Data Science Reality

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[Data Science Reality @Datasreality]

💥 An amazing SQL cheat sheet:

#SQL #CheatSheet #DataScience @DataScienceDojo

[Arif Alam @Arifalam4u]

𝗛𝗼𝘄 𝗱𝗼 𝗡𝗲𝘂𝗿𝗮𝗹 𝗡𝗲𝘁𝘄𝗼𝗿𝗸𝘀 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝘁𝗵𝗶𝗻𝗸?

Everyone’s heard of them. Few actually get them.

No math. No AI jargon.
Just a clean mental model to truly understand how they work.

Let’s start with a relatable story ↓

⤷ Imagine teaching a kid to recognize cats and dogs
You show a picture.
The kid stares. Thinks.
Says “Cat.”

Next day, you show another one.
This time, the kid’s confused maybe it’s a dog?

With time, they start noticing patterns:
→ Pointy ears? Could be a cat
→ Floppy ears? Probably a dog
→ Whiskers? Small paws? Long snout?

They learn by feedback. Adjust. Improve.
That’s exactly what a neural network does.

Let’s map this step-by-step:

𝗟𝗔𝗬𝗘𝗥 𝟭: 𝗧𝗵𝗲 𝗜𝗻𝗽𝘂𝘁 𝗟𝗮𝘆𝗲𝗿
This is the model’s “eyes.”
Each pixel in an image → becomes a number.
An image of size 100x100 → becomes 10,000 numbers.

Just like how your eyes capture light and color, the input layer feeds raw data into the system.

𝗟𝗔𝗬𝗘𝗥 𝟮: 𝗛𝗶𝗱𝗱𝗲𝗻 𝗟𝗮𝘆𝗲𝗿𝘀 (𝘁𝗵𝗲 𝗯𝗿𝗮𝗶𝗻)
Each neuron in this layer tries to extract patterns:

↳ One might detect edges
↳ Another might detect circular shapes
↳ Another starts noticing fur texture

And as we stack more layers…
They start learning higher-level concepts:

↳ Two eyes + triangle ears + fur → likely a cat
↳ Long tongue + floppy ears + big nose → likely a dog

Each layer transforms the data into increasingly useful features.

𝗪𝗛𝗔𝗧 𝗔𝗥𝗘 𝗪𝗘𝗜𝗚𝗛𝗧𝗦 𝗔𝗡𝗗 𝗔𝗖𝗧𝗜𝗩𝗔𝗧𝗜𝗢𝗡𝗦?
Think of weights as how much the model “trusts” a signal.
The more useful a feature is (like whiskers for cats), the higher the weight.

Activations are like switches.
If a neuron sees a strong enough signal, it turns on. If not, it stays silent.

Together, these build a brain that pays attention to the right stuff.

𝗢𝗨𝗧𝗣𝗨𝗧 𝗟𝗔𝗬𝗘𝗥: 𝗧𝗵𝗲 𝗔𝗻𝘀𝘄𝗲𝗿
The final layer gives you probabilities.

⤷ 82% Cat
⤷ 18% Dog

But what if it’s wrong? That’s where learning kicks in.

𝗘𝗥𝗥𝗢𝗥 + 𝗙𝗘𝗘𝗗𝗕𝗔𝗖𝗞 = 𝗜𝗠𝗣𝗥𝗢𝗩𝗘𝗠𝗘𝗡𝗧
The model compares its guess with the actual label.
It calculates “how wrong” it was → then adjusts its weights using backpropagation.

Each time it sees more data, it gets slightly better.

The goal?
To minimize the error → until the network becomes an expert at prediction.

𝗘𝗫𝗔𝗠𝗣𝗟𝗘 𝗜𝗡 𝗔𝗖𝗧𝗜𝗢𝗡:

You feed a neural net 1000s of images of handwritten digits (like 3s, 5s, 9s).
You don’t code “how to recognize a 3”.
You just show it examples.

The model learns:
↳ 3 has two curves
↳ 5 has a flat top
↳ 9 has a loop at the top and a stick below

This is pattern learning at scale.

𝗕𝗢𝗧𝗧𝗢𝗠 𝗟𝗜𝗡𝗘:
A neural network is just a system that:

→ Takes input
→ Learns patterns
→ Adjusts with feedback
→ Gets smarter with experience

It’s not magic.
It’s a smart loop of trial → error → improvement.

That’s the brain of AI.
And now… you actually get it.

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Follow @Arifalam4u

[Arif Alam @Arifalam4u]

𝗬𝗼𝘂𝗿 𝗖𝗵𝗮𝘁𝗚𝗣𝗧 𝗶𝘀 𝗱𝘂𝗺𝗯.
𝗜𝘁 𝗵𝗮𝘀 𝗻𝗼 𝗺𝗲𝗺𝗼𝗿𝘆 𝗼𝗳 𝘆𝗼𝘂𝗿 𝗱𝗮𝘁𝗮.
𝗜𝘁 𝗴𝘂𝗲𝘀𝘀𝗲𝘀. 𝗜𝘁 𝗺𝗮𝗸𝗲𝘀 𝘀𝘁𝘂𝗳𝗳 𝘂𝗽.
𝗬𝗼𝘂 𝗷𝘂𝘀𝘁 𝗱𝗶𝗱𝗻’𝘁 𝗻𝗼𝘁𝗶𝗰𝗲 𝗯𝗲𝗰𝗮𝘂𝘀𝗲 𝗶𝘁 𝘄𝗿𝗼𝘁𝗲 𝗶𝘁 𝗻𝗶𝗰𝗲𝗹𝘆.

𝗪𝗮𝗻𝗻𝗮 𝗺𝗮𝗸𝗲 𝗶𝘁 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝘀𝗺𝗮𝗿𝘁?

𝗨𝘀𝗲 𝗥𝗔𝗚 → Retrieval-Augmented Generation

Let’s break it down ⬇️

𝗪𝗵𝘆 𝗟𝗟𝗠𝘀 𝗳𝗮𝗶𝗹 𝘄𝗶𝘁𝗵 𝗿𝗲𝗮𝗹 𝗱𝗮𝘁𝗮

These models were trained on books, forums, Wikipedia…

But they’ve never seen:

↳ 𝗬𝗼𝘂𝗿 𝗲𝗺𝗮𝗶𝗹𝘀
↳ 𝗬𝗼𝘂𝗿 𝗽𝗿𝗼𝗱𝘂𝗰𝘁 𝗱𝗼𝗰𝘀
↳ 𝗬𝗼𝘂𝗿 𝗰𝘂𝘀𝘁𝗼𝗺𝗲𝗿 𝗾𝘂𝗲𝗿𝗶𝗲𝘀

𝗦𝗼 𝘁𝗵𝗲𝘆 𝗴𝘂𝗲𝘀𝘀.
And they guess wrong confidently.

𝗥𝗔𝗚 𝗳𝗶𝘅𝗲𝘀 𝘁𝗵𝗮𝘁. 𝗛𝗲𝗿𝗲’𝘀 𝗵𝗼𝘄:

↳ Take a user’s question
↳ Search your private knowledge base for answers
↳ Inject the most relevant chunks into the prompt
↳ Let the LLM reply with grounded context

𝗧𝗵𝗲 𝗟𝗟𝗠 𝗿𝗲𝗮𝗱𝘀 𝗯𝗲𝗳𝗼𝗿𝗲 𝗶𝘁 𝘄𝗿𝗶𝘁𝗲𝘀.

𝗘𝘅𝗮𝗺𝗽𝗹𝗲

→ Without RAG: What’s the refund policy?
Model replies: Refunds not allowed after 14 days. (Made-up nonsense)

→ With RAG:
Model searches your internal return-policy. pdf, finds the 2024 clause, and answers:
Refunds are allowed within 30 days if the product is unopened and in original condition.

Same ChatGPT.
But now? It actually knows stuff.

