Data Analytics Projects - SQL, Excel, Tableau, Python & Power BI Interview Resources

SQL Beginner Roadmap 🗄️ 📂 Start Here ∟📂 Install SQL Server / MySQL / SQLite ∟📂 Learn How to Run SQL Queries 📂 SQL Basics ∟📂 What is SQL? ∟📂 Basic SELECT Statements ∟📂 Filtering with WHERE Clause ∟📂 Sorting with ORDER BY ∟📂 Using LIMIT / TOP 📂 Data Manipulation ∟📂 INSERT INTO ∟📂 UPDATE ∟📂 DELETE 📂 Table Management ∟📂 CREATE TABLE ∟📂 ALTER TABLE ∟📂 DROP TABLE 📂 SQL Joins ∟📂 INNER JOIN ∟📂 LEFT JOIN ∟📂 RIGHT JOIN ∟📂 FULL OUTER JOIN 📂 Advanced Queries ∟📂 GROUP BY & HAVING ∟📂 Subqueries ∟📂 Aggregate Functions (COUNT, SUM, AVG) 📂 Practice Projects ∟📌 Build a Simple Library DB ∟📌 Employee Management System ∟📌 Sales Report Analysis 📂 ✅ Move to Next Level (Only After Basics) ∟📂 Learn Indexing & Performance Tuning ∟📂 Stored Procedures & Triggers ∟📂 Database Design & Normalization Credits: https://whatsapp.com/channel/0029VanC5rODzgT6TiTGoa1v React "❤️" For More!

𝗛𝗶𝗴𝗵 𝗗𝗲𝗺𝗮𝗻𝗱𝗶𝗻𝗴 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝗪𝗶𝘁𝗵 𝗣𝗹𝗮𝗰𝗲𝗺𝗲𝗻𝘁 𝗔𝘀𝘀𝗶𝘀𝘁𝗮𝗻𝗰𝗲😍 Learn from IIT faculty and industry experts. IIT Roorkee DS & AI Program :- https://pdlink.in/4qHVFkI IIT Patna AI & ML :- https://pdlink.in/4pBNxkV IIM Mumbai DM & Analytics :- https://pdlink.in/4jvuHdE IIM Rohtak Product Management:- https://pdlink.in/4aMtk8i IIT Roorkee Agentic Systems:- https://pdlink.in/4aTKgdc Upskill in today’s most in-demand tech domains and boost your career 🚀

📊 Data Analyst Roadmap (2025) Master the Skills That Top Companies Are Hiring For! 📍 1. Learn Excel / Google Sheets Basic formulas & formatting VLOOKUP, Pivot Tables, Charts Data cleaning & conditional formatting 📍 2. Master SQL SELECT, WHERE, ORDER BY JOINs (INNER, LEFT, RIGHT) GROUP BY, HAVING, LIMIT Subqueries, CTEs, Window Functions 📍 3. Learn Data Visualization Tools Power BI / Tableau (choose one) Charts, filters, slicers Dashboards & storytelling 📍 4. Get Comfortable with Statistics Mean, Median, Mode, Std Dev Probability basics A/B Testing, Hypothesis Testing Correlation & Regression 📍 5. Learn Python for Data Analysis (Optional but Powerful) Pandas & NumPy for data handling Seaborn, Matplotlib for visuals Jupyter Notebooks for analysis 📍 6. Data Cleaning & Wrangling Handle missing values Fix data types, remove duplicates Text processing & date formatting 📍 7. Understand Business Metrics KPIs: Revenue, Churn, CAC, LTV Think like a business analyst Deliver actionable insights 📍 8. Communication & Storytelling Present insights with clarity Simplify complex data Speak the language of stakeholders 📍 9. Version Control (Git & GitHub) Track your projects Build a data portfolio Collaborate with the community 📍 10. Interview & Resume Preparation Excel, SQL, case-based questions Mock interviews + real projects Resume with measurable achievements ✨ React ❤️ for more

𝗙𝗥𝗘𝗘 𝗢𝗻𝗹𝗶𝗻𝗲 𝗠𝗮𝘀𝘁𝗲𝗿𝗰𝗹𝗮𝘀𝘀 𝗢𝗻 𝗟𝗮𝘁𝗲𝘀𝘁 𝗧𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝗶𝗲𝘀😍 - Data Science  - AI/ML - Data Analytics - UI/UX - Full-stack Development  Get Job-Ready Guidance in Your Tech Journey 𝗥𝗲𝗴𝗶𝘀𝘁𝗲𝗿 𝗙𝗼𝗿 𝗙𝗥𝗘𝗘👇:-  https://pdlink.in/4sw5Ev8 Date :- 11th January 2026

𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗮𝗻𝗱 𝗔𝗿𝘁𝗶𝗳𝗶𝗰𝗶𝗮𝗹 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗣𝗿𝗼𝗴𝗿𝗮𝗺 𝗯𝘆 𝗜𝗜𝗧 𝗥𝗼𝗼𝗿𝗸𝗲𝗲😍 Deadline: 11th January 2026 Eligibility: Open to everyone Duration: 6 Months Program Mode: Online Taught By: IIT Roorkee Professors Companies majorly hire candidates having Data Science and Artificial Intelligence knowledge these days. 𝗥𝗲𝗴𝗶𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻 𝗟𝗶𝗻𝗸👇:  https://pdlink.in/4qNGMO6 Only Limited Seats Available!

✅ Complete Roadmap to Mastering SQL 🚀 🗄️ 📂 1. SQL Fundamentals – What is a database & DBMS – Basic Syntax: SELECT, FROM, WHERE – Data Types: INT, VARCHAR, DATE, etc. – Operators: =, >, <, LIKE, IN – Aliases & Comments 📂 2. Filtering & Sorting – WHERE Clause: Advanced conditions – ORDER BY: Sorting results – LIMIT: Restricting rows – DISTINCT: Unique values 📂 3. Aggregate Functions – COUNT(), SUM(), AVG(), MIN(), MAX() – GROUP BY: Grouping data – HAVING: Filtering grouped data 📂 4. Joins & Relationships – INNER JOIN: Matching rows – LEFT/RIGHT JOIN: All rows from one table – FULL OUTER JOIN: All rows from both tables – Self Join: Joining a table to itself – Subqueries: Queries within queries 📂 5. Advanced Filtering – IN, BETWEEN, LIKE operators – NULL values: IS NULL, IS NOT NULL – EXISTS operator 📂 6. Subqueries & CTEs – Subqueries in SELECT, FROM, WHERE – Common Table Expressions (CTEs): Reusable queries 📂 7. Window Functions – RANK(), DENSE_RANK(), ROW_NUMBER() – LAG(), LEAD() – OVER() clause: Defining the window – Partitioning: PARTITION BY 📂 8. Data Manipulation – INSERT: Adding new data – UPDATE: Modifying existing data – DELETE: Removing data – MERGE: Combining data (upsert) 📂 9. Database Design – Normalization: Reducing redundancy – Primary & Foreign Keys: Relationships – Data types & Constraints – Indexing: Improving query performance 📂 10. Advanced Topics – Stored Procedures: Precompiled SQL – Triggers: Automatic actions – Views: Virtual tables – Performance Tuning: Optimizing queries – Security: User permissions 📂 11. Practice & Projects – Solve coding challenges on platforms like *LeetCode, HackerRank* – Work on real-world projects using datasets from *Kaggle, Data.gov* – Build a portfolio to showcase your SQL skills 💬 Tap ❤️ if you found this helpful!

𝗧𝗼𝗽 𝟱 𝗜𝗻-𝗗𝗲𝗺𝗮𝗻𝗱 𝗦𝗸𝗶𝗹𝗹𝘀 𝘁𝗼 𝗙𝗼𝗰𝘂𝘀 𝗼𝗻 𝗶𝗻 𝟮𝟬𝟮𝟲😍 Start learning industry-relevant data skills today at zero cost! 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀:- https://pdlink.in/497MMLw 𝗔𝗜 & 𝗠𝗟 :- https://pdlink.in/4bhetTu 𝗖𝗹𝗼𝘂𝗱 𝗖𝗼𝗺𝗽𝘂𝘁𝗶𝗻𝗴:- https://pdlink.in/3LoutZd 𝗖𝘆𝗯𝗲𝗿 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆:- https://pdlink.in/3N9VOyW 𝗢𝘁𝗵𝗲𝗿 𝗧𝗲𝗰𝗵 𝗖𝗼𝘂𝗿𝘀𝗲𝘀:- https://pdlink.in/4qgtrxU 🎓 Enroll Now & Get Certified

