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

𝗙𝗥𝗘𝗘 𝗚𝗼𝗼𝗴𝗹𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗣𝗮𝘁𝗵! 𝗕𝗲𝗰𝗼𝗺𝗲 𝗮 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗲𝗱 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝘁 𝗶𝗻 𝟮𝟬𝟮𝟱😍

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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 ❤️

ENJOY LEARNING 👍👍

𝟰 𝗙𝗥𝗘𝗘 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝗯𝘆 𝗛𝗮𝗿𝘃𝗮𝗿𝗱 𝗮𝗻𝗱 𝗦𝘁𝗮𝗻𝗳𝗼𝗿𝗱 𝘁𝗼 𝗟𝗲𝗮𝗿𝗻 𝗔𝗜😍

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Essential Python Libraries for Data Analytics 😄👇

Python Free Resources: https://t.me/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.me/sqlspecialist

Hope it helps :)

𝟱 𝗙𝗥𝗘𝗘 𝗜𝗕𝗠 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝘁𝗼 𝗦𝗸𝘆𝗿𝗼𝗰𝗸𝗲𝘁 𝗬𝗼𝘂𝗿 𝗥𝗲𝘀𝘂𝗺𝗲😍

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Complete Roadmap to learn Generative AI in 2 months 👇👇

Weeks 1-2: Foundations
1. Learn Basics of Python: If not familiar, grasp the fundamentals of Python, a widely used language in AI.
2. Understand Linear Algebra and Calculus: Brush up on basic linear algebra and calculus as they form the foundation of machine learning.

Weeks 3-4: Machine Learning Basics
1. Study Machine Learning Fundamentals: Understand concepts like supervised learning, unsupervised learning, and evaluation metrics.
2. Get Familiar with TensorFlow or PyTorch: Choose one deep learning framework and learn its basics.

Weeks 5-6: Deep Learning
1. Neural Networks: Dive into neural networks, understanding architectures, activation functions, and training processes.
2. CNNs and RNNs: Learn Convolutional Neural Networks (CNNs) for image data and Recurrent Neural Networks (RNNs) for sequential data.

Weeks 7-8: Generative Models
1. Understand Generative Models: Study the theory behind generative models, focusing on GANs (Generative Adversarial Networks) and VAEs (Variational Autoencoders).
2. Hands-On Projects: Implement small generative projects to solidify your understanding. Experimenting with generative models will give you a deeper understanding of how they work. You can use platforms such as Google's Colab or Kaggle to experiment with different types of generative models.

Additional Tips:
- Read Research Papers: Explore seminal papers on GANs and VAEs to gain a deeper insight into their workings.
- Community Engagement: Join AI communities on platforms like Reddit or Stack Overflow to ask questions and learn from others.

Pro Tip: Roadmap won't help unless you start working on it consistently. Start working on projects as early as possible.

2 months are good as a starting point to get grasp the basics of Generative AI but mastering it is very difficult as AI keeps evolving every day.

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𝟲 𝗕𝗲𝘀𝘁 𝗬𝗼𝘂𝗧𝘂𝗯𝗲 𝗖𝗵𝗮𝗻𝗻𝗲𝗹𝘀 𝘁𝗼 𝗠𝗮𝘀𝘁𝗲𝗿 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜😍

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🔟 Data Science Project Ideas for Freshers

Exploratory Data Analysis (EDA) on a Dataset: Choose a dataset of interest and perform thorough EDA to extract insights, visualize trends, and identify patterns.

Predictive Modeling: Build a simple predictive model, such as linear regression, to predict a target variable based on input features. Use libraries like scikit-learn to implement the model.

Classification Problem: Work on a classification task using algorithms like decision trees, random forests, or support vector machines. It could involve classifying emails as spam or not spam, or predicting customer churn.

Time Series Analysis: Analyze time-dependent data, like stock prices or temperature readings, to forecast future values using techniques like ARIMA or LSTM.

Image Classification: Use convolutional neural networks (CNNs) to build an image classification model, perhaps classifying different types of objects or animals.

Natural Language Processing (NLP): Create a sentiment analysis model that classifies text as positive, negative, or neutral, or build a text generator using recurrent neural networks (RNNs).

Clustering Analysis: Apply clustering algorithms like k-means to group similar data points together, such as segmenting customers based on purchasing behaviour.

Recommendation System: Develop a recommendation engine using collaborative filtering techniques to suggest products or content to users.

Anomaly Detection: Build a model to detect anomalies in data, which could be useful for fraud detection or identifying defects in manufacturing processes.

A/B Testing: Design and analyze an A/B test to compare the effectiveness of two different versions of a web page or app feature.

Remember to document your process, explain your methodology, and showcase your projects on platforms like GitHub or a personal portfolio website.

Free datasets to build the projects
👇👇
https://t.me/datasciencefun/1126

ENJOY LEARNING 👍👍

50 Linux commands for our day-to-day work:

1. ls - List directory contents.
2. pwd - Display current directory path.
3. cd - Change directory.
4. mkdir - Create a new directory.
5. mv - Move or rename files.
6. cp - Copy files.
7. rm - Delete files.
8. touch - Create an empty file.
9. rmdir - Remove directory.
10. cat - Display file content.
11. clear - Clear terminal screen.
12. echo - Output text or data to a file.
13. less - View text files page-by-page.
14. man - Display command manual.
15. sudo - Execute commands with root privileges.
16. top - Show system processes.
17. tar - Archive files into tarball.
18. grep - Search for text within files.
19. head - Display file's beginning lines.
20. tail - Show file's ending lines.
21. diff - Compare two files' content.
22. kill - Terminate processes.
23. jobs - List active jobs.
24. sort - Sort lines of a text file.
25. df - Display disk usage.
26. du - Show file or directory size.
27. zip - Compress files into zip format.
28. unzip - Extract zip archives.
29. ssh - Secure connection between hosts.
30. cal - Display calendar.
31. apt - Manage packages.
32. alias - Create command shortcuts.
33. w - Show current user details.
34. whereis - Locate binaries, sources, and manuals.
35. whatis - Provide command description.
36. useradd - Add a new user.
37. passwd - Change user password.
38. whoami - Display current user name.
39. uptime - Show system runtime.
40. free - Display memory status.
41. history - List command history.
42. uname - Provide system details.
43. ping - Check network connectivity.
44. chmod - Modify file/directory permissions.
45. chown - Change file/directory owner.
46. find - Search for files/directories.
47. locate - Find files quickly.
48. ifconfig - Display network interfaces.
49. ip a - List network interfaces succinctly.
50. finger - Retrieve user information.

𝗧𝗖𝗦 𝗙𝗥𝗘𝗘 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀😍

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Important questions to ace your machine learning interview with an approach to answer:

1. Machine Learning Project Lifecycle:
   - Define the problem
   - Gather and preprocess data
   - Choose a model and train it
   - Evaluate model performance
   - Tune and optimize the model
   - Deploy and maintain the model

2. Supervised vs Unsupervised Learning:
   - Supervised Learning: Uses labeled data for training (e.g., predicting house prices from features).
   - Unsupervised Learning: Uses unlabeled data to find patterns or groupings (e.g., clustering customer segments).

