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Sber presented Europe’s largest open-source project at AI Journey as it opened access to its flagship models — the GigaChat Ultra-Preview and Lightning, in addition to a new generation of the GigaAM-v3 open-source models for speech recognition and a full range of image and video generation models in the new Kandinsky 5.0 line, including the Video Pro, Video Lite and Image Lite. The GigaChat Ultra-Preview, a new MoE model featuring 702 billion parameters, has been compiled specifically with the Russian language in mind and trained entirely from scratch. Read a detailed post from the team here. For the first time in Russia, an MoE model of this scale has been trained entirely from scratch — without relying on any foreign weights. Training from scratch, and on such a scale to boot, is a challenge that few teams in the world have taken on. Our flagship Kandinsky Video Pro model has caught up with Veo 3 in terms of visual quality and surpassed Wan 2.2-A14B. Read a detailed post from the team here. The code and weights for all models are now available to all users under MIT license, including commercial use.

📊 Data Analytics Basics Cheatsheet 1. What is Data Analytics? Analyzing raw data to find patterns, trends, and insights to support decision-making. 2. Types of Data Analytics: ⦁ Descriptive: What happened? ⦁ Diagnostic: Why did it happen? ⦁ Predictive: What might happen next? ⦁ Prescriptive: What should be done? 3. Key Tools & Languages: ⦁ Excel – Quick analysis & charts ⦁ SQL – Query and manage databases ⦁ Python (Pandas, NumPy, Matplotlib) ⦁ Power BI / Tableau – Dashboards & visualization 4. Data Cleaning Basics: ⦁ Handle missing values ⦁ Remove duplicates ⦁ Convert data types ⦁ Standardize formats 5. Exploratory Data Analysis (EDA): ⦁ Summary stats (mean, median, mode) ⦁ Data distribution ⦁ Correlation matrix ⦁ Visual tools: bar charts, boxplots, scatter plots 6. Data Visualization: ⦁ Use charts to simplify insights ⦁ Choose chart types based on data (line for trends, bar for comparisons, pie for proportions) 7. SQL Essentials: ⦁ SELECT, WHERE, JOIN, GROUP BY, HAVING, ORDER BY ⦁ Aggregate functions: COUNT, SUM, AVG, MAX, MIN 8. Python for Analysis: ⦁ Pandas for dataframes ⦁ Matplotlib/Seaborn for plotting ⦁ Scikit-learn for basic ML models *9. Metrics to Know: ⦁ Growth %, Conversion rate, Retention rate ⦁ KPIs specific to domain (finance, marketing, etc.) *10. Real-World Use Cases: ⦁ Customer segmentation ⦁ Sales trend analysis ⦁ A/B testing ⦁ Forecasting demand 💬 Tap ❤️ for more!

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📊 Core Data Analyst Interview Topics You Should Know ✅ 1️⃣ Excel/Spreadsheet Skills ⦁ VLOOKUP, INDEX-MATCH, XLOOKUP (newer Excel fave) ⦁ Pivot Tables for summarizing data ⦁ Conditional Formatting to highlight trends ⦁ Data Cleaning & Validation with formulas like IFERROR 2️⃣ SQL & Databases ⦁ SELECT, JOINs (INNER, LEFT, RIGHT, FULL) ⦁ GROUP BY, HAVING, ORDER BY for aggregations ⦁ Subqueries & Window Functions (ROW_NUMBER, LAG) ⦁ CTEs for cleaner, reusable queries 3️⃣ Data Visualization ⦁ Tools: Power BI, Tableau, Excel, Google Data Studio ⦁ Best practices: Choose charts wisely (bar for comparisons, line for trends) ⦁ Dashboards & Interactivity with slicers/drill-downs ⦁ Storytelling with Data to make insights pop 4️⃣ Statistics & Probability ⦁ Mean, Median, Mode, Standard Deviation for summaries ⦁ Correlation vs. Causation (correlation doesn't imply cause!) ⦁ Hypothesis Testing (t-test, p-value for significance) ⦁ Confidence Intervals to gauge reliability 5️⃣ Python for Data Analysis ⦁ Libraries: Pandas for dataframes, NumPy for arrays, Matplotlib/Seaborn for plots ⦁ Data wrangling & cleaning (handling nulls, merging) ⦁ Basic EDA: Describe stats, visualizations, correlations 6️⃣ Business Understanding ⦁ KPI identification (e.g., conversion rate, churn) ⦁ Funnel analysis for drop-offs ⦁ A/B Testing basics to validate changes ⦁ Decision-making support with actionable recommendations 7️⃣ Problem Solving & Case Studies ⦁ Product metrics (DAU/MAU, retention) ⦁ Customer segmentation (RFM analysis) ⦁ Market trend analysis with time-series 8️⃣ ETL Concepts ⦁ Extract from sources, Transform (clean/aggregate), Load to warehouses ⦁ Data pipeline basics using tools like Airflow or dbt 9️⃣ Data Cleaning Techniques ⦁ Handling missing values (impute or drop) ⦁ Duplicates, outliers detection/removal ⦁ Data formatting (standardize dates, text) 🔟 Soft Skills & Communication ⦁ Explaining insights to non-technical stakeholders simply ⦁ Clear visualization storytelling (avoid clutter) ⦁ Collaborating with cross-functional teams for context 💬 Tap ❤️ for more! These cover 80% of what you'll face in 2025 interviews—focus on SQL and Python for tech-heavy roles! Which topic are you prepping most? 😊

