Essential Python Libraries for Data Analytics ๐๐
Python Free Resources: https://t.iss.one/pythondevelopersindia
1. NumPy:
- Efficient numerical operations and array manipulation.
2. Pandas:
- Data manipulation and analysis with powerful data structures (DataFrame, Series).
3. Matplotlib:
- 2D plotting library for creating visualizations.
4. Scikit-learn:
- Machine learning toolkit for classification, regression, clustering, etc.
5. TensorFlow:
- Open-source machine learning framework for building and deploying ML models.
6. PyTorch:
- Deep learning library, particularly popular for neural network research.
7. Django:
- High-level web framework for building robust, scalable web applications.
8. Flask:
- Lightweight web framework for building smaller web applications and APIs.
9. Requests:
- HTTP library for making HTTP requests.
10. Beautiful Soup:
- Web scraping library for pulling data out of HTML and XML files.
As a beginner, you can start with Pandas and Numpy libraries for data analysis. If you want to transition from Data Analyst to Data Scientist, then you can start applying ML libraries like Scikit-learn, Tensorflow, Pytorch, etc. in your data projects.
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
Python Free Resources: https://t.iss.one/pythondevelopersindia
1. NumPy:
- Efficient numerical operations and array manipulation.
2. Pandas:
- Data manipulation and analysis with powerful data structures (DataFrame, Series).
3. Matplotlib:
- 2D plotting library for creating visualizations.
4. Scikit-learn:
- Machine learning toolkit for classification, regression, clustering, etc.
5. TensorFlow:
- Open-source machine learning framework for building and deploying ML models.
6. PyTorch:
- Deep learning library, particularly popular for neural network research.
7. Django:
- High-level web framework for building robust, scalable web applications.
8. Flask:
- Lightweight web framework for building smaller web applications and APIs.
9. Requests:
- HTTP library for making HTTP requests.
10. Beautiful Soup:
- Web scraping library for pulling data out of HTML and XML files.
As a beginner, you can start with Pandas and Numpy libraries for data analysis. If you want to transition from Data Analyst to Data Scientist, then you can start applying ML libraries like Scikit-learn, Tensorflow, Pytorch, etc. in your data projects.
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
๐2
๐ฐ ๐๐ฅ๐๐ ๐๐ผ๐๐ฟ๐๐ฒ๐ ๐ฏ๐ ๐๐ฎ๐ฟ๐๐ฎ๐ฟ๐ฑ ๐ฎ๐ป๐ฑ ๐ฆ๐๐ฎ๐ป๐ณ๐ผ๐ฟ๐ฑ ๐๐ผ ๐๐ฒ๐ฎ๐ฟ๐ป ๐๐๐
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Hereโs your golden ticket to the future!โ
Dreaming of Mastering AI? ๐ฏ
Harvard and Stanfordโtwo of the most prestigious universities in the worldโare offering FREE AI courses๐จโ๐ป
No hidden fees, no long applicationsโjust pure, world-class education, accessible to everyone๐ฅ
๐๐ข๐ง๐ค๐:-
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Hereโs your golden ticket to the future!โ
๐1
Python Interview Questions for Data/Business Analysts:
Question 1:
Given a dataset in a CSV file, how would you read it into a Pandas DataFrame? And how would you handle missing values?
Question 2:
Describe the difference between a list, a tuple, and a dictionary in Python. Provide an example for each.
Question 3:
Imagine you are provided with two datasets, 'sales_data' and 'product_data', both in the form of Pandas DataFrames. How would you merge these datasets on a common column named 'ProductID'?
Question 4:
How would you handle duplicate rows in a Pandas DataFrame? Write a Python code snippet to demonstrate.
Question 5:
Describe the difference between '.iloc[] and '.loc[]' in the context of Pandas.
Question 6:
In Python's Matplotlib library, how would you plot a line chart to visualize monthly sales? Assume you have a list of months and a list of corresponding sales numbers.
Question 7:
How would you use Python to connect to a SQL database and fetch data into a Pandas DataFrame?