Data Analytics Roadmap | |-- Fundamentals |   |-- Mathematics |   |   |-- Descriptive Statistics |   |   |-- Inferential Statistics |   |   |-- Probability Theory |   | |   |-- Programming |   |   |-- Python (Focus on Libraries like Pandas, NumPy) |   |   |-- R (For Statistical Analysis) |   |   |-- SQL (For Data Extraction) | |-- Data Collection and Storage |   |-- Data Sources |   |   |-- APIs |   |   |-- Web Scraping |   |   |-- Databases |   | |   |-- Data Storage |   |   |-- Relational Databases (MySQL, PostgreSQL) |   |   |-- NoSQL Databases (MongoDB, Cassandra) |   |   |-- Data Lakes and Warehousing (Snowflake, Redshift) | |-- Data Cleaning and Preparation |   |-- Handling Missing Data |   |-- Data Transformation |   |-- Data Normalization and Standardization |   |-- Outlier Detection | |-- Exploratory Data Analysis (EDA) |   |-- Data Visualization Tools |   |   |-- Matplotlib |   |   |-- Seaborn |   |   |-- ggplot2 |   | |   |-- Identifying Trends and Patterns |   |-- Correlation Analysis | |-- Advanced Analytics |   |-- Predictive Analytics (Regression, Forecasting) |   |-- Prescriptive Analytics (Optimization Models) |   |-- Segmentation (Clustering Techniques) |   |-- Sentiment Analysis (Text Data) | |-- Data Visualization and Reporting |   |-- Visualization Tools |   |   |-- Power BI |   |   |-- Tableau |   |   |-- Google Data Studio |   | |   |-- Dashboard Design |   |-- Interactive Visualizations |   |-- Storytelling with Data | |-- Business Intelligence (BI) |   |-- KPI Design and Implementation |   |-- Decision-Making Frameworks |   |-- Industry-Specific Use Cases (Finance, Marketing, HR) | |-- Big Data Analytics |   |-- Tools and Frameworks |   |   |-- Hadoop |   |   |-- Apache Spark |   | |   |-- Real-Time Data Processing |   |-- Stream Analytics (Kafka, Flink) | |-- Domain Knowledge |   |-- Industry Applications |   |   |-- E-commerce |   |   |-- Healthcare |   |   |-- Supply Chain | |-- Ethical Data Usage |   |-- Data Privacy Regulations (GDPR, CCPA) |   |-- Bias Mitigation in Analysis |   |-- Transparency in Reporting Free Resources to learn Data Analytics skills👇👇 1. SQL https://mode.com/sql-tutorial/introduction-to-sql https://t.me/sqlspecialist/738 2. Python https://www.learnpython.org/ https://t.me/pythondevelopersindia/873 https://bit.ly/3T7y4ta https://www.geeksforgeeks.org/python-programming-language/learn-python-tutorial 3. R https://datacamp.pxf.io/vPyB4L 4. Data Structures https://leetcode.com/study-plan/data-structure/ https://www.udacity.com/course/data-structures-and-algorithms-in-python--ud513 5. Data Visualization https://www.freecodecamp.org/learn/data-visualization/ https://t.me/Data_Visual/2 https://www.tableau.com/learn/training/20223 https://www.workout-wednesday.com/power-bi-challenges/ 6. Excel https://excel-practice-online.com/ https://t.me/excel_data https://www.w3schools.com/EXCEL/index.php Join @free4unow_backup for more free courses Like for more ❤️ ENJOY LEARNING 👍👍

𝗙𝗥𝗘𝗘 𝗢𝗻𝗹𝗶𝗻𝗲 𝗠𝗮𝘀𝘁𝗲𝗿𝗰𝗹𝗮𝘀𝘀 𝗕𝘆 𝗜𝗻𝗱𝘂𝘀𝘁𝗿𝘆 𝗘𝘅𝗽𝗲𝗿𝘁𝘀 😍 Roadmap to land your dream job in top product-based companies 𝗛𝗶𝗴𝗵𝗹𝗶𝗴𝗵𝘁𝗲𝘀:- - 90-Day Placement Plan - Tech & Non-Tech Career Path - Interview Preparation Tips - Live Q&A 𝗥𝗲𝗴𝗶𝘀𝘁𝗲𝗿 𝗙𝗼𝗿 𝗙𝗥𝗘𝗘👇:-  https://pdlink.in/3Ltb3CE Date & Time:- 06th January 2026 , 7PM