3. Evaluation Metrics for Regression:
   - Mean Absolute Error (MAE)
   - Mean Squared Error (MSE)
   - Root Mean Squared Error (RMSE)
   - R-squared (coefficient of determination)

4. Overfitting and Prevention:
   - Overfitting: Model learns the noise instead of the underlying pattern.
   - Prevention: Use simpler models, cross-validation, regularization.

5. Bias-Variance Tradeoff:
   - Balancing error due to bias (underfitting) and variance (overfitting) to find an optimal model complexity.

6. Cross-Validation:
   - Technique to assess model performance by splitting data into multiple subsets for training and validation.

7. Feature Selection Techniques:
   - Filter methods (e.g., correlation analysis)
   - Wrapper methods (e.g., recursive feature elimination)
   - Embedded methods (e.g., Lasso regularization)

8. Assumptions of Linear Regression:
   - Linearity
   - Independence of errors
   - Homoscedasticity (constant variance)
   - No multicollinearity

9. Regularization in Linear Models:
   - Adds a penalty term to the loss function to prevent overfitting by shrinking coefficients.

10. Classification vs Regression:
    - Classification: Predicts a categorical outcome (e.g., class labels).
    - Regression: Predicts a continuous numerical outcome (e.g., house price).

11. Dimensionality Reduction Algorithms:
    - Principal Component Analysis (PCA)
    - t-Distributed Stochastic Neighbor Embedding (t-SNE)

12. Decision Tree:
    - Tree-like model where internal nodes represent features, branches represent decisions, and leaf nodes represent outcomes.

13. Ensemble Methods:
    - Combine predictions from multiple models to improve accuracy (e.g., Random Forest, Gradient Boosting).

14. Handling Missing or Corrupted Data:
    - Imputation (e.g., mean substitution)
    - Removing rows or columns with missing data
    - Using algorithms robust to missing values

15. Kernels in Support Vector Machines (SVM):
    - Linear kernel
    - Polynomial kernel
    - Radial Basis Function (RBF) kernel

𝗪𝗮𝗻𝘁 𝘁𝗼 𝗟𝗲𝗮𝗿𝗻 𝗜𝗻-𝗗𝗲𝗺𝗮𝗻𝗱 𝗧𝗲𝗰𝗵 𝗦𝗸𝗶𝗹𝗹𝘀 — 𝗳𝗼𝗿 𝗙𝗥𝗘𝗘 — 𝗗𝗶𝗿𝗲𝗰𝘁𝗹𝘆 𝗳𝗿𝗼𝗺 𝗚𝗼𝗼𝗴𝗹𝗲?😍

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Projects to boost your resume for data roles

Pandas Operations for working with data

𝟱 𝗙𝗿𝗲𝗲 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀 𝗧𝗵𝗮𝘁’𝗹𝗹 𝗠𝗮𝗸𝗲 𝗦𝗤𝗟 𝗙𝗶𝗻𝗮𝗹𝗹𝘆 𝗖𝗹𝗶𝗰𝗸.😍

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Here is the list of few projects (found on kaggle). They cover Basics of Python, Advanced Statistics, Supervised Learning (Regression and Classification problems) & Data Science

Please also check the discussions and notebook submissions for different approaches and solution after you tried yourself.

1. Basic python and statistics

Pima Indians :- https://www.kaggle.com/uciml/pima-indians-diabetes-database
Cardio Goodness fit :- https://www.kaggle.com/saurav9786/cardiogoodfitness
Automobile :- https://www.kaggle.com/toramky/automobile-dataset

2. Advanced Statistics

Game of Thrones:-https://www.kaggle.com/mylesoneill/game-of-thrones
World University Ranking:-https://www.kaggle.com/mylesoneill/world-university-rankings
IMDB Movie Dataset:- https://www.kaggle.com/carolzhangdc/imdb-5000-movie-dataset

3. Supervised Learning

a) Regression Problems

How much did it rain :- https://www.kaggle.com/c/how-much-did-it-rain-ii/overview
Inventory Demand:- https://www.kaggle.com/c/grupo-bimbo-inventory-demand
Property Inspection predictiion:- https://www.kaggle.com/c/liberty-mutual-group-property-inspection-prediction
Restaurant Revenue prediction:- https://www.kaggle.com/c/restaurant-revenue-prediction/data
IMDB Box office Prediction:-https://www.kaggle.com/c/tmdb-box-office-prediction/overview

b) Classification problems

Employee Access challenge :- https://www.kaggle.com/c/amazon-employee-access-challenge/overview
Titanic :- https://www.kaggle.com/c/titanic
San Francisco crime:- https://www.kaggle.com/c/sf-crime
Customer satisfcation:-https://www.kaggle.com/c/santander-customer-satisfaction
Trip type classification:- https://www.kaggle.com/c/walmart-recruiting-trip-type-classification
Categorize cusine:- https://www.kaggle.com/c/whats-cooking

4. Some helpful Data science projects for beginners

https://www.kaggle.com/c/house-prices-advanced-regression-techniques

https://www.kaggle.com/c/digit-recognizer

https://www.kaggle.com/c/titanic

5. Intermediate Level Data science Projects

Black Friday Data : https://www.kaggle.com/sdolezel/black-friday

Human Activity Recognition Data : https://www.kaggle.com/uciml/human-activity-recognition-with-smartphones

Trip History Data : https://www.kaggle.com/pronto/cycle-share-dataset

Million Song Data : https://www.kaggle.com/c/msdchallenge

Census Income Data : https://www.kaggle.com/c/census-income/data

Movie Lens Data : https://www.kaggle.com/grouplens/movielens-20m-dataset

Twitter Classification Data : https://www.kaggle.com/c/twitter-sentiment-analysis2

Share with credits: https://t.me/sqlproject

ENJOY LEARNING 👍👍

Top 5 data science projects for freshers

1. Predictive Analytics on a Dataset:
   - Use a dataset to predict future trends or outcomes using machine learning algorithms. This could involve predicting sales, stock prices, or any other relevant domain.

2. Customer Segmentation:
   - Analyze and segment customers based on their behavior, preferences, or demographics. This project could provide insights for targeted marketing strategies.

3. Sentiment Analysis on Social Media Data:
   - Analyze sentiment in social media data to understand public opinion on a particular topic. This project helps in mastering natural language processing (NLP) techniques.

4. Recommendation System:
   - Build a recommendation system, perhaps for movies, music, or products, using collaborative filtering or content-based filtering methods.

5. Fraud Detection:
   - Develop a fraud detection system using machine learning algorithms to identify anomalous patterns in financial transactions or any domain where fraud detection is crucial.

