I unlocked my perplexity pro with college email id today, I think it's valid till 31st May only

Now, Let’s learn about Support Vector Machines (SVM) — sounds fancy, but I’ll break it down super chill.


Imagine, You’ve got two types of animals — let’s say cats and dogs — scattered around on a piece of paper.

Your job? Draw a straight line that separates all the cats from the dogs.

There might be lots of possible lines, but you want the best one — the one that keeps cats on one side, dogs on the other, and is as far away from both groups as possible.

That’s exactly what SVM does.


SVM finds the clearest boundary (called a hyperplane) between two groups. And not just any boundary — the one with the maximum margin, meaning the most space between the two groups.

Because more margin = better separation = fewer mistakes.


Real-Life Example:

Let’s say you're a bouncer at a club.

People line up outside and you need to decide:

Let them in? (Yes)

Turn them away? (No)


You make your call based on their age, dress code, and maybe how confident they walk up.

Now you want the cleanest rule possible to decide this every time — that’s what SVM builds.

Extras:

If the data isn’t linearly separable (i.e., you can’t split it with a straight line), SVM can do some math magic (called kernel trick) and bend the space so you can split it — like adding another dimension.


Imagine drawing a circle in 2D vs slicing with a plane in 3D — yeah, that kind of cool.

When to Use SVM:

- Face detection

- Text classification (like spam or not spam)

- Bioinformatics (disease prediction, gene classification)


SVM can be a bit heavy and sensitive to scaling, but it’s super powerful when tuned right.

React with ♥️ if you want to keep the things going?

Next up: Naive Bayes — it’s got the word “naive” but don’t let that fool you. 😂

Data Science & Machine Learning resources: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D

ENJOY LEARNING 👍👍

Awesome — time for Naive Bayes, the underdog of ML algorithms that’s way smarter than it sounds!


Let’s start with the name:

“Naive” — because it assumes that all the features (inputs) are independent of each other.
“Bayes” — comes from Bayes’ Theorem, a rule in probability that helps us update our belief based on new evidence.

Sounds a bit nerdy? Let me simplify.


Real-Life Example:

Imagine you're trying to guess if someone is a morning person or night owl based on:

Do they drink coffee?

Do they watch Netflix late?

Do they wake up early?


Now, a Naive Bayes model would assume that each of these habits independently contributes to the final guess — even if in real life, they might be related (like Netflix late = wakes up late).

Despite this "naive" assumption — it works shockingly well, especially with text data.


Think of It Like This:

It calculates the probability of each possible outcome and chooses the one with the highest chance.

Let’s say you're checking an email and deciding:

Spam or Not Spam


Naive Bayes looks at:

Does the email have the word "free"?

Does it mention "limited offer"?

Is there a weird link?


It uses all these clues (independently) to guess: “Hmm, looks like spam.”


Why It’s Awesome:

Blazing fast — great for real-time stuff

Works really well for:

- Spam detection

- Sentiment analysis (positive or negative reviews)

- News classification (sports, politics, tech)


It’s not perfect when features are heavily dependent on each other, but for text and high-dimensional data — it’s a beast.

React with ❤️ if you're ready for the next algorithm Logistic Regression — don’t be fooled by the name, it’s more about classification algorithm than regression.

Data Science & Machine Learning resources: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D

ENJOY LEARNING 👍👍

Let’s go! Time to understand our next algorithm Logistic Regression

First things first:

Despite the name, it’s not used for regression (predicting numbers) — it’s actually used for classification (like yes/no, spam/not spam, 1/0).

So think of it more like:

> “Will this happen or not?”
“Yes or No?”
“True or False?”


Real-Life Example:

Let’s say you're a recruiter looking at resumes.

You want to predict: Will this candidate get hired?

You’ve got features like:

Years of experience

Skill match

Education level


You feed those into a Logistic Regression model, and it gives you a probability, like:

> “There’s an 82% chance this person will be hired.”



If it’s above a certain threshold (like 50%), it predicts “Yes” — otherwise “No.”


How It Works (Simply):

It draws a boundary between two classes — like a straight line (or curve) that separates:

All the YES cases on one side

All the NO cases on the other


It uses something called a sigmoid function to convert numbers into probabilities between 0 and 1.

