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✅ Data Analytics Roadmap for Freshers 🚀📊 1️⃣ Understand What a Data Analyst Does 🔍 Analyze data, find insights, create dashboards, support business decisions. 2️⃣ Start with Excel 📈 Learn: – Basic formulas – Charts & Pivot Tables – Data cleaning 💡 Excel is still the #1 tool in many companies. 3️⃣ Learn SQL 🧩 SQL helps you pull and analyze data from databases. Start with: – SELECT, WHERE, JOIN, GROUP BY 🛠️ Practice on platforms like W3Schools or Mode Analytics. 4️⃣ Pick a Programming Language 🐍 Start with Python (easier) or R – Learn pandas, matplotlib, numpy – Do small projects (e.g. analyze sales data) 5️⃣ Data Visualization Tools 📊 Learn: – Power BI or Tableau – Build simple dashboards 💡 Start with free versions or YouTube tutorials. 6️⃣ Practice with Real Data 🔍 Use sites like Kaggle or Data.gov – Clean, analyze, visualize – Try small case studies (sales report, customer trends) 7️⃣ Create a Portfolio 💻 Share projects on: – GitHub – Notion or a simple website 📌 Add visuals + brief explanations of your insights. 8️⃣ Improve Soft Skills 🗣️ Focus on: – Presenting data in simple words – Asking good questions – Thinking critically about patterns 9️⃣ Certifications to Stand Out 🎓 Try: – Google Data Analytics (Coursera) – IBM Data Analyst – LinkedIn Learning basics 🔟 Apply for Internships & Entry Jobs 🎯 Titles to look for: – Data Analyst (Intern) – Junior Analyst – Business Analyst 💬 React ❤️ for more!

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If you’re just starting out in Data Analytics, it’s super important to build the right habits early. Here’s a simple plan for beginners to grow both technical and problem-solving skills together: If You Just Started Learning Data Analytics, Focus on These 5 Baby Steps: 1. Don’t Just Watch Tutorials — Build Small Projects After learning a new tool (like SQL or Excel), create mini-projects: - Analyze your expenses - Explore a free dataset (like Netflix movies, COVID data) 2. Ask Business-Like Questions Early Whenever you see a dataset, practice asking: - What problem could this data solve? - Who would care about this insight? 3. Start a ‘Data Journal’ Every day, note down: - What you learned - One business question you could answer with data (Helps you build real-world thinking!) 4. Practice the Basics 100x Get very comfortable with: - SELECT, WHERE, GROUP BY (SQL) - Pivot tables and charts (Excel) - Basic cleaning (Power Query / Python pandas) _Mastering basics > learning 50 fancy functions._ 5. Learn to Communicate Early Explain your mini-projects like this: - What was the business goal? - What did you find? - What should someone do based on it? React with ❤️ if you need a beginner-friendly roadmap to start your data analytics career Data Analytics Free Resources: https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02 ENJOY LEARNING 👍👍

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📝 12 Essential Articles for Data Scientists 🏷 Article: Seq2Seq Learning with NN https://arxiv.org/pdf/1409.3215 An introduction to Seq2Seq models, which serve as the foundation for machine translation utilizing deep learning. 🏷 Article: GANs https://arxiv.org/pdf/1406.2661 An introduction to Generative Adversarial Networks (GANs) and the concept of generating synthetic data. This forms the basis for creating images and videos with artificial intelligence. 🏷 Article: Attention is All You Need https://arxiv.org/pdf/1706.03762 This paper was revolutionary in natural language processing. It introduced the Transformer architecture, which underlies GPT, BERT, and contemporary intelligent language models. 🏷 Article: Deep Residual Learning https://arxiv.org/pdf/1512.03385 This work introduced the ResNet model, enabling neural networks to achieve greater depth and accuracy without compromising the learning process. 🏷 Article: Batch Normalization https://arxiv.org/pdf/1502.03167 This paper introduced a technique that facilitates faster and more stable training of neural networks. 🏷 Article: Dropout https://jmlr.org/papers/volume15/srivastava14a/srivastava14a.pdf A straightforward method designed to prevent overfitting in neural networks. 🏷 Article: ImageNet Classification with DCNN https://proceedings.neurips.cc/paper_files/paper/2012/file/c399862d3b9d6b76c8436e924a68c45b-Paper.pdf The first successful application of a deep neural network for image recognition. 🏷 Article: Support-Vector Machines https://link.springer.com/content/pdf/10.1007/BF00994018.pdf This seminal work introduced the Support Vector Machine (SVM) algorithm, a widely utilized method for data classification. 🏷 Article: A Few Useful Things to Know About ML https://homes.cs.washington.edu/~pedro/papers/cacm12.pdf A comprehensive collection of practical and empirical insights regarding machine learning. 🏷 Article: Gradient Boosting Machine https://www.cse.iitb.ac.in/~soumen/readings/papers/Friedman1999GreedyFuncApprox.pdf This paper introduced the "Gradient Boosting" method, which serves as the foundation for many modern machine learning models, including XGBoost and LightGBM. 🏷 Article: Latent Dirichlet Allocation https://jmlr.org/papers/volume3/blei03a/blei03a.pdf This work introduced a model for text analysis capable of identifying the topics discussed within an article. 🏷 Article: Random Forests https://www.stat.berkeley.edu/~breiman/randomforest2001.pdf This paper introduced the "Random Forest" algorithm, a powerful machine learning method that aggregates multiple models to achieve enhanced accuracy. https://t.me/CodeProgrammer 🌟

