like if you need similar content ๐๐
Hope this helps you ๐
โค2