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.

Complete Roadmap to land a Data Scientist job in 2025

Phase 1: Build Foundations (3-6 months)

1. Learn Python programming basics
2. Understand statistics and mathematics concepts (linear algebra, calculus, probability)
3. Familiarize yourself with data visualization tools (Matplotlib, Seaborn)

Phase 2: Data Science Skills (6-9 months)

1. Master machine learning algorithms (scikit-learn, TensorFlow)
2. Learn data manipulation frameworks (Pandas, NumPy)
3. Study data visualization libraries (Plotly, Bokeh)
4. Understand database management systems (SQL, NoSQL)

Phase 3: Practice and Projects (3-6 months)

1. Work on personal projects (Kaggle competitions, datasets)
2. Participate in data science communities (GitHub, Reddit)
3. Build a portfolio showcasing skills

Phase 4: Job Preparation (1-3 months)

1. Update resume and online profiles (LinkedIn)
2. Practice whiteboarding and coding interviews
3. Prepare answers for common data science questions

Best Resources to learn Data Science 👇👇

Python Tutorial

Data Science Course by Kaggle

Machine Learning Course by Google

Best Data Science & Machine Learning Resources

Interview Process for Data Science Role at Amazon

Python Interview Resources

Join @free4unow_backup for more free courses

Like for more ❤️

ENJOY LEARNING👍👍

5 Data Analytics Project Ideas to boost your resume:

1. Stock Market Portfolio Optimization

2. YouTube Data Collection & Analysis

3. Elections Ad Spending & Voting Patterns Analysis

4. EV Market Size Analysis

5. Metro Operations Optimization

Jupyter Notebooks are essential for data analysts working with Python.

Here’s how to make the most of this great tool:

1. 𝗢𝗿𝗴𝗮𝗻𝗶𝘇𝗲 𝗬𝗼𝘂𝗿 𝗖𝗼𝗱𝗲 𝘄𝗶𝘁𝗵 𝗖𝗹𝗲𝗮𝗿 𝗦𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲:

Break your notebook into logical sections using markdown headers. This helps you and your colleagues navigate the notebook easily and understand the flow of analysis. You could use headings (#, ##, ###) and bullet points to create a table of contents.


2. 𝗗𝗼𝗰𝘂𝗺𝗲𝗻𝘁 𝗬𝗼𝘂𝗿 𝗣𝗿𝗼𝗰𝗲𝘀𝘀:

Add markdown cells to explain your methodology, code, and guidelines for the user. This Enhances the readability and makes your notebook a great reference for future projects. You might want to include links to relevant resources and detailed docs where necessary.


3. 𝗨𝘀𝗲 𝗜𝗻𝘁𝗲𝗿𝗮𝗰𝘁𝗶𝘃𝗲 𝗪𝗶𝗱𝗴𝗲𝘁𝘀:

Leverage ipywidgets to create interactive elements like sliders, dropdowns, and buttons. With those, you can make your analysis more dynamic and allow users to explore different scenarios without changing the code. Create widgets for parameter tuning and real-time data visualization.


𝟰. 𝗞𝗲𝗲𝗽 𝗜𝘁 𝗖𝗹𝗲𝗮𝗻 𝗮𝗻𝗱 𝗠𝗼𝗱𝘂𝗹𝗮𝗿:

Write reusable functions and classes instead of long, monolithic code blocks. This will improve the code maintainability and efficiency of your notebook. You should store frequently used functions in separate Python scripts and import them when needed.


5. 𝗩𝗶𝘀𝘂𝗮𝗹𝗶𝘇𝗲 𝗬𝗼𝘂𝗿 𝗗𝗮𝘁𝗮 𝗘𝗳𝗳𝗲𝗰𝘁𝗶𝘃𝗲𝗹𝘆:

Utilize libraries like Matplotlib, Seaborn, and Plotly for your data visualizations. These clear and insightful visuals will help you to communicate your findings. Make sure to customize your plots with labels, titles, and legends to make them more informative.


6. 𝗩𝗲𝗿𝘀𝗶𝗼𝗻 𝗖𝗼𝗻𝘁𝗿𝗼𝗹 𝗬𝗼𝘂𝗿 𝗡𝗼𝘁𝗲𝗯𝗼𝗼𝗸𝘀:

Jupyter Notebooks are great for exploration, but they often lack systematic version control. Use tools like Git and nbdime to track changes, collaborate effectively, and ensure that your work is reproducible.

7. 𝗣𝗿𝗼𝘁𝗲𝗰𝘁 𝗬𝗼𝘂𝗿 𝗡𝗼𝘁𝗲𝗯𝗼𝗼𝗸𝘀:

Clean and secure your notebooks by removing sensitive information before sharing. This helps to prevent the leakage of private data. You should consider using environment variables for credentials.