Question 8:
Explain the concept of list comprehensions in Python. Can you provide an example where it's useful for data analysis?
Question 9:
How would you reshape a long-format DataFrame to a wide format using Pandas? Explain with an example.
Question 10:
What are lambda functions in Python? How are they beneficial in data wrangling tasks?
Question 11:
Describe a scenario where you would use the 'groupby()' method in Pandas. How would you aggregate data after grouping?
Question 12:
You are provided with a Pandas DataFrame that contains a column with date strings. How would you convert this column to a datetime format? Additionally, how would you extract the month and year from these datetime objects?
Question 13:
Explain the purpose of the 'pivot_table' method in Pandas and describe a business scenario where it might be useful.
Question 14:
How would you handle large datasets that don't fit into memory? Are you familiar with Dask or any similar libraries?
Python Interview Q&A: https://topmate.io/coding/898340
Like for more โค๏ธ
ENJOY LEARNING ๐๐
Question 1:
Given a dataset in a CSV file, how would you read it into a Pandas DataFrame? And how would you handle missing values?
Question 2:
Describe the difference between a list, a tuple, and a dictionary in Python. Provide an example for each.
Question 3:
Imagine you are provided with two datasets, 'sales_data' and 'product_data', both in the form of Pandas DataFrames. How would you merge these datasets on a common column named 'ProductID'?
Question 4:
How would you handle duplicate rows in a Pandas DataFrame? Write a Python code snippet to demonstrate.
Question 5:
Describe the difference between '.iloc[] and '.loc[]' in the context of Pandas.
Question 6:
In Python's Matplotlib library, how would you plot a line chart to visualize monthly sales? Assume you have a list of months and a list of corresponding sales numbers.
Question 7:
How would you use Python to connect to a SQL database and fetch data into a Pandas DataFrame?
Question 8:
Explain the concept of list comprehensions in Python. Can you provide an example where it's useful for data analysis?
Question 9:
How would you reshape a long-format DataFrame to a wide format using Pandas? Explain with an example.
Question 10:
What are lambda functions in Python? How are they beneficial in data wrangling tasks?
Question 11:
Describe a scenario where you would use the 'groupby()' method in Pandas. How would you aggregate data after grouping?
Question 12:
You are provided with a Pandas DataFrame that contains a column with date strings. How would you convert this column to a datetime format? Additionally, how would you extract the month and year from these datetime objects?
Question 13:
Explain the purpose of the 'pivot_table' method in Pandas and describe a business scenario where it might be useful.
Question 14:
How would you handle large datasets that don't fit into memory? Are you familiar with Dask or any similar libraries?
Python Interview Q&A: https://topmate.io/coding/898340
Like for more โค๏ธ
ENJOY LEARNING ๐๐
๐3
๐๐ฅ๐๐ ๐๐ผ๐ผ๐ด๐น๐ฒ ๐๐ฒ๐ฎ๐ฟ๐ป๐ถ๐ป๐ด ๐ฃ๐ฎ๐๐ต! ๐๐ฒ๐ฐ๐ผ๐บ๐ฒ ๐ฎ ๐๐ฒ๐ฟ๐๐ถ๐ณ๐ถ๐ฒ๐ฑ ๐๐ฎ๐๐ฎ ๐๐ป๐ฎ๐น๐๐๐ ๐ถ๐ป ๐ฎ๐ฌ๐ฎ๐ฑ๐
If youโre dreaming of starting a high-paying data career or switching into the booming tech industry, Google just made it a whole lot easier โ and itโs completely FREE๐จโ๐ป
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Youโll get access to hands-on labs, real datasets, and industry-grade training created directly by Googleโs own experts๐ป
If youโre dreaming of starting a high-paying data career or switching into the booming tech industry, Google just made it a whole lot easier โ and itโs completely FREE๐จโ๐ป
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Youโll get access to hands-on labs, real datasets, and industry-grade training created directly by Googleโs own experts๐ป
โค1๐1
Some interview questions related to Data science
1- what is difference between structured data and unstructured data.