That’s the trick — instead of predicting a raw score, it predicts how confident it is.


Why It’s Used:

- Easy to understand

- Works well with smaller data

- Good baseline model for many classification problems


Some good usecases:

Credit scoring (Will you repay the loan?)

Medical diagnosis (Is it cancerous or not?)

Marketing (Will the customer click the ad?)


It’s like the entry-level, but highly reliable classifier in your ML toolkit.

React with ♥️ if you want to dive into the next one — Gradient Boosting

ENJOY LEARNING 👍👍

Now, let’s understand Gradient Boosting Algorithm


Let's say, You’re trying to guess someone’s age just by looking at them.

You ask your friend, and they say:

> “Hmm, looks like 30.”



You know they’re not great at guessing, but not totally wrong either.

So, you ask a second friend to fix the mistake made by the first one.
Then a third friend tries to fix the errors of both.

Now combine all their guesses — the final answer is a smarter, more accurate prediction.

That’s exactly how Gradient Boosting works.


Simply, It doesn’t build one big smart model.

Instead, it builds lots of small, weak models (usually decision trees), and each one tries to correct the mistakes made by the previous ones.

- First model gives a rough prediction.

- Second model looks at where the first went wrong.

- Third model fixes that again.

And so on…


By the end, all those tiny models work together like a squad to give a powerful prediction.

Why “Gradient” Boosting?

“Gradient” refers to using gradient descent — a fancy way of saying:

> "Let's go step-by-step in the right direction to reduce errors."



Every new tree is built in a way that reduces the error made by the previous ones — kind of like learning from feedback.


Where to use Gradient Boosting:

- Loan default prediction

- Customer churn modeling

- Kaggle competitions (it’s a fan favorite)

- Stock price movements


It’s used in powerful libraries like XGBoost, LightGBM, and CatBoost — all variations of this technique.

Super powerful, but can be slow and needs good tuning.

React with ♥️ if you want to me to talk about Random Forest — another tree-based algorithm, but with a different twist!

Data Science & Machine Learning Resources: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D

ENJOY LEARNING 👍👍

🔍 Machine Learning Cheat Sheet 🔍

1. Key Concepts:
- Supervised Learning: Learn from labeled data (e.g., classification, regression).
- Unsupervised Learning: Discover patterns in unlabeled data (e.g., clustering, dimensionality reduction).
- Reinforcement Learning: Learn by interacting with an environment to maximize reward.

2. Common Algorithms:
- Linear Regression: Predict continuous values.
- Logistic Regression: Binary classification.
- Decision Trees: Simple, interpretable model for classification and regression.
- Random Forests: Ensemble method for improved accuracy.
- Support Vector Machines: Effective for high-dimensional spaces.
- K-Nearest Neighbors: Instance-based learning for classification/regression.
- K-Means: Clustering algorithm.
- Principal Component Analysis(PCA)

3. Performance Metrics:
- Classification: Accuracy, Precision, Recall, F1-Score, ROC-AUC.
- Regression: Mean Absolute Error (MAE), Mean Squared Error (MSE), R^2 Score.

4. Data Preprocessing:
- Normalization: Scale features to a standard range.
- Standardization: Transform features to have zero mean and unit variance.
- Imputation: Handle missing data.
- Encoding: Convert categorical data into numerical format.

5. Model Evaluation:
- Cross-Validation: Ensure model generalization.
- Train-Test Split: Divide data to evaluate model performance.

6. Libraries:
- Python: Scikit-Learn, TensorFlow, Keras, PyTorch, Pandas, Numpy, Matplotlib.
- R: caret, randomForest, e1071, ggplot2.

7. Tips for Success:
- Feature Engineering: Enhance data quality and relevance.
- Hyperparameter Tuning: Optimize model parameters (Grid Search, Random Search).
- Model Interpretability: Use tools like SHAP and LIME.
- Continuous Learning: Stay updated with the latest research and trends.

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

All the best 👍👍

If you're serious about getting into Data Science with Python, follow this 5-step roadmap.

Each phase builds on the previous one, so don’t rush.

Take your time, build projects, and keep moving forward.

Step 1: Python Fundamentals
Before anything else, get your hands dirty with core Python.
This is the language that powers everything else.

