๐ ๐ถ๐ฐ๐ฟ๐ผ๐๐ผ๐ณ๐โ๐ ๐๐ฅ๐๐ ๐๐ ๐๐ด๐ฒ๐ป๐๐ ๐๐ผ๐๐ฟ๐๐ฒ โ ๐๐ฒ๐ฎ๐ฟ๐ป ๐๐ผ๐ ๐๐ต๐ฒ ๐๐๐๐๐ฟ๐ฒ ๐ผ๐ณ ๐๐ ๐ช๐ผ๐ฟ๐ธ๐๐
๐จ Microsoft just dropped a brand-new FREE course on AI Agents โ and itโs a must-watch!๐ฒ
If youโve ever wondered how AI copilots, autonomous agents, and decision-making systems actually work๐จโ๐๐ซ
๐๐ข๐ง๐ค๐:-
https://pdlink.in/4kuGLLe
This course is your launchpad into the future of artificial intelligenceโ ๏ธ
๐จ Microsoft just dropped a brand-new FREE course on AI Agents โ and itโs a must-watch!๐ฒ
If youโve ever wondered how AI copilots, autonomous agents, and decision-making systems actually work๐จโ๐๐ซ
๐๐ข๐ง๐ค๐:-
https://pdlink.in/4kuGLLe
This course is your launchpad into the future of artificial intelligenceโ ๏ธ
โค1
Top 10 Excel Interview Questions with Answers ๐๐
Free Resources to learn Excel: https://t.iss.one/excel_analyst
1. Question: What is the difference between CONCATENATE and "&" in Excel?
Answer: CONCATENATE and "&" both combine text, but "&" is more concise. For example,
2. Question: How can you freeze rows and columns simultaneously in Excel?
Answer: Use the "Freeze Panes" option under the "View" tab. Select the cell below and to the right of the rows and columns you want to freeze, and then click on "Freeze Panes."
3. Question: Explain the VLOOKUP function and when would you use it?
Answer: VLOOKUP searches for a value in the first column of a range and returns a corresponding value in the same row from another column. It's useful for looking up information in a table based on a specific criteria.
4. Question: What is the purpose of the IFERROR function?
Answer: IFERROR is used to handle errors in Excel formulas. It returns a specified value if a formula results in an error, and the actual result if there's no error.
5. Question: How do you create a PivotTable, and what is its purpose?
Answer: To create a PivotTable, select your data, go to the "Insert" tab, and choose "PivotTable." It summarizes and analyzes data in a spreadsheet, allowing you to make sense of large datasets.
6. Question: Explain the difference between relative and absolute cell references.
Answer: Relative references change when you copy a formula to another cell, while absolute references stay fixed. Use a
7. Question: What is the purpose of the INDEX and MATCH functions?
Answer: INDEX returns a value in a specified range based on the row and column number, while MATCH searches for a value in a range and returns its relative position. Combined, they provide a flexible way to look up data.
8. Question: How can you find and remove duplicate values in Excel?
Answer: Use the "Remove Duplicates" feature under the "Data" tab. Select the range containing duplicates, go to "Data" -> "Remove Duplicates," and choose the columns to check for duplicates.
9. Question: Explain the difference between a workbook and a worksheet.
Answer: A workbook is the entire Excel file, while a worksheet is a single sheet within that file. Workbooks can contain multiple worksheets.
10. Question: What is the purpose of the COUNTIF function?
Answer: COUNTIF counts the number of cells within a range that meet a specified condition. For example,
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
Free Resources to learn Excel: https://t.iss.one/excel_analyst
1. Question: What is the difference between CONCATENATE and "&" in Excel?
Answer: CONCATENATE and "&" both combine text, but "&" is more concise. For example,
=A1&B1 achieves the same result as =CONCATENATE(A1, B1).2. Question: How can you freeze rows and columns simultaneously in Excel?
Answer: Use the "Freeze Panes" option under the "View" tab. Select the cell below and to the right of the rows and columns you want to freeze, and then click on "Freeze Panes."
3. Question: Explain the VLOOKUP function and when would you use it?
Answer: VLOOKUP searches for a value in the first column of a range and returns a corresponding value in the same row from another column. It's useful for looking up information in a table based on a specific criteria.
4. Question: What is the purpose of the IFERROR function?
Answer: IFERROR is used to handle errors in Excel formulas. It returns a specified value if a formula results in an error, and the actual result if there's no error.
5. Question: How do you create a PivotTable, and what is its purpose?
Answer: To create a PivotTable, select your data, go to the "Insert" tab, and choose "PivotTable." It summarizes and analyzes data in a spreadsheet, allowing you to make sense of large datasets.
6. Question: Explain the difference between relative and absolute cell references.
Answer: Relative references change when you copy a formula to another cell, while absolute references stay fixed. Use a
$ symbol to make a reference absolute (e.g., $A$1).7. Question: What is the purpose of the INDEX and MATCH functions?
Answer: INDEX returns a value in a specified range based on the row and column number, while MATCH searches for a value in a range and returns its relative position. Combined, they provide a flexible way to look up data.
8. Question: How can you find and remove duplicate values in Excel?
Answer: Use the "Remove Duplicates" feature under the "Data" tab. Select the range containing duplicates, go to "Data" -> "Remove Duplicates," and choose the columns to check for duplicates.
