The Foundation of Data Science

𝟰 𝗙𝗥𝗘𝗘 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀😍 

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

Want to turn raw data into stunning visual stories?📊

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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

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

𝗜𝗻𝗳𝗼𝘀𝘆𝘀 𝟭𝟬𝟬% 𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀😍

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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
👇👇
https://whatsapp.com/channel/0029Va4QUHa6rsQjhITHK82y

Like for more 😄

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

- Python
- Artificial Intelligence,
- Cybersecurity
- Cloud Computing, and
- Machine Learning

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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.

🔥 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

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.iss.one/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

📚 Join @free4unow_backup for more free resources.

ENJOY LEARNING! 👍👍

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.

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.

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