✅ What to learn:
type(), int(), float(), str(), list(), dict()
if, elif, else, for, while, range()
def, return, function arguments
List comprehensions: [x for x in list if condition]
– Mini Checkpoint:
Build a mini console-based data calculator (inputs, basic operations, conditionals, loops).

Step 2: Data Cleaning with Pandas
Pandas is the tool you'll use to clean, reshape, and explore data in real-world scenarios.

✅ What to learn:
Cleaning: df.dropna(), df.fillna(), df.replace(), df.drop_duplicates()
Merging & reshaping: pd.merge(), df.pivot(), df.melt()
Grouping & aggregation: df.groupby(), df.agg()
– Mini Checkpoint:
Build a data cleaning script for a messy CSV file. Add comments to explain every step.

Step 3: Data Visualization with Matplotlib
Nobody wants raw tables.
Learn to tell stories through charts.

✅ What to learn:
Basic charts: plt.plot(), plt.scatter()
Advanced plots: plt.hist(), plt.kde(), plt.boxplot()
Subplots & customizations: plt.subplots(), fig.add_subplot(), plt.title(), plt.legend(), plt.xlabel()
– Mini Checkpoint:
Create a dashboard-style notebook visualizing a dataset, include at least 4 types of plots.

Step 4: Exploratory Data Analysis (EDA)
This is where your analytical skills kick in.
You’ll draw insights, detect trends, and prepare for modeling.

✅ What to learn:
Descriptive stats: df.mean(), df.median(), df.mode(), df.std(), df.var(), df.min(), df.max(), df.quantile()
Correlation analysis: df.corr(), plt.imshow(), scipy.stats.pearsonr()
— Mini Checkpoint:
Write an EDA report (Markdown or PDF) based on your findings from a public dataset.

Step 5: Intro to Machine Learning with Scikit-Learn
Now that your data skills are sharp, it's time to model and predict.

✅ What to learn:
Training & evaluation: train_test_split(), .fit(), .predict(), cross_val_score()
Regression: LinearRegression(), mean_squared_error(), r2_score()
Classification: LogisticRegression(), accuracy_score(), confusion_matrix()
Clustering: KMeans(), silhouette_score()

– Final Checkpoint:

Build your first ML project end-to-end
✅ Load data
✅ Clean it
✅ Visualize it
✅ Run EDA
✅ Train & test a model
✅ Share the project with visuals and explanations on GitHub

Don’t just complete tutorialsm create things.

Explain your work.
Build your GitHub.
Write a blog.

That’s how you go from “learning” to “landing a job

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

All the best 👍👍

Let's move on to the next Machine Learning Algorithm Random Forest

Let's say, you’ve got a really tough question to answer — so you don’t just ask one expert.
You ask a whole panel of experts, each with their own opinion.

Then, you take a vote — and go with what the majority says.

That’s how Random Forest works.


At its core, it builds lots of decision trees, not just one.

Each tree gets:

- A random subset of the data

- A random subset of the features (columns)


Each tree makes a prediction — and then the forest says:

> “Alright, let’s vote!” 😄



For classification, it picks the class most trees agree on.
For regression, it averages the numbers predicted by each tree.


Why Randomness? 🤔

That randomness actually makes the model more robust.

Instead of every tree seeing the same stuff and making the same mistakes, each tree gets its own “view,” which reduces overfitting and makes the whole forest more balanced and fair.


In Real Life:

Let’s say you’re predicting whether a loan applicant is risky.

One tree might focus on income and age.
Another tree might focus on employment history and loan amount.
Another might consider credit score and existing debt.

Together, they make a better decision than any single tree.


When to Use Random Forst:

- Credit scoring

- Stock market analysis

- Fraud detection

- Healthcare diagnosis


It’s often the go-to when you want high accuracy and don’t mind the model being a bit of a black box.