9. Question: Explain the difference between a workbook and a worksheet.
Answer: A workbook is the entire Excel file, while a worksheet is a single sheet within that file. Workbooks can contain multiple worksheets.
10. Question: What is the purpose of the COUNTIF function?
Answer: COUNTIF counts the number of cells within a range that meet a specified condition. For example,
=COUNTIF(A1:A10, ">50") counts the cells in A1 to A10 that are greater than 50.Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
โค1
Forwarded from Python Projects & Resources
๐ช๐ถ๐ฝ๐ฟ๐ผโ๐ ๐๐ฟ๐ฒ๐ฒ ๐๐ฎ๐๐ฎ ๐ฆ๐ฐ๐ถ๐ฒ๐ป๐ฐ๐ฒ ๐๐ฐ๐ฐ๐ฒ๐น๐ฒ๐ฟ๐ฎ๐๐ผ๐ฟ: ๐ฌ๐ผ๐๐ฟ ๐๐ฎ๐๐-๐ง๐ฟ๐ฎ๐ฐ๐ธ ๐๐ผ ๐ฎ ๐๐ฎ๐๐ฎ ๐๐ฎ๐ฟ๐ฒ๐ฒ๐ฟ!๐
Want to break into Data Science but donโt have a degree or years of experience? Wipro just made it easier than ever!๐จโ๐โจ๏ธ
With the Wipro Data Science Accelerator, you can start learning for FREEโno fancy credentials needed. Whether youโre a beginner or an aspiring data professional๐จโ๐ป๐
๐๐ข๐ง๐ค๐:-
https://pdlink.in/4hOXcR7
Ready to start? Explore Wiproโs Data Science Accelerator hereโ ๏ธ
Want to break into Data Science but donโt have a degree or years of experience? Wipro just made it easier than ever!๐จโ๐โจ๏ธ
With the Wipro Data Science Accelerator, you can start learning for FREEโno fancy credentials needed. Whether youโre a beginner or an aspiring data professional๐จโ๐ป๐
๐๐ข๐ง๐ค๐:-
https://pdlink.in/4hOXcR7
Ready to start? Explore Wiproโs Data Science Accelerator hereโ ๏ธ
โค2
Step-by-step guide to become a Data Analyst in 2025โ๐
1. Learn the Fundamentals:
Start with Excel, basic statistics, and data visualization concepts.
2. Pick Up Key Tools & Languages:
Master SQL, Python (or R), and data visualization tools like Tableau or Power BI.
3. Get Formal Education or Certification:
A bachelorโs degree in a relevant field (like Computer Science, Math, or Economics) helps, but you can also do online courses or certifications in data analytics.
4. Build Hands-on Experience:
Work on real-world projectsโuse Kaggle datasets, internships, or freelance gigs to practice data cleaning, analysis, and visualization.
5. Create a Portfolio:
Showcase your projects on GitHub or a personal website. Include dashboards, reports, and code samples.
6. Develop Soft Skills:
Focus on communication, problem-solving, teamwork, and attention to detailโthese are just as important as technical skills.
7. Apply for Entry-Level Jobs:
Look for roles like โJunior Data Analystโ or โBusiness Analyst.โ Tailor your resume to highlight your skills and portfolio.
8. Keep Learning:
Stay updated with new tools (like AI-driven analytics), trends, and advanced topics such as machine learning or domain-specific analytics.
React โค๏ธ for more
1. Learn the Fundamentals:
Start with Excel, basic statistics, and data visualization concepts.
2. Pick Up Key Tools & Languages:
Master SQL, Python (or R), and data visualization tools like Tableau or Power BI.
3. Get Formal Education or Certification:
A bachelorโs degree in a relevant field (like Computer Science, Math, or Economics) helps, but you can also do online courses or certifications in data analytics.
4. Build Hands-on Experience:
Work on real-world projectsโuse Kaggle datasets, internships, or freelance gigs to practice data cleaning, analysis, and visualization.
5. Create a Portfolio:
Showcase your projects on GitHub or a personal website. Include dashboards, reports, and code samples.
6. Develop Soft Skills:
Focus on communication, problem-solving, teamwork, and attention to detailโthese are just as important as technical skills.
7. Apply for Entry-Level Jobs:
Look for roles like โJunior Data Analystโ or โBusiness Analyst.โ Tailor your resume to highlight your skills and portfolio.
8. Keep Learning:
Stay updated with new tools (like AI-driven analytics), trends, and advanced topics such as machine learning or domain-specific analytics.
React โค๏ธ for more
โค3๐ฅ1
Forwarded from Artificial Intelligence
๐๐ถ๐ฑ๐ฑ๐ฒ๐ป ๐๐ฒ๐บ ๐ณ๐ผ๐ฟ ๐๐ฟ๐ฒ๐ฒ ๐๐ผ๐๐ฟ๐๐ฒ๐ ๐ณ๐ฟ๐ผ๐บ ๐ ๐๐ง, ๐๐ฎ๐ฟ๐๐ฎ๐ฟ๐ฑ & ๐ฆ๐๐ฎ๐ป๐ณ๐ผ๐ฟ๐ฑ!๐
Still searching for quality learning resources?๐
What if I told you thereโs a platform offering free full-length courses from top universities like MIT, Stanford, and Harvard โ and most people have never even heard of it? ๐คฏ
๐๐ถ๐ป๐ธ๐:-๐
https://pdlink.in/4lN7aF1
Donโt skip this chanceโ ๏ธ
Still searching for quality learning resources?๐
What if I told you thereโs a platform offering free full-length courses from top universities like MIT, Stanford, and Harvard โ and most people have never even heard of it? ๐คฏ
๐๐ถ๐ป๐ธ๐:-๐
https://pdlink.in/4lN7aF1
Donโt skip this chanceโ ๏ธ
โค1
Artificial Intelligence (AI) is the simulation of human intelligence in machines that are designed to think, learn, and make decisions. From virtual assistants to self-driving cars, AI is transforming how we interact with technology.
