Deepak Jose

I write about Data Science & Machine Learning🚀. Helping over 400K+ community to excel in Data Science🎯.
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[Deepak Jose @dsbrain_TM]

♻️ Share if someone on your team needs to see this .
#RAG #LLM #AIEngineering #GenerativeAI #Python #OpenSource

[Deepak Jose @dsbrain_TM]

🚨 𝗩𝗲𝗰𝘁𝗼𝗿 𝗱𝗮𝘁𝗮𝗯𝗮𝘀𝗲𝘀 𝗮𝗿𝗲 𝗻𝗼 𝗹𝗼𝗻𝗴𝗲𝗿 𝗿𝗲𝗾𝘂𝗶𝗿𝗲𝗱 𝗳𝗼𝗿 𝗥𝗔𝗚.
I came across a tool called PageIndex this week and honestly didn't expect to be this impressed.

𝘕𝘰 𝘦𝘮𝘣𝘦𝘥𝘥𝘪𝘯𝘨𝘴. 𝘕𝘰 𝘤𝘩𝘶𝘯𝘬𝘪𝘯𝘨. 𝘕𝘰 𝘷𝘦𝘤𝘵𝘰𝘳 𝘴𝘦𝘢𝘳𝘤𝘩.

[Deepak Jose @dsbrain_TM]

Also supports PDFs, markdown, and raw page images without any OCR pipeline. MIT licensed. Fully open source. If you're working on document-heavy AI, this is genuinely worth exploring.

GitHub: https://t.co/ddeDyueKGP

[Deepak Jose @dsbrain_TM]

6) Runway ML → AI video editing
7) ElevenLabs → realistic AI voice generation
8) GitHub Copilot → AI coding assistant
9) Zapier AI → automate repetitive workflows
10) Fireflies AI → meeting transcription + summaries

🎯 People who use AI tools well work 2–3× faster.

[Deepak Jose @dsbrain_TM]

🔥 10 AI Tools That Can Save You 10+ Hours Per Week

1) ChatGPT → research, coding help, brainstorming
2) Perplexity → AI search with sources
3) Notion AI → notes + summarization
4) Grammarly → writing + grammar improvement
5) Midjourney / DALL·E → image generation

[Deepak Jose @dsbrain_TM]

6) Scikit-learn
→ Machine learning models

7) Statsmodels
→ Statistical analysis

8) OpenPyXL
→ Work with Excel files

9) Requests
→ Fetch data from APIs

10) BeautifulSoup
→ Web scraping data

🎯 Master Pandas + SQL first — that’s 70% of real work.

[Deepak Jose @dsbrain_TM]

🔥 10 Python Libraries Every Data Analyst Should Know

1) Pandas
→ Data cleaning & analysis

2) NumPy
→ Fast numerical computing

3) Matplotlib
→ Basic data visualization

4) Seaborn
→ Statistical visualizations

5) Plotly
→ Interactive dashboards & charts

[Deepak Jose @dsbrain_TM]

5) Conversion Rate
→ Visitors → Customers %

6) Churn Rate
→ % of customers who leave

7) Average Order Value (AOV)
→ Avg money spent per order

8) Monthly Active Users (MAU)
→ Number of active users in a month

[Deepak Jose @dsbrain_TM]

📊 Data Analysts Are Hired to Track These KPIs

1) Revenue
→ Total money generated

2) Profit Margin
→ Profit / Revenue

3) Customer Acquisition Cost (CAC)
→ Cost to get one new customer

4) Customer Lifetime Value (LTV)
→ Total revenue from one customer

[Deepak Jose @dsbrain_TM]

📈 Data Analytics with AI - IIT Roorkee

https://t.co/20p6K54inp

⚠️Deadline approaching soon. Limited seats!

[Deepak Jose @dsbrain_TM]

Hi friend 👋,Masai School is offering Certification programs from IIT Roorkee, IIM Mumbai, IIT Patna, MIT & IIM Ranchi.

Book an assessment test now! 👇:

🎓 AI & Data Science - IIT Roorkee

https://t.co/v1uq567CXj

[Deepak Jose @dsbrain_TM]

💼 Business Analytics & AI - IIM Ranchi

https://t.co/I6BfseidWd

🤖 AI & Machine Learning - IIT Patna

https://t.co/G7nE4i4iPd

🐍 ML with Python - MIT

https://t.co/TIIdXUaOgR

[Deepak Jose @dsbrain_TM]

→ Likelihood of an event (0 to 1)

5) Normal Distribution
→ Bell curve (68–95–99.7 rule)

6) p-value
→ Probability results happened by chance
→ p < 0.05 = statistically significant (commonly)

7) Confidence Interval
→ Range where true value likely lies

[Deepak Jose @dsbrain_TM]

📊 Statistics You MUST Know for Data Interviews

1) Mean vs Median
→ Mean = average | Median = middle (robust to outliers)

2) Standard Deviation
→ How spread out the data is

3) Correlation (−1 to +1)
→ Strength + direction of relationship (not causation)

4) Probability

[Deepak Jose @dsbrain_TM]

Is Age > 30?
├── Yes → Income > 50K?
│ ├── Yes → Approve Loan
│ └── No → Reject Loan
└── No → Reject Loan

Key point to remember:
Easy to understand and explain, but can overfit if the tree grows too deep.

[Deepak Jose @dsbrain_TM]

What is a decision tree?

A decision tree is a supervised learning model that makes predictions by repeatedly splitting data based on feature conditions. Each internal node represents a decision, branches represent outcomes, and leaf nodes give the final prediction.
Intuition

[Deepak Jose @dsbrain_TM]

(how to think about it):
It works like a flowchart of yes/no questions, narrowing down decisions step by step until a final answer is reached.
Example logic: