Data Science Projects

SQL Roadmap

🎯 GigaChat 3.5 Reasoning: 5 Key Features

1️⃣ Advanced Reasoning: Explores multiple step-by-step paths, using automated verification to reinforce correct answers and self-correct

2️⃣ Autonomous Tool Usage: Independently decides when to call external APIs or revise earlier steps

3️⃣ Linear Attention: Proprietary architecture retains key context points without re-matching from scratch

4️⃣ Token Economy: Uses 37% fewer tokens than DeepSeek V4 Flash Preview on math problems

5️⃣ Proven Performance: Open-source LLM (built on GigaChat 3.5 Ultra) with massive benchmark gains:
• IFBench: 44 → 77
• Natural Plan: 64 → 80
• LiveCodeBench v6: 56 → 85

🔗 MIT License. Weights on Hugging Face:  fp8 | bf16

📌 Roadmap to Master Machine Learning in 6 Steps

Whether you're just starting or looking to go pro in ML, this roadmap will keep you on track:

1️⃣ Learn the Fundamentals
Build a math foundation (algebra, calculus, stats) + Python + libraries like NumPy & Pandas

2️⃣ Learn Essential ML Concepts
Start with supervised learning (regression, classification), then unsupervised learning (K-Means, PCA)

3️⃣ Understand Data Handling
Clean, transform, and visualize data effectively using summary stats & feature engineering

4️⃣ Explore Advanced Techniques
Delve into ensemble methods, CNNs, deep learning, and NLP fundamentals

5️⃣ Learn Model Deployment
Use Flask, FastAPI, and cloud platforms (AWS, GCP) for scalable deployment

6️⃣ Build Projects & Network
Participate in Kaggle, create portfolio projects, and connect with the ML community

React ❤️ for more

How to Become a Data Analyst from Scratch! 🚀

Whether you're starting fresh or upskilling, here's your roadmap:

➜ Master Excel and SQL - solve SQL problems from leetcode & hackerank
➜ Get the hang of either Power BI or Tableau - do some hands-on projects
➜ learn what the heck ATS is and how to get around it
➜ learn to be ready for any interview question
➜ Build projects for a data portfolio
➜ And you don't need to do it all at once!
➜ Fail and learn to pick yourself up whenever required

Whether it's acing interviews or building an impressive portfolio, give yourself the space to learn, fail, and grow. Good things take time ✅

Like if it helps ❤️

I have curated best 80+ top-notch Data Analytics Resources 👇👇
https://topmate.io/analyst/861634

Hope it helps :)

Use These AI APIs in Your Projects to Get Hired👇

1. OpenAI API
https://developers.openai.com/api/docs

2. Anthropic Claude API
https://docs.anthropic.com

3. Google Gemini API
https://ai.google.dev

4. Stability AI API
https://platform.stability.ai

5. Hugging Face Inference API
https://huggingface.co/docs/api-inference/index

6. ElevenLabs API
https://elevenlabs.io/docs

7. AssemblyAI API
https://assemblyai.com/docs

If you are targeting your first Data Analyst job then this is why you should avoid guided projects

The common thing nowadays is "Coffee Sales Analysis" and "Pizza Sales Analysis"

I don't see these projects as PROJECTS

But as big RED flags

We are showing our SKILLS through projects, RIGHT?

Then what's WRONG with these projects?

Don't think from YOUR side

Think from the HIRING team's side

These projects have more than a MILLION views on YouTube

Even if you consider 50% of this NUMBER

Then just IMAGINE how many aspiring Data Analysts would have created this same project

Hiring teams see hundreds of resumes and portfolios on a DAILY basis

Just imagine how many times they would have seen the SAME titles of projects again and again

They would know that these projects are PUBLICLY available for EVERYONE

You have simply copied pasted the ENTIRE project from YouTube

So now if I want to hire a Data Analyst then how would I JUDGE you or your technical skills?

What is the USE of Pizza or Coffee sales analysis projects for MY company?

By doing such guided projects, you are involving yourself in a big circle of COMPETITION

I repeat, there were more than a MILLION views

So please AVOID guided projects at all costs

Guided projects are good for your personal PRACTICE and LinkedIn CONTENT

But try not to involve them in your PORTFOLIO or RESUME

🚦Top 10 Data Science Tools🚦

Here we will examine the top best Data Science tools that are utilized generally by data researchers and analysts. But prior to beginning let us discuss about what is Data Science.

🛰What is Data Science ?

Data science is a quickly developing field that includes the utilization of logical strategies, calculations, and frameworks to extract experiences and information from organized and unstructured data .

🗽Top Data Science Tools that are normally utilized :

1.) Jupyter Notebook : Jupyter Notebook is an open-source web application that permits clients to make and share archives that contain live code, conditions, representations, and narrative text .

2.) Keras : Keras is a famous open-source brain network library utilized in data science. It is known for its usability and adaptability.
Keras provides a range of tools and techniques for dealing with common data science problems, such as overfitting, underfitting, and regularization.