𝗥𝗔𝗚 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲 (𝗥𝗲𝗮𝗹 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴)
1. Ingest
↳ Upload your PDFs, Notion docs, Confluence pages, etc.
2. Chunk + Embed
↳ Break into readable chunks
↳ Convert each chunk to a vector using embedding models (OpenAI, BGE, etc.)
3. Store in Vector DB
↳ FAISS, Pinecone, Weaviate use whichever fits your stack
4. Search on Query
↳ User types a question
↳ You retrieve top relevant chunks (semantic similarity)
5. Augment Prompt
↳ Inject the retrieved context into the prompt
↳ Then pass it to the LLM for response

That’s it.
Now your bot can access any internal info like a pro.

𝗪𝗵𝗲𝗿𝗲 𝗶𝘀 𝗥𝗔𝗚 𝗯𝗲𝗶𝗻𝗴 𝘂𝘀𝗲𝗱 𝘁𝗼𝗱𝗮𝘆?

↳ Slack AI Search
↳ Notion AI
↳ GitHub Copilot Chat for private repos
↳ Almost every AI chatbot in enterprise

It’s not just useful It’s the default architecture for grounded, real-world LLM apps

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Follow @Arifalam4u

[Arif Alam @Arifalam4u]

𝗧𝗵𝗲 𝗯𝗲𝘀𝘁 𝗠𝗟 𝗮𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺 𝗶𝗻 𝗮 𝗰𝗿𝗶𝘀𝗶𝘀? 𝗥𝗮𝗻𝗱𝗼𝗺 𝗙𝗼𝗿𝗲𝘀𝘁.

No matter if you’re building a fraud detection model or a credit scoring system
𝗥𝗮𝗻𝗱𝗼𝗺 𝗙𝗼𝗿𝗲𝘀𝘁 often beats everything else without deep tuning.

Here’s how it works, 𝗮𝘀 𝗶𝗳 𝘆𝗼𝘂’𝗿𝗲 𝗲𝘅𝗽𝗹𝗮𝗶𝗻𝗶𝗻𝗴 𝗶𝘁 𝘁𝗼 𝗮 𝗰𝗼𝗹𝗹𝗲𝗮𝗴𝘂𝗲

𝗧𝗵𝗲 𝗣𝗿𝗼𝗯𝗹𝗲𝗺 𝗪𝗶𝘁𝗵 𝗗𝗲𝗰𝗶𝘀𝗶𝗼𝗻 𝗧𝗿𝗲𝗲𝘀

Decision Trees are simple and powerful.
But they tend to overfit. Badly.

They’ll memorize your training data, split too many times, and get confused by noise.
Which means great accuracy on paper, but terrible performance in real-world data.

So what if we don’t rely on just one tree?

𝗘𝗻𝘁𝗲𝗿: 𝗥𝗮𝗻𝗱𝗼𝗺 𝗙𝗼𝗿𝗲𝘀𝘁 🌲🌲🌲

Imagine 𝟭𝟬𝟬 different Decision Trees,
Each trained on a 𝗿𝗮𝗻𝗱𝗼𝗺 𝘀𝘂𝗯𝘀𝗲𝘁 of your data
Each considering a 𝗿𝗮𝗻𝗱𝗼𝗺 𝘀𝘂𝗯𝘀𝗲𝘁 of features

↳ That’s a Random Forest: a collection of weak learners voting together.

Why does this work so well?

Because randomization → lowers correlation → reduces overfitting → increases generalization.

Think of it like this:

1 dumb voter = noisy
100 random voters = surprisingly wise

𝗛𝗼𝘄 𝗶𝘁 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝘄𝗼𝗿𝗸𝘀

↳ First, we use bootstrapping
 Randomly sample data (with replacement) to create different training sets

↳ Then we build many Decision Trees
 Each one picks random features at each split

↳ Final output?
 Majority vote for classification
 Average value for regression

Result: High accuracy, low variance, no manual tuning needed.

𝗥𝗲𝗮𝗹-𝗪𝗼𝗿𝗹𝗱 𝗘𝘅𝗮𝗺𝗽𝗹𝗲

Let’s say you work in FinTech.

You’re building a fraud detection model.
But fraud patterns change.
A simple Decision Tree overfits to old patterns.

𝗪𝗶𝘁𝗵 𝗥𝗮𝗻𝗱𝗼𝗺 𝗙𝗼𝗿𝗲𝘀𝘁:

↳ You feed it transaction history, user metadata, device info
↳ It trains 100+ trees
↳ Learns generalized patterns of fraud
↳ And flags anomalies with surprisingly high precision even on unseen data

You just saved your company millions.

𝗪𝗵𝗲𝗻 𝗦𝗵𝗼𝘂𝗹𝗱 𝗬𝗼𝘂 𝗨𝘀𝗲 𝗜𝘁?

✅ When you need high accuracy out of the box
✅ When you want to avoid heavy tuning
✅ When your data is structured and tabular
✅ When interpretability is less important than performance

𝗕𝗲𝘀𝘁 𝗣𝗮𝗿𝘁?
Random Forest can also give you feature importance
↳ So you learn what actually drives predictions.

𝗧𝗶𝗽: Use 𝗦𝗰𝗶𝗸𝗶𝘁-𝗟𝗲𝗮𝗿𝗻

from sklearn.ensemble import RandomForestClassifier

model = RandomForestClassifier(n_estimators=100, max_depth=10)
https://t.co/HmRW1WJDhS(X_train, y_train)
predictions = model.predict(X_test)

↳ Add model.feature_importances_ to see what mattered most.

𝗧𝗼 𝗿𝗲𝗰𝗮𝗽:

Random Forest is not “fancy”
It’s just clever, practical, and battle-tested.

If you’re not sure where to start with a supervised ML problem
Start with Random Forest.

It’ll surprise you with how far it takes you.

[Arif Alam @Arifalam4u]

𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗠𝗟 𝘄𝗶𝘁𝗵𝗼𝘂𝘁 𝘁𝗶𝗺𝗲 𝗰𝗼𝗺𝗽𝗹𝗲𝘅𝗶𝘁𝘆 𝗶𝘀 𝗹𝗶𝗸𝗲 𝗰𝗼𝗱𝗶𝗻𝗴 𝗯𝗹𝗶𝗻𝗱𝗳𝗼𝗹𝗱𝗲𝗱.

You might train the model…
You might get good accuracy…

But if you don’t know how the algorithm scales with data,
You’ll end up deploying a model that collapses in production.

𝗟𝗶𝗻𝗲𝗮𝗿 𝗥𝗲𝗴𝗿𝗲𝘀𝘀𝗶𝗼𝗻 / 𝗟𝗼𝗴𝗶𝘀𝘁𝗶𝗰 𝗥𝗲𝗴𝗿𝗲𝘀𝘀𝗶𝗼𝗻
↳ 𝗧𝗶𝗺𝗲: 𝑶(𝑛𝑓² + 𝑓³) if closed-form, 𝑶(𝑛𝑓) per iteration in gradient descent
Simple, scalable, great for baseline models
Struggles only with ultra-high dimensionality (think text data with 100K+ tokens)

𝗞-𝗡𝗲𝗮𝗿𝗲𝘀𝘁 𝗡𝗲𝗶𝗴𝗵𝗯𝗼𝗿𝘀 (𝗞𝗡𝗡)
↳ 𝗧𝗶𝗺𝗲: 𝑶(𝑛𝑓) at inference yes, prediction time
There’s no training cost
But the larger your data, the slower your predictions become
Imagine a search through 1 million rows per query

𝗗𝗲𝗰𝗶𝘀𝗶𝗼𝗻 𝗧𝗿𝗲𝗲
↳ 𝗧𝗶𝗺𝗲: 𝑶(𝑛𝑓 log 𝑛)
Very efficient on both training and inference
But trees tend to overfit, especially on noisy datasets

𝗥𝗮𝗻𝗱𝗼𝗺 𝗙𝗼𝗿𝗲𝘀𝘁
↳ 𝗧𝗶𝗺𝗲: 𝑶(𝑡 × 𝑛𝑓 log 𝑛) → t = number of trees
Training can be parallelized
So while it’s heavier than a single tree, it’s still production-grade at scale

𝗚𝗿𝗮𝗱𝗶𝗲𝗻𝘁 𝗕𝗼𝗼𝘀𝘁𝗲𝗱 𝗧𝗿𝗲𝗲𝘀 (XGBoost, LightGBM)
↳ 𝗧𝗶𝗺𝗲: 𝑶(𝑘𝑡 × 𝑛 log 𝑛)
Outperforms RF on many tasks
But the training time can grow with too many boosting rounds (𝑘)
LightGBM is faster on large, sparse datasets due to leaf-wise splits

