Femto Insights Analytics

5 ?????? ?????????????????? ???? ?????????? 95% ???????????????????? Check the questions list below ???? 1. Explain order of execution of SQL. 2.) What is difference between where and having? 3.) What is the use of group by? 4.) Explain all types of joins in SQL? 5.) What are triggers in SQL? 6.) What is stored procedure in SQL 7.) Explain all types of window functions? (Mainly rank, row_num, dense_rank, lead & lag) 8.) What is difference between Delete and Truncate? 9.) What is difference between DML, DDL and DCL? 10.) What are aggregate function and when do we use them? explain with few example. 11.) Which is faster between CTE and Subquery? 12.) What are constraints and types of Constraints? 13.) Types of Keys? 14.) Different types of Operators ? 15.) Difference between Group By and Where? 16.) What are Views? 17.) What are different types of constraints? 18.) What is difference between varchar and narchar? 19.) Similar for char and nchar? 20.) What are index and their types? 21.) What is an index? Explain its different types. 22.) List the different types of relationships in SQL. 23. Differentiate between UNION and UNION ALL. 24.) How many types of clauses in SQL? 25.) What is the difference between UNION and UNION ALL in SQL? 26.) What are the various types of relationships in SQL? 27.) Difference between Primary Key and Secondary Key? 28.) What is the difference between where and having? 29.) Find the second highest salary of an employee? 30.) Write retention query in SQL? 31.) Write year-on-year growth in SQL? 32.) Write a query for cummulative sum in SQL? 33.) Difference between Function and Store procedure? 34.) Do we use variable in views? 35.) What are the limitations of views? Follow PRAJJVAL MISHRA for more ? Feel free to re-share this post if you find it helpful.

Unleash the Power of SQL: Your Ultimate Guide to SQL Basics & Interview Mastery Mastering SQL is essential for any data professional. Our latest blog post covers everything from SQL fundamentals to high-frequency interview questions, helping you sharpen your skills and prepare for success! What you’ll learn: 1.SQL essentials: SELECT, FROM, WHERE, ORDER BY, LIMIT, and more 2. Deep dive into WHERE, GROUP BY, and HAVING—when and how to use them 3. 6 must-know SQL interview questions with expert sample answers Whether you're preparing for an interview or enhancing your data analysis skills, this guide has you covered! ?? Read the full blog here：https://lnkd.in/eNy-A9n8 Drop a comment with your favorite SQL trick or interview experience! ?? #DataAnalysis #SQL #TechSkills #CareerGrowth #InterviewPrep

?? Is AI Product Management the Next Big Career Move? AI won’t take your PM job, but an AI-powered PM might. AI is transforming product management faster than ever. Since 2023, the rise of large language models (LLMs) has made AI a must-know skill for PMs. But does that mean every PM needs to become an AI expert? Not exactly. The best product managers don’t just manage AI—they use it to supercharge their work. ?? Traditional AI PMs build purpose-driven ML models (think self-driving cars, fraud detection, recommendation engines). ?? AI-powered PMs leverage AI to automate workflows, enhance decision-making, and gain a competitive edge. If you're ignoring AI, you’re falling behind. Here’s why: ? AI-powered research is faster and deeper. ? AI-driven decision-making beats manual data analysis. ? AI automation frees up time for strategic thinking. How to Become an AI-Powered PM (Without a PhD): ? Experiment with Gen AI tools (ChatGPT, Midjourney, RunwayML). ? Learn AI fundamentals (Coursera’s AI for Everyone is a great start). ? Understand prompt engineering & API integrations. ? Identify AI use cases in your industry. ? Collaborate with engineers & data teams. ?? Want to stay ahead in AI-powered product management? Subscribe to our GenAI Works newsletter for insights, tools, and strategies to level up https://lnkd.in/dybPUGhE #ai #ainewsletter #innovation #productmanagement #career

Understanding Machine Learning Algorithms! ?? Supervised Learning Algorithms: 1?? Linear Regression: Used for predicting a continuous dependent variable based on one or more independent variables. 2?? Logistic Regression: Used for binary classification problems where the outcome is a categorical variable. 3?? Naive Bayes: Based on Bayes' theorem, it is used for classification tasks, particularly text classification. 4?? Decision Tree: A tree-like model used for both classification and regression tasks, which splits data into branches to make predictions. 5?? Random Forest: An ensemble method that uses multiple decision trees to improve accuracy and prevent overfitting. 6?? Gradient Boosted Trees: An ensemble technique that builds models sequentially, with each new model correcting errors made by the previous ones. ?? Unsupervised Learning Algorithms: 1?? Principal Component Analysis (PCA): A dimensionality reduction technique used to reduce the number of variables in a dataset while retaining as much information as possible. 2?? K-Means Clustering: A clustering algorithm that partitions data into K distinct clusters based on similarity. ?? Instance-Based Learning: 1?? K-Nearest Neighbors (KNN): A simple, instance-based learning algorithm used for classification and regression tasks. It predicts the label of a data point based on the labels of its nearest neighbors. ?? Deep Learning: 1?? Dense Neural Network: A type of artificial neural network where each neuron is connected to every neuron in the previous and next layer. Used for a wide range of tasks, including image and speech recognition. 10 Best Advanced Machine Learning Resources- https://lnkd.in/gi2jPvJN Image Credit: Data Interview Happy Learning! #MachineLearning #DataScience #Algorithms #AI #ML #DeepLearning #DataScienceCommunity #ArtificialIntelligence

Data Analyst Roadmap in 2025 |-- Week 1: Introduction to Data Analysis | |-- Data Analysis Fundamentals | | |-- What is Data Analysis? | | |-- Types of Data Analysis | | |-- Data Analysis Workflow | |-- Tools and Environment Setup | | |-- Overview of Tools (Excel, SQL) | | |-- Installing Necessary Software | | |-- Setting Up Your Workspace | |-- First Data Analysis Project | | |-- Data Collection | | |-- Data Cleaning | | |-- Basic Data Exploration | |-- Week 2: Data Collection and Cleaning | |-- Data Collection Methods | | |-- Primary vs. Secondary Data | | |-- Web Scraping | | |-- APIs | |-- Data Cleaning Techniques | | |-- Handling Missing Values | | |-- Data Transformation | | |-- Data Normalization | |-- Data Quality | | |-- Ensuring Data Accuracy | | |-- Data Integrity | | |-- Data Validation | |-- Week 3: Data Exploration and Visualization | |-- Exploratory Data Analysis (EDA) | | |-- Descriptive Statistics | | |-- Data Distribution | | |-- Correlation Analysis | |-- Data Visualization Basics | | |-- Choosing the Right Chart Type | | |-- Creating Basic Charts | | |-- Customizing Visuals | |-- Advanced Data Visualization | | |-- Interactive Dashboards | | |-- Storytelling with Data | | |-- Data Presentation Techniques | |-- Week 4: Statistical Analysis | |-- Introduction to Statistics | | |-- Descriptive vs. Inferential Statistics | | |-- Probability Theory | |-- Hypothesis Testing | | |-- Null and Alternative Hypotheses | | |-- t-tests, Chi-square tests | | |-- p-values and Significance Levels | |-- Regression Analysis | | |-- Simple Linear Regression | | |-- Multiple Linear Regression | | |-- Logistic Regression | |-- Week 5: SQL for Data Analysis | |-- SQL Basics | | |-- SQL Syntax | | |-- Select, Insert, Update, Delete | |-- Advanced SQL | | |-- Joins and Subqueries | | |-- Window Functions | | |-- Stored Procedures | |-- Week 6-8: Python for Data Analysis | |-- Python Basics | | |-- Python Syntax | | |-- Data Types and Structures | | |-- Functions and Loops | |-- Data Analysis with Python | | |-- NumPy for Numerical Data | | |-- Pandas for Data Manipulation | | |-- Matplotlib and Seaborn for Visualization | |-- Advanced Data Analysis in Python | |-- Week 9-11: Real-world Applications and Projects | |-- Capstone Project | | |-- Project Planning | | |-- Data Collection and Preparation | | |-- Building and Optimizing Models | | |-- Creating and Publishing Reports Free resources to learn these skills ?? SQL - https://lnkd.in/gQkjdAWP Python - https://lnkd.in/gQk8siKn Excel - https://lnkd.in/d-txjPJn Power BI - https://lnkd.in/gs6RgH2m Projects - https://t.me/sqlproject I have curated best 80+ top-notch Data Analytics Resources ???? https://t.me/sqlspecialist Like this post for more content like this ??

Are you gearing up for Data Analyst positions in 2025? As we wrap up the festivities and step into the New Year, aspiring professionals in Analytics eagerly anticipate the resumption of hiring processes to kickstart their careers in 2025. The landscape for Data Analytics roles is poised to be fiercely competitive. Regardless of the industry or organization, every Data Analyst must equip themselves with a fundamental toolkit comprising essential skills, knowledge, and tools necessary for proficient job performance. To stand out in the job market, it is crucial to ensure that your skill set and resumes are tailored to match the criteria that recruiters will be seeking. Here's a comprehensive checklist outlining the key skills, crucial topics, concepts, and techniques that all Data Analytics applicants and enthusiasts should master to enhance their prospects of securing their initial analytics role. Pdf credit - Varun Sagar Theegala If you found this guide beneficial, show your support by liking and sharing. Don't forget to follow PRAJJVAL MISHRA for more such post.