3.) PyTorch : PyTorch is one more famous open-source AI library utilized in information science. PyTorch also offers easy-to-use interfaces for various tasks such as data loading, model building, training, and deployment, making it accessible to beginners as well as experts in the field of machine learning.

4.) TensorFlow : TensorFlow allows data researchers to play out an extensive variety of AI errands, for example, image recognition , natural language processing , and deep learning.

5.) Spark : Spark allows data researchers to perform data processing tasks like data control, investigation, and machine learning , rapidly and effectively.

6.) Hadoop : Hadoop provides a distributed file system (HDFS) and a distributed processing framework (MapReduce) that permits data researchers to handle enormous datasets rapidly.

7.) Tableau : Tableau is a strong data representation tool that permits data researchers to make intuitive dashboards and perceptions. Tableau allows users to combine multiple charts.

8.) SQL : SQL (Structured Query Language) SQL permits data researchers to perform complex queries , join tables, and aggregate data, making it simple to extricate bits of knowledge from enormous datasets. It is a powerful tool for data management, especially for large datasets.

9.) Power BI : Power BI is a business examination tool that conveys experiences and permits clients to make intuitive representations and reports without any problem.

10.) Excel : Excel is a spreadsheet program that broadly utilized in data science. It is an amazing asset for information the board, examination, and visualization .Excel can be used to explore the data by creating pivot tables, histograms, scatterplots, and other types of visualizations.

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Get FREE access to company-specific interview kits, previous questions, preparation strategies, and important resources! 👇

Google :- https://pdlink.in/4xtUyIG

Amazon :- https://pdlink.in/45Q0YWR

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Wipro :- https://pdlink.in/4fMo1rA

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📌 share it with friends preparing for placements

Myths About Data Science:

✅ Data Science is Just Coding

Coding is a part of data science. It also involves statistics, domain expertise, communication skills, and business acumen. Soft skills are as important or even more important than technical ones

✅ Data Science is a Solo Job

I wish. I wanted to be a data scientist so I could sit quietly in a corner and code. Data scientists often work in teams, collaborating with engineers, product managers, and business analysts

✅ Data Science is All About Big Data

Big data is a big buzzword (that was more popular 10 years ago), but not all data science projects involve massive datasets. It’s about the quality of the data and the questions you’re asking, not just the quantity.

✅ You Need to Be a Math Genius

Many data science problems can be solved with basic statistical methods and simple logistic regression. It’s more about applying the right techniques rather than knowing advanced math theories.

✅ Data Science is All About Algorithms

Algorithms are a big part of data science, but understanding the data and the business problem is equally important. Choosing the right algorithm is crucial, but it’s not just about complex models. Sometimes simple models can provide the best results. Logistic regression!

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🚀 Data Analyst Roadmap


First things first 👇
❌ Don’t buy expensive courses to become a Data Analyst.

💡 Consistency > Certifications > Courses

Skills and practice are what actually get you hired.


✅ Mandatory Skills for a Data Analyst

1️⃣ SQL

Practice as much as possible.

This is the most important skill for any Data Analyst.

📚 Resource
YouTube Channel: Ankit Bansal
Playlist: SQL Practice / SQL Interview Questions


2️⃣ Excel

Advanced Excel is required.

Focus on:

• Formulas
• Pivot Tables
• Power Query Basics
• Data Cleaning
• Data Analysis functions


3️⃣ BI Tools

Choose ONE:

• Power BI
• Tableau

❌ Do NOT learn both at the same time.

If you choose Power BI, learn these deeply:

• Power Query
• DAX
• M Code

📚 Resources

YouTube Channel: Learnit Training
Video: Power BI DAX Full Tutorial for Beginners

YouTube Channel: Enterprise DNA
Playlist: DAX Practice Series

YouTube Channel: Goodly (Chandeep Chhabra)
Playlists: Power Query Tutorials and M Code Tutorials


4️⃣ Python

Focus mainly on:

• NumPy
• Pandas
• Basic visualization libraries (Matplotlib / Seaborn)

You don’t need deep ML knowledge for Data Analyst roles.


⭐ Good-to-Have Skills

These are not mandatory but help in career growth:

• Machine Learning (basic understanding)
• PySpark
• Databricks (becoming popular in data teams)
• Cloud platforms

Cloud options:

• Azure
• GCP


🎓 Certifications (Optional)

Certifications can help but are not required.

Useful ones:

• Microsoft Power BI Certification – PL-300
• Tableau Certification
• Azure Cloud Certification


❌ No other certifications are required.

Save your money.

Focus on skills, projects, and practice.