React with ❤️ if you want me to cover next important algorithm K-Nearest Neighbors (KNN)

Data Science & Machine Learning Resources: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D

ENJOY LEARNING 👍👍

Roadmap to become a Data Scientist:

📂 Learn Python & R
∟📂 Learn Statistics & Probability
∟📂 Learn SQL & Data Handling
∟📂 Learn Data Cleaning & Preprocessing
∟📂 Learn Data Visualization (Matplotlib, Seaborn, Power BI/Tableau)
∟📂 Learn Machine Learning (Supervised, Unsupervised)
∟📂 Learn Deep Learning (Neural Nets, CNNs, RNNs)
∟📂 Learn Model Deployment (Flask, Streamlit, FastAPI)
∟📂 Build Real-world Projects & Case Studies
∟✅ Apply for Jobs & Internships

React ❤️ for more

Free Data Science Resources: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D

Machine Learning Algorithms every data scientist should know:

📌 Supervised Learning:

🔹 Regression
∟ Linear Regression
∟ Ridge & Lasso Regression
∟ Polynomial Regression

🔹 Classification
∟ Logistic Regression
∟ K-Nearest Neighbors (KNN)
∟ Decision Tree
∟ Random Forest
∟ Support Vector Machine (SVM)
∟ Naive Bayes
∟ Gradient Boosting (XGBoost, LightGBM, CatBoost)


📌 Unsupervised Learning:

🔹 Clustering
∟ K-Means
∟ Hierarchical Clustering
∟ DBSCAN

🔹 Dimensionality Reduction
∟ PCA (Principal Component Analysis)
∟ t-SNE
∟ LDA (Linear Discriminant Analysis)


📌 Reinforcement Learning (Basics):
∟ Q-Learning
∟ Deep Q Network (DQN)


📌 Ensemble Techniques:
∟ Bagging (Random Forest)
∟ Boosting (XGBoost, AdaBoost, Gradient Boosting)
∟ Stacking

Don’t forget to learn model evaluation metrics: accuracy, precision, recall, F1-score, AUC-ROC, confusion matrix, etc.

Free Machine Learning Resources: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D

React ❤️ for more free resources

Machine Learning – Essential Concepts 🚀

1️⃣ Types of Machine Learning

Supervised Learning – Uses labeled data to train models.

Examples: Linear Regression, Decision Trees, Random Forest, SVM


Unsupervised Learning – Identifies patterns in unlabeled data.

Examples: Clustering (K-Means, DBSCAN), PCA


Reinforcement Learning – Models learn through rewards and penalties.

Examples: Q-Learning, Deep Q Networks



2️⃣ Key Algorithms

Regression – Predicts continuous values (Linear Regression, Ridge, Lasso).

Classification – Categorizes data into classes (Logistic Regression, Decision Tree, SVM, Naïve Bayes).

Clustering – Groups similar data points (K-Means, Hierarchical Clustering, DBSCAN).

Dimensionality Reduction – Reduces the number of features (PCA, t-SNE, LDA).


3️⃣ Model Training & Evaluation

Train-Test Split – Dividing data into training and testing sets.

Cross-Validation – Splitting data multiple times for better accuracy.

Metrics – Evaluating models with RMSE, Accuracy, Precision, Recall, F1-Score, ROC-AUC.


4️⃣ Feature Engineering

Handling missing data (mean imputation, dropna()).

Encoding categorical variables (One-Hot Encoding, Label Encoding).

Feature Scaling (Normalization, Standardization).


5️⃣ Overfitting & Underfitting

Overfitting – Model learns noise, performs well on training but poorly on test data.

Underfitting – Model is too simple and fails to capture patterns.

Solution: Regularization (L1, L2), Hyperparameter Tuning.


6️⃣ Ensemble Learning

Combining multiple models to improve performance.

Bagging (Random Forest)

Boosting (XGBoost, Gradient Boosting, AdaBoost)



7️⃣ Deep Learning Basics

Neural Networks (ANN, CNN, RNN).

Activation Functions (ReLU, Sigmoid, Tanh).

Backpropagation & Gradient Descent.


8️⃣ Model Deployment

Deploy models using Flask, FastAPI, or Streamlit.

Model versioning with MLflow.

Cloud deployment (AWS SageMaker, Google Vertex AI).

Join our WhatsApp channel: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D

New Data Scientists - When you learn, it's easy to get distracted by Machine Learning & Deep Learning terms like "XGBoost", "Neural Networks", "RNN", "LSTM" or Advanced Technologies like "Spark", "Julia", "Scala", "Go", etc.