Free Datsets -> https://t.me/DataPortfolio/2?single

These projects showcase practical application of data science skills and can be highlighted on a resume for entry-level positions.

Join @pythonspecialist for more data science projects

𝗡𝗼 𝗗𝗲𝗴𝗿𝗲𝗲? 𝗡𝗼 𝗣𝗿𝗼𝗯𝗹𝗲𝗺. 𝗧𝗵𝗲𝘀𝗲 𝟰 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 𝗖𝗮𝗻 𝗟𝗮𝗻𝗱 𝗬𝗼𝘂 𝗮 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝘁 𝗝𝗼𝗯😍

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Like for more ❤️

𝗗𝗿𝗲𝗮𝗺 𝗝𝗼𝗯 𝗮𝘁 𝗚𝗼𝗼𝗴𝗹𝗲? 𝗧𝗵𝗲𝘀𝗲 𝟰 𝗙𝗥𝗘𝗘 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀 𝗪𝗶𝗹𝗹 𝗛𝗲𝗹𝗽 𝗬𝗼𝘂 𝗚𝗲𝘁 𝗧𝗵𝗲𝗿𝗲😍

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Because if someone else can do it, so can you. Why not you? Why not now?✅️

1. How can we deal with problems that arise when the data flows in from a variety of sources?

There are many ways to go about dealing with multi-source problems. However, these are done primarily to solve the problems of:

Identifying the presence of similar/same records and merging them into a single recordRe-structuring the schema to ensure there is good schema integration



2. Where is Time Series Analysis used?

Since time series analysis (TSA) has a wide scope of usage, it can be used in multiple domains. Here are some of the places where TSA plays an important role:

Statistics
Signal processing
Econometrics
Weather forecasting
Earthquake prediction
Astronomy
Applied science


3. What are the ideal situations in which t-test or z-test can be used?

It is a standard practice that a t-test is used when there is a sample size less than 30 and the z-test is considered when the sample size exceeds 30 in most cases.


4. What is the usage of the NVL() function?

The NVL() function is used to convert the NULL value to the other value. The function returns the value of the second parameter if the first parameter is NULL. If the first parameter is anything other than NULL, it is left unchanged. This function is used in Oracle, not in SQL and MySQL. Instead of NVL() function, MySQL have IFNULL() and SQL Server have ISNULL() function.


5. What is the difference between DROP and TRUNCATE commands?

If a table is dropped, all things associated with that table are dropped as well. This includes the relationships defined on the table with other tables, access privileges, and grants that the table has, as well as the integrity checks and constraints.

However, if a table is truncated, there are no such problems as mentioned above. The table retains its original structure and the data is dropped.

𝗣𝗼𝘄𝗲𝗿𝗕𝗜 𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲 𝗙𝗿𝗼𝗺 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁😍

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🚀 Complete Roadmap to Become a Data Scientist in 5 Months

📅 Week 1-2: Fundamentals
✅ Day 1-3: Introduction to Data Science, its applications, and roles.
✅ Day 4-7: Brush up on Python programming 🐍.
✅ Day 8-10: Learn basic statistics 📊 and probability 🎲.

🔍 Week 3-4: Data Manipulation & Visualization
📝 Day 11-15: Master Pandas for data manipulation.
📈 Day 16-20: Learn Matplotlib & Seaborn for data visualization.

🤖 Week 5-6: Machine Learning Foundations
🔬 Day 21-25: Introduction to scikit-learn.
📊 Day 26-30: Learn Linear & Logistic Regression.

🏗 Week 7-8: Advanced Machine Learning
🌳 Day 31-35: Explore Decision Trees & Random Forests.
📌 Day 36-40: Learn Clustering (K-Means, DBSCAN) & Dimensionality Reduction.

🧠 Week 9-10: Deep Learning
🤖 Day 41-45: Basics of Neural Networks with TensorFlow/Keras.
📸 Day 46-50: Learn CNNs & RNNs for image & text data.

🏛 Week 11-12: Data Engineering
🗄 Day 51-55: Learn SQL & Databases.
🧹 Day 56-60: Data Preprocessing & Cleaning.

📊 Week 13-14: Model Evaluation & Optimization
📏 Day 61-65: Learn Cross-validation & Hyperparameter Tuning.
📉 Day 66-70: Understand Evaluation Metrics (Accuracy, Precision, Recall, F1-score).

🏗 Week 15-16: Big Data & Tools
🐘 Day 71-75: Introduction to Big Data Technologies (Hadoop, Spark).
☁️ Day 76-80: Learn Cloud Computing (AWS, GCP, Azure).

🚀 Week 17-18: Deployment & Production
🛠 Day 81-85: Deploy models using Flask or FastAPI.
📦 Day 86-90: Learn Docker & Cloud Deployment (AWS, Heroku).

🎯 Week 19-20: Specialization
📝 Day 91-95: Choose NLP or Computer Vision, based on your interest.

🏆 Week 21-22: Projects & Portfolio
📂 Day 96-100: Work on Personal Data Science Projects.

💬 Week 23-24: Soft Skills & Networking
🎤 Day 101-105: Improve Communication & Presentation Skills.
🌐 Day 106-110: Attend Online Meetups & Forums.

🎯 Week 25-26: Interview Preparation
💻 Day 111-115: Practice Coding Interviews (LeetCode, HackerRank).
📂 Day 116-120: Review your projects & prepare for discussions.

👨‍💻 Week 27-28: Apply for Jobs
📩 Day 121-125: Start applying for Entry-Level Data Scientist positions.

🎤 Week 29-30: Interviews
📝 Day 126-130: Attend Interviews & Practice Whiteboard Problems.

🔄 Week 31-32: Continuous Learning
📰 Day 131-135: Stay updated with the Latest Data Science Trends.

🏆 Week 33-34: Accepting Offers
📝 Day 136-140: Evaluate job offers & Negotiate Your Salary.

🏢 Week 35-36: Settling In
🎯 Day 141-150: Start your New Data Science Job, adapt & keep learning!

