Essential statistics topics for data science
1. Denoscriptive statistics: Measures of central tendency, measures of dispersion, and graphical representations of data.
2. Inferential statistics: Hypothesis testing, confidence intervals, and regression analysis.
3. Probability theory: Concepts of probability, random variables, and probability distributions.
4. Sampling techniques: Simple random sampling, stratified sampling, and cluster sampling.
5. Statistical modeling: Linear regression, logistic regression, and time series analysis.
6. Machine learning algorithms: Supervised learning, unsupervised learning, and reinforcement learning.
7. Bayesian statistics: Bayesian inference, Bayesian networks, and Markov chain Monte Carlo methods.
8. Data visualization: Techniques for visualizing data and communicating insights effectively.
9. Experimental design: Designing experiments, analyzing experimental data, and interpreting results.
10. Big data analytics: Handling large volumes of data using tools like Hadoop, Spark, and SQL.
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
Credits: https://news.1rj.ru/str/datasciencefun
Like if you need similar content 😄👍
1. Denoscriptive statistics: Measures of central tendency, measures of dispersion, and graphical representations of data.
2. Inferential statistics: Hypothesis testing, confidence intervals, and regression analysis.
3. Probability theory: Concepts of probability, random variables, and probability distributions.
4. Sampling techniques: Simple random sampling, stratified sampling, and cluster sampling.
5. Statistical modeling: Linear regression, logistic regression, and time series analysis.
6. Machine learning algorithms: Supervised learning, unsupervised learning, and reinforcement learning.
7. Bayesian statistics: Bayesian inference, Bayesian networks, and Markov chain Monte Carlo methods.
8. Data visualization: Techniques for visualizing data and communicating insights effectively.
9. Experimental design: Designing experiments, analyzing experimental data, and interpreting results.
10. Big data analytics: Handling large volumes of data using tools like Hadoop, Spark, and SQL.
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
Credits: https://news.1rj.ru/str/datasciencefun
Like if you need similar content 😄👍
👍1
𝗠𝗮𝘀𝘁𝗲𝗿 𝗣𝘆𝘁𝗵𝗼𝗻 𝗙𝘂𝗻𝗱𝗮𝗺𝗲𝗻𝘁𝗮𝗹𝘀 𝗳𝗼𝗿 𝗧𝗲𝗰𝗵 & 𝗗𝗮𝘁𝗮 𝗥𝗼𝗹𝗲𝘀 – 𝗙𝗿𝗲𝗲 𝗕𝗲𝗴𝗶𝗻𝗻𝗲𝗿 𝗚𝘂𝗶𝗱𝗲😍
If you’re aiming for a role in tech, data analytics, or software development, one of the most valuable skills you can master is Python🎯
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/4jg88I8
All The Best 🎊
If you’re aiming for a role in tech, data analytics, or software development, one of the most valuable skills you can master is Python🎯
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/4jg88I8
All The Best 🎊
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🤗 HuggingFace is offering 9 AI courses for FREE!
These 9 courses covers LLMs, Agents, Deep RL, Audio and more
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These 9 courses covers LLMs, Agents, Deep RL, Audio and more
1️⃣ LLM Course:
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2️⃣ Agents Course:
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3️⃣ Deep Reinforcement Learning Course:
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4️⃣ Open-Source AI Cookbook:
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5️⃣ Machine Learning for Games Course
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6️⃣ Hugging Face Audio course:
https://huggingface.co/learn/audio-course/chapter0/introduction
7️⃣ Vision Course:
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8️⃣ Machine Learning for 3D Course:
https://huggingface.co/learn/ml-for-3d-course/unit0/introduction
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Guys, Big Announcement!
We’ve officially hit 2 MILLION followers — and it’s time to take our Python journey to the next level!
I’m super excited to launch the 30-Day Python Coding Challenge — perfect for absolute beginners, interview prep, or anyone wanting to build real projects from scratch.
This challenge is your daily dose of Python — bite-sized lessons with hands-on projects so you actually code every day and level up fast.
