tgindex
P

Python Interviews

описание

Join this channel to learn python for web development, data science, artificial intelligence and machine learning with quizzes, projects and amazing resources for free For collaborations: @coderfun

← к каналу@PythonInterviews
28 923
подписчиков
Охват к подписчикам
8,5%
ERR
Реакции к просмотрам
0,16%
80 на 20 постов
Пересылки к просмотрам
0,00%
0
Постов в день
0,1
всего 20

Где отзываются чаще

доля реакций к просмотрам
  • 2 июн.Master Python the Right Way – Without Procrastination. 🐍✨ When I first started learning Python, I quickly realized: You can't master a programming language just by reading syntax or watching tutorials. 📚🚫 Real growth happens when you practice, build, and solve problems on your own. 🛠💻 That's exactly why I've compiled a collection of Python programs – designed to take you from basics to advanced logic-building. 📈🧠 What is this collection about? 🤔 ✔️ Beginner to advanced programs with clear explanations ✔️ Pattern-based exercises to strengthen core fundamentals ✔️ Problem-solving programs that sharpen logical thinking Why is this important? 🌟 You don't just learn "how to code", you start learning "how to think like a programmer". 🧠⚡️ This is perfect for: 🎯 • Preparing for technical interviews 🤝 • Participating in coding challenges 🏆 • Building real-world Python projects 🚀0,53%
  • 28 июл.🔰 Pass in Python0,45%
  • 11 маяComplete Syllabus for Data Analytics interview: SQL: 1. Basic - SELECT statements with WHERE, ORDER BY, GROUP BY, HAVING - Basic JOINS (INNER, LEFT, RIGHT, FULL) - Creating and using simple databases and tables 2. Intermediate - Aggregate functions (COUNT, SUM, AVG, MAX, MIN) - Subqueries and nested queries - Common Table Expressions (WITH clause) - CASE statements for conditional logic in queries 3. Advanced - Advanced JOIN techniques (self-join, non-equi join) - Window functions (OVER, PARTITION BY, ROW_NUMBER, RANK, DENSE_RANK, lead, lag) - optimization with indexing - Data manipulation (INSERT, UPDATE, DELETE) Python: 1. Basic - Syntax, variables, data types (integers, floats, strings, booleans) - Control structures (if-else, for and while loops) - Basic data structures (lists, dictionaries, sets, tuples) - Functions, lambda functions, error handling (try-except) - Modules and packages 2. Pandas & Numpy - Creating and manipulating DataFrames and Series - Indexing, selecting, and filtering data - Handling missing data (fillna, dropna) - Data aggregation with groupby, summarizing data - Merging, joining, and concatenating datasets 3. Basic Visualization - Basic plotting with Matplotlib (line plots, bar plots, histograms) - Visualization with Seaborn (scatter plots, box plots, pair plots) - Customizing plots (sizes, labels, legends, color palettes) - Introduction to interactive visualizations (e.g., Plotly) Excel: 1. Basic - Cell operations, basic formulas (SUMIFS, COUNTIFS, AVERAGEIFS, IF, AND, OR, NOT & Nested Functions etc.) - Introduction to charts and basic data visualization - Data sorting and filtering - Conditional formatting 2. Intermediate - Advanced formulas (V/XLOOKUP, INDEX-MATCH, nested IF) - PivotTables and PivotCharts for summarizing data - Data validation tools - What-if analysis tools (Data Tables, Goal Seek) 3. Advanced - Array formulas and advanced functions - Data Model & Power Pivot - Advanced Filter - Slicers and Timelines in Pivot Tables - Dynamic charts and interactive dashboards Power BI: 1. Data Modeling - Importing data from various sources - Creating and managing relationships between different datasets - Data modeling basics (star schema, snowflake schema) 2. Data Transformation - Using Power Query for data cleaning and transformation - Advanced data shaping techniques - Calculated columns and measures using DAX 3. Data Visualization and Reporting - Creating interactive reports and dashboards - Visualizations (bar, line, pie charts, maps) - Publishing and sharing reports, scheduling data refreshes Statistics Fundamentals: Mean, Median, Mode, Standard Deviation, Variance, Probability Distributions, Hypothesis Testing, P-values, Confidence Intervals, Correlation, Simple Linear Regression, Normal Distribution, Binomial Distribution, Poisson Distribution. Hope it helps :)0,43%
  • 29 июн.Top linked list questions to practice: 1. 🔄 Reverse a Linked List 2. 🔁 Detect a Cycle in a Linked List 3. 🤝 Find the Merge Point of Two Linked Lists 4. 🚫 Remove N-th Node From End of List 5. 🔗 Merge Two Sorted Linked Lists 6. 🖼️ Check if a Linked List is a Palindrome 7. 🚨 Remove Duplicates from a Sorted List 8. 🎯 Find the Middle of a Linked List 9. 🔄 Rotate a Linked List 10. 📑 Implement a Doubly Linked List 11. 📊 Implement a Circular Linked List 12. 🛠️ Add Two Numbers Represented by Linked Lists 13. 🧹 Remove Linked List Elements 14. 🧩 Partition List around a value 15. 🔄 Reverse Nodes in k-Group0,37%
  • 22 июн.🔰 Take Screenshots using Python0,34%