Hers is the brief A-Z overview of the terms used in Artificial Intelligence World
A - Algorithm: A set of rules or instructions that an AI system follows to solve problems or make decisions.
B - Bias: Prejudice in AI systems due to skewed training data, leading to unfair outcomes.
C - Chatbot: AI software that can hold conversations with users via text or voice.
D - Deep Learning: A type of machine learning using layered neural networks to analyze data and make decisions.
E - Expert System: An AI that replicates the decision-making ability of a human expert in a specific domain.
F - Fine-Tuning: The process of refining a pre-trained model on a specific task or dataset.
G - Generative AI: AI that can create new content like text, images, audio, or code.
H - Heuristic: A rule-of-thumb or shortcut used by AI to make decisions efficiently.
I - Image Recognition: The ability of AI to detect and classify objects or features in an image.
J - Jupyter Notebook: A tool widely used in AI for interactive coding, data visualization, and documentation.
K - Knowledge Representation: How AI systems store, organize, and use information for reasoning.
L - LLM (Large Language Model): An AI trained on large text datasets to understand and generate human language (e.g., GPT-4).
M - Machine Learning: A branch of AI where systems learn from data instead of being explicitly programmed.
N - NLP (Natural Language Processing): AI's ability to understand, interpret, and generate human language.
O - Overfitting: When a model performs well on training data but poorly on unseen data due to memorizing instead of generalizing.
P - Prompt Engineering: Crafting effective inputs to steer generative AI toward desired responses.
Q - Q-Learning: A reinforcement learning algorithm that helps agents learn the best actions to take.
R - Reinforcement Learning: A type of learning where AI agents learn by interacting with environments and receiving rewards.
S - Supervised Learning: Machine learning where models are trained on labeled datasets.
T - Transformer: A neural network architecture powering models like GPT and BERT, crucial in NLP tasks.
U - Unsupervised Learning: A method where AI finds patterns in data without labeled outcomes.
V - Vision (Computer Vision): The field of AI that enables machines to interpret and process visual data.
W - Weak AI: AI designed to handle narrow tasks without consciousness or general intelligence.
X - Explainable AI (XAI): Techniques that make AI decision-making transparent and understandable to humans.
Y - YOLO (You Only Look Once): A popular real-time object detection algorithm in computer vision.
Z - Zero-shot Learning: The ability of AI to perform tasks it hasnโt been explicitly trained on.
Credits: https://whatsapp.com/channel/0029Va4QUHa6rsQjhITHK82y
Hers is the brief A-Z overview of the terms used in Artificial Intelligence World
A - Algorithm: A set of rules or instructions that an AI system follows to solve problems or make decisions.
B - Bias: Prejudice in AI systems due to skewed training data, leading to unfair outcomes.
C - Chatbot: AI software that can hold conversations with users via text or voice.
D - Deep Learning: A type of machine learning using layered neural networks to analyze data and make decisions.
E - Expert System: An AI that replicates the decision-making ability of a human expert in a specific domain.
F - Fine-Tuning: The process of refining a pre-trained model on a specific task or dataset.
G - Generative AI: AI that can create new content like text, images, audio, or code.
H - Heuristic: A rule-of-thumb or shortcut used by AI to make decisions efficiently.
I - Image Recognition: The ability of AI to detect and classify objects or features in an image.
J - Jupyter Notebook: A tool widely used in AI for interactive coding, data visualization, and documentation.
K - Knowledge Representation: How AI systems store, organize, and use information for reasoning.
L - LLM (Large Language Model): An AI trained on large text datasets to understand and generate human language (e.g., GPT-4).
M - Machine Learning: A branch of AI where systems learn from data instead of being explicitly programmed.
N - NLP (Natural Language Processing): AI's ability to understand, interpret, and generate human language.
O - Overfitting: When a model performs well on training data but poorly on unseen data due to memorizing instead of generalizing.
P - Prompt Engineering: Crafting effective inputs to steer generative AI toward desired responses.
Q - Q-Learning: A reinforcement learning algorithm that helps agents learn the best actions to take.
R - Reinforcement Learning: A type of learning where AI agents learn by interacting with environments and receiving rewards.
S - Supervised Learning: Machine learning where models are trained on labeled datasets.
T - Transformer: A neural network architecture powering models like GPT and BERT, crucial in NLP tasks.
U - Unsupervised Learning: A method where AI finds patterns in data without labeled outcomes.
V - Vision (Computer Vision): The field of AI that enables machines to interpret and process visual data.
W - Weak AI: AI designed to handle narrow tasks without consciousness or general intelligence.
X - Explainable AI (XAI): Techniques that make AI decision-making transparent and understandable to humans.
Y - YOLO (You Only Look Once): A popular real-time object detection algorithm in computer vision.
Z - Zero-shot Learning: The ability of AI to perform tasks it hasnโt been explicitly trained on.