Datasets for Data Science Projects

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

🚀 If you’re entering an AI career right now, here’s the truth: It’s not about learning “everything.” It’s about learning the right technical foundations — the ones the industry actually uses. These are the core skills that will matter for the next 5–10 years, no matter how fast AI evolves 👇 1️⃣ Learn how modern LLMs actually work You don’t need to know the math behind transformers, but you must understand: • tokens & embeddings • context windows • attention • prompting vs reasoning • fine-tuning vs RAG • when models hallucinate (and why) If you don’t know how the engine works, you can’t drive it well. 2️⃣ Learn Retrieval — the real backbone of enterprise AI Most AI applications in companies rely on RAG, not fine-tuning. Focus on: • chunking strategies • embedding models • hybrid retrieval (dense + sparse) • vector databases • knowledge graphs • context filtering • evaluation of retrieved docs If you master retrieval, you instantly become valuable. 3️⃣ Learn how to evaluate AI systems, not just build them Engineers build models. Professionals who can evaluate them are the ones who get promoted. Learn to measure: • grounding accuracy • relevance • completeness • tool-use correctness • consistency across runs • latency • safety This is where the real skill gap is. 4️⃣ Learn prompting as an engineering discipline Not “try random prompts.” But systematic methods like: • template prompts • tool-calling prompts • guardrail prompts • chain-of-thought • reflection prompts • constraint-based prompting Prompting is becoming the new API design. 5️⃣ Learn how to build agentic workflows AI is moving from answers → decisions → actions. You should know: • planner → executor → verifier agent structure • tool routing • action space design • human-in-the-loop workflows • permissioning • error recovery loops This is what separates beginners from real AI engineers. 6️⃣ Learn Python + APIs deeply You don’t need to be a software engineer, but you must be comfortable with: • Python basics • API calls • JSON • LangChain / LlamaIndex / DSPy • building small scripts • reading logs • debugging AI pipelines This is the “plumbing” behind AI systems. 7️⃣ Build real projects, not toy demos Instead of “build a chatbot,” build: • a support email classifier • a RAG system on company policies • a customer insights extractor • an automatic meeting summarizer • a multimodal analyzer (text + image) • an internal tool-calling agent Projects that solve real problems get you hired. 8️⃣ Learn one domain deeply AI generalists struggle. AI + domain experts win. Choose one: • finance • healthcare • retail • manufacturing • real estate • cybersecurity • operations • supply chain • HR tech AI skill + domain depth = career acceleration. If you’re entering AI today: Focus on retrieval, reasoning, evaluation, agents, and real projects. These are the skills companies are desperate for.

✅ Top Projects Every Data Analyst Should Build 🧪📊 1️⃣ Sales Dashboard Dive into revenue trends, product performance, and regional sales breakdowns. Tools: Excel, Power BI, SQL 2️⃣ Customer Churn Analysis Spot patterns in customer drop-off and predict who might leave next. Skills: Pandas, Logistic Regression, Data Cleaning 3️⃣ Marketing Campaign Report Measure ad ROI, CTRs, and conversion funnels for better targeting. Tools: Google Sheets, Tableau, SQL 4️⃣ HR Analytics Track turnover, hiring efficiency, and team performance metrics. Tools: Python, Excel, Power BI 5️⃣ E-commerce Order Analysis Analyze order flows, delivery delays, and return rates. Skills: SQL joins, Data Wrangling 6️⃣ Survey Data Analysis Process feedback, visualize sentiment, and pull key insights. Tools: Python (Pandas, Seaborn), Excel 7️⃣ Financial Performance Tracker Monitor monthly P&L, expense trends, and profitability. Tools: Excel dashboards or Tableau 8️⃣ COVID-19 Data Tracker Explore time series, regional impacts, and recovery patterns (timeless for public health analysis). Skills: APIs, Pandas, Plotly 9️⃣ Movie/Book Rating Analysis Uncover genre trends, rating correlations, and recommendation basics. Tools: Python, SQL, Matplotlib 🔟 Real-time Data Dashboard Build live feeds for stocks, weather, or crypto with interactive updates. Tools: Python, Streamlit, APIs These projects are straight from 2025 guides like DataCamp and GeeksforGeeks—start with public datasets to build your portfolio and land that analyst gig! 💬 Tap ❤️ for more! Which one are you tackling first? 😊

𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 & 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗙𝗥𝗘𝗘 𝗠𝗮𝘀𝘁𝗲𝗿𝗰𝗹𝗮𝘀𝘀😍 Kickstart Your Data Science Career This Masterclass will help you build a strong foundation in Data Science Eligibility :- Students ,Freshers & Working Professionals  𝗥𝗲𝗴𝗶𝘀𝘁𝗲𝗿 𝗙𝗼𝗿 𝗙𝗥𝗘𝗘👇:-  https://pdlink.in/3XDI0ie Date & Time:- 5th Dec 2025 ,7PM

✅ Data Analyst Project Ideas to Build Your Portfolio 🗂️📊 1️⃣ Sales Dashboard ⦁ Analyze monthly revenue, top products, regions ⦁ Use Excel, Power BI, or Tableau ⦁ Add filters for category, region, and time 2️⃣ Customer Churn Analysis ⦁ Predict which users may leave ⦁ Use Python (Pandas, Scikit-learn) + classification models ⦁ Visualize churn trends and risk factors 3️⃣ Marketing Campaign Analysis ⦁ Track CTR, conversion rate, and ROI ⦁ Use SQL for data extraction, Power BI for dashboard ⦁ Show pre/post performance 4️⃣ E-commerce Product Analysis ⦁ Analyze product ratings, sales, and returns ⦁ Use Python and SQL ⦁ Recommend improvements to pricing or stock 5️⃣ HR Analytics Dashboard ⦁ Track attrition, headcount, hiring trends ⦁ Segment by department, gender, experience ⦁ Use Tableau/Power BI 6️⃣ Finance Report ⦁ Budget vs actuals ⦁ Forecasting using time series models ⦁ Use Excel, Python (statsmodels) 7️⃣ COVID-19 Data Analysis ⦁ Use public datasets ⦁ Track trends by country, cases, deaths, vaccination ⦁ Build interactive visuals 8️⃣ Social Media Insights ⦁ Analyze Twitter, LinkedIn, or Instagram engagement ⦁ Use Python + APIs ⦁ Highlight what content works best These project ideas are highly recommended in 2025 guides such as GeeksforGeeks and DataCamp to demonstrate diverse skills in data cleaning, analysis, visualization, and machine learning deployment. Which one will you tackle first? 😊

Sometimes reality outpaces expectations in the most unexpected ways. While global AI development seems increasingly fragmented, Sber just released Europe's largest open-source AI collection—full weights, code, and commercial rights included. ✅ No API paywalls. ✅ No usage restrictions. ✅ Just four complete model families ready to run in your private infrastructure, fine-tuned on your data, serving your specific needs. What makes this release remarkable isn't merely the technical prowess, but the quiet confidence behind sharing it openly when others are building walls. Find out more in the article from the developers. GigaChat Ultra Preview: 702B-parameter MoE model (36B active per token) with 128K context window. Trained from scratch, it outperforms DeepSeek V3.1 on specialized benchmarks while maintaining faster inference than previous flagships. Enterprise-ready with offline fine-tuning for secure environments. GitHub | HuggingFace | GitVerse GigaChat Lightning offers the opposite balance: compact yet powerful MoE architecture running on your laptop. It competes with Qwen3-4B in quality, matches the speed of Qwen3-1.7B, yet is significantly smarter and larger in parameter count. Lightning holds its own against the best open-source models in its class, outperforms comparable models on different tasks, and delivers ultra-fast inference—making it ideal for scenarios where Ultra would be overkill and speed is critical. Plus, it features stable expert routing and a welcome bonus: 256K context support. GitHub | Hugging Face | GitVerse Kandinsky 5.0 brings a significant step forward in open generative models. The flagship Video Pro matches Veo 3 in visual quality and outperforms Wan 2.2-A14B, while Video Lite and Image Lite offer fast, lightweight alternatives for real-time use cases. The suite is powered by K-VAE 1.0, a high-efficiency open-source visual encoder that enables strong compression and serves as a solid base for training generative models. This stack balances performance, scalability, and practicality—whether you're building video pipelines or experimenting with multimodal generation. GitHub | GitVerse | Hugging Face | Technical report Audio gets its upgrade too: GigaAM-v3 delivers speech recognition model with 50% lower WER than Whisper-large-v3, trained on 700k hours of audio with punctuation/normalization for spontaneous speech. GitHub | HuggingFace | GitVerse Every model can be deployed on-premises, fine-tuned on your data, and used commercially. It's not just about catching up – it's about building sovereign AI infrastructure that belongs to everyone who needs it.