Keeping these techniques in mind will help to transform your Jupyter Notebooks into great tools for analysis and communication.

I have curated the best interview resources to crack Python Interviews 👇👇
https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L

Hope you'll like it

Like this post if you need more resources like this 👍❤️

For working professionals willing to pivot their careers to AI:

Here are the steps you can take right now:

1. Learn the basics of AI
==================

You need to understand the differences among various AI jargons (e.g., what is the difference between statistical ML vs. deep learning? What exactly is an LLM?) and when to use which to solve a given business problem. Many fast-paced courses can teach you all of this without having to learn coding. (Shameless plug: I have a course that I will add in the comments section below)

2. Build an AI project in your current work
==============================

Find a problem statement in your current work that can be solved using AI and will deliver some value. Work on this during your extra hours, then showcase it to your management to get official approval to make it a full-fledged project.

3. Collaborate with the AI team in your company for inner sourcing
================================================

Many companies have the concept of inner sourcing where, say, an AI team is too busy and has a list of tasks they have opened on their GitHub repository that others can work on. Use this as an opportunity to do some real AI work and build rapport with the AI team.

4. Attend AI conferences
==================

By attending AI conferences, you will not only learn but also build a network with AI professionals who will help you in your AI career journey.

5. Attend an AI bootcamp at a university or online learning company
=================================================

Artificial Intelligence

👉Telegram Link: https://t.iss.one/addlist/4q2PYC0pH_VjZDk5

Like for more ❤️

All the best 👍👍

Food is Medicine:

🧄. Garlic - good for the immune system
🍌. Bananas - good for the nerves
🍠. Sweet potatoes - good for digestion
🌰. Walnuts - good for memory
🍊. Oranges - good for the skin
🥬. Kale - good for the bones
🌻. Chia seeds - good for the heart
🌶. Peppers - good for metabolism
🍄. Mushrooms - good for the immune system
🍅. Tomatoes - good for the blood
🫐. Blueberries - good for the brain

- If you aren't currently following us, you'll probably never see us again. 🗿

⭐⭐⭐ Advance Level Data science Projects ⭐⭐⭐

1) Identify your Digits Dataset : https://www.kaggle.com/c/digit-recognizer/data

2) Recommendation Engine : https://cseweb.ucsd.edu/~jmcauley/datasets.html

3) Visual QA : https://visualqa.org/download.html

4) Vox Celebrity : https://www.robots.ox.ac.uk/~vgg/data/voxceleb/

5) Breast cancer classification : https://www.kaggle.com/martinab/breast-cancer-classification-wisconsin-dataset

6) Traffic signals : https://benchmark.ini.rub.de/?section=gtsrb&subsection=dataset

7) Image caption generator : https://academictorrents.com/details/9dea07ba660a722ae1008c4c8afdd303b6f6e53b

Practice projects to consider:

1. Implement a basic search engine: Read a set of documents and build an index of keywords. Then, implement a search function that returns a list of documents that match the query.

2. Build a recommendation system: Read a set of user-item interactions and build a recommendation system that suggests items to users based on their past behavior.

3. Create a data analysis tool: Read a large dataset and implement a tool that performs various analyses, such as calculating summary statistics, visualizing distributions, and identifying patterns and correlations.

4. Implement a graph algorithm: Study a graph algorithm such as Dijkstra's shortest path algorithm, and implement it in Python. Then, test it on real-world graphs to see how it performs.

Pandas Cheatsheet For Data Science

Data Analytics Projects for Beginners 👇


Web Scraping
https://github.com/shreyaswankhede/IMDb-Web-Scraping-and-Sentiment-Analysis

Product Price Scraping and Analysis
https://github.com/CodesdaLu/Web-Scrapping

News Scraping
https://github.com/rohit-yadav/scraping-news-articles

Real Time Stock Price Scraping with Python
https://youtu.be/rONhdonaWUo?si=A3oDEVbLIAP78cCz

Zomato Analysis
https://youtu.be/fFi_TBw27is?si=E0iLd3J06YHfQkRk

IPL Analysis
https://github.com/Yashmenaria1/IPL-Data-Exploration

https://www.youtube.com/watch?v=ur-v0dv0Qtw

https://www.youtube.com/watch?v=ur-v0dv0Qtw

Football Data Analysis
https://youtu.be/yat7soj__4w?si=h5CLIvVFzzKm8IEP

Market Basket Analysis
https://youtu.be/Ne8Sbp2hJIk?si=ThEuvdOnRrpcVjOg

Customer Churn Prediction
https://github.com/Pradnya1208/Telecom-Customer-Churn-prediction

Employee’s Performance for HR Analytics
https://www.kaggle.com/code/rajatraj0502/employee-s-performance-for-hr-analytics

Food Price Prediction
https://github.com/VectorInstitute/foodprice-forecasting

If you want to get a job as a machine learning engineer, don’t start by diving into the hottest libraries like PyTorch,TensorFlow, Langchain, etc.