Credits: https://whatsapp.com/channel/0029Va4QUHa6rsQjhITHK82y
โค2
๐ฏ ๐๐ฅ๐๐ ๐๐ผ๐๐ฟ๐๐ฒ๐ ๐๐ผ ๐ฆ๐๐ฎ๐ฟ๐ ๐ฌ๐ผ๐๐ฟ ๐๐ฎ๐๐ฎ ๐๐ป๐ฎ๐น๐๐๐ถ๐ฐ๐ ๐๐ฎ๐ฟ๐ฒ๐ฒ๐ฟ ๐ถ๐ป ๐ฎ๐ฌ๐ฎ๐ฑ!๐
Want to break into Data Analytics but donโt know where to start? ๐ค
These 3 beginner-friendly and 100% FREE courses will help you build real skills โ no degree required!๐จโ๐
๐๐ถ๐ป๐ธ:-๐
https://pdlink.in/3IohnJO
No confusion, no fluff โ just pure valueโ ๏ธ
Want to break into Data Analytics but donโt know where to start? ๐ค
These 3 beginner-friendly and 100% FREE courses will help you build real skills โ no degree required!๐จโ๐
๐๐ถ๐ป๐ธ:-๐
https://pdlink.in/3IohnJO
No confusion, no fluff โ just pure valueโ ๏ธ
Data Science isn't easy!
Itโs the field that turns raw data into meaningful insights and predictions.
To truly excel in Data Science, focus on these key areas:
0. Understanding the Basics of Statistics: Master probability, distributions, and hypothesis testing to make informed decisions.
1. Mastering Data Preprocessing: Clean, transform, and structure your data for effective analysis.
2. Exploring Data with Visualizations: Use tools like Matplotlib, Seaborn, and Tableau to create compelling data stories.
3. Learning Machine Learning Algorithms: Get hands-on with supervised and unsupervised learning techniques, like regression, classification, and clustering.
4. Mastering Python for Data Science: Learn libraries like Pandas, NumPy, and Scikit-learn for data manipulation and analysis.
5. Building and Evaluating Models: Train, validate, and tune models using cross-validation, performance metrics, and hyperparameter optimization.
6. Understanding Deep Learning: Dive into neural networks and frameworks like TensorFlow or PyTorch for advanced predictive modeling.
7. Staying Updated with Research: The field evolves fastโkeep up with the latest methods, research papers, and tools.
8. Developing Problem-Solving Skills: Data science is about solving real-world problems, so practice by tackling real datasets and challenges.
9. Communicating Results Effectively: Learn to present your findings in a clear and actionable way for both technical and non-technical audiences.
Data Science is a journey of learning, experimenting, and refining your skills.
๐ก Embrace the challenge of working with messy data, building predictive models, and uncovering hidden patterns.
โณ With persistence, curiosity, and hands-on practice, you'll unlock the power of data to change the world!
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
Credits: https://t.iss.one/datasciencefun
Like if you need similar content ๐๐
Hope this helps you ๐
#datascience
Itโs the field that turns raw data into meaningful insights and predictions.
To truly excel in Data Science, focus on these key areas:
0. Understanding the Basics of Statistics: Master probability, distributions, and hypothesis testing to make informed decisions.
1. Mastering Data Preprocessing: Clean, transform, and structure your data for effective analysis.
2. Exploring Data with Visualizations: Use tools like Matplotlib, Seaborn, and Tableau to create compelling data stories.
3. Learning Machine Learning Algorithms: Get hands-on with supervised and unsupervised learning techniques, like regression, classification, and clustering.
4. Mastering Python for Data Science: Learn libraries like Pandas, NumPy, and Scikit-learn for data manipulation and analysis.
5. Building and Evaluating Models: Train, validate, and tune models using cross-validation, performance metrics, and hyperparameter optimization.
6. Understanding Deep Learning: Dive into neural networks and frameworks like TensorFlow or PyTorch for advanced predictive modeling.
7. Staying Updated with Research: The field evolves fastโkeep up with the latest methods, research papers, and tools.
8. Developing Problem-Solving Skills: Data science is about solving real-world problems, so practice by tackling real datasets and challenges.
9. Communicating Results Effectively: Learn to present your findings in a clear and actionable way for both technical and non-technical audiences.
Data Science is a journey of learning, experimenting, and refining your skills.
๐ก Embrace the challenge of working with messy data, building predictive models, and uncovering hidden patterns.
โณ With persistence, curiosity, and hands-on practice, you'll unlock the power of data to change the world!
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
Credits: https://t.iss.one/datasciencefun
Like if you need similar content ๐๐
Hope this helps you ๐
#datascience
โค2
Forwarded from Python Projects & Resources
๐ฒ ๐ฅ๐ฒ๐ฎ๐น-๐ช๐ผ๐ฟ๐น๐ฑ ๐ฆ๐ค๐ ๐ฃ๐ฟ๐ผ๐ท๐ฒ๐ฐ๐๐ ๐๐ผ ๐๐ผ๐ผ๐๐ ๐ฌ๐ผ๐๐ฟ ๐๐ป๐ฎ๐น๐๐๐ถ๐ฐ๐ ๐ฃ๐ผ๐ฟ๐๐ณ๐ผ๐น๐ถ๐ผ (๐๐ฅ๐๐ ๐๐ฎ๐๐ฎ๐๐ฒ๐๐!)๐
๐ฏ Want to level up your SQL skills with real business scenarios?๐
These 6 hands-on SQL projects will help you go beyond basic SELECT queries and practice what hiring managers actually care about๐จโ๐ป๐
๐๐ข๐ง๐ค๐:-
https://pdlink.in/40kF1x0
Save this post โ even completing 1 project can power up your SQL profile!โ ๏ธ
๐ฏ Want to level up your SQL skills with real business scenarios?๐
These 6 hands-on SQL projects will help you go beyond basic SELECT queries and practice what hiring managers actually care about๐จโ๐ป๐
๐๐ข๐ง๐ค๐:-
https://pdlink.in/40kF1x0
Save this post โ even completing 1 project can power up your SQL profile!โ ๏ธ
โค1
Amazon Interview Process for Data Scientist position
๐Round 1- Phone Screen round
This was a preliminary round to check my capability, projects to coding, Stats, ML, etc.