✅ Data Analyst Interview Questions for Freshers 📊 1) What is the role of a data analyst? Answer: A data analyst collects, processes, and performs statistical analyses on data to provide actionable insights that support business decision-making. 2) What are the key skills required for a data analyst? Answer: Strong skills in SQL, Excel, data visualization tools (like Tableau or Power BI), statistical analysis, and problem-solving abilities are essential. 3) What is data cleaning? Answer: Data cleaning involves identifying and correcting inaccuracies, inconsistencies, or missing values in datasets to improve data quality. 4) What is the difference between structured and unstructured data? Answer: Structured data is organized in rows and columns (e.g., spreadsheets), while unstructured data includes formats like text, images, and videos that lack a predefined structure. 5) What is a KPI? Answer: KPI stands for Key Performance Indicator, which is a measurable value that demonstrates how effectively a company is achieving its business goals. 6) What tools do you use for data analysis? Answer: Common tools include Excel, SQL, Python (with libraries like Pandas), R, Tableau, and Power BI. 7) Why is data visualization important? Answer: Data visualization helps translate complex data into understandable charts and graphs, making it easier for stakeholders to grasp insights and trends. 8) What is a pivot table? Answer: A pivot table is a feature in Excel that allows you to summarize, analyze, and explore data by reorganizing and grouping it dynamically. 9) What is correlation? Answer: Correlation measures the statistical relationship between two variables, indicating whether they move together and how strongly. 10) What is a data warehouse? Answer: A data warehouse is a centralized repository that consolidates data from multiple sources, optimized for querying and analysis. 11) Explain the difference between INNER JOIN and OUTER JOIN in SQL. Answer: INNER JOIN returns only the matching rows between two tables, while OUTER JOIN returns all matching rows plus unmatched rows from one or both tables, depending on whether it’s LEFT, RIGHT, or FULL OUTER JOIN. 12) What is hypothesis testing? Answer: Hypothesis testing is a statistical method used to determine if there is enough evidence in a sample to infer that a certain condition holds true for the entire population. 13) What is the difference between mean, median, and mode? Answer: ⦁ Mean: The average of all numbers. ⦁ Median: The middle value when data is sorted. ⦁ Mode: The most frequently occurring value in a dataset. 14) What is data normalization? Answer: Normalization is the process of organizing data to reduce redundancy and improve integrity, often by dividing data into related tables. 15) How do you handle missing data? Answer: Missing data can be handled by removing rows, imputing values (mean, median, mode), or using algorithms that support missing data. 💬 React ❤️ for more!

Matrix Exponential Attention (MEA) An experimental attention mechanism for transformers MEA offers an alternative to classic softmax-attention. Instead of normalization via softmax, a matrix exponential is used, which allows modeling more complex, high-order interactions between tokens. 🟢 How it works?

