Data Analysis Books | Python | SQL | Excel | Artificial Intelligence | Power BI | Tableau | AI Resources

Complete SQL road map 👇👇 1.Intro to SQL • Definition • Purpose • Relational DBs • DBMS 2.Basic SQL Syntax • SELECT • FROM • WHERE • ORDER BY • GROUP BY 3. Data Types • Integer • Floating-Point • Character • Date • VARCHAR • TEXT • BLOB • BOOLEAN 4.Sub languages • DML • DDL • DQL • DCL • TCL 5. Data Manipulation • INSERT • UPDATE • DELETE 6. Data Definition • CREATE • ALTER • DROP • Indexes 7.Query Filtering and Sorting • WHERE • AND • OR Conditions • Ascending • Descending 8. Data Aggregation • SUM • AVG • COUNT • MIN • MAX 9.Joins and Relationships • INNER JOIN • LEFT JOIN • RIGHT JOIN • Self-Joins • Cross Joins • FULL OUTER JOIN 10.Subqueries • Subqueries used in • Filtering data • Aggregating data • Joining tables • Correlated Subqueries 11.Views • Creating • Modifying • Dropping Views 12.Transactions • ACID Properties • COMMIT • ROLLBACK • SAVEPOINT • ROLLBACK TO SAVEPOINT 13.Stored Procedures • CREATE PROCEDURE • ALTER PROCEDURE • DROP PROCEDURE • EXECUTE PROCEDURE • User-Defined Functions (UDFs) 14.Triggers • Trigger Events • Trigger Execution and Syntax 15. Security and Permissions • CREATE USER • GRANT • REVOKE • ALTER USER • DROP USER 16.Optimizations • Indexing Strategies • Query Optimization 17.Normalization • 1NF(Normal Form) • 2NF • 3NF • BCNF 18.Backup and Recovery • Database Backups • Point-in-Time Recovery 19.NoSQL Databases • MongoDB • Cassandra etc... • Key differences 20. Data Integrity • Primary Key • Foreign Key 21.Advanced SQL Queries • Window Functions • Common Table Expressions (CTEs) 22.Full-Text Search • Full-Text Indexes • Search Optimization 23. Data Import and Export • Importing Data • Exporting Data (CSV, JSON) • Using SQL Dump Files 24.Database Design • Entity-Relationship Diagrams • Normalization Techniques 25.Advanced Indexing • Composite Indexes • Covering Indexes 26.Database Transactions • Savepoints • Nested Transactions • Two-Phase Commit Protocol 27.Performance Tuning • Query Profiling and Analysis • Query Cache Optimization ------------------ END ------------------- Some good resources to learn SQL 1.Tutorial & Courses • Learn SQL: https://bit.ly/3FxxKPz • Udacity: imp.i115008.net/AoAg7K 2. YouTube Channel's • FreeCodeCamp:rb.gy/pprz73 • Programming with Mosh: rb.gy/g62hpe 3. Books • SQL in a Nutshell: https://t.me/DataAnalystInterview/158 4. SQL Interview Questions https://t.me/sqlanalyst/72?single Join @free4unow_backup for more free resourses ENJOY LEARNING 👍👍

Tired of AI that refuses to help? @UnboundGPT_bot doesn't lecture. It just works. ✓ Multiple models (GPT-4o, Gemini, DeepSeek)  ✓ Image generation & editing  ✓ Video creation  ✓ Persistent memory  ✓ Actually uncensored Free to try → @UnboundGPT_bot or https://ko2bot.com

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!