After clearing this round the technical Interview rounds started. There were 5-6 rounds (Multiple rounds in one day).
๐ ๐ฅ๐ผ๐๐ป๐ฑ ๐ฎ- ๐๐ฎ๐๐ฎ ๐ฆ๐ฐ๐ถ๐ฒ๐ป๐ฐ๐ฒ ๐๐ฟ๐ฒ๐ฎ๐ฑ๐๐ต:
In this round the interviewer tested my knowledge on different kinds of topics.
๐๐ฅ๐ผ๐๐ป๐ฑ ๐ฏ- ๐๐ฒ๐ฝ๐๐ต ๐ฅ๐ผ๐๐ป๐ฑ:
In this round the interviewers grilled deeper into 1-2 topics. I was asked questions around:
Standard ML tech, Linear Equation, Techniques, etc.
๐๐ฅ๐ผ๐๐ป๐ฑ ๐ฐ- ๐๐ผ๐ฑ๐ถ๐ป๐ด ๐ฅ๐ผ๐๐ป๐ฑ-
This was a Python coding round, which I cleared successfully.
๐๐ฅ๐ผ๐๐ป๐ฑ ๐ฑ- This was ๐๐ถ๐ฟ๐ถ๐ป๐ด ๐ ๐ฎ๐ป๐ฎ๐ด๐ฒ๐ฟ where my fitment for the team got assessed.
๐๐๐ฎ๐๐ ๐ฅ๐ผ๐๐ป๐ฑ- ๐๐ฎ๐ฟ ๐ฅ๐ฎ๐ถ๐๐ฒ๐ฟ- Very important round, I was asked heavily around Leadership principles & Employee dignity questions.
So, here are my Tips if youโre targeting any Data Science role:
-> Never make up stuff & donโt lie in your Resume.
-> Projects thoroughly study.
-> Practice SQL, DSA, Coding problem on Leetcode/Hackerank.
-> Download data from Kaggle & build EDA (Data manipulation questions are asked)
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
ENJOY LEARNING ๐๐
๐Round 1- Phone Screen round
This was a preliminary round to check my capability, projects to coding, Stats, ML, etc.
After clearing this round the technical Interview rounds started. There were 5-6 rounds (Multiple rounds in one day).
๐ ๐ฅ๐ผ๐๐ป๐ฑ ๐ฎ- ๐๐ฎ๐๐ฎ ๐ฆ๐ฐ๐ถ๐ฒ๐ป๐ฐ๐ฒ ๐๐ฟ๐ฒ๐ฎ๐ฑ๐๐ต:
In this round the interviewer tested my knowledge on different kinds of topics.
๐๐ฅ๐ผ๐๐ป๐ฑ ๐ฏ- ๐๐ฒ๐ฝ๐๐ต ๐ฅ๐ผ๐๐ป๐ฑ:
In this round the interviewers grilled deeper into 1-2 topics. I was asked questions around:
Standard ML tech, Linear Equation, Techniques, etc.
๐๐ฅ๐ผ๐๐ป๐ฑ ๐ฐ- ๐๐ผ๐ฑ๐ถ๐ป๐ด ๐ฅ๐ผ๐๐ป๐ฑ-
This was a Python coding round, which I cleared successfully.
๐๐ฅ๐ผ๐๐ป๐ฑ ๐ฑ- This was ๐๐ถ๐ฟ๐ถ๐ป๐ด ๐ ๐ฎ๐ป๐ฎ๐ด๐ฒ๐ฟ where my fitment for the team got assessed.
๐๐๐ฎ๐๐ ๐ฅ๐ผ๐๐ป๐ฑ- ๐๐ฎ๐ฟ ๐ฅ๐ฎ๐ถ๐๐ฒ๐ฟ- Very important round, I was asked heavily around Leadership principles & Employee dignity questions.
So, here are my Tips if youโre targeting any Data Science role:
-> Never make up stuff & donโt lie in your Resume.
-> Projects thoroughly study.
-> Practice SQL, DSA, Coding problem on Leetcode/Hackerank.
-> Download data from Kaggle & build EDA (Data manipulation questions are asked)
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
ENJOY LEARNING ๐๐
โค2
Building Your Personal Brand as a Data Analyst ๐
A strong personal brand can help you land better job opportunities, attract freelance clients, and position you as a thought leader in data analytics.
Hereโs how to build and grow your brand effectively:
1๏ธโฃ Optimize Your LinkedIn Profile ๐
Use a clear, professional profile picture and a compelling headline (e.g., Data Analyst | SQL | Power BI | Python Enthusiast).