✅ Top Data Analytics Projects That Strengthen Your Resume 📊💼 These analytics projects, pulled from 2025 insights by GeeksforGeeks and DataCamp, focus on cleaning, visualization, and insights—key for roles where 75% of hires showcase real-world data handling to stand out! 1. Sales Data Analysis → Analyze trends using Python/Pandas on retail datasets → Create dashboards with Tableau for revenue forecasts and patterns 2. Customer Churn Prediction → Build models with SQL queries and Scikit-learn on telecom data → Visualize retention strategies and key churn factors 3. Market Basket Analysis → Use association rules on transaction data for product recommendations → Implement in R or Python to uncover buying behaviors 4. COVID-19 Data Visualization → Aggregate global datasets with joins and aggregations → Design interactive maps and charts for trend analysis 5. Housing Price Analysis → Perform EDA on real estate data with correlations and regressions → Predict prices using linear models and feature engineering 6. Uber Trips Dashboard → Query ride data for peak hours and route optimization → Build BI reports highlighting efficiency metrics 7. Stock Market Time Series → Forecast prices with ARIMA or Prophet on financial data → Generate reports on volatility and investment insights Tips: ⦁ Use tools like SQL, Python (Pandas/Seaborn), and Tableau for end-to-end workflows ⦁ Document findings in Jupyter notebooks and host on GitHub ⦁ Emphasize storytelling: insights over raw code 💬 Tap ❤️ for more! Sales analysis is a crowd-pleaser for e-commerce gigs! Which project fits your skill level? 😊

Tune in to the 10th AI Journey 2025 international conference: scientists, visionaries, and global AI practitioners will come together on one stage. Here, you will hear the voices of those who don't just believe in the future—they are creating it! Speakers include visionaries Kai-Fu Lee and Chen Qufan, as well as dozens of global AI gurus! Do you agree with their predictions about AI? On November 20, we will focus on the role of AI in business and economic development and present technologies that will help businesses and developers be more effective by unlocking human potential. On November 21, we will talk about how engineers and scientists are making scientific and technological breakthroughs and creating the future today! The day's program includes presentations by scientists from around the world: - Ajit Abraham (Sai University, India) will present on “Generative AI in Healthcare” - Nebojša Bačanin Džakula (Singidunum University, Serbia) will talk about the latest advances in bio-inspired metaheuristics - AIexandre Ferreira Ramos (University of São Paulo, Brazil) will present his work on using thermodynamic models to study the regulatory logic of transcriptional control at the DNA level - Anderson Rocha (University of Campinas, Brazil) will give a presentation entitled “AI in the New Era: From Basics to Trends, Opportunities, and Global Cooperation”. And in the special AIJ Junior track, we will talk about how AI helps us learn, create and ride the wave with AI. The day will conclude with an award ceremony for the winners of the AI Challenge for aspiring data scientists and the AIJ Contest for experienced AI specialists. The results of an open selection of AIJ Science research papers will be announced. Ride the wave with AI into the future! Tune in to the AI Journey webcast on November 19-21.

The program for the 10th AI Journey 2025 international conference has been unveiled: scientists, visionaries, and global AI practitioners will come together on one stage. Here, you will hear the voices of those who don't just believe in the future—they are creating it! Speakers include visionaries Kai-Fu Lee and Chen Qufan, as well as dozens of global AI gurus from around the world! On the first day of the conference, November 19, we will talk about how AI is already being used in various areas of life, helping to unlock human potential for the future and changing creative industries, and what impact it has on humans and on a sustainable future. On November 20, we will focus on the role of AI in business and economic development and present technologies that will help businesses and developers be more effective by unlocking human potential. On November 21, we will talk about how engineers and scientists are making scientific and technological breakthroughs and creating the future today! Ride the wave with AI into the future! Tune in to the AI Journey webcast on November 19-21.

𝗔𝗜/𝗠𝗟 𝗙𝗥𝗘𝗘 𝗢𝗻𝗹𝗶𝗻𝗲 𝗠𝗮𝘀𝘁𝗲𝗿𝗹𝗰𝗹𝗮𝘀𝘀😍 Kickstart Your AI & Machine Learning Career - Leverage your skills in the AI-driven job market - Get exposed to the Generative AI Tools, Technologies, and Platforms Eligibility :- Working Professionals & Graduates  𝗥𝗲𝗴𝗶𝘀𝘁𝗲𝗿 𝗙𝗼𝗿 𝗙𝗥𝗘𝗘👇:-  https://pdlink.in/47fcsF5 Date :- October 30, 2025  Time:-7:00 PM