Machine Learning Roadmap

PowerBI Interview Questions 🚀🔥

Useful websites to practice and enhance your data analytics skills 👇👇 1. Python http://learnpython.org 2. SQL https://www.sql-practice.com/ 3. Excel https://excel-practice-online.com/ 4. Power BI https://www.workout-wednesday.com/power-bi-challenges/ 5. Quiz and Interview Questions https://t.me/sqlspecialist Haven't shared lot of resources to avoid too much distraction Just focus on the basics, practice learnings and work on building projects to improve your skills. Thats the best way to learn in my opinion 😄 Join @free4unow_backup for more free courses ENJOY LEARNING 👍👍

Greetings from PVR Cloud Tech!! 🌈 🚀 Along with our highly successful Azure Data Engineering program, we are now launching a brand-new Data Engineering with Snowflake, DBT, and Airflow training track! Course: Snowflake + DBT + Airflow 📌 Start Date: 24th Nov 2025 ⏰ Time:  8 PM – 9 PM IST | Monday 🔹 Course Content: https://drive.google.com/file/d/1luKHrhYZ6zKuXZpVPGzMydrU_6R2yQnL/view 📱 Join WhatsApp Group: https://chat.whatsapp.com/EZghn5PVmryDgJZ1TjIMRk?mode=wwt 📥 Register Now: https://forms.gle/Vaofd52rkJcUpKPV7 📺 WhatsApp Channel: https://www.whatsapp.com/channel/0029Vb60rGU8V0thkpbFFW2n Team   PVR Cloud Tech:)  +91-9346060794

Skills Needed To Become Data Analyst

Hey guys 👋 I was working on something big from last few days. Finally, I have curated best 80+ top-notch Data Analytics Resources 👇👇 https://topmate.io/analyst/861634 If you go on purchasing these books, it will cost you more than 15000 but I kept the minimal price for everyone's benefit. I hope these resources will help you in data analytics journey. I will add more resources here in the future without any additional cost. All the best for your career ❤️

🎯 2 Power-Packed Courses to Boost Your Tech Career! 💻🚀 Whether you're preparing for placements or starting your coding journey — we’ve got you covered! ✅ 1. DSA Self-Paced Course 📌 Master Data Structures & Algorithms – Perfect for SDE interviews & competitive coding – Covers Arrays, Trees, Graphs, DP & more 🔗 Join now: https://gfgcdn.com/tu/W84/ ✅ 2. Python Beginner to Advanced 📌 Learn Python from scratch to expert level – Covers basics, OOPs, file handling, projects & more 🔗 Start here: https://gfgcdn.com/tu/W8D/ 🎁 Use Coupon: GFGWINTERARC for 25% OFF (Limited Time!) These GeeksforGeeks courses are top-rated for 2025 placements—DSA's got 60+ hours of videos, 200+ problems in C++/Python/Java, and real projects to nail interviews at FAANG-level companies! Which one are you grabbing first? 😊

🎯 2 Power-Packed Courses to Boost Your Tech Career! 💻🚀 Whether you're preparing for placements or starting your coding journey — we’ve got you covered! ✅ 1. DSA Self-Paced Course  📌 Master Data Structures & Algorithms  – Perfect for SDE interviews & competitive coding  – Covers Arrays, Trees, Graphs, DP & more  🔗 Join now: https://gfgcdn.com/tu/W84/ ✅ 2. Python Beginner to Advanced  📌 Learn Python from scratch to expert level  – Covers basics, OOPs, file handling, projects & more  🔗 Start here: https://gfgcdn.com/tu/W8D/ 🎁 Use Coupon: GFGWINTERARC for 25% OFF (Limited Time!)

Free Data Analytics Courses With Certificate 👇👇 https://www.linkedin.com/posts/sql-analysts_dataanalyst-datascience-datacamp-activity-7392164126371958784-cFIc Double Tap ♥️ For More Free Resources

📊 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? 😊

Excel Formulas every data analyst should know

Free Access to our premium Data Science Channel 👇👇 https://whatsapp.com/channel/0029Va4QUHa6rsQjhITHK82y Amazing premium resources only for my subscribers 🎁 Free Data Science Courses 🎁 Machine Learning Notes 🎁 Python Free Learning Resources 🎁 Learn AI with ChatGPT 🎁 Build Chatbots using LLM 🎁 Learn Generative AI 🎁 Free Coding Certified Courses Join fast ❤️ ENJOY LEARNING 👍👍

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 😊

Grab the deal before it over Best deal from admin One month premium of perplexity ai with comet browser worth 200$ for free No payment no checkout direct redeem. Open link, login & download comet browser. Ask anything to comet you get best answers for your learning https://pplx.ai/bhavinahir537

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