Write an engaging "About" section showcasing your skills, experience, and passion for data analytics.
Share projects, case studies, and insights to demonstrate expertise.
Engage with industry leaders, recruiters, and fellow analysts.
2๏ธโฃ Share Valuable Content Consistently โ๏ธ
Post insightful LinkedIn posts, Medium articles, or Twitter threads on SQL, Power BI, Python, and industry trends.
Write about real-world case studies, common mistakes, and career advice.
Share data visualization tips, SQL tricks, or step-by-step tutorials.
3๏ธโฃ Contribute to Open-Source & GitHub ๐ป
Publish SQL queries, Python scripts, Jupyter notebooks, and dashboards.
Share projects with real datasets to showcase your hands-on skills.
Collaborate on open-source data analytics projects to gain exposure.
4๏ธโฃ Engage in Online Data Analytics Communities ๐
Join and contribute to Reddit (r/dataanalysis, r/SQL), Stack Overflow, and Data Science Discord groups.
Participate in Kaggle competitions to gain practical experience.
Answer questions on Quora, LinkedIn, or Twitter to establish credibility.
5๏ธโฃ Speak at Webinars & Meetups ๐ค
Host or participate in webinars on LinkedIn, YouTube, or data conferences.
Join local meetups or online communities like DataCamp and Tableau User Groups.
Share insights on career growth, best practices, and analytics trends.
6๏ธโฃ Create a Portfolio Website ๐
Build a personal website showcasing your projects, resume, and blog.
Include interactive dashboards, case studies, and problem-solving examples.
Use Wix, WordPress, or GitHub Pages to get started.
7๏ธโฃ Network & Collaborate ๐ค
Connect with hiring managers, recruiters, and senior analysts.
Collaborate on guest blog posts, podcasts, or YouTube interviews.
Attend data science and analytics conferences to expand your reach.
8๏ธโฃ Start a YouTube Channel or Podcast ๐ฅ
Share short tutorials on SQL, Power BI, Python, and Excel.
Interview industry experts and discuss data analytics career paths.
Offer career guidance, resume tips, and interview prep content.
9๏ธโฃ Offer Free Value Before Monetizing ๐ก
Give away free e-books, templates, or mini-courses to attract an audience.
Provide LinkedIn Live Q&A sessions, career guidance, or free tutorials.
Once you build trust, you can monetize through consulting, courses, and coaching.
๐ Stay Consistent & Keep Learning
Building a brand takes timeโstay consistent with content creation and engagement.
Keep learning new skills and sharing your journey to stay relevant.
Follow industry leaders, subscribe to analytics blogs, and attend workshops.
A strong personal brand in data analytics can open unlimited opportunitiesโfrom job offers to freelance gigs and consulting projects.
Start small, be consistent, and showcase your expertise! ๐ฅ
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
#dataanalyst
A strong personal brand can help you land better job opportunities, attract freelance clients, and position you as a thought leader in data analytics.
Hereโs how to build and grow your brand effectively:
1๏ธโฃ Optimize Your LinkedIn Profile ๐
Use a clear, professional profile picture and a compelling headline (e.g., Data Analyst | SQL | Power BI | Python Enthusiast).
Write an engaging "About" section showcasing your skills, experience, and passion for data analytics.
Share projects, case studies, and insights to demonstrate expertise.
Engage with industry leaders, recruiters, and fellow analysts.
2๏ธโฃ Share Valuable Content Consistently โ๏ธ
Post insightful LinkedIn posts, Medium articles, or Twitter threads on SQL, Power BI, Python, and industry trends.
Write about real-world case studies, common mistakes, and career advice.
Share data visualization tips, SQL tricks, or step-by-step tutorials.
3๏ธโฃ Contribute to Open-Source & GitHub ๐ป
Publish SQL queries, Python scripts, Jupyter notebooks, and dashboards.
Share projects with real datasets to showcase your hands-on skills.
Collaborate on open-source data analytics projects to gain exposure.
4๏ธโฃ Engage in Online Data Analytics Communities ๐
Join and contribute to Reddit (r/dataanalysis, r/SQL), Stack Overflow, and Data Science Discord groups.
Participate in Kaggle competitions to gain practical experience.
Answer questions on Quora, LinkedIn, or Twitter to establish credibility.
5๏ธโฃ Speak at Webinars & Meetups ๐ค
Host or participate in webinars on LinkedIn, YouTube, or data conferences.
Join local meetups or online communities like DataCamp and Tableau User Groups.
Share insights on career growth, best practices, and analytics trends.
6๏ธโฃ Create a Portfolio Website ๐
Build a personal website showcasing your projects, resume, and blog.
Include interactive dashboards, case studies, and problem-solving examples.
Use Wix, WordPress, or GitHub Pages to get started.
7๏ธโฃ Network & Collaborate ๐ค
Connect with hiring managers, recruiters, and senior analysts.
Collaborate on guest blog posts, podcasts, or YouTube interviews.
Attend data science and analytics conferences to expand your reach.
8๏ธโฃ Start a YouTube Channel or Podcast ๐ฅ
Share short tutorials on SQL, Power BI, Python, and Excel.
Interview industry experts and discuss data analytics career paths.
Offer career guidance, resume tips, and interview prep content.
9๏ธโฃ Offer Free Value Before Monetizing ๐ก
Give away free e-books, templates, or mini-courses to attract an audience.