Here’s what you’ll learn over the next 30 days:
Week 1: Python Fundamentals
- Variables & Data Types (Build your own bio/profile noscript)
- Operators (Mini calculator to sharpen math skills)
- Strings & String Methods (Word counter & palindrome checker)
- Lists & Tuples (Manage a grocery list like a pro)
- Dictionaries & Sets (Create your own contact book)
- Conditionals (Make a guess-the-number game)
- Loops (Multiplication tables & pattern printing)
Week 2: Functions & Logic — Make Your Code Smarter
- Functions (Prime number checker)
- Function Arguments (Tip calculator with custom tips)
- Recursion Basics (Factorials & Fibonacci series)
- Lambda, map & filter (Process lists efficiently)
- List Comprehensions (Filter odd/even numbers easily)
- Error Handling (Build a safe input reader)
- Review + Mini Project (Command-line to-do list)
Week 3: Files, Modules & OOP
- Reading & Writing Files (Save and load notes)
- Custom Modules (Create your own utility math module)
- Classes & Objects (Student grade tracker)
- Inheritance & OOP (RPG character system)
- Dunder Methods (Build a custom string class)
- OOP Mini Project (Simple bank account system)
- Review & Practice (Quiz app using OOP concepts)
Week 4: Real-World Python & APIs — Build Cool Apps
- JSON & APIs (Fetch weather data)
- Web Scraping (Extract noscripts from HTML)
- Regular Expressions (Find emails & phone numbers)
- Tkinter GUI (Create a simple counter app)
- CLI Tools (Command-line calculator with argparse)
- Automation (File organizer noscript)
- Final Project (Choose, build, and polish your app!)
React with ❤️ if you're ready for this new journey
You can join our WhatsApp channel to access it for free: https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L/1661
We’ve officially hit 2 MILLION followers — and it’s time to take our Python journey to the next level!
I’m super excited to launch the 30-Day Python Coding Challenge — perfect for absolute beginners, interview prep, or anyone wanting to build real projects from scratch.
This challenge is your daily dose of Python — bite-sized lessons with hands-on projects so you actually code every day and level up fast.
Here’s what you’ll learn over the next 30 days:
Week 1: Python Fundamentals
- Variables & Data Types (Build your own bio/profile noscript)
- Operators (Mini calculator to sharpen math skills)
- Strings & String Methods (Word counter & palindrome checker)
- Lists & Tuples (Manage a grocery list like a pro)
- Dictionaries & Sets (Create your own contact book)
- Conditionals (Make a guess-the-number game)
- Loops (Multiplication tables & pattern printing)
Week 2: Functions & Logic — Make Your Code Smarter
- Functions (Prime number checker)
- Function Arguments (Tip calculator with custom tips)
- Recursion Basics (Factorials & Fibonacci series)
- Lambda, map & filter (Process lists efficiently)
- List Comprehensions (Filter odd/even numbers easily)
- Error Handling (Build a safe input reader)
- Review + Mini Project (Command-line to-do list)
Week 3: Files, Modules & OOP
- Reading & Writing Files (Save and load notes)
- Custom Modules (Create your own utility math module)
- Classes & Objects (Student grade tracker)
- Inheritance & OOP (RPG character system)
- Dunder Methods (Build a custom string class)
- OOP Mini Project (Simple bank account system)
- Review & Practice (Quiz app using OOP concepts)
Week 4: Real-World Python & APIs — Build Cool Apps
- JSON & APIs (Fetch weather data)
- Web Scraping (Extract noscripts from HTML)
- Regular Expressions (Find emails & phone numbers)
- Tkinter GUI (Create a simple counter app)
- CLI Tools (Command-line calculator with argparse)
- Automation (File organizer noscript)
- Final Project (Choose, build, and polish your app!)
React with ❤️ if you're ready for this new journey
You can join our WhatsApp channel to access it for free: https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L/1661
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Are you looking to become a machine learning engineer?
I created a free and comprehensive roadmap. Let's go through this post and explore what you need to know to become an expert machine learning engineer:
Math & Statistics
Just like most other data roles, machine learning engineering starts with strong foundations from math, precisely linear algebra, probability and statistics.