Credit: Mohan

9 tips to get started with Data Analysis:

Learn Excel, SQL, and a programming language (Python or R)

Understand basic statistics and probability

Practice with real-world datasets (Kaggle, Data.gov)

Clean and preprocess data effectively

Visualize data using charts and graphs

Ask the right questions before diving into data

Use libraries like Pandas, NumPy, and Matplotlib

Focus on storytelling with data insights

Build small projects to apply what you learn

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

ENJOY LEARNING 👍👍

𝗔𝗜 & 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗣𝗿𝗼𝗴𝗿𝗮𝗺 (𝗡𝗼 𝗖𝗼𝗱𝗶𝗻𝗴 𝗡𝗲𝗲𝗱𝗲𝗱)

Apply Now👉:- https://pdlink.in/4aYWald

By E&ICT Academy, IIT Roorkee

Batch Closing Soon - 18th July 2026

Data Science Benefits

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These FREE courses can help you learn Data Analytics, Power BI & Excel skills that companies actually hire for 🚀

✨ What you’ll learn:
✔ Excel + Power BI 📊
✔ Data Cleaning with Power Query
✔ Interactive Dashboards
✔ Modern Analytics Skills

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🎓 Perfect for Students, Freshers & Career Switchers

✅ Step-by-Step Guide to Create a Data Science Portfolio 🎯📊

✅ 1️⃣ Pick Your Focus Area
Decide what kind of data scientist you want to be:
• Data Analyst → Excel, SQL, Power BI/Tableau 📈
• Machine Learning → Python, Scikit-learn, TensorFlow 🧠
• Data Engineer → Python, Spark, Airflow, Cloud ⚙️
• Full-stack DS → Mix of analysis + ML + deployment 🧑‍💻

✅ 2️⃣ Plan Your Portfolio Sections
Your portfolio should include:
• Home Page – Quick intro about you 👋
• About Me – Education, tools, skills 📝
• Projects – With code, visuals & explanations 📊
• Blog (optional) – Share insights & tutorials ✍️
• Contact – Email, LinkedIn, GitHub, etc. ✉️

✅ 3️⃣ Build the Portfolio Website
Options to build:
• Use Jupyter Notebook + GitHub Pages 🌐
• Create with Streamlit or Gradio (for interactive apps) ✨
• Full site: HTML/CSS or React + deploy on Netlify/Vercel 🚀

✅ 4️⃣ Add 2–4 Quality Projects
Project ideas:
• EDA on real-world datasets 🔍
• Machine learning prediction model 🔮
• NLP app (e.g., sentiment analysis) 💬
• Dashboard in Power BI/Tableau 📈
• Time series forecasting ⏳

Each project should include:
• Problem statement ❓
• Dataset source 📁
• Visualizations 📊
• Model performance ✅
• GitHub repo + live app link (if any) 🔗
• Brief write-up or blog 📄

✅ 5️⃣ Showcase on GitHub
• Create clean repos with README files 🌟
• Add visuals, summaries, and instructions 📸
• Use Jupyter notebooks or Markdown ✏️

✅ 6️⃣ Deploy and Share
• Use Streamlit Cloud, Hugging Face, or Netlify 🚀
• Share on LinkedIn & Kaggle 🤝
• Use Medium/Hashnode for blogs 📝
• Create a resume link to your portfolio 🔗

💡 Pro Tips:
• Focus on storytelling: Why the project matters 📖
• Show your thought process, not just code 🤔
• Keep UI simple and clean ✨
• Add certifications and tools logos if needed 🏅
• Keep your portfolio updated every 2–3 months 🔄

🎯 Goal: When someone views your site, they should instantly see your skills, your projects, and your ability to solve real-world data problems.

💬 Tap ❤️ if this helped you!

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🤝 Types of Machine Learning

GigaChat 3.5 Ultra Publicly Released — The New Generation of the Flagship Model

The GigaChat team has released GigaChat 3.5 Ultra as open source—a new 432B model under the MIT license. This is the first open-source hybrid of GatedDeltaNet and MLA scaled to hundreds of billions of parameters, featuring a proprietary training recipe we refined through more than 1,500 experiments. The model has grown in terms of code, mathematics, agent scenarios, and application domains—yet it’s 40% smaller than GigaChat 3.1 Ultra.

What’s inside:

🔘A proprietary hybrid MLA + Gated DeltaNet architecture with a dedicated stabilization framework, without which this hybrid setup would not train reliably at this scale;
🔘 Gated Attention: the model can locally down-weight overly strong signals from the attention layer;
🔘GatedNorm: normalization with an explicit gate that controls signal magnitude across features;
🔘Approximately 4x lower KV cache per token: with the same memory budget, the model can support 2.14x longer context and deliver a 20% throughput increase under load;
🔘Two MTP heads, enabling up to 2.2x faster generation;
🔘FP8 across all training stages with no quality degradation compared with bf16, enabled by custom Triton and CUDA kernels;
🔘A new online RL stage after SFT and DPO.

Results:

🔘 GigaChat-3.5-Ultra-Base outperforms DeepSeek V3.2 Exp Base and DeepSeek V4 Flash Base on average across a set of general, math, and code benchmarks:
🔘 GigaChat-3.5-Ultra-Instruct is comparable to DeepSeek V3.2 in terms of average score, despite having half the size;
🔘 According to the MiniMax-M2.7 LLM judge, the average win rate against GigaChat 3.1 Ultra is 75.9%, and against GPT-5 is 68.7%.

The entire stack — data (our own LLM-filtered Common Crawl, 600+ programming languages in the code), architecture, training methodology, and infrastructure — was built end-to-end by GigaChat team.

➡️ HuggingFace