2- what is multicollinearity.and how to remove them
3- which algorithms you use to find the most correlated features in the datasets.
4- define entropy
5- what is the workflow of principal component analysis
6- what are the applications of principal component analysis not with respect to dimensionality reduction
7- what is the Convolutional neural network. Explain me its working
1- what is difference between structured data and unstructured data.
2- what is multicollinearity.and how to remove them
3- which algorithms you use to find the most correlated features in the datasets.
4- define entropy
5- what is the workflow of principal component analysis
6- what are the applications of principal component analysis not with respect to dimensionality reduction
7- what is the Convolutional neural network. Explain me its working
๐2
๐๐ฒ๐๐ ๐ฌ๐ผ๐๐ง๐๐ฏ๐ฒ ๐๐ต๐ฎ๐ป๐ป๐ฒ๐น๐ ๐๐ผ ๐๐ฒ๐ฎ๐ฟ๐ป ๐๐๐๐ฒ๐ป๐๐ถ๐ฎ๐น ๐๐ฎ๐๐ฎ ๐๐ป๐ฎ๐น๐๐๐ถ๐ฐ๐ ๐ฆ๐ธ๐ถ๐น๐น๐ ๐ณ๐ผ๐ฟ ๐๐ฅ๐๐๐
Dreaming of becoming a Data Analyst but feel overwhelmed by where to start?๐จโ๐ป
Hereโs the truth: YouTube is packed with goldmine content, and the best part โ itโs all 100% FREE๐ฅ
๐๐ข๐ง๐ค๐:-
https://pdlink.in/4cL3SyM
๐ If Youโre Serious About Data Analytics, You Canโt Sleep on These YouTube Channels!
Dreaming of becoming a Data Analyst but feel overwhelmed by where to start?๐จโ๐ป
Hereโs the truth: YouTube is packed with goldmine content, and the best part โ itโs all 100% FREE๐ฅ
๐๐ข๐ง๐ค๐:-
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๐ If Youโre Serious About Data Analytics, You Canโt Sleep on These YouTube Channels!
๐1
To start with Machine Learning:
1. Learn Python
2. Practice using Google Colab
Take these free courses:
https://t.iss.one/datasciencefun/290
If you need a bit more time before diving deeper, finish the Kaggle tutorials.
At this point, you are ready to finish your first project: The Titanic Challenge on Kaggle.
If Math is not your strong suit, don't worry. I don't recommend you spend too much time learning Math before writing code. Instead, learn the concepts on-demand: Find what you need when needed.
From here, take the Machine Learning specialization in Coursera. It's more advanced, and it will stretch you out a bit.
The top universities worldwide have published their Machine Learning and Deep Learning classes online. Here are some of them:
https://t.iss.one/datasciencefree/259
Many different books will help you. The attached image will give you an idea of my favorite ones.
Finally, keep these three ideas in mind:
1. Start by working on solved problems so you can find help whenever you get stuck.
2. ChatGPT will help you make progress. Use it to summarize complex concepts and generate questions you can answer to practice.
3. Find a community on LinkedIn or ๐ and share your work. Ask questions, and help others.
During this time, you'll deal with a lot. Sometimes, you will feel it's impossible to keep up with everything happening, and you'll be right.
Here is the good news:
Most people understand a tiny fraction of the world of Machine Learning. You don't need more to build a fantastic career in space.
Focus on finding your path, and Write. More. Code.
That's how you win.โ๏ธโ๏ธ
1. Learn Python
2. Practice using Google Colab
Take these free courses:
https://t.iss.one/datasciencefun/290
If you need a bit more time before diving deeper, finish the Kaggle tutorials.
At this point, you are ready to finish your first project: The Titanic Challenge on Kaggle.
If Math is not your strong suit, don't worry. I don't recommend you spend too much time learning Math before writing code. Instead, learn the concepts on-demand: Find what you need when needed.
From here, take the Machine Learning specialization in Coursera. It's more advanced, and it will stretch you out a bit.