Here are the probability units you will need to focus on:
Basic probability concepts statistics
Inferential statistics
Regression analysis
Experimental design and A/B testing Bayesian statistics
Calculus
Linear algebra
Python:
You can choose Python, R, Julia, or any other language, but Python is the most versatile and flexible language for machine learning.
Variables, data types, and basic operations
Control flow statements (e.g., if-else, loops)
Functions and modules
Error handling and exceptions
Basic data structures (e.g., lists, dictionaries, tuples)
Object-oriented programming concepts
Basic work with APIs
Detailed data structures and algorithmic thinking
Machine Learning Prerequisites:
Exploratory Data Analysis (EDA) with NumPy and Pandas
Basic data visualization techniques to visualize the variables and features.
Feature extraction
Feature engineering
Different types of encoding data
Machine Learning Fundamentals
Using scikit-learn library in combination with other Python libraries for:
Supervised Learning: (Linear Regression, K-Nearest Neighbors, Decision Trees)
Unsupervised Learning: (K-Means Clustering, Principal Component Analysis, Hierarchical Clustering)
Reinforcement Learning: (Q-Learning, Deep Q Network, Policy Gradients)
Solving two types of problems:
Regression
Classification
Neural Networks:
Neural networks are like computer brains that learn from examples, made up of layers of "neurons" that handle data. They learn without explicit instructions.
Types of Neural Networks:
Feedforward Neural Networks: Simplest form, with straight connections and no loops.
Convolutional Neural Networks (CNNs): Great for images, learning visual patterns.
Recurrent Neural Networks (RNNs): Good for sequences like text or time series, because they remember past information.
In Python, it’s the best to use TensorFlow and Keras libraries, as well as PyTorch, for deeper and more complex neural network systems.
Deep Learning:
Deep learning is a subset of machine learning in artificial intelligence (AI) that has networks capable of learning unsupervised from data that is unstructured or unlabeled.
Convolutional Neural Networks (CNNs)
Recurrent Neural Networks (RNNs)
Long Short-Term Memory Networks (LSTMs)
Generative Adversarial Networks (GANs)
Autoencoders
Deep Belief Networks (DBNs)
Transformer Models
Machine Learning Project Deployment
Machine learning engineers should also be able to dive into MLOps and project deployment. Here are the things that you should be familiar or skilled at:
Version Control for Data and Models
Automated Testing and Continuous Integration (CI)
Continuous Delivery and Deployment (CD)
Monitoring and Logging
Experiment Tracking and Management
Feature Stores
Data Pipeline and Workflow Orchestration
Infrastructure as Code (IaC)
Model Serving and APIs
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
Credits: https://news.1rj.ru/str/datasciencefun
Like if you need similar content 😄👍
I created a free and comprehensive roadmap. Let's go through this post and explore what you need to know to become an expert machine learning engineer:
Math & Statistics
Just like most other data roles, machine learning engineering starts with strong foundations from math, precisely linear algebra, probability and statistics.
Here are the probability units you will need to focus on:
Basic probability concepts statistics
Inferential statistics
Regression analysis
Experimental design and A/B testing Bayesian statistics
Calculus
Linear algebra
Python:
You can choose Python, R, Julia, or any other language, but Python is the most versatile and flexible language for machine learning.
Variables, data types, and basic operations
Control flow statements (e.g., if-else, loops)
Functions and modules
Error handling and exceptions
Basic data structures (e.g., lists, dictionaries, tuples)
Object-oriented programming concepts
Basic work with APIs
Detailed data structures and algorithmic thinking
Machine Learning Prerequisites:
Exploratory Data Analysis (EDA) with NumPy and Pandas
Basic data visualization techniques to visualize the variables and features.
Feature extraction
Feature engineering
Different types of encoding data
Machine Learning Fundamentals
Using scikit-learn library in combination with other Python libraries for:
Supervised Learning: (Linear Regression, K-Nearest Neighbors, Decision Trees)
Unsupervised Learning: (K-Means Clustering, Principal Component Analysis, Hierarchical Clustering)
Reinforcement Learning: (Q-Learning, Deep Q Network, Policy Gradients)
Solving two types of problems:
Regression
Classification
Neural Networks:
Neural networks are like computer brains that learn from examples, made up of layers of "neurons" that handle data. They learn without explicit instructions.
Types of Neural Networks:
Feedforward Neural Networks: Simplest form, with straight connections and no loops.
Convolutional Neural Networks (CNNs): Great for images, learning visual patterns.
Recurrent Neural Networks (RNNs): Good for sequences like text or time series, because they remember past information.
In Python, it’s the best to use TensorFlow and Keras libraries, as well as PyTorch, for deeper and more complex neural network systems.
Deep Learning:
Deep learning is a subset of machine learning in artificial intelligence (AI) that has networks capable of learning unsupervised from data that is unstructured or unlabeled.
Convolutional Neural Networks (CNNs)
Recurrent Neural Networks (RNNs)
Long Short-Term Memory Networks (LSTMs)
Generative Adversarial Networks (GANs)
Autoencoders
Deep Belief Networks (DBNs)
Transformer Models
Machine Learning Project Deployment
Machine learning engineers should also be able to dive into MLOps and project deployment. Here are the things that you should be familiar or skilled at:
Version Control for Data and Models
Automated Testing and Continuous Integration (CI)
Continuous Delivery and Deployment (CD)
Monitoring and Logging
Experiment Tracking and Management
Feature Stores
Data Pipeline and Workflow Orchestration
Infrastructure as Code (IaC)
Model Serving and APIs
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
Credits: https://news.1rj.ru/str/datasciencefun
Like if you need similar content 😄👍
❤2👍1
📝GOOGLE (GOOGL) Stock Financial News: 2000–Today
📌 Alphabet (GOOG) Daily News Feed | 2000–2025 for Investors & Analysts
🔍
#StockMarketAnalysis#FinancialNLP#SentimentAnalysis#GOOGL#TimeSeriesData
📌 Alphabet (GOOG) Daily News Feed | 2000–2025 for Investors & Analysts
🔍
This dataset provides a comprehensive daily news feed about Alphabet Inc. (GOOGL) from 2000 to 2025. It's ideal for NLP applications, sentiment analysis, and exploring how financial news impacts stock prices. When combined with the accompanying dataset containing Google’s financial statements and stock prices, it becomes a powerful tool for building predictive models, conducting event-driven investment analysis, and understanding the interplay between corporate news and market behavior.
#StockMarketAnalysis#FinancialNLP#SentimentAnalysis#GOOGL#TimeSeriesData
❤1
𝗕𝗿𝗲𝗮𝗸 𝗜𝗻𝘁𝗼 𝗗𝗲𝗲𝗽 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗶𝗻 𝟮𝟬𝟮𝟱 𝘄𝗶𝘁𝗵 𝗧𝗵𝗶𝘀 𝗙𝗥𝗘𝗘 𝗠𝗜𝗧 𝗖𝗼𝘂𝗿𝘀𝗲😍
If you’re serious about AI, you can’t skip Deep Learning—and this FREE course from MIT is one of the best ways to start👨💻📌
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Why wait to get started when you can learn from MIT for free?✅️
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Q. Explain the data preprocessing steps in data analysis.
Ans. Data preprocessing transforms the data into a format that is more easily and effectively processed in data mining, machine learning and other data science tasks.
1. Data profiling.
2. Data cleansing.
3. Data reduction.
4. Data transformation.
5. Data enrichment.
6. Data validation.
Q. What Are the Three Stages of Building a Model in Machine Learning?
Ans. The three stages of building a machine learning model are:
Model Building: Choosing a suitable algorithm for the model and train it according to the requirement
Model Testing: Checking the accuracy of the model through the test data
Applying the Model: Making the required changes after testing and use the final model for real-time projects
Q. What are the subsets of SQL?
Ans. The following are the four significant subsets of the SQL:
Data definition language (DDL): It defines the data structure that consists of commands like CREATE, ALTER, DROP, etc.
Data manipulation language (DML): It is used to manipulate existing data in the database. The commands in this category are SELECT, UPDATE, INSERT, etc.
Data control language (DCL): It controls access to the data stored in the database. The commands in this category include GRANT and REVOKE.
Transaction Control Language (TCL): It is used to deal with the transaction operations in the database. The commands in this category are COMMIT, ROLLBACK, SET TRANSACTION, SAVEPOINT, etc.