IDEA: Attention is formulated as exp(QKᵀ), and the calculation of the exponential is approximated by a truncated series. This makes it possible to calculate attention linearly along the length of the sequence, without creating huge n×n matrices. What does this provide - More expressive attention compared to softmax - Higher-order interactions between tokens - Linear complexity in memory and time - Suitable for long contexts and research architectures The project is at the intersection of Linear Attention and Higher-order Attention and is of a research nature. This is not a ready-made replacement for standard attention, but an attempt to expand its mathematical form.

For ML researchers and engineers who are studying new forms of attention, alternatives to softmax, and architectures for long sequences. GitHub Not for production yet •••••••••••••••••••••••••••••••••••••• 🤖 Data Science, ML & Big Data with @DataXplore

🚀 Startup Accelerator Roadmap: Sber500 Batch 7 📊 📌 Who Should Apply • Startups with MVP and early traction • DeepTech teams in: 🔹 GenAI & Applied AI for Scientific Research 🔹 Robotics & Autonomous Transport Systems 🔹 Advanced Materials & Photonics 🔹 Quantum Computing 🔹 Earth Remote Sensing (Space & Ground-based) • International founders exploring the Russian market 📌 Program Structure 1️⃣ Stage 1: Online Bootcamp • 150 teams selected • Strengthen product strategy & business model • Identify market use cases • Assess collaboration with Sber ecosystem 2️⃣ Stage 2: Intensive Mentorship • 25 best teams selected • Work with international mentors (Europe, US, Asia, Middle East) • Access to actively investing funds • Direct discussions with corporate customers 3️⃣ Stage 3: Demo Day • Moscow Startup Summit, Fall 2026 • Present to wider audience • In 2024 & 2025, every 5th startup was international 📌 What You Get ✅ 12-week online program in English ✅ International mentors (serial founders, VC partners, corporate executives) ✅ Access to investors & corporations ✅ Long-term community (work continues after program ends) 📌 Results That Speak 📈 Revenue grows 4x on average after program 🚀 Some teams scale up to 1,000x 🤝 10,900+ contracts and pilots with corporations (6 seasons) 📌 Previous International Teams From: India, South Korea, Armenia, China, Turkey, Algeria 📌 Key Details 📅 Deadline: 10 April 2026 ⏱️ Duration: Up to 12 weeks 🌐 Format: Online 💬 Language: English 💰 Participation: Free of charge 👉 Apply via the link ⚔️ Quick Comparison: Why Apply? • Without Accelerator 🔹 Find mentors on your own 🔹 Pitch investors individually 🔹 Build corporate connections from scratch • With Sber500 🔹 Access to curated mentor network 🔹 Demo Day with active investors 🔹 Direct path to corporate pilots 🎯 Best For: • Data Science Startups → AI/ML solutions • Analytics Teams → Enterprise data products • DeepTech Founders → Science-intensive technology Which stage interests you most? Bootcamp 👌 Mentorship 🤝 Demo Day 👍 ℹ️ Learn More Tap ♥️ for more startup resources!

SQL vs Python Programming: Quick Comparison ✍ 📌 SQL Programming • Query data from databases • Filter, join, aggregate rows Best fields • Data Analytics • Business Intelligence • Reporting and MIS • Entry-level Data Engineering Job titles • Data Analyst • Business Analyst • BI Analyst • SQL Developer Hiring reality • Asked in most analyst interviews • Used daily in analyst roles India salary range • Fresher: 4–8 LPA • Mid-level: 8–15 LPA Real tasks • Monthly sales report • Top customers by revenue • Duplicate removal 📌 Python Programming • Clean and analyze data • Automate workflows • Build models Where you work • Notebooks • Scripts • ML pipelines Best fields • Data Science • Machine Learning • Automation • Advanced Analytics Job titles • Data Scientist • ML Engineer • Analytics Engineer • Python Developer Hiring reality • Common in mid to senior roles • Strong demand in AI teams India salary range • Fresher: 6–10 LPA • Mid-level: 12–25 LPA Real tasks • Churn prediction • Report automation • File handling CSV, Excel, JSON ⚔️ Quick comparison • Data source SQL stays inside databases Python pulls data from anywhere • Speed SQL runs fast on large tables Python slows with raw big data • Learning SQL is beginner-friendly Python needs coding basics 🎯 Role-based choice • Data Analyst SQL required Python adds value • Data Scientist Python required SQL used to fetch data • Business Analyst SQL works for most roles Python helps automate work • Data Engineer SQL for pipelines Python for processing ✅ Best career move • Learn SQL first for entry • Add Python for growth • Use both in real projects Which one do you prefer? SQL 👍 Python ❤️ Both 🙏 None 😮