Don't get bogged down trying to learn every new term & technology you come across.

Instead, focus on foundations.
- data wrangling
- visualizing
- exploring
- modeling
- understanding the results.

The best tools are often basic, Build yourself up. You'll advance much faster. Keep learning!

Artificial Intelligence isn't easy!

It’s the cutting-edge field that enables machines to think, learn, and act like humans.

To truly master Artificial Intelligence, focus on these key areas:

0. Understanding AI Fundamentals: Learn the basic concepts of AI, including search algorithms, knowledge representation, and decision trees.


1. Mastering Machine Learning: Since ML is a core part of AI, dive into supervised, unsupervised, and reinforcement learning techniques.


2. Exploring Deep Learning: Learn neural networks, CNNs, RNNs, and GANs to handle tasks like image recognition, NLP, and generative models.


3. Working with Natural Language Processing (NLP): Understand how machines process human language for tasks like sentiment analysis, translation, and chatbots.


4. Learning Reinforcement Learning: Study how agents learn by interacting with environments to maximize rewards (e.g., in gaming or robotics).


5. Building AI Models: Use popular frameworks like TensorFlow, PyTorch, and Keras to build, train, and evaluate your AI models.


6. Ethics and Bias in AI: Understand the ethical considerations and challenges of implementing AI responsibly, including fairness, transparency, and bias.


7. Computer Vision: Master image processing techniques, object detection, and recognition algorithms for AI-powered visual applications.


8. AI for Robotics: Learn how AI helps robots navigate, sense, and interact with the physical world.


9. Staying Updated with AI Research: AI is an ever-evolving field—stay on top of cutting-edge advancements, papers, and new algorithms.



Artificial Intelligence is a multidisciplinary field that blends computer science, mathematics, and creativity.

💡 Embrace the journey of learning and building systems that can reason, understand, and adapt.

⏳ With dedication, hands-on practice, and continuous learning, you’ll contribute to shaping the future of intelligent systems!

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

Credits: https://xn--r1a.website/datasciencefun

Like if you need similar content 😄👍

Hope this helps you 😊

Everything you need to become Data Scientist ❤️

Data Science in 100 Days

Essential Data Science Concepts Everyone Should Know:

1. Data Types and Structures:

• Categorical: Nominal (unordered, e.g., colors) and Ordinal (ordered, e.g., education levels)

• Numerical: Discrete (countable, e.g., number of children) and Continuous (measurable, e.g., height)

• Data Structures: Arrays, Lists, Dictionaries, DataFrames (for organizing and manipulating data)

2. Descriptive Statistics:

• Measures of Central Tendency: Mean, Median, Mode (describing the typical value)

• Measures of Dispersion: Variance, Standard Deviation, Range (describing the spread of data)

• Visualizations: Histograms, Boxplots, Scatterplots (for understanding data distribution)

3. Probability and Statistics:

• Probability Distributions: Normal, Binomial, Poisson (modeling data patterns)

• Hypothesis Testing: Formulating and testing claims about data (e.g., A/B testing)

• Confidence Intervals: Estimating the range of plausible values for a population parameter

4. Machine Learning:

• Supervised Learning: Regression (predicting continuous values) and Classification (predicting categories)

• Unsupervised Learning: Clustering (grouping similar data points) and Dimensionality Reduction (simplifying data)

• Model Evaluation: Accuracy, Precision, Recall, F1-score (assessing model performance)

5. Data Cleaning and Preprocessing:

• Missing Value Handling: Imputation, Deletion (dealing with incomplete data)

• Outlier Detection and Removal: Identifying and addressing extreme values

• Feature Engineering: Creating new features from existing ones (e.g., combining variables)

6. Data Visualization:

• Types of Charts: Bar charts, Line charts, Pie charts, Heatmaps (for communicating insights visually)

• Principles of Effective Visualization: Clarity, Accuracy, Aesthetics (for conveying information effectively)

7. Ethical Considerations in Data Science:

• Data Privacy and Security: Protecting sensitive information

• Bias and Fairness: Ensuring algorithms are unbiased and fair

8. Programming Languages and Tools:

• Python: Popular for data science with libraries like NumPy, Pandas, Scikit-learn

• R: Statistical programming language with strong visualization capabilities

• SQL: For querying and manipulating data in databases

9. Big Data and Cloud Computing:

• Hadoop and Spark: Frameworks for processing massive datasets

• Cloud Platforms: AWS, Azure, Google Cloud (for storing and analyzing data)

10. Domain Expertise:

• Understanding the Data: Knowing the context and meaning of data is crucial for effective analysis

• Problem Framing: Defining the right questions and objectives for data-driven decision making

Bonus:

• Data Storytelling: Communicating insights and findings in a clear and engaging manner

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

ENJOY LEARNING 👍👍

Planning for Data Science or Data Engineering Interview.