The top universities worldwide have published their Machine Learning and Deep Learning classes online. Here are some of them:
https://t.iss.one/datasciencefree/259
Many different books will help you. The attached image will give you an idea of my favorite ones.
Finally, keep these three ideas in mind:
1. Start by working on solved problems so you can find help whenever you get stuck.
2. ChatGPT will help you make progress. Use it to summarize complex concepts and generate questions you can answer to practice.
3. Find a community on LinkedIn or ๐ and share your work. Ask questions, and help others.
During this time, you'll deal with a lot. Sometimes, you will feel it's impossible to keep up with everything happening, and you'll be right.
Here is the good news:
Most people understand a tiny fraction of the world of Machine Learning. You don't need more to build a fantastic career in space.
Focus on finding your path, and Write. More. Code.
That's how you win.โ๏ธโ๏ธ
โค2
Artificial Intelligence (AI) Roadmap
|
|-- Fundamentals
| |-- Mathematics
| | |-- Linear Algebra
| | |-- Calculus
| | |-- Probability and Statistics
| |
| |-- Programming
| | |-- Python (Focus on Libraries like NumPy, Pandas)
| | |-- Java or C++ (optional but useful)
| |
| |-- Algorithms and Data Structures
| | |-- Graphs and Trees
| | |-- Dynamic Programming
| | |-- Search Algorithms (e.g., A*, Minimax)
|
|-- Core AI Concepts
| |-- Knowledge Representation
| |-- Search Methods (DFS, BFS)
| |-- Constraint Satisfaction Problems
| |-- Logical Reasoning
|
|-- Machine Learning (ML)
| |-- Supervised Learning (Regression, Classification)
| |-- Unsupervised Learning (Clustering, Dimensionality Reduction)
| |-- Reinforcement Learning (Q-Learning, Policy Gradient Methods)
| |-- Ensemble Methods (Random Forest, Gradient Boosting)
|
|-- Deep Learning (DL)
| |-- Neural Networks
| |-- Convolutional Neural Networks (CNNs)
| |-- Recurrent Neural Networks (RNNs)
| |-- Transformers (BERT, GPT)
| |-- Frameworks (TensorFlow, PyTorch)
|
|-- Natural Language Processing (NLP)
| |-- Text Preprocessing (Tokenization, Lemmatization)
| |-- NLP Models (Word2Vec, BERT)
| |-- Applications (Chatbots, Sentiment Analysis, NER)
|
|-- Computer Vision
| |-- Image Processing
| |-- Object Detection (YOLO, SSD)
| |-- Image Segmentation
| |-- Applications (Facial Recognition, OCR)
|
|-- Ethical AI
| |-- Fairness and Bias
| |-- Privacy and Security
| |-- Explainability (SHAP, LIME)
|
|-- Applications of AI
| |-- Healthcare (Diagnostics, Personalized Medicine)
| |-- Finance (Fraud Detection, Algorithmic Trading)
| |-- Retail (Recommendation Systems, Inventory Management)
| |-- Autonomous Vehicles (Perception, Control Systems)
|
|-- AI Deployment
| |-- Model Serving (Flask, FastAPI)
| |-- Cloud Platforms (AWS SageMaker, Google AI)
| |-- Edge AI (TensorFlow Lite, ONNX)
|
|-- Advanced Topics