𝗦𝗨𝗣𝗣𝗢𝗥𝗧 𝗩𝗘𝗖𝗧𝗢𝗥 𝗠𝗔𝗖𝗛𝗜𝗡𝗘 (𝗦𝗩𝗠)
↳ 𝗧𝗶𝗺𝗲: Worst case 𝑶(𝑛³)
Great for small datasets
But scales very poorly with more rows
Avoid for big data unless using linear SVM + approximate solvers

𝗡𝗮𝗶𝘃𝗲 𝗕𝗮𝘆𝗲𝘀
↳ 𝗧𝗶𝗺𝗲: 𝑶(𝑛𝑓)
Lightning fast, even on huge feature sets
Why? No optimization just counting + probabilities
Perfect for spam detection, text classification at scale

𝗞-𝗠𝗲𝗮𝗻𝘀 𝗖𝗹𝘂𝘀𝘁𝗲𝗿𝗶𝗻𝗴
↳ 𝗧𝗶𝗺𝗲: 𝑶(𝑛𝑘𝑡𝑓) → k = clusters, t = iterations
Scales decently
But sensitive to initialization
Use k-means++ for smarter centroids and faster convergence

𝗣𝗖𝗔 (𝗣𝗿𝗶𝗻𝗰𝗶𝗽𝗮𝗹 𝗖𝗼𝗺𝗽𝗼𝗻𝗲𝗻𝘁 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀)
↳ 𝗧𝗶𝗺𝗲: 𝑶(𝑓²𝑛 + 𝑓³)
Used for dimensionality reduction
But gets heavy when features go into tens of thousands
If using on image/text data, combine with truncation or randomized SVD

𝗡𝗲𝘂𝗿𝗮𝗹 𝗡𝗲𝘁𝘀 (𝗠𝗟𝗣s, CNNs, RNNs)
↳ 𝗧𝗶𝗺𝗲: Varies wildly
Basic MLP = 𝑶(𝑛𝑙𝑓²) where l = layers
CNNs depend on filter sizes
RNNs = slow for sequence length
Use batching, GPU acceleration, and smart layer design to scale deep learning

𝗧𝗵𝗲 𝗧𝗮𝗸𝗲𝗮𝘄𝗮𝘆?

→ 𝗗𝗼𝗻’𝘁 𝗽𝗶𝗰𝗸 𝗮𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺𝘀 𝗯𝗮𝘀𝗲𝗱 𝗼𝗻 𝗮𝗰𝗰𝘂𝗿𝗮𝗰𝘆 𝗮𝗹𝗼𝗻𝗲

---

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[Data Science Reality @Datasreality]

@Arifalam4u Great Share

[Arif Alam @Arifalam4u]

𝗦𝗤𝗟 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗖𝗵𝗲𝗮𝘁𝗦𝗵𝗲𝗲𝘁 (𝟮𝟬𝟮𝟱 𝗘𝗱𝗶𝘁𝗶𝗼𝗻)
𝗟𝗲𝘁’𝘀 𝗯𝗲 𝗵𝗼𝗻𝗲𝘀𝘁: 𝗦𝗤𝗟 𝗰𝗮𝗻 𝗺𝗮𝗸𝗲 𝗼𝗿 𝗯𝗿𝗲𝗮𝗸 𝘆𝗼𝘂𝗿 𝗶𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄.

