AI Engineer vs Software Engineer 👆

𝟱 𝗖𝗼𝗱𝗶𝗻𝗴 𝗖𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲𝘀 𝗧𝗵𝗮𝘁 𝗔𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗠𝗮𝘁𝘁𝗲𝗿 𝗙𝗼𝗿 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝘁𝗶𝘀𝘁𝘀 💻

You don’t need to be a LeetCode grandmaster.
But data science interviews still test your problem-solving mindset—and these 5 types of challenges are the ones that actually matter.

Here’s what to focus on (with examples) 👇

🔹 1. String Manipulation (Common in Data Cleaning)

✅ Parse messy columns (e.g., split “Name_Age_City”)
✅ Regex to extract phone numbers, emails, URLs
✅ Remove stopwords or HTML tags in text data

Example: Clean up a scraped dataset from LinkedIn bias

🔹 2. GroupBy and Aggregation with Pandas

✅ Group sales data by product/region
✅ Calculate avg, sum, count using .groupby()
✅ Handle missing values smartly

Example: “What’s the top-selling product in each region?”

🔹 3. SQL Join + Window Functions

✅ INNER JOIN, LEFT JOIN to merge tables
✅ ROW_NUMBER(), RANK(), LEAD(), LAG() for trends
✅ Use CTEs to break complex queries

Example: “Get 2nd highest salary in each department”

🔹 4. Data Structures: Lists, Dicts, Sets in Python

✅ Use dictionaries to map, filter, and count
✅ Remove duplicates with sets
✅ List comprehensions for clean solutions

Example: “Count frequency of hashtags in tweets”

🔹 5. Basic Algorithms (Not DP or Graphs)

✅ Sliding window for moving averages
✅ Two pointers for duplicate detection
✅ Binary search in sorted arrays

Example: “Detect if a pair of values sum to 100”

🎯 Tip: Practice challenges that feel like real-world data work, not textbook CS exams.

Use platforms like:

StrataScratch
Hackerrank (SQL + Python)
Kaggle Code

I have curated the best interview resources to crack Data Science Interviews
👇👇
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Get File Size using Python 👆

Important data science topics you should definitely be aware of

1. Statistics & Probability

Descriptive Statistics (mean, median, mode, variance, std deviation)
Probability Distributions (Normal, Binomial, Poisson)
Bayes' Theorem
Hypothesis Testing (t-test, chi-square test, ANOVA)
Confidence Intervals

2. Data Manipulation & Analysis

Data wrangling/cleaning
Handling missing values & outliers
Feature engineering & scaling
GroupBy operations
Pivot tables
Time series manipulation

3. Programming (Python/R)

Data structures (lists, dictionaries, sets)
Libraries:
Python: pandas, NumPy, matplotlib, seaborn, scikit-learn
R: dplyr, ggplot2, caret
Writing reusable functions
Working with APIs & files (CSV, JSON, Excel)

4. Data Visualization
Plot types: bar, line, scatter, histograms, heatmaps, boxplots
Dashboards (Power BI, Tableau, Plotly Dash, Streamlit)
Communicating insights clearly

5. Machine Learning

Supervised Learning
Linear & Logistic Regression
Decision Trees, Random Forest, Gradient Boosting (XGBoost, LightGBM)
SVM, KNN

Unsupervised Learning
K-means Clustering
PCA
Hierarchical Clustering

Model Evaluation
Accuracy, Precision, Recall, F1-Score
Confusion Matrix, ROC-AUC
Cross-validation, Grid Search

6. Deep Learning (Basics)
Neural Networks (perceptron, activation functions)
CNNs, RNNs (just an overview unless you're going deep into DL)
Frameworks: TensorFlow, PyTorch, Keras

7. SQL & Databases
SELECT, WHERE, GROUP BY, JOINS, CTEs, Subqueries
Window functions
Indexes and Query Optimization

8. Big Data & Cloud (Basics)
Hadoop, Spark
AWS, GCP, Azure (basic knowledge of data services)

9. Deployment & MLOps (Basic Awareness)
Model deployment (Flask, FastAPI)
Docker basics
CI/CD pipelines
Model monitoring

10. Business & Domain Knowledge
Framing a problem
Understanding business KPIs
Translating data insights into actionable strategies

I have curated the best interview resources to crack Data Science Interviews
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https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D

Like for the detailed explanation on each topic 😄👍

🌮 Data Analyst Vs Data Engineer Vs Data Scientist 🌮


Skills required to become data analyst
👉 Advanced Excel, Oracle/SQL
👉 Python/R

Skills required to become data engineer
👉 Python/ Java.
👉 SQL, NoSQL technologies like Cassandra or MongoDB
👉 Big data technologies like Hadoop, Hive/ Pig/ Spark

Skills required to become data Scientist
👉 In-depth knowledge of tools like R/ Python/ SAS.
👉 Well versed in various machine learning algorithms like scikit-learn, karas and tensorflow
👉 SQL and NoSQL

Bonus skill required: Data Visualization (PowerBI/ Tableau) & Statistics

Today, lets understand Machine Learning in simplest way possible

What is Machine Learning?

Think of it like this:

Machine Learning is when you teach a computer to learn from data, so it can make decisions or predictions without being told exactly what to do step-by-step.

Real-Life Example:
Let’s say you want to teach a kid how to recognize a dog.
You show the kid a bunch of pictures of dogs.

The kid starts noticing patterns — “Oh, they have four legs, fur, floppy ears...”

Next time the kid sees a new picture, they might say, “That’s a dog!” — even if they’ve never seen that exact dog before.

That’s what machine learning does — but instead of a kid, it's a computer.

In Tech Terms (Still Simple):

You give the computer data (like pictures, numbers, or text).
You give it examples of the right answers (like “this is a dog”, “this is not a dog”).
It learns the patterns.

Later, when you give it new data, it makes a smart guess.

Few Common Uses of ML You See Every Day:

Netflix: Suggesting shows you might like.
Google Maps: Predicting traffic.
Amazon: Recommending products.
Banks: Detecting fraud in transactions.

Should we start covering all data Science and machine learning concepts like this?

I have curated the best interview resources to crack Data Science Interviews
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https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D

Like for more ❤️

Machine Learning Types 👆

So now that you know what machine learning is (teaching computers to learn from data), the next thing is.

How do they learn?

That’s where algorithms come in.
Think of algorithms as different learning styles.

Just like people — some learn best by watching videos, others by solving problems — computers have different ways to learn too. These different ways are what we call machine learning algorithms.

Let’s start with the most common and simple ones.

I’ll explain them one by one in a way that makes sense.

Here’s a quick list of popular ML algorithms:
Linear Regression – predicts numbers (like house prices).
Logistic Regression – predicts categories (yes/no, spam/not spam).
Decision Trees – makes decisions by asking questions.
Random Forest – a group of decision trees working together.
K-Nearest Neighbors (KNN) – looks at neighbors to decide.
Support Vector Machine (SVM) – draws lines to separate data.
Naive Bayes – based on probability, good for text (like spam filters).
K-Means Clustering – groups similar things together.
Principal Component Analysis (PCA) – reduces complexity of data.
Neural Networks – the backbone of deep learning (used in face recognition, voice assistants, etc.).

Wanna need a detailed explanation on each algorithm?

React with ♥️ and let me know in the comments if you really want to learn more about the algorithms.

You can now find Data Science & Machine Learning resources on WhatsApp as well: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D

Now let's understand Linear Regression in detail.

Linear Regression is all about predicting a continuous value (like salary, price, temperature) based on another variable (like years of experience, number of products sold, etc.).

Let's say, You’re trying to predict someone's salary based on their years of experience. As experience increases, you generally expect the salary to increase too. What linear regression does is find the best line that fits this trend.

The line is represented by this simple equation:

Salary = m * Years of Experience + b

Here:
m is the slope of the line (it tells you how much salary increases with each additional year of experience).
b is the y-intercept (the starting point, or the salary when there's no experience).

The Process:

Training the model: The algorithm looks at all your data and tries to draw the straightest line possible that fits the pattern between experience and salary. It does this by adjusting the m (slope) and b (intercept) to minimize the difference between predicted and actual salaries.

Making predictions: Once the model has learned the best line, it can predict salaries for new people based on their years of experience. For example, if you tell it someone has 5 years of experience, it will give you the predicted salary.

Linear regression is great when there's a straight-line relationship between variables. It helps you make predictions, and because it’s simple, it’s often used as a starting point for many problems.

React with ♥️ if you need similar explanation for the rest of the algorithms

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

Top Machine Learning Libraries 👆

Let’s move on to the next one: Logistic Regression.

And don’t worry — even though it sounds like “linear regression,” this one’s all about yes or no answers.

What is Logistic Regression?

Let’s say you want to predict if someone will get approved for a loan or not.

You’ve got details like:

Their income
Credit score
Employment status

But the final output is binary — either “Yes” (approved) or “No” (not approved).

That’s where Logistic Regression comes in. It’s used when the outcome is yes/no, true/false, 0/1 — anything with just two categories.

Real-Life Vibe:
Imagine you’re trying to figure out if a student will pass or fail an exam based on the number of hours they study.

Now instead of drawing a straight line (like in linear regression), logistic regression draws an S-shaped curve.

Why?