| |-- Multi-Agent Systems
| |-- Generative Models (GANs, VAEs)
| |-- Knowledge Graphs
| |-- AI in Quantum Computing
Best Resources to learn ML & AI ๐
Learn Python for Free
Prompt Engineering Course
Prompt Engineering Guide
Data Science Course
Google Cloud Generative AI Path
Machine Learning with Python Free Course
Machine Learning Free Book
Artificial Intelligence WhatsApp channel
Hands-on Machine Learning
Deep Learning Nanodegree Program with Real-world Projects
AI, Machine Learning and Deep Learning
Like this post for more roadmaps โค๏ธ
Follow & share the channel link with your friends: t.iss.one/free4unow_backup
ENJOY LEARNING๐๐
|
|-- Fundamentals
| |-- Mathematics
| | |-- Linear Algebra
| | |-- Calculus
| | |-- Probability and Statistics
| |
| |-- Programming
| | |-- Python (Focus on Libraries like NumPy, Pandas)
| | |-- Java or C++ (optional but useful)
| |
| |-- Algorithms and Data Structures
| | |-- Graphs and Trees
| | |-- Dynamic Programming
| | |-- Search Algorithms (e.g., A*, Minimax)
|
|-- Core AI Concepts
| |-- Knowledge Representation
| |-- Search Methods (DFS, BFS)
| |-- Constraint Satisfaction Problems
| |-- Logical Reasoning
|
|-- Machine Learning (ML)
| |-- Supervised Learning (Regression, Classification)
| |-- Unsupervised Learning (Clustering, Dimensionality Reduction)
| |-- Reinforcement Learning (Q-Learning, Policy Gradient Methods)
| |-- Ensemble Methods (Random Forest, Gradient Boosting)
|
|-- Deep Learning (DL)
| |-- Neural Networks
| |-- Convolutional Neural Networks (CNNs)
| |-- Recurrent Neural Networks (RNNs)
| |-- Transformers (BERT, GPT)
| |-- Frameworks (TensorFlow, PyTorch)
|
|-- Natural Language Processing (NLP)
| |-- Text Preprocessing (Tokenization, Lemmatization)
| |-- NLP Models (Word2Vec, BERT)
| |-- Applications (Chatbots, Sentiment Analysis, NER)
|
|-- Computer Vision
| |-- Image Processing
| |-- Object Detection (YOLO, SSD)
| |-- Image Segmentation
| |-- Applications (Facial Recognition, OCR)
|
|-- Ethical AI
| |-- Fairness and Bias
| |-- Privacy and Security
| |-- Explainability (SHAP, LIME)
|
|-- Applications of AI
| |-- Healthcare (Diagnostics, Personalized Medicine)
| |-- Finance (Fraud Detection, Algorithmic Trading)
| |-- Retail (Recommendation Systems, Inventory Management)
| |-- Autonomous Vehicles (Perception, Control Systems)
|
|-- AI Deployment
| |-- Model Serving (Flask, FastAPI)
| |-- Cloud Platforms (AWS SageMaker, Google AI)
| |-- Edge AI (TensorFlow Lite, ONNX)
|
|-- Advanced Topics
| |-- Multi-Agent Systems
| |-- Generative Models (GANs, VAEs)
| |-- Knowledge Graphs
| |-- AI in Quantum Computing
Best Resources to learn ML & AI ๐
Learn Python for Free
Prompt Engineering Course
Prompt Engineering Guide
Data Science Course
Google Cloud Generative AI Path
Machine Learning with Python Free Course
Machine Learning Free Book
Artificial Intelligence WhatsApp channel