Provide LinkedIn Live Q&A sessions, career guidance, or free tutorials.
Once you build trust, you can monetize through consulting, courses, and coaching.
๐ Stay Consistent & Keep Learning
Building a brand takes timeโstay consistent with content creation and engagement.
Keep learning new skills and sharing your journey to stay relevant.
Follow industry leaders, subscribe to analytics blogs, and attend workshops.
A strong personal brand in data analytics can open unlimited opportunitiesโfrom job offers to freelance gigs and consulting projects.
Start small, be consistent, and showcase your expertise! ๐ฅ
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
#dataanalyst
โค2
Forwarded from Data Analysis Books | Python | SQL | Excel | Artificial Intelligence | Power BI | Tableau | AI Resources
๐๐ฒ๐ฐ๐ผ๐บ๐ฒ ๐ฎ ๐๐ฎ๐๐ฎ ๐๐ป๐ฎ๐น๐๐๐ ๐ถ๐ป ๐ฎ๐ฌ๐ฎ๐ฑ โ ๐ฆ๐๐ฎ๐ฟ๐ ๐๐ถ๐๐ต ๐๐๐๐ ๐ฏ ๐๐ผ๐ฟ๐ฒ ๐ฆ๐ธ๐ถ๐น๐น๐!๐
Want to break into Data Analytics without a degree or expensive bootcamps?๐จโ๐ป๐
All you need are 3 essentials to get started๐
๐ Excel | ๐ข SQL | ๐ง Basic Maths
๐๐ข๐ง๐ค๐:-
https://pdlink.in/3IwVWGE
You can learn & practice them 100% FREEโ ๏ธ
Want to break into Data Analytics without a degree or expensive bootcamps?๐จโ๐ป๐
All you need are 3 essentials to get started๐
๐ Excel | ๐ข SQL | ๐ง Basic Maths
๐๐ข๐ง๐ค๐:-
https://pdlink.in/3IwVWGE
You can learn & practice them 100% FREEโ ๏ธ
1700001429173.pdf
427.3 KB
Top Python libraries for generative AI
Generative AI is a branch of artificial intelligence that focuses on the creation of new content, such as text, images, music, and code. This is done by training models on large datasets of existing content, which the model then uses to generate new content.
Python is a popular programming language for generative AI, as it has a wide range of libraries and frameworks available.
Generative AI is a branch of artificial intelligence that focuses on the creation of new content, such as text, images, music, and code. This is done by training models on large datasets of existing content, which the model then uses to generate new content.
Python is a popular programming language for generative AI, as it has a wide range of libraries and frameworks available.
Programming Practice Python 2023.pdf
5.4 MB
Programming Practice Python
Like for more
Like for more
โค6
๐๐ฟ๐ฎ๐ฐ๐ธ ๐๐๐๐ก๐ ๐๐ป๐๐ฒ๐ฟ๐๐ถ๐ฒ๐๐ ๐ถ๐ป ๐ฎ๐ฌ๐ฎ๐ฑ โ ๐ณ๐ผ๐ฟ ๐๐ฅ๐๐!๐
If youโre serious about cracking top tech interviews โ from FAANG to startups โ this is the roadmap you canโt afford to miss๐
Thousands have used it to land roles at Google, Amazon, Microsoft, and more โ completely free๐คฉ๐
๐๐ข๐ง๐ค๐:-
https://pdlink.in/3TJlpyW
Your dream job might just start here.โ ๏ธ
If youโre serious about cracking top tech interviews โ from FAANG to startups โ this is the roadmap you canโt afford to miss๐
Thousands have used it to land roles at Google, Amazon, Microsoft, and more โ completely free๐คฉ๐
๐๐ข๐ง๐ค๐:-
https://pdlink.in/3TJlpyW
Your dream job might just start here.โ ๏ธ
โค1
WhatsApp is no longer a platform just for chat.
It's an educational goldmine.
If you do, youโre sleeping on a goldmine of knowledge and community. WhatsApp channels are a great way to practice data science, make your own community, and find accountability partners.
I have curated the list of best WhatsApp channels to learn coding & data science for FREE
Free Courses with Certificate
๐๐
https://whatsapp.com/channel/0029Vamhzk5JENy1Zg9KmO2g
Jobs & Internship Opportunities
๐๐
https://whatsapp.com/channel/0029VaI5CV93AzNUiZ5Tt226
Web Development
๐๐
https://whatsapp.com/channel/0029VaiSdWu4NVis9yNEE72z
Python Free Books & Projects
๐๐
https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L
Java Free Resources
๐๐
https://whatsapp.com/channel/0029VamdH5mHAdNMHMSBwg1s
Coding Interviews
๐๐
https://whatsapp.com/channel/0029VammZijATRSlLxywEC3X
SQL For Data Analysis
๐๐
https://whatsapp.com/channel/0029VanC5rODzgT6TiTGoa1v
Power BI Resources
๐๐
https://whatsapp.com/channel/0029Vai1xKf1dAvuk6s1v22c
Programming Free Resources
๐๐
https://whatsapp.com/channel/0029VahiFZQ4o7qN54LTzB17
Data Science Projects
๐๐
https://whatsapp.com/channel/0029Va4QUHa6rsQjhITHK82y
Learn Data Science & Machine Learning
๐๐
https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
ENJOY LEARNING ๐๐
It's an educational goldmine.
If you do, youโre sleeping on a goldmine of knowledge and community. WhatsApp channels are a great way to practice data science, make your own community, and find accountability partners.
