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Изходен канал @clockstackwheels · Post #385 · 17.06

Второй онлайн-день #DotNext закончился. В числе прочего был интересный разговор на тему «Страх и ненависть в Open Source», но я вам о некоторых особо заметных случаях рассказывал уже вот тут и тут. Ещё послушал про обратное соединение распиленного монолита (бывает, что и такое нужно!) и ещё пару докладов. В целом, впечатления противоречивые. Опишу кратко. Плюсы: 1. Высочайшего качества техническая организация. Ничего не глючило, чистая картинка и звук, удобный UI. 2. Были полезные и практичные вещи, интересные. 3. Реально отвечали на вопросы в чате в реалтайм-режиме. Что прошло ниже моих ожиданий (я впервые на такой дорогой IT-конференции): 1. Наверное, подсознательно я ожидал что с учётом цены билета буквально каждый доклад будет супер звёздным уровня "Торвальдс лично рассказывает подробности устройства ядра Linux, и делает это с шутками и котиками". Но доклады в среднем довольно обычные. Некоторые поверхностные, другие на очень далёкую от меня тему. И ещё их не очень много, не то, чтоб был гигантский выбор. Хотя, справедливости ради, больше 2-3 лекций в день тяжело осилить. 2. Интерактивные фишки формально заявлены: виртуальные стенды и квизы. По факту, во-первых, стенды и квизы полностью повторяют друг друга, во-вторых, их было всего два, и интересный (на мой личный взгляд) только один. Я ожидал, что их хотя бы десяток будет. 3. Часть обсуждения в Telegram, часть прямо в онлайн-чате лекции, и это, на мой взгляд, не пошло на пользу. Мне не хотелось вступать в Telegram-чат, но активность в основном чате лекции была низкой, при этом с телефона такой чат вообще не подразумевался. Я для себя убедился, что всё-таки именно мне в таких мероприятиях важна офлайновость: прийти и вживую потусить, получить мерч, поучаствовать в активностях. Чисто в онлайн-формате сугубо на мой взгляд мероприятие себя не окупает. Посмотрим, что будет в офлайне 27-го числа, напишу вам отзыв. #dev

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Repositorio data science

@repo_science · Post #3999 · 22.01.2024 г., 11:01

#TimeSeries#Analysis#Python ⌚️ Forecasting Models and Time Series for Business in Python Time Series Analysis in Python. Demand Planning & Business Forecasting. Forecast with 6 Models: Prophet, ARIMA & More. ----- Main channel: @repo_science Coupons: @freecoupons_reposcience -----

Daily Channels

@dailychannels · Post #6767 · 23.03.2026 г., 01:00

Channel: Propheta Indicator Signals Members: ~2.5K 💢 Username: @propheta_indicator Description: 😎 WE MILK THE EXCHANGES! 🔥 Reviews & Results - @propheta_reviews 📊 Performance Reports - @propheta_reports 🤖 Get Access - @ProphetaAccountBot Contact us: @propheta_help 🏷 Tags: #crypto_fx_trading #crypto#trading#signals#analysis#news https://lve.to/4rck4ca4c6

Venezuelanalysis

@venanalysis · Post #1850 · 11.01.2025 г., 21:16

The Venezuelanalysis staff gathered to discuss the recent events surrounding Maduro’s third presidential term inauguration and the challenges ahead. The topics included an update on the situation on the ground, María Corina Machado's (fake?) arrest and the US response. Click to watch: https://venezuelanalysis.com/video/venezuelas-maduro-presidential-inauguration-recap-and-lookahead/ #Livestream#Analysis#Venezuela#PresidentialInauguration

Daily Channels

@dailychannels · Post #5943 · 26.03.2025 г., 13:00

Channel: Bitcoin Trading Nicole Members: ~20.77K 💢 Username: @bitcointradingnicole Description: Nicole Bitcoin Trading is a place to be, where experts calls are backedup with sound Technical analysis. t.me/PayoutProof t.me/BitcoinAlgoPumps t.me/CryptoTradingNicole For VIP & Pump Contact: @NicoleCrypto 🏷 Tags: #crypto_fx_trading #bitcoin#trading#crypto#analysis#investing https://telegramchannels.me/channels/bitcointradingnicole

Daily Channels

@dailychannels · Post #6000 · 11.04.2025 г., 01:00

Channel: Crypto Trading Signals ✅ Members: ~8.19K 💢 Username: @binancefuturetrading Description: Who are we? We are a group of professional traders who focus mainly on crypto publicity projects and crypto Trading. 🏷 Tags: #crypto_fx_trading #cryptocurrency#bitcoin#trading#analysis#investments https://telegramchannels.me/channels/binancefuturetrading

djangoproject

@djangoproject · Post #336 · 09.05.2017 г., 05:24

https://dzone.com/articles/pyflakes-passive-checker There are several code #analysis tools for Python. The most well known is pylint. Then there’s pychecker and now we’re moving on to #pyflakes. The pyflakes project is a part of something known as the Divmod Project. Pyflakes doesn’t actually execute the code it checks, unlike #pychecker. Of course, #pylint also doesn’t execute the code. Regardless, we’ll take a quick look at it and see how pyflakes works and if it’s better than the competition.