Hands-on Machine Learning
Deep Learning Nanodegree Program with Real-world Projects
AI, Machine Learning and Deep Learning
Like this post for more roadmaps โค๏ธ
Follow & share the channel link with your friends: t.iss.one/free4unow_backup
ENJOY LEARNING๐๐
๐2
๐ง๐๐ฆ ๐๐ฅ๐๐ ๐๐ฒ๐ฟ๐๐ถ๐ณ๐ถ๐ฐ๐ฎ๐๐ถ๐ผ๐ป ๐ข๐ป ๐๐ฎ๐๐ฎ ๐ ๐ฎ๐ป๐ฎ๐ด๐ฒ๐บ๐ฒ๐ป๐ - ๐๐ป๐ฟ๐ผ๐น๐น ๐๐ผ๐ฟ ๐๐ฅ๐๐๐
Want to know how top companies handle massive amounts of data without losing track? ๐
TCS is offering a FREE beginner-friendly course on Master Data Management, and yesโit comes with a certificate! ๐
๐๐ข๐ง๐ค๐:-
https://pdlink.in/4jGFBw0
Just click and start learning!โ ๏ธ
Want to know how top companies handle massive amounts of data without losing track? ๐
TCS is offering a FREE beginner-friendly course on Master Data Management, and yesโit comes with a certificate! ๐
๐๐ข๐ง๐ค๐:-
https://pdlink.in/4jGFBw0
Just click and start learning!โ ๏ธ
๐2
๐ฑ ๐๐ฟ๐ฒ๐ฒ ๐ช๐ฒ๐ฏ๐๐ถ๐๐ฒ๐ ๐๐ผ ๐๐ฒ๐ฎ๐ฟ๐ป ๐ฃ๐๐๐ต๐ผ๐ป ๐ณ๐ฟ๐ผ๐บ ๐ฆ๐ฐ๐ฟ๐ฎ๐๐ฐ๐ต ๐ถ๐ป ๐ฎ๐ฌ๐ฎ๐ฑ (๐ก๐ผ ๐๐ป๐๐ฒ๐๐๐บ๐ฒ๐ป๐ ๐ก๐ฒ๐ฒ๐ฑ๐ฒ๐ฑ!)๐
If youโre serious about starting your tech journey, Python is one of the best languages to master๐จโ๐ป๐จโ๐
Iโve found 5 hidden gems that offer beginner tutorials, advanced exercises, and even real-world projects โ absolutely FREE๐ฅ
๐๐ข๐ง๐ค๐:-
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Start today, and youโll thank yourself tomorrow.โ ๏ธ
If youโre serious about starting your tech journey, Python is one of the best languages to master๐จโ๐ป๐จโ๐
Iโve found 5 hidden gems that offer beginner tutorials, advanced exercises, and even real-world projects โ absolutely FREE๐ฅ
๐๐ข๐ง๐ค๐:-
https://pdlink.in/4lOVqmb
Start today, and youโll thank yourself tomorrow.โ ๏ธ
๐1
Forwarded from Artificial Intelligence
๐๐ผ๐ผ๐ด๐น๐ฒ ๐๐ฅ๐๐ ๐๐ ๐๐ฒ๐ฟ๐๐ถ๐ณ๐ถ๐ฐ๐ฎ๐๐ถ๐ผ๐ป ๐๐ผ๐๐ฟ๐๐ฒ๐๐
Ever wondered how machines describe images in words?๐ป
Want to get hands-on with cutting-edge AI and computer vision โ for FREE?๐
๐๐ข๐ง๐ค๐:-
https://pdlink.in/42FaT0Y
๐ฏ Start Learning AI for FREE
Ever wondered how machines describe images in words?๐ป
Want to get hands-on with cutting-edge AI and computer vision โ for FREE?๐
๐๐ข๐ง๐ค๐:-
https://pdlink.in/42FaT0Y
๐ฏ Start Learning AI for FREE
๐1
Here are 7 FREE courses that will make you smarter:
1. Negotiating Salary:
Learn how to get the pay you deserve by mastering the art of negotiation.
https://pll.harvard.edu/course/negotiating-salary
Share this telegram channel with your friends: https://t.iss.one/udacityfreecourse
2. Entrepreneurship:
Learn how to build a successful business.
https://pll.harvard.edu/course/technology-entrepreneurship-lab-market
3. Intro to AI:
A beginner's guide to artificial intelligence and its applications in the real world.
https://pll.harvard.edu/course/cs50s-introduction-artificial-intelligence-python
4. Managing Happiness:
Did you know you can learn how to be happier?