  • 13 авг.✅ Data Science Interview Prep Guide 📊🧠 Whether you're a fresher or career-switcher, here’s how to prep step-by-step: 1️⃣ Understand the Role Data scientists solve problems using data. Core responsibilities: • Data cleaning & analysis • Building predictive models • Communicating insights • Working with business/product teams 2️⃣ Core Skills Needed ✔️ Python (NumPy, Pandas, Matplotlib, Scikit-learn) ✔️ SQL ✔️ Statistics & probability ✔️ Machine Learning basics ✔️ Data storytelling & visualization (Power BI / Tableau / Seaborn) 3️⃣ Key Interview Areas A. Python & Coding • Write code to clean and analyze data • Solve logic problems (e.g., reverse a list, group data by key) • List vs Dict vs DataFrame usage B. Statistics & Probability • Hypothesis testing • p-values, confidence intervals • Normal distribution, sampling C. Machine Learning Concepts • Supervised vs unsupervised learning • Overfitting, regularization, cross-validation • Algorithms: Linear Regression, Decision Trees, KNN, SVM D. SQL • Joins, GROUP BY, subqueries • Window functions • Data aggregation and filtering E. Business & Communication • Explain model results to non-tech stakeholders • What metrics would you track for [business case]? • Tell me about a time you used data to influence a decision 4️⃣ Build Your Portfolio ✅ Do projects like: • E-commerce sales analysis • Customer churn prediction • Movie recommendation system ✅ Host on GitHub or Kaggle ✅ Add visual dashboards and insights 5️⃣ Practice Platforms • LeetCode (SQL, Python) • HackerRank • StrataScratch (SQL case studies) • Kaggle (competitions & notebooks) 💬 Tap ❤️ for more!0,33%
  • 2 июл.🎯If you want to survive in AI era, you must complete these 5 Free AI Courses by google before the 2026 ends 👇 1/ Introduction to Generative AI: https://www.skills.google/course_templates/536 2/ Introduction to LLM: https://www.skills.google/course_templates/539 3/ Introduction to Responsible AI: https://www.skills.google/course_templates/554 4/ GenAI Bootcamp: https://cloudonair.withgoogle.com/gen-ai-bootcamp 5/ Google AI Essentials: https://www.skills.google/paths/23360,29%
  • 10 июл.🔰 Python Set Methods0,27%
  • 23 июл.Quick Python Cheat Sheet for Beginners 🐍✍️ Python is widely used for data analysis, automation, and AI—perfect for beginners starting their coding journey. Aggregation Functions 📊 • sum(list) → Adds all values 👉 sum([1,2,3]) = 6 • len(list) → Counts total elements 👉 len([1,2,3]) = 3 • max(list) → Highest value 👉 max([4,7,2]) = 7 • min(list) → Lowest value 👉 min([4,7,2]) = 2 • sum(list)/len(list) → Average 👉 sum([10,20])/2 = 15 Lookup / Searching 🔍 • in → Check existence 👉 5 in [1,2,5] = True • list.index(value) → Position of value 👉 [10,20,30].index(20) = 1 • Dictionary lookup 👉 data = {"name": "John", "age": 25} data["name"] # John Logical Operations 🧠 • if condition: → Decision making 👉 if x > 10: print("High") else: print("Low") • and → All conditions true • or → Any condition true • not → Reverse condition Text (String) Functions 🔤 • len(text) → Length 👉 len("hello") = 5 • text.lower() → Lowercase • text.upper() → Uppercase • text.strip() → Remove spaces 👉 " hi ".strip() = "hi" • text.replace(old, new) 👉 "hi".replace("h","H") = "Hi" • String concatenation 👉 "Hello " + "World" Date Time Functions 📅 • from datetime import datetime • datetime.now() → Current date time • Extract values: now = datetime.now() now.year now.month now.day Math Functions ➗ • import math • math.sqrt(x) → Square root • math.ceil(x) → Round up • math.floor(x) → Round down • abs(x) → Absolute value Conditional Aggregation (Like Excel SUMIF) ⚡ • Using list comprehension nums = [10, 20, 30, 40] sum(x for x in nums if x > 20) # 70 • Count condition len([x for x in nums if x > 20]) # 2 Pro Tip for Data Analysts 💡 👉 For real-world work, use libraries: pandas & numpy Example: import pandas as pd df["salary"].mean() Python Resources: https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L Double Tap ♥️ For More0,24%
  • 16 июл.Aaj hi ek certified Hackar bano!💻 Shuru se saari cheeze seekho bilkul basic se!! PW skills leke aaya h certified Ethical Hacking ka course!! Isme milega : ✅ Hands on Practice ✅ LIVE Hacking Labs ✅ Certificate after Completion Sirf Rs 4999 mai Abhi enroll karo HACK30 Coupon code use karke 30% OFF milega! Enroll NOW : https://pwskills.com/web-development/certified-ethical-hacking-course-035473/?source=pwskills.com&position=course_dropdown&from=home_page&utm_source=pwskills&utm_medium=telegram&utm_campaign=ethical_hacking0,21%
  • 24 июл.To be GOOD in Data Science you need to learn: - Python - SQL - PowerBI To be GREAT in Data Science you need to add: - Business Understanding - Knowledge of Cloud - Many-many projects But to LAND a job in Data Science you need to prove you can: - Learn new things - Communicate clearly - Solve problems #datascience0,15%
  • 5 апр.Virtual Env in Python0,13%