🎉 Enjoy Learning & Build Your Dream Career in Data Science! 🚀🔥

𝗧𝗼𝗽 𝟰 𝗙𝗼𝗿 𝗙𝗥𝗘𝗘 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀 𝗧𝗼 𝗠𝗮𝘀𝘁𝗲𝗿 𝗦𝗤𝗟 𝗙𝗼𝗿 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 😍

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🚨30 FREE Dataset Sources for Data Science Projects🔥

Data Simplifier: https://datasimplifier.com/best-data-analyst-projects-for-freshers/

US Government Dataset: https://www.data.gov/

Open Government Data (OGD) Platform India: https://data.gov.in/

The World Bank Open Data: https://data.worldbank.org/

Data World: https://data.world/

BFI - Industry Data and Insights: https://www.bfi.org.uk/data-statistics

The Humanitarian Data Exchange (HDX): https://data.humdata.org/

Data at World Health Organization (WHO): https://www.who.int/data

FBI’s Crime Data Explorer: https://crime-data-explorer.fr.cloud.gov/

AWS Open Data Registry: https://registry.opendata.aws/

FiveThirtyEight: https://data.fivethirtyeight.com/

IMDb Datasets: https://www.imdb.com/interfaces/

Kaggle: https://www.kaggle.com/datasets

UCI Machine Learning Repository: https://archive.ics.uci.edu/ml/index.php

Google Dataset Search: https://datasetsearch.research.google.com/

Nasdaq Data Link: https://data.nasdaq.com/

Recommender Systems and Personalization Datasets: https://cseweb.ucsd.edu/~jmcauley/datasets.html

Reddit - Datasets: https://www.reddit.com/r/datasets/

Open Data Network by Socrata: https://www.opendatanetwork.com/

Climate Data Online by NOAA: https://www.ncdc.noaa.gov/cdo-web/

Azure Open Datasets: https://azure.microsoft.com/en-us/services/open-datasets/

IEEE Data Port: https://ieee-dataport.org/

Wikipedia: Database: https://dumps.wikimedia.org/

BuzzFeed News: https://github.com/BuzzFeedNews/everything

Academic Torrents: https://academictorrents.com/

Yelp Open Dataset: https://www.yelp.com/dataset

The NLP Index by Quantum Stat: https://index.quantumstat.com/

Computer Vision Online: http://www.computervisiononline.com/dataset

Visual Data Discovery: https://www.visualdata.io/

Roboflow Public Datasets: https://public.roboflow.com/

Computer Vision Group, TUM: https://vision.in.tum.de/data/datasets

𝗪𝗲𝗯 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 😍

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8. Set up the user interface and trigger the main function.

• Provides an input field for the user's question
• Triggers the main function when the user clicks "Get Answer"

7. Define the main function to run all LLMs and aggregate results.

• Runs all reference models asynchronously
• Displays individual responses in expandable sections
• Aggregates responses using the aggregator model
• Streams the aggregated response.

5. Define the models and aggregator system prompt.

• Specifies the LLMs to be used for generating responses
• Defines the aggregator model and its system prompt

6. Implement the LLM call function.

• Asynchronously calls the LLM with the user's prompt
• Returns the model name and its response

4. Initialize Together AI clients.

• Sets up Together API key as an environment variable
• Initializes both synchronous and asynchronous Together clients

3. Set up the Streamlit app and API key input.

• Creates a title for the app
• Adds a secure input field for the Together API key

2. Import necessary libraries

• Streamlit for the web interface
• asyncio for asynchronous operations
• Together AI for LLM interactions

1. Install the necessary Python Libraries

Run the following commands from your terminal to install the required libraries:

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𝟱 𝗙𝗥𝗘𝗘 𝗚𝗼𝗼𝗴𝗹𝗲 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 😍

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Here are some project ideas for a data science and machine learning project focused on generating AI:

1. Natural Language Generation (NLG) Model: Build a model that generates human-like text based on input data. This could be used for creating product descriptions, news articles, or personalized recommendations.

2. Code Generation Model: Develop a model that generates code snippets based on a given task or problem statement. This could help automate software development tasks or assist programmers in writing code more efficiently.

3. Image Captioning Model: Create a model that generates captions for images, describing the content of the image in natural language. This could be useful for visually impaired individuals or for enhancing image search capabilities.

4. Music Generation Model: Build a model that generates music compositions based on input data, such as existing songs or musical patterns. This could be used for creating background music for videos or games.

5. Video Synthesis Model: Develop a model that generates realistic video sequences based on input data, such as a series of images or a textual description. This could be used for generating synthetic training data for computer vision models.

6. Chatbot Generation Model: Create a model that generates conversational agents or chatbots based on input data, such as dialogue datasets or user interactions. This could be used for customer service automation or virtual assistants.

7. Art Generation Model: Build a model that generates artistic images or paintings based on input data, such as art styles, color palettes, or themes. This could be used for creating unique digital artwork or personalized designs.

8. Story Generation Model: Develop a model that generates fictional stories or narratives based on input data, such as plot outlines, character descriptions, or genre preferences. This could be used for creative writing prompts or interactive storytelling applications.

9. Recipe Generation Model: Create a model that generates new recipes based on input data, such as ingredient lists, dietary restrictions, or cuisine preferences. This could be used for meal planning or culinary inspiration.

10. Financial Report Generation Model: Build a model that generates financial reports or summaries based on input data, such as company financial statements, market trends, or investment portfolios. This could be used for automated financial analysis or decision-making support.

Any project which sounds interesting to you?

𝗙𝗥𝗘𝗘 𝗪𝗲𝗯𝘀𝗶𝘁𝗲𝘀 𝗧𝗼 𝗠𝗮𝘀𝘁𝗲𝗿 𝗖𝗼𝗱𝗶𝗻𝗴 𝗙𝗼𝗿 𝗙𝗥𝗘𝗘 😍

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Excel vs SQL vs Python (pandas):

1️⃣ Filtering Data
↳ Excel: =FILTER(A2:D100, B2:B100>50) (Excel 365 users)
↳ SQL: SELECT * FROM table WHERE column > 50;
↳ Python: df_filtered = df[df['column'] > 50]

2️⃣ Sorting Data
↳ Excel: Data → Sort (or =SORT(A2:A100, 1, TRUE))
↳ SQL: SELECT * FROM table ORDER BY column ASC;
↳ Python: df_sorted = df.sort_values(by="column")

3️⃣ Counting Rows
↳ Excel: =COUNTA(A:A)
↳ SQL: SELECT COUNT(*) FROM table;
↳ Python: row_count = len(df)

4️⃣ Removing Duplicates
↳ Excel: Data → Remove Duplicates
↳ SQL: SELECT DISTINCT * FROM table;
↳ Python: df_unique = df.drop_duplicates()

5️⃣ Joining Tables
↳ Excel: Power Query → Merge Queries (or VLOOKUP/XLOOKUP)
↳ SQL: SELECT * FROM table1 JOIN table2 ON table1.id = table2.id;
↳ Python: df_merged = pd.merge(df1, df2, on="id")

6️⃣ Ranking Data
↳ Excel: =RANK.EQ(A2, $A$2:$A$100)
↳ SQL: SELECT column, RANK() OVER (ORDER BY column DESC) AS rank FROM table;
↳ Python: df["rank"] = df["column"].rank(method="min", ascending=False)

7️⃣ Moving Average Calculation
↳ Excel: =AVERAGE(B2:B4) (manually for rolling window)
↳ SQL: SELECT date, AVG(value) OVER (ORDER BY date ROWS BETWEEN 2 PRECEDING AND CURRENT ROW) AS moving_avg FROM table;
↳ Python: df["moving_avg"] = df["value"].rolling(window=3).mean()

8️⃣ Running Total
↳ Excel: =SUM($B$2:B2) (drag down)
↳ SQL: SELECT date, SUM(value) OVER (ORDER BY date) AS running_total FROM table;
↳ Python: df["running_total"] = df["value"].cumsum()

𝗧𝗼𝗽 𝗠𝗡𝗖𝘀 𝗛𝗶𝗿𝗶𝗻𝗴 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝘁𝘀 😍

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🔟 Data Science Project Ideas for Freshers

Exploratory Data Analysis (EDA) on a Dataset: Choose a dataset of interest and perform thorough EDA to extract insights, visualize trends, and identify patterns.