📈 Want to Excel at Data Analytics? Master These Essential Skills! ☑️ Core Concepts: • Statistics & Probability – Understand distributions, hypothesis testing • Excel – Pivot tables, formulas, dashboards Programming: • Python – NumPy, Pandas, Matplotlib, Seaborn • R – Data analysis & visualization • SQL – Joins, filtering, aggregation Data Cleaning & Wrangling: • Handle missing values, duplicates • Normalize and transform data Visualization: • Power BI, Tableau – Dashboards • Plotly, Seaborn – Python visualizations • Data Storytelling – Present insights clearly Advanced Analytics: • Regression, Classification, Clustering • Time Series Forecasting • A/B Testing & Hypothesis Testing ETL & Automation: • Web Scraping – BeautifulSoup, Scrapy • APIs – Fetch and process real-world data • Build ETL Pipelines Tools & Deployment: • Jupyter Notebook / Colab • Git & GitHub • Cloud Platforms – AWS, GCP, Azure • Google BigQuery, Snowflake Hope it helps :)

Important Topics to become a data scientist [Advanced Level] 👇👇 1. Mathematics Linear Algebra Analytic Geometry Matrix Vector Calculus Optimization Regression Dimensionality Reduction Density Estimation Classification 2. Probability Introduction to Probability 1D Random Variable The function of One Random Variable Joint Probability Distribution Discrete Distribution Normal Distribution 3. Statistics Introduction to Statistics Data Description Random Samples Sampling Distribution Parameter Estimation Hypotheses Testing Regression 4. Programming Python: Python Basics List Set Tuples Dictionary Function NumPy Pandas Matplotlib/Seaborn R Programming: R Basics Vector List Data Frame Matrix Array Function dplyr ggplot2 Tidyr Shiny DataBase: SQL MongoDB Data Structures Web scraping Linux Git 5. Machine Learning How Model Works Basic Data Exploration First ML Model Model Validation Underfitting & Overfitting Random Forest Handling Missing Values Handling Categorical Variables Pipelines Cross-Validation(R) XGBoost(Python|R) Data Leakage 6. Deep Learning Artificial Neural Network Convolutional Neural Network Recurrent Neural Network TensorFlow Keras PyTorch A Single Neuron Deep Neural Network Stochastic Gradient Descent Overfitting and Underfitting Dropout Batch Normalization Binary Classification 7. Feature Engineering Baseline Model Categorical Encodings Feature Generation Feature Selection 8. Natural Language Processing Text Classification Word Vectors 9. Data Visualization Tools BI (Business Intelligence): Tableau Power BI Qlik View Qlik Sense 10. Deployment Microsoft Azure Heroku Google Cloud Platform Flask Django Join @datasciencefun to learning important data science and machine learning concepts ENJOY LEARNING 👍👍

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Data Analyst Roadmap Like if it helps ❤️

Data Analyst Roadmap 📊 📂 Python Basics ∟📂 Numpy & Pandas ∟📂 Data Cleaning ∟📂 Data Visualization (Matplotlib, Seaborn) ∟📂 SQL for Data Analysis ∟📂 Excel & Google Sheets ∟📂 Statistics for Analysis ∟📂 BI Tools (Power BI / Tableau) ∟📂 Real-World Projects ∟✅ Apply for Data Analyst Roles ❤️ React for More!