Yes, you might hear a lot about them or some other trending technology of the year...but guess what!

Technologies evolve rapidly, especially in the age of AI, but core concepts are always seen as more valuable than expertise in any particular tool. Stop trying to perform a brain surgery without knowing anything about human anatomy.

Instead, here are basic skills that will get you further than mastering any framework:


𝐌𝐚𝐭𝐡𝐞𝐦𝐚𝐭𝐢𝐜𝐬 𝐚𝐧𝐝 𝐒𝐭𝐚𝐭𝐢𝐬𝐭𝐢𝐜𝐬 - My first exposure to probability and statistics was in college, and it felt abstract at the time, but these concepts are the backbone of ML.

You can start here: Khan Academy Statistics and Probability - https://www.khanacademy.org/math/statistics-probability

𝐋𝐢𝐧𝐞𝐚𝐫 𝐀𝐥𝐠𝐞𝐛𝐫𝐚 𝐚𝐧𝐝 𝐂𝐚𝐥𝐜𝐮𝐥𝐮𝐬 - Concepts like matrices, vectors, eigenvalues, and derivatives are fundamental to understanding how ml algorithms work. These are used in everything from simple regression to deep learning.

𝐏𝐫𝐨𝐠𝐫𝐚𝐦𝐦𝐢𝐧𝐠 - Should you learn Python, Rust, R, Julia, JavaScript, etc.? The best advice is to pick the language that is most frequently used for the type of work you want to do. I started with Python due to its simplicity and extensive library support, and it remains my go-to language for machine learning tasks.

You can start here: Automate the Boring Stuff with Python - https://automatetheboringstuff.com/

𝐀𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦 𝐔𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝𝐢𝐧𝐠 - Understand the fundamental algorithms before jumping to deep learning. This includes linear regression, decision trees, SVMs, and clustering algorithms.

𝐃𝐞𝐩𝐥𝐨𝐲𝐦𝐞𝐧𝐭 𝐚𝐧𝐝 𝐏𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧:
Knowing how to take a model from development to production is invaluable. This includes understanding APIs, model optimization, and monitoring. Tools like Docker and Flask are often used in this process.

𝐂𝐥𝐨𝐮𝐝 𝐂𝐨𝐦𝐩𝐮𝐭𝐢𝐧𝐠 𝐚𝐧𝐝 𝐁𝐢𝐠 𝐃𝐚𝐭𝐚:
Familiarity with cloud platforms (AWS, Google Cloud, Azure) and big data tools (Spark) is increasingly important as datasets grow larger. These skills help you manage and process large-scale data efficiently.

You can start here: Google Cloud Machine Learning - https://cloud.google.com/learn/training/machinelearning-ai

I love frameworks and libraries, and they can make anyone's job easier.

But the more solid your foundation, the easier it will be to pick up any new technologies and actually validate whether they solve your problems.

Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624

All the best 👍👍

Top 10 Programming Languages to learn in 2025 (With Free Resources to learn) :-

1. Python
- learnpython.org
- t.iss.one/pythonfreebootcamp

2. Java
- learnjavaonline.org
- t.iss.one/free4unow_backup/550

3. C#
- learncs.org
- w3schools.com

4. JavaScript
- learnjavascript.online
- t.iss.one/javascript_courses

5. Rust
- rust-lang.org
- exercism.org

6. Go Programming
- go.dev
- learn-golang.org

7. Kotlin
- kotlinlang.org
- w3schools.com/KOTLIN

8. TypeScript
- Typescriptlang.org
- learntypescript.dev

9. SQL
- datasimplifier.com
- t.iss.one/sqlanalyst

10. R Programming
- w3schools.com/r/
- r-coder.com

ENJOY LEARNING 👍👍

Randomized experiments are the gold standard for measuring impact. Here’s how to measure impact with randomized trials. 👇

𝟏. 𝐃𝐞𝐬𝐢𝐠𝐧 𝐄𝐱𝐩𝐞𝐫𝐢𝐦𝐞𝐧𝐭
Planning the structure and methodology of the experiment, including defining the hypothesis, selecting metrics, and conducting a power analysis to determine sample size.
⤷ Ensures the experiment is well-structured and statistically sound, minimizing bias and maximizing reliability.

𝟐. 𝐈𝐦𝐩𝐥𝐞𝐦𝐞𝐧𝐭 𝐕𝐚𝐫𝐢𝐚𝐧𝐭𝐬
Creating different versions of the intervention by developing and deploying the control (A) and treatment (B) versions.
⤷ Allows for a clear comparison between the current state and the proposed change.