Predictive Modeling: Build a simple predictive model, such as linear regression, to predict a target variable based on input features. Use libraries like scikit-learn to implement the model.

Classification Problem: Work on a classification task using algorithms like decision trees, random forests, or support vector machines. It could involve classifying emails as spam or not spam, or predicting customer churn.

Time Series Analysis: Analyze time-dependent data, like stock prices or temperature readings, to forecast future values using techniques like ARIMA or LSTM.

Image Classification: Use convolutional neural networks (CNNs) to build an image classification model, perhaps classifying different types of objects or animals.

Natural Language Processing (NLP): Create a sentiment analysis model that classifies text as positive, negative, or neutral, or build a text generator using recurrent neural networks (RNNs).

Clustering Analysis: Apply clustering algorithms like k-means to group similar data points together, such as segmenting customers based on purchasing behaviour.

Recommendation System: Develop a recommendation engine using collaborative filtering techniques to suggest products or content to users.

Anomaly Detection: Build a model to detect anomalies in data, which could be useful for fraud detection or identifying defects in manufacturing processes.

A/B Testing: Design and analyze an A/B test to compare the effectiveness of two different versions of a web page or app feature.

Remember to document your process, explain your methodology, and showcase your projects on platforms like GitHub or a personal portfolio website.

Free datasets to build the projects
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Who is Data Scientist?

He/she is responsible for collecting, analyzing and interpreting the results, through a large amount of data. This process is used to take an important decision for the business, which can affect the growth and help to face compititon in the market.

A data scientist analyzes data to extract actionable insight from it. More specifically, a data scientist:

Determines correct datasets and variables.

Identifies the most challenging data-analytics problems.

Collects large sets of data- structured and unstructured, from different sources.

Cleans and validates data ensuring accuracy, completeness, and uniformity.

Builds and applies models and algorithms to mine stores of big data.

Analyzes data to recognize patterns and trends.

Interprets data to find solutions.

Communicates findings to stakeholders using tools like visualization.

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Data Science Projects to Land a 6 Figure Job

If you want to build agents that don’t break in production...

You must start with the most important pattern:

𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗥𝗔𝗚.

This week in the 𝗦𝗲𝗰𝗼𝗻𝗱 𝗕𝗿𝗮𝗶𝗻 𝗔𝗜 𝗔𝘀𝘀𝗶𝘀𝘁𝗮𝗻𝘁 course, we released the Agentic RAG module

... and today, I’m breaking down how it’s architected from the ground up.

𝗪𝗵𝗮𝘁 𝗶𝘀 𝘁𝗵𝗲 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗥𝗔𝗚 𝗺𝗼𝗱𝘂𝗹𝗲?

The Agentic RAG module takes a user query via a Gradio UI.

The output is a reasoned answer, generated through:

→ Semantic search from a vector DB
→ Multi-step reasoning via an agent
→ Optional summarization through a model/API

𝗢𝗻𝗹𝗶𝗻𝗲 𝘃𝘀. 𝗢𝗳𝗳𝗹𝗶𝗻𝗲 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲𝘀

Here’s the thing...

Most GenAI pipelines are offline.

They’re pre-scheduled, long-running jobs.

(Using tools such as ZenML)

But this module is online.

It runs as a standalone Python app and powers real-time user interactions.

We intentionally decoupled it from our offline feature/training pipelines to preserve a clean separation between ingestion and inference.

𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗟𝗮𝘆𝗲𝗿: 𝗧𝗼𝗼𝗹𝗶𝗻𝗴 𝗕𝗿𝗲𝗮𝗸𝗱𝗼𝘄𝗻

The agent is built using SmolAgents (by Hugging Face) and is powered by 3 tools:

"What can I do?" Tool
→ Helps users explore agent capabilities

Retriever Tool
→ Queries MongoDB 's vector and text indexes (populated offline)

Summarization Tool
→ Hits a REST API for refining long-form web content

Each tool was picked to reflect real-world agent scenarios:

→ Python logic
→ DB queries
→ External API calls

The agent uses these tools iteratively to minimize cost and latency.

All reasoning happens in real time with full traceability via the Gradio UI.

𝗪𝗵𝗮𝘁 𝗵𝗮𝗽𝗽𝗲𝗻𝘀 𝘂𝗻𝗱𝗲𝗿 𝘁𝗵𝗲 𝗵𝗼𝗼𝗱?

User submits a query

The agent decides: “Do I need context?”

If yes → queries the vector DB (retriever tool)

Retrieved chunks optionally go through summarization

The agent reasons → repeats if more context is needed to answer the question fully

Once confident → final response returned

We can swap the summarization model for full customization between our custom small language model (hosted as a real-time API on Hugging Face) and OpenAI (as a fallback).

It’s modular, testable, and future-proof.

𝗖𝗼𝘂𝗹𝗱 𝘄𝗲 𝗵𝗮𝘃𝗲 𝘂𝘀𝗲𝗱 𝗮 𝘀𝗶𝗺𝗽𝗹𝗲 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄?

Yes.

But the agentic approach unlocks scalability and extensibility.

This is critical if you want to:

→ Add new tools
→ Support multi-turn reasoning
→ Layer in observability or eval logic later

But this is just the beginning.

We’ll be expanding this system with observability:

- Evaluation
- Prompt monitoring

𝟯 𝗙𝗿𝗲𝗲 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝘁𝗼 𝗟𝗲𝘃𝗲𝗹 𝗨𝗽 𝗬𝗼𝘂𝗿 𝗧𝗲𝗰𝗵 𝗦𝗸𝗶𝗹𝗹𝘀 𝗶𝗻 𝟮𝟬𝟮𝟱😍

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If you're into deep learning, then you know that students usually one of the two paths:

- Computer vision
- Natural language processing (NLP)

If you're into NLP, here are 5 fundamental concepts you should know:

Before we start, What is NLP?

Natural Language Processing (NLP) is a branch of artificial intelligence that focuses on the interaction between computers and humans through language.

It enables machines to understand, interpret, and respond to human language in a way that is both meaningful and useful.

Data scientists need NLP to analyze, process, and generate insights from large volumes of textual data, aiding in tasks ranging from sentiment analysis to automated summarization.

Tokenization

Tokenization involves breaking down text into smaller units, such as words or phrases. This is the first step in preprocessing textual data for further analysis or NLP applications.