How to Handle Missing Data Properly? Missing values are an inevitable challenge in data analysis and machine learning. If not handled correctly, they can introduce bias and significantly impact model performance. So, how should we deal with missing data effectively? Common Approaches: ? Deletion – Best for cases where missing values are minimal ? Simple Imputation (Mean, Median, Mode) – Suitable for numerical variables ? Forward/Backward Fill – Ideal for time-series data ? Group-wise Imputation – Effective when clear grouping exists in data ? Model-based Imputation – Best for high-quality prediction needs Choosing the right method is crucial—poor handling can distort insights and reduce model reliability! At Femto Insights Analytics, we’ve put together a comprehensive guide that explains each method’s pros, cons, and practical applications. Read the full blog here ?? https://lnkd.in/eDDRj9Rb What challenges have you faced when dealing with missing data? Let’s discuss in the comments! ?? #DataScience #MachineLearning #DataAnalytics #FemtoInsights #MissingData #BusinessIntelligence

Complete SQL Topics for Data Analysts ???? 1. Introduction to SQL: - Basic syntax and structure - Understanding databases and tables 2. Querying Data: - SELECT statement - Filtering data using WHERE clause - Sorting data with ORDER BY 3. Joins: - INNER JOIN, LEFT JOIN, RIGHT JOIN, FULL JOIN - Combining data from multiple tables 4. Aggregation Functions: - GROUP BY - Aggregate functions like COUNT, SUM, AVG, MAX, MIN 5. Subqueries: - Using subqueries in SELECT, WHERE, and HAVING clauses 6. Data Modification: - INSERT, UPDATE, DELETE statements - Transactions and Rollback 7. Data Types and Constraints: - Understanding various data types (e.g., INT, VARCHAR) - Using constraints (e.g., PRIMARY KEY, FOREIGN KEY) 8. Indexes: - Creating and managing indexes for performance optimization 9. Views: - Creating and using views for simplified querying 10. Stored Procedures and Functions: - Writing and executing stored procedures - Creating and using functions 11. Normalization: - Understanding database normalization concepts 12. Data Import and Export: - Importing and exporting data using SQL 13. Window Functions: - ROW_NUMBER(), RANK(), DENSE_RANK(), and others 14. Advanced Filtering: - Using CASE statements for conditional logic 15. Advanced Join Techniques: - Self-joins and other advanced join scenarios 16. Analytical Functions: - LAG(), LEAD(), OVER() for advanced analytics 17. Working with Dates and Times: - Date and time functions and formatting 18. Performance Tuning: - Query optimization strategies 19. Security: - Understanding SQL injection and best practices for security 20. Handling NULL Values: - Dealing with NULL values in queries Ensure hands-on practice on these topics to strengthen your SQL skills. Since SQL is one of the most essential skill for data analysts, I have decided to teach each topic daily in this channel for free. Like this post if you want me to continue this SQL series ???? Free access to our telegram community today ???? https://t.me/sqlspecialist Hope it helps :)

5 Best Github repos to help you pass data analyst and data scientist interviews! 1] 100-????????-????-????-???????? (42???) ? Daily coding challenges for machine learning mastery ? Structured path from ML basics to implementation ? https://lnkd.in/dcftdA57 2] ??????????????-?????????????????????? (22.7???) ? Comprehensive data science resource catalog ? Tools, libraries, and learning materials by category ? https://lnkd.in/dcFYYwx9 3] ????????-??????????????-??????-?????????????????? (14.5???) ? Microsoft's 10-week beginner curriculum ? Project-based approach with practical exercises ? https://lnkd.in/d_zZBadF 4] ?????????????? ???????????????? ????????????????????(8.1???) ? Real tech giant interview questions with solutions ? Targeted preparation for ML positions ? https://lnkd.in/gq_huuZD 5] ????????-??????????????-??????????????-?????????????????? (27.2???) ? Ready-to-run Jupyter notebooks for all DS topics ? Practical implementations of algorithms and techniques ? https://lnkd.in/dPmQuPB9 These repositories are maintained by various individuals and organizations, each offering valuable resources for learning and practicing data analytics and data science. If you find this helpful, feel free to... ?? React ?? Comment ?? Share Let me know if I missed anything! #dataanalytics #datascience #github #sql #ml

Many people pay too much to learn Python, but my mission is to break down barriers. I have shared complete learning series to learn Python from scratch. Here are the links to the Python series Complete Python Topics for Data Analyst: https://lnkd.in/dmTaCBAw Part-1: https://lnkd.in/dX5DXyfK Part-2: https://lnkd.in/dwJpUedu Part-3: https://lnkd.in/dXrB_sb5 Part-4: https://lnkd.in/dTmuFpfw Part-5: https://lnkd.in/dtwfBvVj Part-6: https://lnkd.in/dZ6ZMh6P Part-7: https://lnkd.in/dM4tpZjK Part-8: https://lnkd.in/dkpC2ZPJ Part-9: https://lnkd.in/ddYG6gWv Part-10: https://lnkd.in/dfHvut4n Part-11: https://lnkd.in/ddYG6gWv Part-12: https://lnkd.in/d_CmZ9qv Part-13: https://lnkd.in/dJRJ9X-P Part-14: https://lnkd.in/dup_kKj8 Part-15: https://lnkd.in/dvxHqM5m I saw a lot of big influencers copy pasting my content after removing the credits. It's absolutely fine for me as more people are getting free education because of my content. But I will really appreciate if you share credits for the time and efforts I put in to create such valuable content. I hope you can understand. You can refer these amazing resources for Python Interview Preparation. Complete SQL Topics for Data Analysts: https://lnkd.in/dmFZH-TB Complete Power BI Topics for Data Analysts: https://lnkd.in/d4MKkwaH I'll continue with learning series on Excel & Tableau. Thanks to all who support our channel and share the content with proper credits. You guys are really amazing. Hope it helps :)