Python for Data Analysts

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

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Python for Data Analysts

📚 Frequently Asked Pandas Interview Questions (Beginner Level)

1️⃣ What is the difference between a Series and a DataFrame?

💡 Answer:

Series → A one-dimensional labeled array.

DataFrame → A two-dimensional table with rows and columns.

2️⃣ How do you find missing
values in a DataFrame?

💡 Answer:

df.isnull().sum()

This returns the number of missing values in each column.

3️⃣ What is the difference between loc and iloc?

💡 Answer:

loc → Label-based indexing.

iloc → Integer position-based indexing.

4️⃣ What is the difference between merge() and concat()?

💡 Answer:

merge() combines DataFrames using a common key (similar to an SQL JOIN).

concat() combines DataFrames by stacking them vertically or horizontally.

React ♥️ for more interview questions

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2.29K views05:27

Python for Data Analysts

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2.14K views08:53

Python for Data Analysts

🚀 How to Land a Data Analyst Job Without Experience?

Many people asked me this question, so I thought to answer it here to help everyone. Here is the step-by-step approach i would recommend:

✅ Step 1: Master the Essential Skills

You need to build a strong foundation in:

🔹 SQL – Learn how to extract and manipulate data
🔹 Excel – Master formulas, Pivot Tables, and dashboards
🔹 Python – Focus on Pandas, NumPy, and Matplotlib for data analysis
🔹 Power BI/Tableau – Learn to create interactive dashboards
🔹 Statistics & Business Acumen – Understand data trends and insights

Where to learn?
📌 Google Data Analytics Course
📌 SQL – Mode Analytics (Free)
📌 Python – Kaggle or DataCamp

✅ Step 2: Work on Real-World Projects

Employers care more about what you can do rather than just your degree. Build 3-4 projects to showcase your skills.

🔹 Project Ideas:

✅ Analyze sales data to find profitable products
✅ Clean messy datasets using SQL or Python
✅ Build an interactive Power BI dashboard
✅ Predict customer churn using machine learning (optional)

Use Kaggle, Data.gov, or Google Dataset Search to find free datasets!

✅ Step 3: Build an Impressive Portfolio

Once you have projects, showcase them! Create:
📌 A GitHub repository to store your SQL/Python code
📌 A Tableau or Power BI Public Profile for dashboards
📌 A Medium or LinkedIn post explaining your projects

A strong portfolio = More job opportunities! 💡

✅ Step 4: Get Hands-On Experience

If you don’t have experience, create your own!
📌 Do freelance projects on Upwork/Fiverr
📌 Join an internship or volunteer for NGOs
📌 Participate in Kaggle competitions
📌 Contribute to open-source projects

Real-world practice > Theoretical knowledge!

✅ Step 5: Optimize Your Resume & LinkedIn Profile

Your resume should highlight:
✔️ Skills (SQL, Python, Power BI, etc.)
✔️ Projects (Brief descriptions with links)
✔️ Certifications (Google Data Analytics, Coursera, etc.)

Bonus Tip:
🔹 Write "Data Analyst in Training" on LinkedIn
🔹 Start posting insights from your learning journey
🔹 Engage with recruiters & join LinkedIn groups

✅ Step 6: Start Applying for Jobs

Don’t wait for the perfect job—start applying!
📌 Apply on LinkedIn, Indeed, and company websites
📌 Network with professionals in the industry
📌 Be ready for SQL & Excel assessments

Pro Tip: Even if you don’t meet 100% of the job requirements, apply anyway! Many companies are open to hiring self-taught analysts.

You don’t need a fancy degree to become a Data Analyst. Skills + Projects + Networking = Your job offer!

🔥 Your Challenge: Start your first project today and track your progress!

Share with credits: https://t.me/sqlspecialist

Hope it helps :)

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1.19K views18:51

Python for Data Analysts

𝐓𝐢𝐩𝐬 𝐟𝐨𝐫 𝐏𝐲𝐭𝐡𝐨𝐧 𝐂𝐨𝐝𝐢𝐧𝐠 𝐢𝐧 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬:

𝘐 𝘨𝘦𝘵 𝘴𝘰 𝘮𝘢𝘯𝘺 𝘲𝘶𝘦𝘴𝘵𝘪𝘰𝘯𝘴 𝘧𝘳𝘰𝘮 𝘥𝘢𝘵𝘢 𝘢𝘯𝘢𝘭𝘺𝘵𝘪𝘤𝘴 𝘢𝘴𝘱𝘪𝘳𝘢𝘯𝘵𝘴 𝘢𝘯𝘥 𝘱𝘳𝘰𝘧𝘦𝘴𝘴𝘪𝘰𝘯𝘢𝘭𝘴 𝘰𝘯 𝘩𝘰𝘸 𝘵𝘰 𝘨𝘢𝘪𝘯 𝘤𝘰𝘮𝘮𝘢𝘯𝘥 𝘰𝘧 𝘗𝘺𝘵𝘩𝘰𝘯.

📍𝐋𝐞𝐚𝐫𝐧 𝐂𝐨𝐫𝐞 𝐏𝐲𝐭𝐡𝐨𝐧 𝐋𝐢𝐛𝐫𝐚𝐫𝐢𝐞𝐬: Master Python libraries for data analytics, like
-pandas for dataframes,
-NumPy for numerical operations,
-Matplotlib/Seaborn for plotting,
-scikit-learn for machine learning.

📍𝐔𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝 𝐂𝐨𝐧𝐜𝐞𝐩𝐭𝐬: Important concepts like list comprehensions, lambda functions, object-oriented programming, and error handling to write efficient code.

📍𝐔𝐬𝐞 𝐏𝐫𝐨𝐛𝐥𝐞𝐦-𝐒𝐨𝐥𝐯𝐢𝐧𝐠 𝐌𝐞𝐭𝐡𝐨𝐝𝐬: Apply data wrangling techniques, efficient loops, and vectorized operations in NumPy/pandas for optimized performance.

📍𝐃𝐨 𝐌𝐨𝐜𝐤 𝐏𝐫𝐨𝐣𝐞𝐜𝐭𝐬: Work on end-to-end Python analytics projects—data loading, cleaning, analysis, and visualization.

📍𝐋𝐞𝐚𝐫𝐧 𝐟𝐫𝐨𝐦 𝐏𝐚𝐬𝐭 𝐏𝐫𝐨𝐣𝐞𝐜𝐭𝐬: Review your previous Python projects to see where your code can be more efficient.

Like this post if you need more resources like this 👍❤️

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1.09K views06:23

Python for Data Analysts

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