Because we want to squeeze all predictions into a range between 0 and 1 — where:
Closer to 1 = high chance of “Yes”
Closer to 0 = high chance of “No”

For example:
If the model says 0.95 → Very likely to pass
If it says 0.20 → Not likely to pass

You can set a cut-off point, say 0.5 — anything above that is considered “Yes,” and below it is “No.”

It’s the go-to model for problems like:
Will the customer churn?
Is this email spam?
Will the patient have a disease?
Simple, fast, and surprisingly powerful.

React with ♥️ if you want me to cover the next one — Decision Trees!

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

Data Science Learning Circle 👆

Alright, let’s get into Decision Trees — one of the easiest and most intuitive ML algorithms out there.

Think of it like this:

You're playing 20 Questions — where each question helps you narrow down the possibilities. Decision Trees work just like that.

It’s like teaching a computer how to ask smart questions to reach an answer.

Real-Life Example:

Say you’re trying to decide whether to go for a walk.

Your brain might go:

Is it raining?
→ Yes → Stay home.
→ No → Next question.

Is it too hot?
→ Yes → Stay home.
→ No → Go for a walk.


This “question-answer” logic is exactly how a Decision Tree works.

It keeps splitting the data based on the most useful questions — until it reaches a decision.


In ML Terms (Still super simple):

Let’s say you’re building a model to predict if someone will buy a product online.

The decision tree might ask:

Is their age above 30?

Did they visit the website more than 3 times this week?

Do they have items in their cart?


Depending on the answers (yes/no), the tree branches out until it reaches a final decision: Buy or Not Buy.

Why It’s Cool:

Easy to understand and explain (no complex math).

Works for both classification (yes/no) and regression (predicting numbers).

Looks just like a flowchart — very visual.


But there’s a twist: one tree is cool, but a bunch of trees is even better.

Shall we talk about that next? It’s called Random Forest — and it’s like a team of decision trees working together.

React with ❤️ if you want me to explain Random Forest

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

ENJOY LEARNING 👍👍

Let’s go — time for Random Forest, one of the most powerful and popular algorithms out there!


Let's say, You want to make an important decision — so instead of asking just one person, you ask 100 people and go with the majority opinion.

That’s Random Forest in a nutshell.

It builds many decision trees, lets them all vote, and then takes the most popular answer.

Why?

Because relying on just one decision tree can be risky — it might overfit (aka learn too much from the training data and mess up on new data).

But if you build many trees on slightly different pieces of data, each one learns something different. When you bring all their results together, the final answer is way more accurate and balanced.

It’s like:

One tree might make a mistake.

But a forest of trees? Much smarter together.


Real-Life Analogy:

Let’s say you’re trying to decide which laptop to buy.

You ask one friend (that’s like a decision tree).

Or you ask 10 friends, each with different experiences, and you go with what most of them say (that’s a random forest).


You’ll feel a lot more confident in your decision, right?

That’s exactly what this algorithm does.

Where to use it:

- Predicting whether someone will default on a loan

- Detecting fraud

- Recommending products

Any place where accuracy really matters


It’s a bit heavier computationally, but the trade-off is often worth it.

React with ♥️ if you want me to cover all ML Algorithms

Up next: K-Nearest Neighbors (KNN) — the friendly neighbor algorithm!

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

ENJOY LEARNING 👍👍

Cool! Let’s jump into K-Nearest Neighbors (KNN) — the friendly, simple, but surprisingly smart algorithm.

Let's say, You move into a new neighborhood and you want to figure out what kind of food the locals like.

So, you knock on the doors of your nearest 5 neighbors and ask them.

If 3 say “we love pizza” and 2 say “we love sushi,” you assume — “Alright, this area probably loves pizza.”

That’s how KNN works.


How It Works:

Let’s say you have a bunch of data points (people, items, whatever) and each one is labeled — like:

This customer bought the product.

This one didn’t.


Now you get a new customer and want to predict if they’ll buy.

KNN looks at the K closest points (neighbors) in the data — maybe 3, 5, or 7 — and checks:

What decision did those neighbors make?

Whichever label is in the majority becomes the prediction for the new one.


Simple voting system — based on closeness.


But Wait, What’s “Nearest”?

It means:

Whose values (like age, income, etc.) are most similar?

“Closeness” is measured using math — like distance in space.


So, it’s not literal neighbors — it’s more like “closest match” in the data.”


Where It Works Well:

Classifying handwritten digits (0–9)

Recommendation systems

Face recognition

When you need something simple but effective


The beauty? No training phase! It just stores the data and looks around at prediction time.


React with ♥️ if you're ready for the next algorithm, Support Vector Machines (SVM). It’s like drawing the cleanest line possible between two groups.

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

ENJOY LEARNING 👍👍

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 👍👍