Crypto M - Crypto News

@CryptoM · Post #64526 · 09.04.2026 г., 06:14

🚀 Polymarket Traders' Earnings: Only 0.015% Achieve $5,000 Monthly Crypto analyst Andrey Sergeenkov's recent analysis reveals that a mere 0.015% of Polymarket traders managed to earn at least $5,000 monthly for four consecutive months. According to NS3.AI, the study examined trading data spanning from April 2024 to April 1, 2026, highlighting the challenges faced by traders in achieving consistent profitability on the platform. #Polymarket#Crypto#Trading#Earnings#Profitability#Analysis#NS3AI

Data Analytics

@sqlspecialist · Post #1644 · 23.05.2025 г., 18:46

✨The STAR method is a powerful technique used to answer behavioral interview questions effectively. It helps structure responses by focusing on Situation, Task, Action, and Result. For analytics professionals, using the STAR method ensures that you demonstrate your problem-solving abilities, technical skills, and business acumen in a clear and concise way. Here’s how the STAR method works, tailored for an analytics interview: 📍 1. Situation Describe the context or challenge you faced. For analysts, this might be related to data challenges, business processes, or system inefficiencies. Be specific about the setting, whether it was a project, a recurring task, or a special initiative. Example: “At my previous role as a data analyst at XYZ Company, we were experiencing a high churn rate among our subscription customers. This was a critical issue because it directly impacted revenue.”* 📍 2. Task Explain the responsibilities you had or the goals you needed to achieve in that situation. In analytics, this usually revolves around diagnosing the problem, designing experiments, or conducting data analysis. Example: “I was tasked with identifying the factors contributing to customer churn and providing actionable insights to the marketing team to help them improve retention.”* 📍 3. Action Detail the specific actions you took to address the problem. Be sure to mention any tools, software, or methodologies you used (e.g., SQL, Python, data #visualization tools, #statistical#models). This is your opportunity to showcase your technical expertise and approach to problem-solving. Example: “I collected and analyzed customer data using #SQL to extract key trends. I then used #Python for data cleaning and statistical analysis, focusing on engagement metrics, product usage patterns, and customer feedback. I also collaborated with the marketing and product teams to understand business priorities.”* 📍 4. Result Highlight the outcome of your actions, especially any measurable impact. Quantify your results if possible, as this demonstrates your effectiveness as an analyst. Show how your analysis directly influenced business decisions or outcomes. Example: “As a result of my analysis, we discovered that customers were disengaging due to a lack of certain product features. My insights led to a targeted marketing campaign and product improvements, reducing churn by 15% over the next quarter.”* Example STAR Answer for an Analytics Interview Question: Question: *"Tell me about a time you used data to solve a business problem."* Answer (STAR format): 🔻*S*: “At my previous company, our sales team was struggling with inconsistent performance, and management wasn’t sure which factors were driving the variance.” 🔻*T*: “I was assigned the task of conducting a detailed analysis to identify key drivers of sales performance and propose data-driven recommendations.” 🔻*A*: “I began by collecting sales data over the past year and segmented it by region, product line, and sales representative. I then used Python for #statistical#analysis and developed a regression model to determine the key factors influencing sales outcomes. I also visualized the data using #Tableau to present the findings to non-technical stakeholders.” 🔻*R*: “The analysis revealed that product mix and regional seasonality were significant contributors to the variability. Based on my findings, the company adjusted their sales strategy, leading to a 20% increase in sales efficiency in the next quarter.” Hope this helps you 😊

Repositorio data science

@repo_science · Post #3078 · 18.04.2023 г., 15:54

#analysis#AWS#Databases#ETL#MongoDB#pipelines#RDS#S3#Scala#Spark#SQL ⚙️ 50 HOURS OF BIG DATA, PYSPARK, AWS, SCALA, AND SCRAPING (2022) 🌐 Inglés ⚖️17.03GB 🔗Link ----- Canal principal:@repo_science Cupones: @freecoupons_reposcience -----

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