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Python for Data Analysts

Python for Data Analysts

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Find top Python resources from global universities, cool projects, and learning materials for data analytics. For promotions: @coderfun Useful links: heylink.me/DataAnalytics

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πŸ“ˆ Telegram kanali Python for Data Analysts analitikasi

Python for Data Analysts (@pythonanalyst) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 51 569 obunachidan iborat bo'lib, Texnologiyalar & Aralashmalar toifasida 2 585-o'rinni va Hindiston mintaqasida 6 735-o'rinni egallagan.

πŸ“Š Auditoriya koβ€˜rsatkichlari va dinamika

Π½Π΅Π²Ρ–Π΄ΠΎΠΌΠΎ sanasidan buyon loyiha tez oβ€˜sib, 51 569 obunachiga ega boβ€˜ldi.

30 Oktabr, 2026 dagi oxirgi ma’lumotlarga koβ€˜ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni -222 ga, soβ€˜nggi 24 soatda esa -13 ga oβ€˜zgardi va umumiy qamrov yuqori darajada qolmoqda.

  • Tasdiqlash holati: Tasdiqlanmagan
  • Jalb etish (ER): Auditoriya oβ€˜rtacha 5.96% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining 0.97% ini tashkil etuvchi reaksiyalarni toβ€˜playdi.
  • Post qamrovi: Har bir post oβ€˜rtacha 0 marta koβ€˜riladi; birinchi sutkada odatda 498 ta koβ€˜rish yigβ€˜iladi.
  • Reaksiyalar va oβ€˜zaro ta’sir: Auditoriya faol: har bir postga oβ€˜rtacha 0 ta reaksiya keladi.
  • Tematik yoβ€˜nalishlar: Kontent visualization, panda, analyst, sql, analytic kabi asosiy mavzularga jamlangan.

πŸ“ Tavsif va kontent siyosati

Muallif resursni shaxsiy fikrni ifoda etish maydoni sifatida ta’riflaydi:
β€œFind top Python resources from global universities, cool projects, and learning materials for data analytics. For promotions: @coderfun Useful links: heylink.me/DataAnalytics”

Yuqori yangilanish chastotasi (oxirgi ma’lumot 31 Oktabr, 2026 da olingan) sababli kanal doimo dolzarb va katta qamrovli boβ€˜lib qoladi. Analitika auditoriya kontent bilan faol hamkorlik qilishini, uni Texnologiyalar & Aralashmalar toifasidagi muhim ta’sir nuqtasiga aylantirishini koβ€˜rsatadi.

51 569
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Postlar arxiv
5 High-growth career paths in Tech: Tech careers in 2024 Discover the trends, skills, and opportunities that will define tech careers in 2024. πŸ“† Date: December 28th, 2023 πŸ•’ Time:  8PM-10PM Free Registration Link: πŸ‘‡πŸ‘‡ https://bit.ly/47clbV6 Don't miss out on this opportunity to elevate your Data Science career!  ENJOY LEARNING πŸ‘πŸ‘

Python Cheat Sheet.pdf17.29 MB

Head First Python Paul Barry, 2023

Sent to all who liked and commented on the post πŸ˜„

Python for Data Science Roadmap πŸ‘‡πŸ‘‡ https://t.me/DataScienceInterviews/6

Complete Python Course with Building 10 Real-world Applications πŸ‘‡πŸ‘‡ https://www.linkedin.com/posts/sql-analysts_python-viral-pythonprogramming-activity-7141826568485605376-tf7V

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Natural Language Processing - 2024 #python #en

Python Most Important Interview Questions Question 1: Calculate the average stock price for Company X over the last 6 months. Question 2: Identify the month with the highest total sales for Company Y using their monthly sales data. Question 3: Find the maximum and minimum stock price for Company Z on any given day in the last year. Question 4: Create a column in the DataFrame showing the percentage change in stock price from the previous day for Company X. Question 5: Determine the number of days when the stock price of Company Y was above its 30-day moving average. Question 6: Compare the average stock price of Companies X and Z in the first quarter of the year. #Data# ---------------------------------------------- import pandas as pd data = {   'Date': pd.date_range(start='2023-01-01', periods=180, freq='D'),   'CompanyX_StockPrice': pd.np.random.randint(50, 150, 180),   'CompanyY_Sales': pd.np.random.randint(20000, 50000, 180),   'CompanyZ_StockPrice': pd.np.random.randint(70, 200, 180) } df = pd.DataFrame(data)

PythonCrashCourse.pdf5.38 MB

150 SQL Queries for Practice πŸ‘‡πŸ‘‡ https://t.me/DataAnalystInterview/170

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Data Visualization with Python Dr. Pooja, 2023

Python Data Cleaning Cookbook Michael Walker, 2023

Python Interview Questions for data analyst interview Question 1: Find the top 5 dates when the percentage change in Company A's stock price was the highest. Question 2: Calculate the annualized volatility of Company B's stock price. (Hint: Annualized volatility is the standard deviation of daily returns multiplied by the square root of the number of trading days in a year.) Question 3: Identify the longest streaks of consecutive days when the stock price of Company A was either increasing or decreasing continuously. Question 4: Create a new column that represents the cumulative returns of Company A's stock price over the year. Question 5: Calculate the 7-day rolling average of both Company A's and Company B's stock prices and find the date when the two rolling averages were closest to each other. Question 6: Create a new DataFrame that contains only the dates when Company A's stock price was above its 50-day moving average, and Company B's stock price was below its 50-day moving average

Learning Professional Pythonβœ… https://www.linkedin.com/posts/sql-analysts_learn-python-activity-7129340888232714240-ZuMM Like for more πŸ‘β€οΈ

Python for Everybody Charles R. Severance, 2023

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Profound Python Libraries Onder Teker, 2022