Focus on SQL & Python first. Here are some important questions which you should know.

𝐈𝐦𝐩𝐨𝐫𝐭𝐚𝐧𝐭 𝐒𝐐𝐋 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬

1- Find out nth Order/Salary from the tables.
2- Find the no of output records in each join from given Table 1 & Table 2
3- YOY,MOM Growth related questions.
4- Find out Employee ,Manager Hierarchy (Self join related question) or
Employees who are earning more than managers.
5- RANK,DENSERANK related questions
6- Some row level scanning medium to complex questions using CTE or recursive CTE, like (Missing no /Missing Item from the list etc.)
7- No of matches played by every team or Source to Destination flight combination using CROSS JOIN.
8-Use window functions to perform advanced analytical tasks, such as calculating moving averages or detecting outliers.
9- Implement logic to handle hierarchical data, such as finding all descendants of a given node in a tree structure.
10-Identify and remove duplicate records from a table.

𝐈𝐦𝐩𝐨𝐫𝐭𝐚𝐧𝐭 𝐏𝐲𝐭𝐡𝐨𝐧 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬

1- Reversing a String using an Extended Slicing techniques.
2- Count Vowels from Given words .
3- Find the highest occurrences of each word from string and sort them in order.
4- Remove Duplicates from List.
5-Sort a List without using Sort keyword.
6-Find the pair of numbers in this list whose sum is n no.
7-Find the max and min no in the list without using inbuilt functions.
8-Calculate the Intersection of Two Lists without using Built-in Functions
9-Write Python code to make API requests to a public API (e.g., weather API) and process the JSON response.
10-Implement a function to fetch data from a database table, perform data manipulation, and update the database.

Join for more: https://xn--r1a.website/datasciencefun

ENJOY LEARNING 👍👍

Data Science Interview Questions

1. What are the different subsets of SQL?

Data Definition Language (DDL) – It allows you to perform various operations on the database such as CREATE, ALTER, and DELETE objects.
Data Manipulation Language(DML) – It allows you to access and manipulate data. It helps you to insert, update, delete and retrieve data from the database.
Data Control Language(DCL) – It allows you to control access to the database. Example – Grant, Revoke access permissions.

2. List the different types of relationships in SQL.

There are different types of relations in the database:
One-to-One – This is a connection between two tables in which each record in one table corresponds to the maximum of one record in the other.
One-to-Many and Many-to-One – This is the most frequent connection, in which a record in one table is linked to several records in another.
Many-to-Many – This is used when defining a relationship that requires several instances on each sides.
Self-Referencing Relationships – When a table has to declare a connection with itself, this is the method to employ.

3. How to create empty tables with the same structure as another table?

To create empty tables:
Using the INTO operator to fetch the records of one table into a new table while setting a WHERE clause to false for all entries, it is possible to create empty tables with the same structure. As a result, SQL creates a new table with a duplicate structure to accept the fetched entries, but nothing is stored into the new table since the WHERE clause is active.

4. What is Normalization and what are the advantages of it?

Normalization in SQL is the process of organizing data to avoid duplication and redundancy. Some of the advantages are:
Better Database organization
More Tables with smaller rows
Efficient data access
Greater Flexibility for Queries
Quickly find the information
Easier to implement Security

Roadmap to become Data Scientist

Data Science Roadmap: 🗺

📂 Math & Stats
 ∟📂 Python/R
  ∟📂 Data Wrangling
   ∟📂 Visualization
    ∟📂 ML
     ∟📂 DL & NLP
      ∟📂 Projects
       ∟ ✅ Apply For Job

Like if you need detailed explanation step-by-step ❤️