Learn how!
https://pll.harvard.edu/course/managing-happiness
5. Mobile App Development:
Learn how to create your mobile app and reach a wider audience.
https://cs50.harvard.edu/mobile/2018/
6. Entrepreneurship in Emerging Economies:
Learn how to start a successful business in countries where the economy is growing fast.
https://pll.harvard.edu/course/entrepreneurship-in-emerging-economies
7. Web Programming:
Learn how to build your website.
https://pll.harvard.edu/course/cs50s-web-programming-python-and-javascript
Share this telegram channel with your friends: https://t.iss.one/udacityfreecourse
1. Negotiating Salary:
Learn how to get the pay you deserve by mastering the art of negotiation.
https://pll.harvard.edu/course/negotiating-salary
Share this telegram channel with your friends: https://t.iss.one/udacityfreecourse
2. Entrepreneurship:
Learn how to build a successful business.
https://pll.harvard.edu/course/technology-entrepreneurship-lab-market
3. Intro to AI:
A beginner's guide to artificial intelligence and its applications in the real world.
https://pll.harvard.edu/course/cs50s-introduction-artificial-intelligence-python
4. Managing Happiness:
Did you know you can learn how to be happier?
Learn how!
https://pll.harvard.edu/course/managing-happiness
5. Mobile App Development:
Learn how to create your mobile app and reach a wider audience.
https://cs50.harvard.edu/mobile/2018/
6. Entrepreneurship in Emerging Economies:
Learn how to start a successful business in countries where the economy is growing fast.
https://pll.harvard.edu/course/entrepreneurship-in-emerging-economies
7. Web Programming:
Learn how to build your website.
https://pll.harvard.edu/course/cs50s-web-programming-python-and-javascript
Share this telegram channel with your friends: https://t.iss.one/udacityfreecourse
โค1๐1
๐ง๐ต๐ฒ ๐ฐ ๐ฃ๐ฟ๐ผ๐ท๐ฒ๐ฐ๐๐ ๐ง๐ต๐ฎ๐ ๐๐ฎ๐ป ๐๐ฎ๐ป๐ฑ ๐ฌ๐ผ๐ ๐ฎ ๐๐ฎ๐๐ฎ ๐ฆ๐ฐ๐ถ๐ฒ๐ป๐ฐ๐ฒ ๐๐ผ๐ฏ (๐๐๐ฒ๐ป ๐ช๐ถ๐๐ต๐ผ๐๐ ๐๐
๐ฝ๐ฒ๐ฟ๐ถ๐ฒ๐ป๐ฐ๐ฒ) ๐ผ
Recruiters donโt want to see more certificatesโthey want proof you can solve real-world problems. Thatโs where the right projects come in. Not toy datasets, but projects that demonstrate storytelling, problem-solving, and impact.
Here are 4 killer projects thatโll make your portfolio stand out ๐
๐น 1. Exploratory Data Analysis (EDA) on Real-World Dataset
Pick a messy dataset from Kaggle or public sources. Show your thought process.
โ Clean data using Pandas
โ Visualize trends with Seaborn/Matplotlib
โ Share actionable insights with graphs and markdown
Bonus: Turn it into a Jupyter Notebook with detailed storytelling
๐น 2. Predictive Modeling with ML
Solve a real problem using machine learning. For example:
โ Predict customer churn using Logistic Regression
โ Predict housing prices with Random Forest or XGBoost
โ Use scikit-learn for training + evaluation
Bonus: Add SHAP or feature importance to explain predictions
๐น 3. SQL-Powered Business Dashboard
Use real sales or ecommerce data to build a dashboard.
โ Write complex SQL queries for KPIs
โ Visualize with Power BI or Tableau
โ Show trends: Revenue by Region, Product Performance, etc.
Bonus: Add filters & slicers to make it interactive
๐น 4. End-to-End Data Science Pipeline Project
Build a complete pipeline from scratch.
โ Collect data via web scraping (e.g., IMDb, LinkedIn Jobs)
โ Clean + Analyze + Model + Deploy
โ Deploy with Streamlit/Flask + GitHub + Render
Bonus: Add a blog post or LinkedIn write-up explaining your approach
๐ฏ One solid project > 10 certificates.
Make it visible. Make it valuable. Share it confidently.
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Recruiters donโt want to see more certificatesโthey want proof you can solve real-world problems. Thatโs where the right projects come in. Not toy datasets, but projects that demonstrate storytelling, problem-solving, and impact.