I have curated the list of best WhatsApp channels to learn coding & data science for FREE
Free Courses with Certificate
๐๐
https://whatsapp.com/channel/0029Vamhzk5JENy1Zg9KmO2g
Jobs & Internship Opportunities
๐๐
https://whatsapp.com/channel/0029VaI5CV93AzNUiZ5Tt226
Web Development
๐๐
https://whatsapp.com/channel/0029VaiSdWu4NVis9yNEE72z
Python Free Books & Projects
๐๐
https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L
Java Free Resources
๐๐
https://whatsapp.com/channel/0029VamdH5mHAdNMHMSBwg1s
Coding Interviews
๐๐
https://whatsapp.com/channel/0029VammZijATRSlLxywEC3X
SQL For Data Analysis
๐๐
https://whatsapp.com/channel/0029VanC5rODzgT6TiTGoa1v
Power BI Resources
๐๐
https://whatsapp.com/channel/0029Vai1xKf1dAvuk6s1v22c
Programming Free Resources
๐๐
https://whatsapp.com/channel/0029VahiFZQ4o7qN54LTzB17
Data Science Projects
๐๐
https://whatsapp.com/channel/0029Va4QUHa6rsQjhITHK82y
Learn Data Science & Machine Learning
๐๐
https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
ENJOY LEARNING ๐๐
โค3
๐ฐ ๐๐ฟ๐ฒ๐ฒ ๐ ๐ถ๐ฐ๐ฟ๐ผ๐๐ผ๐ณ๐ ๐ฅ๐ฒ๐๐ผ๐๐ฟ๐ฐ๐ฒ๐ ๐๐ผ ๐ ๐ฎ๐๐๐ฒ๐ฟ ๐๐ฎ๐๐ฎ ๐ฆ๐ฐ๐ถ๐ฒ๐ป๐ฐ๐ฒ ๐ถ๐ป ๐ฎ๐ฌ๐ฎ๐ฑ๐
Want to break into data science in 2025โwithout spending a single rupee?๐ฐ๐จโ๐ป
Youโre in luck! Microsoft is offering powerful, beginner-friendly resources that teach you everything from Python fundamentals to AI and data analyticsโfor free๐คฉโ๏ธ
๐๐ข๐ง๐ค๐:-
https://pdlink.in/42vCIrb
Level up your career in the booming field of dataโ ๏ธ
Want to break into data science in 2025โwithout spending a single rupee?๐ฐ๐จโ๐ป
Youโre in luck! Microsoft is offering powerful, beginner-friendly resources that teach you everything from Python fundamentals to AI and data analyticsโfor free๐คฉโ๏ธ
๐๐ข๐ง๐ค๐:-
https://pdlink.in/42vCIrb
Level up your career in the booming field of dataโ ๏ธ
โค2
Letโs analyze the Python code snippet from the image:
โ Correct answer: C. 7
python
Copy
Edit
def add_n(a, b):
return (a + b)
a = 5
b = 5
print(add_n(4, 3))
Step-by-step explanation:
A function add_n(a, b) is defined to return the sum of a and b.
The variables a = 5 and b = 5 are declared but not used inside the function call โ they are irrelevant in this context.
The function is called with explicit arguments: add_n(4, 3), so:
python
Copy
Edit
return 4 + 3 # = 7
โ Correct answer: C. 7
โค6
Forwarded from Artificial Intelligence
๐ฐ ๐ ๐๐๐-๐ช๐ฎ๐๐ฐ๐ต ๐ฌ๐ผ๐๐ง๐๐ฏ๐ฒ ๐๐ผ๐๐ฟ๐๐ฒ๐ ๐ณ๐ผ๐ฟ ๐๐๐ฒ๐ฟ๐ ๐๐ฎ๐๐ฎ ๐๐ป๐ฎ๐น๐๐๐ถ๐ฐ๐ ๐ฆ๐๐๐ฑ๐ฒ๐ป๐ ๐ถ๐ป ๐ฎ๐ฌ๐ฎ๐ฑ๐
If youโre starting your data analytics journey, these 4 YouTube courses are pure gold โ and the best part? ๐ป๐คฉ
Theyโre completely free๐ฅ๐ฏ
๐๐ข๐ง๐ค๐:-
https://pdlink.in/44DvNP1
Each course can help you build the right foundation for a successful tech careerโ ๏ธ
If youโre starting your data analytics journey, these 4 YouTube courses are pure gold โ and the best part? ๐ป๐คฉ
Theyโre completely free๐ฅ๐ฏ
๐๐ข๐ง๐ค๐:-
https://pdlink.in/44DvNP1
Each course can help you build the right foundation for a successful tech careerโ ๏ธ
โค1
hands-on-data-science.pdf
15.3 MB
Hands-On Data Science and Python Machine Learning
Frank Kane, 2017
Frank Kane, 2017
XML_JSON_Programming,_For_Beginners,_Learn_Coding.epub
876.1 KB
XML JSON Programming
Yao, Ray, 2020
Yao, Ray, 2020
System design terminologies.pdf
23.7 MB
๐ฆ๐๐๐๐ฒ๐บ ๐๐ฒ๐๐ถ๐ด๐ป ๐ง๐ฒ๐ฟ๐บ๐ถ๐ป๐ผ๐น๐ผ๐ด๐ถ๐ฒ๐
โค5