Part-of-Speech Tagging:

This process involves identifying the part of speech for each word in a sentence (e.g., noun, verb, adjective). It is crucial for various NLP tasks that require understanding the grammatical structure of text.

Stemming and Lemmatization

These techniques reduce words to their base or root form. Stemming cuts off prefixes and suffixes, while lemmatization considers the morphological analysis of the words, leading to more accurate results.

Named Entity Recognition (NER)

NER identifies and classifies named entities in text into predefined categories such as the names of persons, organizations, locations, etc. It's essential for tasks like data extraction from documents and content classification.

Sentiment Analysis

This technique determines the emotional tone behind a body of text. It's widely used in business and social media monitoring to gauge public opinion and customer sentiment.

𝟱 𝗣𝗼𝘄𝗲𝗿𝗳𝘂𝗹 𝗙𝗿𝗲𝗲 𝗔𝗜 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝗳𝗿𝗼𝗺 𝗛𝗮𝗿𝘃𝗮𝗿𝗱 & 𝗦𝘁𝗮𝗻𝗳𝗼𝗿𝗱😍

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Here is a list of 50 data science interview questions that can help you prepare for a data science job interview. These questions cover a wide range of topics and levels of difficulty, so be sure to review them thoroughly and practice your answers.

Mathematics and Statistics:

1. What is the Central Limit Theorem, and why is it important in statistics?
2. Explain the difference between population and sample.
3. What is probability and how is it calculated?
4. What are the measures of central tendency, and when would you use each one?
5. Define variance and standard deviation.
6. What is the significance of hypothesis testing in data science?
7. Explain the p-value and its significance in hypothesis testing.
8. What is a normal distribution, and why is it important in statistics?
9. Describe the differences between a Z-score and a T-score.
10. What is correlation, and how is it measured?
11. What is the difference between covariance and correlation?
12. What is the law of large numbers?

Machine Learning:

13. What is machine learning, and how is it different from traditional programming?
14. Explain the bias-variance trade-off.
15. What are the different types of machine learning algorithms?
16. What is overfitting, and how can you prevent it?
17. Describe the k-fold cross-validation technique.
18. What is regularization, and why is it important in machine learning?
19. Explain the concept of feature engineering.
20. What is gradient descent, and how does it work in machine learning?
21. What is a decision tree, and how does it work?
22. What are ensemble methods in machine learning, and provide examples.
23. Explain the difference between supervised and unsupervised learning.
24. What is deep learning, and how does it differ from traditional neural networks?
25. What is a convolutional neural network (CNN), and where is it commonly used?
26. What is a recurrent neural network (RNN), and where is it commonly used?
27. What is the vanishing gradient problem in deep learning?
28. Describe the concept of transfer learning in deep learning.

Data Preprocessing:

29. What is data preprocessing, and why is it important in data science?
30. Explain missing data imputation techniques.
31. What is one-hot encoding, and when is it used?
32. How do you handle categorical data in machine learning?
33. Describe the process of data normalization and standardization.
34. What is feature scaling, and why is it necessary?
35. What is outlier detection, and how can you identify outliers in a dataset?

Data Exploration:

36. What is exploratory data analysis (EDA), and why is it important?
37. Explain the concept of data distribution.
38. What are box plots, and how are they used in EDA?
39. What is a histogram, and what insights can you gain from it?
40. Describe the concept of data skewness.
41. What are scatter plots, and how are they useful in data analysis?
42. What is a correlation matrix, and how is it used in EDA?
43. How do you handle imbalanced datasets in machine learning?

Model Evaluation:

44. What are the common metrics used for evaluating classification models?
45. Explain precision, recall, and F1-score.
46. What is ROC curve analysis, and what does it measure?
47. How do you choose the appropriate evaluation metric for a regression problem?
48. Describe the concept of confusion matrix.
49. What is cross-entropy loss, and how is it used in classification problems?
50. Explain the concept of AUC-ROC.

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Complete Machine Learning Roadmap
👇👇

1. Introduction to Machine Learning
- Definition
- Purpose
- Types of Machine Learning (Supervised, Unsupervised, Reinforcement)

2. Mathematics for Machine Learning
- Linear Algebra
- Calculus
- Statistics and Probability

3. Programming Languages for ML
- Python and Libraries (NumPy, Pandas, Matplotlib)
- R

4. Data Preprocessing
- Handling Missing Data
- Feature Scaling
- Data Transformation

5. Exploratory Data Analysis (EDA)
- Data Visualization
- Descriptive Statistics

6. Supervised Learning
- Regression
- Classification
- Model Evaluation

7. Unsupervised Learning
- Clustering (K-Means, Hierarchical)
- Dimensionality Reduction (PCA)

8. Model Selection and Evaluation
- Cross-Validation
- Hyperparameter Tuning
- Evaluation Metrics (Precision, Recall, F1 Score)

9. Ensemble Learning
- Random Forest
- Gradient Boosting

10. Neural Networks and Deep Learning
- Introduction to Neural Networks
- Building and Training Neural Networks
- Convolutional Neural Networks (CNN)
- Recurrent Neural Networks (RNN)

11. Natural Language Processing (NLP)
- Text Preprocessing
- Sentiment Analysis
- Named Entity Recognition (NER)

12. Reinforcement Learning
- Basics
- Markov Decision Processes
- Q-Learning

13. Machine Learning Frameworks
- TensorFlow
- PyTorch
- Scikit-Learn

14. Deployment of ML Models
- Flask for Web Deployment
- Docker and Kubernetes

15. Ethical and Responsible AI
- Bias and Fairness
- Ethical Considerations

16. Machine Learning in Production
- Model Monitoring
- Continuous Integration/Continuous Deployment (CI/CD)

17. Real-world Projects and Case Studies

18. Machine Learning Resources
- Online Courses
- Books
- Blogs and Journals

📚 Learning Resources for Machine Learning:
- [Python for Machine Learning](https://t.me/udacityfreecourse/167)
- [Fast.ai: Practical Deep Learning for Coders](https://course.fast.ai/)
- [Intro to Machine Learning](https://learn.microsoft.com/en-us/training/paths/intro-to-ml-with-python/)

📚 Books:
- Machine Learning Interviews
- Machine Learning for Absolute Beginners

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🔥 Data Science Roadmap 2025

Step 1: 🐍 Python Basics
Step 2: 📊 Data Analysis (Pandas, NumPy)
Step 3: 📈 Data Visualization (Matplotlib, Seaborn)
Step 4: 🤖 Machine Learning (Scikit-learn)
Step 5: � Deep Learning (TensorFlow/PyTorch)
Step 6: 🗃️ SQL & Big Data (Spark)
Step 7: 🚀 Deploy Models (Flask, FastAPI)
Step 8: 📢 Showcase Projects
Step 9: 💼 Land a Job!