Here are 4 killer projects thatโll make your portfolio stand out ๐
๐น 1. Exploratory Data Analysis (EDA) on Real-World Dataset
Pick a messy dataset from Kaggle or public sources. Show your thought process.
โ Clean data using Pandas
โ Visualize trends with Seaborn/Matplotlib
โ Share actionable insights with graphs and markdown
Bonus: Turn it into a Jupyter Notebook with detailed storytelling
๐น 2. Predictive Modeling with ML
Solve a real problem using machine learning. For example:
โ Predict customer churn using Logistic Regression
โ Predict housing prices with Random Forest or XGBoost
โ Use scikit-learn for training + evaluation
Bonus: Add SHAP or feature importance to explain predictions
๐น 3. SQL-Powered Business Dashboard
Use real sales or ecommerce data to build a dashboard.
โ Write complex SQL queries for KPIs
โ Visualize with Power BI or Tableau
โ Show trends: Revenue by Region, Product Performance, etc.
Bonus: Add filters & slicers to make it interactive
๐น 4. End-to-End Data Science Pipeline Project
Build a complete pipeline from scratch.
โ Collect data via web scraping (e.g., IMDb, LinkedIn Jobs)
โ Clean + Analyze + Model + Deploy
โ Deploy with Streamlit/Flask + GitHub + Render
Bonus: Add a blog post or LinkedIn write-up explaining your approach
๐ฏ One solid project > 10 certificates.
Make it visible. Make it valuable. Share it confidently.
I have curated the best interview resources to crack Data Science Interviews
๐๐
https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
Like if you need similar content ๐๐
๐4
Statistics Interview Questions
Topics to Cover:
โข Descriptive statistics
โข Probability
โข Hypothesis testing
โข Regression analysis
Questions and Answers:
1 Q: What is the difference between descriptive and inferential statistics?
A: Descriptive statistics summarize the main features of a dataset (e.g., mean, median, mode), while inferential statistics use samples to make inferences about a larger population.
2 Q: Define p-value in hypothesis testing.
A: The p-value is the probability of obtaining test results at least as extreme as the observed results, assuming the null hypothesis is true. A low p-value (< 0.05) indicates strong evidence against the null hypothesis.
3 Q: What is the central limit theorem?
A: The central limit theorem states that the distribution of the sample mean approximates a normal distribution as the sample size becomes large, regardless of the population's distribution.
4 Q: Explain the concept of correlation.
A: Correlation measures the strength and direction of the relationship between two variables. It ranges from -1 (perfect negative) to +1 (perfect positive), with 0 indicating no correlation.
5 Q: What is linear regression?
A: Linear regression is a statistical method for modeling the relationship between a dependent variable and one or more independent variables by fitting a linear equation to observed data.
I have curated best 80+ top-notch Data Analytics Resources ๐๐
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Like if it helps :)
Topics to Cover:
โข Descriptive statistics
โข Probability
โข Hypothesis testing
โข Regression analysis
Questions and Answers:
1 Q: What is the difference between descriptive and inferential statistics?
A: Descriptive statistics summarize the main features of a dataset (e.g., mean, median, mode), while inferential statistics use samples to make inferences about a larger population.
2 Q: Define p-value in hypothesis testing.
A: The p-value is the probability of obtaining test results at least as extreme as the observed results, assuming the null hypothesis is true. A low p-value (< 0.05) indicates strong evidence against the null hypothesis.
3 Q: What is the central limit theorem?
A: The central limit theorem states that the distribution of the sample mean approximates a normal distribution as the sample size becomes large, regardless of the population's distribution.
4 Q: Explain the concept of correlation.
A: Correlation measures the strength and direction of the relationship between two variables. It ranges from -1 (perfect negative) to +1 (perfect positive), with 0 indicating no correlation.
5 Q: What is linear regression?
A: Linear regression is a statistical method for modeling the relationship between a dependent variable and one or more independent variables by fitting a linear equation to observed data.
I have curated best 80+ top-notch Data Analytics Resources ๐๐
https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02
Like if it helps :)
โค3๐2