📈 Data Visualisation Cheatsheet: 13 Must-Know Chart Types ✅ 1️⃣ Gantt Chart Tracks project schedules over time. 🔹 Advantage: Clarifies timelines & tasks 🔹 Use case: Project management & planning 2️⃣ Bubble Chart Shows data with bubble size variations. 🔹 Advantage: Displays 3 data dimensions 🔹 Use case: Comparing social media engagement 3️⃣ Scatter Plots Plots data points on two axes. 🔹 Advantage: Identifies correlations & clusters 🔹 Use case: Analyzing variable relationships 4️⃣ Histogram Chart Visualizes data distribution in bins. 🔹 Advantage: Easy to see frequency 🔹 Use case: Understanding age distribution in surveys 5️⃣ Bar Chart Uses rectangular bars to visualize data. 🔹 Advantage: Easy comparison across groups 🔹 Use case: Comparing sales across regions 6️⃣ Line Chart Shows trends over time with lines. 🔹 Advantage: Clear display of data changes 🔹 Use case: Tracking stock market performance 7️⃣ Pie Chart Represents data in circular segments. 🔹 Advantage: Simple proportion visualization 🔹 Use case: Displaying market share distribution 8️⃣ Maps Geographic data representation on maps. 🔹 Advantage: Recognizes spatial patterns 🔹 Use case: Visualizing population density by area 9️⃣ Bullet Charts Measures performance against a target. 🔹 Advantage: Compact alternative to gauges 🔹 Use case: Tracking sales vs quotas 🔟 Highlight Table Colors tabular data based on values. 🔹 Advantage: Quickly identifies highs & lows 🔹 Use case: Heatmapping survey responses 1️⃣1️⃣ Tree Maps Hierarchical data with nested rectangles. 🔹 Advantage: Efficient space usage 🔹 Use case: Displaying file system usage 1️⃣2️⃣ Box & Whisker Plot Summarizes data distribution & outliers. 🔹 Advantage: Concise data spread representation 🔹 Use case: Comparing exam scores across classes 1️⃣3️⃣ Waterfall Charts / Walks Visualizes sequential cumulative effect. 🔹 Advantage: Clarifies source of final value 🔹 Use case: Understanding profit & loss components 💡 Use the right chart to tell your data story clearly. Power BI Resources: https://whatsapp.com/channel/0029Vai1xKf1dAvuk6s1v22c Tap ♥️ for more!

Kandinsky 5.0 Video Lite and Kandinsky 5.0 Video Pro generative models on the global text-to-video landscape 🔘Pro is currently the #1 open-source model worldwide 🔘Lite (2B parameters) outperforms Sora v1. 🔘Only Google (Veo 3.1, Veo 3), OpenAI (Sora 2), Alibaba (Wan 2.5), and KlingAI (Kling 2.5, 2.6) outperform Pro — these are objectively the strongest video generation models in production today. We are on par with Luma AI (Ray 3) and MiniMax (Hailuo 2.3): the maximum ELO gap is 3 points, with a 95% CI of ±21. Useful links 🔘Full leaderboard: LM Arena 🔘Kandinsky 5.0 details: technical report 🔘Open-source Kandinsky 5.0: GitHub and Hugging Face

The best fine-tuning guide you'll find on arXiv this year. Covers: > NLP basics > PEFT/LoRA/QLoRA techniques > Mixture of Experts > Seven-stage fine-tuning pipeline Source: https://arxiv.org/pdf/2408.13296v1

Don't forget to check these 10 SQL projects with corresponding datasets that you could use to practice your SQL skills: 1. Analysis of Sales Data: (https://www.kaggle.com/kyanyoga/sample-sales-data) 2. HR Analytics: (https://www.kaggle.com/pavansubhasht/ibm-hr-analytics-attrition-dataset) 3. Social Media Analytics: (https://www.kaggle.com/datasets/ramjasmaurya/top-1000-social-media-channels) 4. Financial Data Analysis: (https://www.kaggle.com/datasets/nitindatta/finance-data) 5. Healthcare Data Analysis: (https://www.kaggle.com/cdc/mortality) 6. Customer Relationship Management: (https://www.kaggle.com/pankajjsh06/ibm-watson-marketing-customer-value-data) 7. Web Analytics: (https://www.kaggle.com/zynicide/wine-reviews) 8. E-commerce Analysis: (https://www.kaggle.com/olistbr/brazilian-ecommerce) 9. Supply Chain Management: (https://www.kaggle.com/datasets/harshsingh2209/supply-chain-analysis) 10. Inventory Management: (https://www.kaggle.com/datasets?search=inventory+management) Share this channel with your friends 🤝🤩 Join for more -> https://whatsapp.com/channel/0029VaxbzNFCxoAmYgiGTL3Z ENJOY LEARNING 👍👍