🔓 Pro Tip: Compete on Kaggle

#datascience

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Three different learning styles in machine learning algorithms:

1. Supervised Learning

Input data is called training data and has a known label or result such as spam/not-spam or a stock price at a time.

A model is prepared through a training process in which it is required to make predictions and is corrected when those predictions are wrong. The training process continues until the model achieves a desired level of accuracy on the training data.

Example problems are classification and regression.

Example algorithms include: Logistic Regression and the Back Propagation Neural Network.

2. Unsupervised Learning

Input data is not labeled and does not have a known result.

A model is prepared by deducing structures present in the input data. This may be to extract general rules. It may be through a mathematical process to systematically reduce redundancy, or it may be to organize data by similarity.

Example problems are clustering, dimensionality reduction and association rule learning.

Example algorithms include: the Apriori algorithm and K-Means.

3. Semi-Supervised Learning

Input data is a mixture of labeled and unlabelled examples.

There is a desired prediction problem but the model must learn the structures to organize the data as well as make predictions.

Example problems are classification and regression.

Example algorithms are extensions to other flexible methods that make assumptions about how to model the unlabeled data.

𝟱 𝗙𝗥𝗘𝗘 𝗧𝗲𝗰𝗵 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝗙𝗿𝗼𝗺 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁, 𝗔𝗪𝗦, 𝗜𝗕𝗠, 𝗖𝗶𝘀𝗰𝗼, 𝗮𝗻𝗱 𝗦𝘁𝗮𝗻𝗳𝗼𝗿𝗱. 😍

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Data Science Learning Plan

Step 1: Mathematics for Data Science (Statistics, Probability, Linear Algebra)

Step 2: Python for Data Science (Basics and Libraries)

Step 3: Data Manipulation and Analysis (Pandas, NumPy)

Step 4: Data Visualization (Matplotlib, Seaborn, Plotly)

Step 5: Databases and SQL for Data Retrieval

Step 6: Introduction to Machine Learning (Supervised and Unsupervised Learning)

Step 7: Data Cleaning and Preprocessing

Step 8: Feature Engineering and Selection

Step 9: Model Evaluation and Tuning

Step 10: Deep Learning (Neural Networks, TensorFlow, Keras)

Step 11: Working with Big Data (Hadoop, Spark)

Step 12: Building Data Science Projects and Portfolio

Data Science Resources
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COMMON TERMINOLOGIES IN PYTHON - PART 1

Have you ever gotten into a discussion with a programmer before? Did you find some of the Terminologies mentioned strange or you didn't fully understand them?

In this series, we would be looking at the common Terminologies in python.

It is important to know these Terminologies to be able to professionally/properly explain your codes to people and/or to be able to understand what people say in an instant when these codes are mentioned. Below are a few:

IDLE (Integrated Development and Learning Environment) - this is an environment that allows you to easily write Python code. IDLE can be used to execute a single statements and create, modify, and execute Python scripts.

Python Shell - This is the interactive environment that allows you to type in python code and execute them immediately

System Python - This is the version of python that comes with your operating system

Prompt - usually represented by the symbol ">>>" and it simply means that python is waiting for you to give it some instructions

REPL (Read-Evaluate-Print-Loop) - this refers to the sequence of events in your interactive window in form of a loop (python reads the code inputted>the code is evaluated>output is printed)

Argument - this is a value that is passed to a function when called eg print("Hello World")... "Hello World" is the argument that is being passed.

Function - this is a code that takes some input, known as arguments, processes that input and produces an output called a return value. E.g print("Hello World")... print is the function

Return Value - this is the value that a function returns to the calling script or function when it completes its task (in other words, Output). E.g.
>>> print("Hello World")
Hello World
Where Hello World is your return value.

Note: A return value can be any of these variable types: handle, integer, object, or string

Script - This is a file where you store your python code in a text file and execute all of the code with a single command

Script files - this is a file containing a group of python scripts

DATA SCIENCE CONCEPTS

The Data Science skill no one talks about...

Every aspiring data scientist I talk to thinks their job starts when someone else gives them:
    1. a dataset, and
    2. a clearly defined metric to optimize for, e.g. accuracy

But it doesn’t.

It starts with a business problem you need to understand, frame, and solve. This is the key data science skill that separates senior from junior professionals.

Let’s go through an example.

Example

Imagine you are a data scientist at Uber. And your product lead tells you:

    👩‍💼: “We want to decrease user churn by 5% this quarter”

We say that a user churns when she decides to stop using Uber.

But why?

There are different reasons why a user would stop using Uber. For example:

   1.  “Lyft is offering better prices for that geo” (pricing problem)
   2. “Car waiting times are too long” (supply problem)
   3. “The Android version of the app is very slow” (client-app performance problem)

You build this list ↑ by asking the right questions to the rest of the team. You need to understand the user’s experience using the app, from HER point of view.

Typically there is no single reason behind churn, but a combination of a few of these. The question is: which one should you focus on?

This is when you pull out your great data science skills and EXPLORE THE DATA 🔎.

You explore the data to understand how plausible each of the above explanations is. The output from this analysis is a single hypothesis you should consider further. Depending on the hypothesis, you will solve the data science problem differently.

For example…

Scenario 1: “Lyft Is Offering Better Prices” (Pricing Problem)

One solution would be to detect/predict the segment of users who are likely to churn (possibly using an ML Model) and send personalized discounts via push notifications. To test your solution works, you will need to run an A/B test, so you will split a percentage of Uber users into 2 groups:

    The A group. No user in this group will receive any discount.

    The B group. Users from this group that the model thinks are likely to churn, will receive a price discount in their next trip.

You could add more groups (e.g. C, D, E…) to test different pricing points.

In a nutshell
    1. Translating business problems into data science problems is the key data science skill that separates a senior from a junior data scientist.
2. Ask the right questions, list possible solutions, and explore the data to narrow down the list to one.
3. Solve this one data science problem

𝗟𝗲𝗮𝗿𝗻 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜 𝗳𝗼𝗿 𝗙𝗥𝗘𝗘 & 𝗘𝗹𝗲𝘃𝗮𝘁𝗲 𝗬𝗼𝘂𝗿 𝗗𝗮𝘀𝗵𝗯𝗼𝗮𝗿𝗱 𝗚𝗮𝗺𝗲!😍

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⚡️ Big ML cheat sheet

Here you will find the basic theory of Machine Learning and examples of the implementation of specific ML algorithms - in general, this is just the thing to brush up on your knowledge before the interview.