Q. What is a Parameter in Tableau? Give an Example.
Ans. A parameter is a dynamic value that a customer could select, and you can use it to replace constant values in calculations, filters, and reference lines.
For example, when creating a filter to show the top 10 products based on total profit instead of the fixed value, you can update the filter to show the top 10, 20, or 30 products using a parameter.
Ans. Data preprocessing transforms the data into a format that is more easily and effectively processed in data mining, machine learning and other data science tasks.
1. Data profiling.
2. Data cleansing.
3. Data reduction.
4. Data transformation.
5. Data enrichment.
6. Data validation.
Q. What Are the Three Stages of Building a Model in Machine Learning?
Ans. The three stages of building a machine learning model are:
Model Building: Choosing a suitable algorithm for the model and train it according to the requirement
Model Testing: Checking the accuracy of the model through the test data
Applying the Model: Making the required changes after testing and use the final model for real-time projects
Q. What are the subsets of SQL?
Ans. The following are the four significant subsets of the SQL:
Data definition language (DDL): It defines the data structure that consists of commands like CREATE, ALTER, DROP, etc.
Data manipulation language (DML): It is used to manipulate existing data in the database. The commands in this category are SELECT, UPDATE, INSERT, etc.
Data control language (DCL): It controls access to the data stored in the database. The commands in this category include GRANT and REVOKE.
Transaction Control Language (TCL): It is used to deal with the transaction operations in the database. The commands in this category are COMMIT, ROLLBACK, SET TRANSACTION, SAVEPOINT, etc.
Q. What is a Parameter in Tableau? Give an Example.
Ans. A parameter is a dynamic value that a customer could select, and you can use it to replace constant values in calculations, filters, and reference lines.
For example, when creating a filter to show the top 10 products based on total profit instead of the fixed value, you can update the filter to show the top 10, 20, or 30 products using a parameter.
❤6
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ChatGPT: https://whatsapp.com/channel/0029VapThS265yDAfwe97c23
OpenAI: https://whatsapp.com/channel/0029VbAbfqcLtOj7Zen5tt3o
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Perplexity AI: https://whatsapp.com/channel/0029VbAa05yISTkGgBqyC00U
Copilot: https://whatsapp.com/channel/0029VbAW0QBDOQIgYcbwBd1l
Generative AI: https://whatsapp.com/channel/0029VazaRBY2UPBNj1aCrN0U
Prompt Engineering: https://whatsapp.com/channel/0029Vb6ISO1Fsn0kEemhE03b
Artificial Intelligence: https://whatsapp.com/channel/0029VaoePz73bbV94yTh6V2E
Grok AI: https://whatsapp.com/channel/0029VbAU3pWChq6T5bZxUk1r
Deeplearning AI: https://whatsapp.com/channel/0029VbAKiI1FSAt81kV3lA0t
AI Studio: https://whatsapp.com/channel/0029VbAWNue1iUxjLo2DFx2U
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ChatGPT: https://whatsapp.com/channel/0029VapThS265yDAfwe97c23
OpenAI: https://whatsapp.com/channel/0029VbAbfqcLtOj7Zen5tt3o
Deepseek: https://whatsapp.com/channel/0029Vb9js9sGpLHJGIvX5g1w
Perplexity AI: https://whatsapp.com/channel/0029VbAa05yISTkGgBqyC00U
Copilot: https://whatsapp.com/channel/0029VbAW0QBDOQIgYcbwBd1l
Generative AI: https://whatsapp.com/channel/0029VazaRBY2UPBNj1aCrN0U
Prompt Engineering: https://whatsapp.com/channel/0029Vb6ISO1Fsn0kEemhE03b
Artificial Intelligence: https://whatsapp.com/channel/0029VaoePz73bbV94yTh6V2E
Grok AI: https://whatsapp.com/channel/0029VbAU3pWChq6T5bZxUk1r
Deeplearning AI: https://whatsapp.com/channel/0029VbAKiI1FSAt81kV3lA0t
AI Studio: https://whatsapp.com/channel/0029VbAWNue1iUxjLo2DFx2U
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Generative AI Mindmap
🔥3
Forwarded from Artificial Intelligence
𝟰 𝗙𝗿𝗲𝗲 𝗣𝘆𝘁𝗵𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝘁𝗼 𝗦𝘁𝗮𝗿𝘁 𝗖𝗼𝗱𝗶𝗻𝗴 𝗟𝗶𝗸𝗲 𝗮 𝗣𝗿𝗼 𝗶𝗻 𝟮𝟬𝟮𝟱😍
Looking to kickstart your coding journey with Python? 🐍
Whether you’re an aspiring data analyst, a student, or preparing for tech roles, these free Python courses are perfect for beginners!📊📌
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/4jtpf9M
These platforms offer high-quality learning — no fees, no catch✅️
Looking to kickstart your coding journey with Python? 🐍
Whether you’re an aspiring data analyst, a student, or preparing for tech roles, these free Python courses are perfect for beginners!📊📌