➤ 𝗝𝗢𝗜𝗡𝘀 𝗮𝗿𝗲 𝗮 𝗳𝗮𝘃𝗼𝗿𝗶𝘁𝗲 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻

⟶ INNER JOIN: Returns matching rows
⟶ LEFT JOIN: All rows from left + matched from right
⟶ RIGHT JOIN: All rows from right + matched from left
⟶ FULL JOIN: All rows with matches from both
⟶ SELF JOIN: Join table with itself

Pro tip: Add ON conditions carefully or get a cartesian disaster

➤ 𝗪𝗶𝗻𝗱𝗼𝘄 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀 𝗮𝗿𝗲 𝗮𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝗽𝗹𝘂𝘀 𝗲𝗮𝘀𝘆 𝘀𝗰𝗼𝗿𝗲

⟶ ROW_NUMBER() OVER(PARTITION BY dept ORDER BY salary DESC)
⟶ RANK(), DENSE_RANK(), LAG(), LEAD()

Use case: Find second-highest salary per department
Why it’s asked: Checks if you understand partitioning

➤ 𝗖𝗼𝗺𝗺𝗼𝗻 𝗧𝗮𝗯𝗹𝗲 𝗘𝘅𝗽𝗿𝗲𝘀𝘀𝗶𝗼𝗻 (𝗖𝗧𝗘)

WITH top_emps AS (
SELECT name, salary,
RANK() OVER(PARTITION BY dept ORDER BY salary DESC) AS rnk
FROM employees
)
SELECT * FROM top_emps WHERE rnk = 1;

Bonus: Cleaner than subqueries, super readable

➤ 𝗧𝗿𝗶𝗰𝗸𝘆 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀 𝗬𝗼𝘂 𝗠𝗨𝗦𝗧 𝗽𝗿𝗮𝗰𝘁𝗶𝗰𝗲

⟶ Find second highest salary
⟶ Detect duplicates
⟶ Running total by date
⟶ Find employees who never submitted reports
⟶ Department with highest avg salary
⟶ Show top 3 items sold per category
⟶ Users active 3 days in a row

➤ 𝗚𝗿𝗼𝘂𝗽𝗶𝗻𝗴 𝗮𝗻𝗱 𝗔𝗚𝗚𝗥𝗘𝗚𝗔𝗧𝗘𝗦

⟶ GROUP BY, HAVING, COUNT(DISTINCT ...)
⟶ GROUPING SETS, CUBE, ROLLUP for advanced analysis
⟶ Aggregate + CASE combo to count conditionally

➤ 𝗗𝗔𝗧𝗘𝗦 + 𝗦𝗨𝗕𝗤𝗨𝗘𝗥𝗜𝗘𝗦 + 𝗘𝗫𝗜𝗦𝗧𝗦

⟶ DATE_DIFF(), CURRENT_DATE, DATE_TRUNC()
⟶ WHERE EXISTS (...) to filter on child records
⟶ Subqueries in SELECT and WHERE — but use wisely

➤ 𝗗𝗘𝗕𝗨𝗚𝗚𝗜𝗡𝗚 𝗦𝗞𝗜𝗟𝗟

⟶ Check joins: run SELECT COUNT(*) before & after
⟶ Run in parts: test subqueries in isolation
⟶ Check NULL handling: COALESCE() or logic fails

➤ 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗧𝗜𝗣𝗦

⟶ Always explain your approach first
⟶ If stuck: narrate your thought process
⟶ Use LIMIT 5 to validate outputs
⟶ Ask: “Can I assume table names and structure?”

➤ 𝗕𝗼𝗻𝘂𝘀: 𝗠𝗨𝗦𝗧-𝗞𝗡𝗢𝗪 𝗖𝗢𝗠𝗠𝗔𝗡𝗗𝗦

DISTINCT, CASE, IS NULL, BETWEEN, IN, ANY, ALL, CAST(), LIKE, REGEXP

𝗖𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀 𝗹𝗼𝘃𝗲 𝗰𝗮𝗻𝗱𝗶𝗱𝗮𝘁𝗲𝘀 𝘄𝗵𝗼 𝗰𝗮𝗻 𝗿𝗲𝗮𝗱 + 𝗱𝗲𝗯𝘂𝗴 𝗰𝗼𝗺𝗽𝗹𝗲𝘅 𝗤𝗨𝗘𝗥𝗜𝗘𝗦.
It’s not about knowing everything it’s about solving smartly.

---

That's a wrap!!

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Everyday, I share post on above topics.

𝐑𝐞𝐬𝐨𝐮𝐫𝐜𝐞𝐬 𝐭𝐨 𝐆𝐞𝐭 𝐒𝐭𝐚𝐫𝐭𝐞𝐝
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📸/ Datalemur. com

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