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The Foundation of Data Science

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Machine Learning Project Ideas

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Data Scientist Roadmap
|
|-- 1. Basic Foundations
|   |-- a. Mathematics
|   |   |-- i. Linear Algebra
|   |   |-- ii. Calculus
|   |   |-- iii. Probability
|   |   -- iv. Statistics
|   |
|   |-- b. Programming
|   |   |-- i. Python
|   |   |   |-- 1. Syntax and Basic Concepts
|   |   |   |-- 2. Data Structures
|   |   |   |-- 3. Control Structures
|   |   |   |-- 4. Functions
|   |   |   -- 5. Object-Oriented Programming
|   |   |
|   |   -- ii. R (optional, based on preference)
|   |
|   |-- c. Data Manipulation
|   |   |-- i. Numpy (Python)
|   |   |-- ii. Pandas (Python)
|   |   -- iii. Dplyr (R)
|   |
|   -- d. Data Visualization
|       |-- i. Matplotlib (Python)
|       |-- ii. Seaborn (Python)
|       -- iii. ggplot2 (R)
|
|-- 2. Data Exploration and Preprocessing
|   |-- a. Exploratory Data Analysis (EDA)
|   |-- b. Feature Engineering
|   |-- c. Data Cleaning
|   |-- d. Handling Missing Data
|   -- e. Data Scaling and Normalization
|
|-- 3. Machine Learning
|   |-- a. Supervised Learning
|   |   |-- i. Regression
|   |   |   |-- 1. Linear Regression
|   |   |   -- 2. Polynomial Regression
|   |   |
|   |   -- ii. Classification
|   |       |-- 1. Logistic Regression
|   |       |-- 2. k-Nearest Neighbors
|   |       |-- 3. Support Vector Machines
|   |       |-- 4. Decision Trees
|   |       -- 5. Random Forest
|   |
|   |-- b. Unsupervised Learning
|   |   |-- i. Clustering
|   |   |   |-- 1. K-means
|   |   |   |-- 2. DBSCAN
|   |   |   -- 3. Hierarchical Clustering
|   |   |
|   |   -- ii. Dimensionality Reduction
|   |       |-- 1. Principal Component Analysis (PCA)
|   |       |-- 2. t-Distributed Stochastic Neighbor Embedding (t-SNE)
|   |       -- 3. Linear Discriminant Analysis (LDA)
|   |
|   |-- c. Reinforcement Learning
|   |-- d. Model Evaluation and Validation
|   |   |-- i. Cross-validation
|   |   |-- ii. Hyperparameter Tuning
|   |   -- iii. Model Selection
|   |
|   -- e. ML Libraries and Frameworks
|       |-- i. Scikit-learn (Python)
|       |-- ii. TensorFlow (Python)
|       |-- iii. Keras (Python)
|       -- iv. PyTorch (Python)
|
|-- 4. Deep Learning
|   |-- a. Neural Networks
|   |   |-- i. Perceptron
|   |   -- ii. Multi-Layer Perceptron
|   |
|   |-- b. Convolutional Neural Networks (CNNs)
|   |   |-- i. Image Classification
|   |   |-- ii. Object Detection
|   |   -- iii. Image Segmentation
|   |
|   |-- c. Recurrent Neural Networks (RNNs)
|   |   |-- i. Sequence-to-Sequence Models
|   |   |-- ii. Text Classification
|   |   -- iii. Sentiment Analysis
|   |
|   |-- d. Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU)
|   |   |-- i. Time Series Forecasting
|   |   -- ii. Language Modeling
|   |
|   -- e. Generative Adversarial Networks (GANs)
|       |-- i. Image Synthesis
|       |-- ii. Style Transfer
|       -- iii. Data Augmentation
|
|-- 5. Big Data Technologies
|   |-- a. Hadoop
|   |   |-- i. HDFS
|   |   -- ii. MapReduce
|   |
|   |-- b. Spark
|   |   |-- i. RDDs
|   |   |-- ii. DataFrames
|   |   -- iii. MLlib
|   |
|   -- c. NoSQL Databases
|       |-- i. MongoDB
|       |-- ii. Cassandra
|       |-- iii. HBase
|       -- iv. Couchbase
|
|-- 6. Data Visualization and Reporting
|   |-- a. Dashboarding Tools
|   |   |-- i. Tableau
|   |   |-- ii. Power BI
|   |   |-- iii. Dash (Python)
|   |   -- iv. Shiny (R)
|   |
|   |-- b. Storytelling with Data
|   -- c. Effective Communication
|
|-- 7. Domain Knowledge and Soft Skills
|   |-- a. Industry-specific Knowledge
|   |-- b. Problem-solving
|   |-- c. Communication Skills
|   |-- d. Time Management
|   -- e. Teamwork
|
-- 8. Staying Updated and Continuous Learning
    |-- a. Online Courses
    |-- b. Books and Research Papers
    |-- c. Blogs and Podcasts
    |-- d. Conferences and Workshops
    `-- e. Networking and Community Engagement

𝗛𝗼𝘄 𝘁𝗼 𝗕𝗲𝗰𝗼𝗺𝗲 𝗮 𝗙𝗶𝗻𝗮𝗻𝗰𝗶𝗮𝗹 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝘁 𝗶𝗻 𝟮𝟬𝟮𝟱😍

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Top 5 data science projects for freshers

1. Predictive Analytics on a Dataset:
   - Use a dataset to predict future trends or outcomes using machine learning algorithms. This could involve predicting sales, stock prices, or any other relevant domain.

2. Customer Segmentation:
   - Analyze and segment customers based on their behavior, preferences, or demographics. This project could provide insights for targeted marketing strategies.

3. Sentiment Analysis on Social Media Data:
   - Analyze sentiment in social media data to understand public opinion on a particular topic. This project helps in mastering natural language processing (NLP) techniques.

4. Recommendation System:
   - Build a recommendation system, perhaps for movies, music, or products, using collaborative filtering or content-based filtering methods.

5. Fraud Detection:
   - Develop a fraud detection system using machine learning algorithms to identify anomalous patterns in financial transactions or any domain where fraud detection is crucial.

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These projects showcase practical application of data science skills and can be highlighted on a resume for entry-level positions.

Join @pythonspecialist for more data science projects

𝗖𝗶𝘀𝗰𝗼 𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 😍

Upgrade Your Tech Skills in 2025—For FREE!

🔹 Introduction to Cybersecurity
🔹 Networking Essentials
🔹 Introduction to Modern AI
🔹 Discovering Entrepreneurship
🔹 Python for Beginners

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⚠️ O'Reilly Media, one of the most reputable publishers in the fields of programming, data mining, and AI, has made 10 data science books available to those interested in this field for free .

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0⃣ Python Data Science Handbook
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1⃣ Python for Data Analysis book
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🔢 Fundamentals of Data Visualization book
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🔢 R for Data Science book
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🔢 Deep Learning for Coders book
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🔢 DS at the Command Line book
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🔢 Hands-On Data Visualization Book
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🔢 Think Stats book
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🔢 Think Bayes book
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🔢 Kafka, The Definitive Guide
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- Introduction to SQL (Simplilearn) 

- Intro to SQL (Kaggle) 

- Introduction to Database & SQL Querying 

- SQL for Beginners – Microsoft SQL Server

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