𝟑. 𝐂𝐨𝐧𝐝𝐮𝐜𝐭 𝐓𝐞𝐬𝐭
Choosing the right statistical test and calculating test statistics, such as confidence intervals, p-values, and effect sizes.
⤷ Ensures the results are statistically valid and interpretable.

𝟒. 𝐀𝐧𝐚𝐥𝐲𝐳𝐞 𝐑𝐞𝐬𝐮𝐥𝐭𝐬
Evaluating the data collected from the experiment, interpreting confidence intervals, p-values, and effect sizes to determine statistical significance and practical impact.
⤷ Helps determine whether the observed changes are meaningful and should be implemented.

𝟓. 𝐀𝐝𝐝𝐢𝐭𝐢𝐨𝐧𝐚𝐥 𝐅𝐚𝐜𝐭𝐨𝐫𝐬
⤷ Network Effects: User interactions affecting experiment outcomes.
⤷ P-Hacking: Manipulating data for significant results.
⤷ Novelty Effects: Temporary boost from new features.

Hope this helps you 😊

This is a quick and easy guide to the four main categories: Supervised, Unsupervised, Semi-Supervised, and Reinforcement Learning.

1. Supervised Learning
In supervised learning, the model learns from examples that already have the answers (labeled data). The goal is for the model to predict the correct result when given new data.

Some common supervised learning algorithms include:

➡️ Linear Regression – For predicting continuous values, like house prices.
➡️ Logistic Regression – For predicting categories, like spam or not spam.
➡️ Decision Trees – For making decisions in a step-by-step way.
➡️ K-Nearest Neighbors (KNN) – For finding similar data points.
➡️ Random Forests – A collection of decision trees for better accuracy.
➡️ Neural Networks – The foundation of deep learning, mimicking the human brain.

2. Unsupervised Learning
With unsupervised learning, the model explores patterns in data that doesn’t have any labels. It finds hidden structures or groupings.

Some popular unsupervised learning algorithms include:

➡️ K-Means Clustering – For grouping data into clusters.
➡️ Hierarchical Clustering – For building a tree of clusters.
➡️ Principal Component Analysis (PCA) – For reducing data to its most important parts.
➡️ Autoencoders – For finding simpler representations of data.

3. Semi-Supervised Learning
This is a mix of supervised and unsupervised learning. It uses a small amount of labeled data with a large amount of unlabeled data to improve learning.

Common semi-supervised learning algorithms include:

➡️ Label Propagation – For spreading labels through connected data points.
➡️ Semi-Supervised SVM – For combining labeled and unlabeled data.
➡️ Graph-Based Methods – For using graph structures to improve learning.

4. Reinforcement Learning
In reinforcement learning, the model learns by trial and error. It interacts with its environment, receives feedback (rewards or penalties), and learns how to act to maximize rewards.

Popular reinforcement learning algorithms include:

➡️ Q-Learning – For learning the best actions over time.
➡️ Deep Q-Networks (DQN) – Combining Q-learning with deep learning.
➡️ Policy Gradient Methods – For learning policies directly.
➡️ Proximal Policy Optimization (PPO) – For stable and effective learning.

🔟 Project Ideas for a data analyst

Customer Segmentation: Analyze customer data to segment them based on their behaviors, preferences, or demographics, helping businesses tailor their marketing strategies.

Churn Prediction: Build a model to predict customer churn, identifying factors that contribute to churn and proposing strategies to retain customers.

Sales Forecasting: Use historical sales data to create a predictive model that forecasts future sales, aiding inventory management and resource planning.

Market Basket Analysis: Analyze
transaction data to identify associations between products often purchased together, assisting retailers in optimizing product placement and cross-selling.

Sentiment Analysis: Analyze social media or customer reviews to gauge public sentiment about a product or service, providing valuable insights for brand reputation management.

Healthcare Analytics: Examine medical records to identify trends, patterns, or correlations in patient data, aiding in disease prediction, treatment optimization, and resource allocation.

Financial Fraud Detection: Develop algorithms to detect anomalous transactions and patterns in financial data, helping prevent fraud and secure transactions.

A/B Testing Analysis: Evaluate the results of A/B tests to determine the effectiveness of different strategies or changes on websites, apps, or marketing campaigns.

Energy Consumption Analysis: Analyze energy usage data to identify patterns and inefficiencies, suggesting strategies for optimizing energy consumption in buildings or industries.

Real Estate Market Analysis: Study housing market data to identify trends in property prices, rental rates, and demand, assisting buyers, sellers, and investors in making informed decisions.

Remember to choose a project that aligns with your interests and the domain you're passionate about.

Data Analyst Roadmap
👇👇
https://t.iss.one/sqlspecialist/379

ENJOY LEARNING 👍👍

🔅 Convert Video to Audio using Python