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If I had to start learning data analyst all over again, I'd follow this: 1- Learn SQL: ---- Joins (Inner, Left, Full outer and Self) ---- Aggregate Functions (COUNT, SUM, AVG, MIN, MAX) ---- Group by and Having clause ---- CTE and Subquery ---- Windows Function (Rank, Dense Rank, Row number, Lead, Lag etc) 2- Learn Excel: ---- Mathematical (COUNT, SUM, AVG, MIN, MAX, etc) ---- Logical Functions (IF, AND, OR, NOT) ---- Lookup and Reference (VLookup, INDEX, MATCH etc) ---- Pivot Table, Filters, Slicers 3- Learn BI Tools: ---- Data Integration and ETL (Extract, Transform, Load) ---- Report Generation ---- Data Exploration and Ad-hoc Analysis ---- Dashboard Creation 4- Learn Python (Pandas) Optional: ---- Data Structures, Data Cleaning and Preparation ---- Data Manipulation ---- Merging and Joining Data (Merging and joining DataFrames -similar to SQL joins) ---- Data Visualization (Basic plotting using Matplotlib and Seaborn) Hope this helps you 😊

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Python Interview Questions with Answers Part-1: ☑️ 1. What is Python and why is it popular for data analysis?     Python is a high-level, interpreted programming language known for simplicity and readability. It’s popular in data analysis due to its rich ecosystem of libraries like Pandas, NumPy, and Matplotlib that simplify data manipulation, analysis, and visualization. 2. Differentiate between lists, tuples, and sets in Python. ⦁ List: Mutable, ordered, allows duplicates. ⦁ Tuple: Immutable, ordered, allows duplicates. ⦁ Set: Mutable, unordered, no duplicates. 3. How do you handle missing data in a dataset?     Common methods: removing rows/columns with missing values, filling with mean/median/mode, or using interpolation. Libraries like Pandas provide .dropna(), .fillna() functions to do this easily. 4. What are list comprehensions and how are they useful?     Concise syntax to create lists from iterables using a single readable line, often replacing loops for cleaner and faster code.     Example: [x**2 for x in range(5)] → `` 5. Explain Pandas DataFrame and Series. ⦁ Series: 1D labeled array, like a column. ⦁ DataFrame: 2D labeled data structure with rows and columns, like a spreadsheet. 6. How do you read data from different file formats (CSV, Excel, JSON) in Python?     Using Pandas: ⦁ CSV: pd.read_csv('file.csv') ⦁ Excel: pd.read_excel('file.xlsx') ⦁ JSON: pd.read_json('file.json') 7. What is the difference between Python’s append() and extend() methods? ⦁ append() adds its argument as a single element to the end of a list. ⦁ extend() iterates over its argument adding each element to the list. 8. How do you filter rows in a Pandas DataFrame?     Using boolean indexing:     df[df['column'] > value] filters rows where ‘column’ is greater than value. 9. Explain the use of groupby() in Pandas with an example.     groupby() splits data into groups based on column(s), then you can apply aggregation.     Example: df.groupby('category')['sales'].sum() gives total sales per category. 10. What are lambda functions and how are they used?      Anonymous, inline functions defined with lambda keyword. Used for quick, throwaway functions without formally defining with def.      Example: df['new'] = df['col'].apply(lambda x: x*2) React ♥️ for Part 2