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/4jtpf9M
These platforms offer high-quality learning — no fees, no catch✅️
Power BI Interview Questions Asked Bajaj Auto Ltd
1. Self Introduction
2. What are your roles and responsibilities of your project?
3. Difference between Import Mode and Direct Mode?
4. What kind of projects have you worked on Domain?
5. How do you handle complex data transformations in Power Query? Can you provide an example of a challenging transformation you implemented?
6. What challenges you faced while doing a projects?
7. Types of Refreshes in Power BI?
8. What is DAX in Power BI?
9. How do you perform data cleansing and transformation in Power BI?
10. How do you connect to data sources in Power BI?
11. What are the components in Power BI?
12. What is Power Pivot will do in Power BI?
13. Write a query to fetch top 5 employees having highest salary?
14. Write a query to find 2nd highest salary from employee table?
15. Difference between Rank function & Dense Rank function in SQL?
16. Difference between Power BI Desktop & Power BI Service?
17. How will you optimize Power BI reports?
18. What are the difficulties you have faced when doing a projects?
19. How can you optimize a SQL query?
20. What is Indexes?
21. How ETL process happen in Power BI?
22. What is difference between Star schema & Snowflake schema and how will know when to use which schemas respectively?
23. How will you perform filtering & it's types?
24. What is Bookmarks?
25. Difference between Drilldown and Drill through in Power BI?
26. Difference between Calculated column and measure?
27. Difference between Slicer and Filter?
28. What is a use Pandas, Matplotlib, seaborn Libraries?
29. Difference between Sum and SumX?
30. Do you have any questions?
1. Self Introduction
2. What are your roles and responsibilities of your project?
3. Difference between Import Mode and Direct Mode?
4. What kind of projects have you worked on Domain?
5. How do you handle complex data transformations in Power Query? Can you provide an example of a challenging transformation you implemented?
6. What challenges you faced while doing a projects?
7. Types of Refreshes in Power BI?
8. What is DAX in Power BI?
9. How do you perform data cleansing and transformation in Power BI?
10. How do you connect to data sources in Power BI?
11. What are the components in Power BI?
12. What is Power Pivot will do in Power BI?
13. Write a query to fetch top 5 employees having highest salary?
14. Write a query to find 2nd highest salary from employee table?
15. Difference between Rank function & Dense Rank function in SQL?
16. Difference between Power BI Desktop & Power BI Service?
17. How will you optimize Power BI reports?
18. What are the difficulties you have faced when doing a projects?
19. How can you optimize a SQL query?
20. What is Indexes?
21. How ETL process happen in Power BI?
22. What is difference between Star schema & Snowflake schema and how will know when to use which schemas respectively?
23. How will you perform filtering & it's types?
24. What is Bookmarks?
25. Difference between Drilldown and Drill through in Power BI?
26. Difference between Calculated column and measure?
27. Difference between Slicer and Filter?
28. What is a use Pandas, Matplotlib, seaborn Libraries?
29. Difference between Sum and SumX?
30. Do you have any questions?
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Google :- https://pdlink.in/3H2YJX7
Microsoft :- https://pdlink.in/4iq8QlM
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IBM :- https://pdlink.in/3QyJyqk
Cisco :- https://pdlink.in/4fYr1xO
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