数据分析
2,125 个结果
- Bigdata Investment MemoBigdata.comWrite a full institutional investment memo on a public company using Bigdata.com data — thesis, variant perception versus consensus, valuation, risks, catalysts, and an explicit recommendation with conviction.
- Bigdata Moat Governance ReviewBigdata.comAssess how durable a public company's competitive advantage is and whether management can be trusted with the capital, using Bigdata.com data. Covers moat identification by type with evidence, moat strength via ROIC versus WACC, pricing power and share trend, a competitive advantage period estimate with erosion signals, industry structure via five forces, the capital allocation track record…
- Bigdata Peer ComparablesBigdata.comCompare a public company against its peer set using Bigdata.com data — valuation multiples, growth, profitability, returns, leverage, and sentiment — to judge relative attractiveness. Builds the peer set with an explicit rationale for inclusion and exclusion, tabulates like-for-like metrics with peer median and quartile positioning, decomposes any premium or discount into what fundamentals…
- Bigdata Post Ipo Day14Bigdata.comWrite a day-14 post-IPO note on potential NASDAQ-100 fast-track index inclusion for a recently listed large-cap, using Bigdata.com data plus web search for index methodology and market data. Covers two-week trading status, an eligibility check against Nasdaq's current published rules (cited, never assumed), a float-adjusted index weight and implied passive-demand estimate with the math shown…
- Bigdata Post Ipo Day179Bigdata.comWrite a day-179 post-IPO note on the 180-day lock-up expiry using Bigdata.com data plus filings and market data. Covers lock-up terms from the prospectus (expiry date, covered holders, share count, early-release provisions), float and overhang math (post-expiry float, days-to-trade versus ADV), insider and VC selling-intention signals, positioning into the event (short interest, borrow, options…
- Bigdata Post Ipo Day365Bigdata.comWrite a day-365 post-IPO note on the 366-day founder and significant-investor lock-up expiry and float expansion toward 15-20%, using Bigdata.com data plus filings and market data. Covers the staggered lock-up structure from the prospectus, float expansion math and days-to-trade, the offsetting float-adjusted index reweight demand netted against new supply, a realistic read on whether founders…
- Bigdata Pre Ipo AnalysisBigdata.comProduce a balanced pre-IPO research note on an upcoming, not-yet-listed company using its S-1/F-1 plus Bigdata.com data. Covers deal structure (price range, shares, greenshoe, implied valuation, underwriters, lock-ups, share classes), two years plus interim financials, business model and funding history, TAM and listed comparables, IPO-window conditions, and 90-day sentiment — closing with bull…
- Bigdata Quick TakeBigdata.comGive a fast, PM-style quick take on a stock using Bigdata.com data — a one-line current view, the 2-3 drivers that actually matter right now, the key risks and what would change the view, and the near-term setup with the next catalyst. Deliberately short: one page, no full thesis, no model.
- Bigdata Regional ComparisonBigdata.comCompare regions or blocs using Bigdata.com data — economic indicators, market performance, and cross-asset views — and turn that into an allocation recommendation. Covers growth, inflation, policy and labor per region, comparative developed-versus-emerging analysis, regional equity valuations, and fixed income and currency views for each.
- Bigdata Risk AssessmentBigdata.comProduce a comprehensive risk assessment for a public company using Bigdata.com data (10-K risk factors, 8-K material events, news, tearsheet financials). Covers six categories — regulatory and legal, competitive and moat erosion, operational, financial and balance sheet, macro, and management and governance — each rated by likelihood and impact, with a distress screen when leverage is stretched…
- Bigdata Scenario AnalysisBigdata.comBuild bull, base, and bear cases for a public company using Bigdata.com data — with explicit line-item assumptions, justified probability weights summing to 100%, a value or price per scenario with the bridge shown, a probability-weighted expected value and expected return versus spot, the upside/downside skew and risk-reward ratio, and what would move probability between the cases.
- Bigdata Sector AnalysisBigdata.comAnalyze a market sector using Bigdata.com data — performance, valuations, themes, sub-industries, and upcoming catalysts. Maps the sector to its own operating and valuation KPIs rather than generic P/E, reads cycle and profitability positioning as early, mid, or late versus history, aggregates bellwether tearsheet metrics, and closes with a positioning call plus top picks and areas to avoid.
- Bigdata Sector PlaybookBigdata.comBuild an actionable investment playbook for a sector using Bigdata.com data and sector-specific frameworks — the KPIs that actually matter in that sector, how to value companies in it and why, the live debates and where consensus sits on each, valuation context against the sector's own history, a sub-industry map with cycle position, screening criteria and sector-specific red flags, and an…
- Bigdata Valuation SnapshotBigdata.comAnswer what a public company is worth and whether it is cheap or expensive, using Bigdata.com data (tearsheet multiples, estimates, margins, peer context). Produces a multiples cross-check against the company's own history and peer median, an implied-expectations read on what the current price already embeds (reverse-DCF reasoning, no model build required), the 2-3 value drivers that dominate…
- Bigdata Variant PerceptionBigdata.comState explicitly where your view on a public company differs from consensus, using Bigdata.com data. Establishes the consensus baseline from estimates and sell-side posture, applies the EPIC filter to candidate differentiators, frames the view on FaVeS (fundamentals, valuation, sentiment), and states the variant view as a specific, falsifiable claim with a time horizon — plus what the market is…
- Connector QuickbookscaffeinelabsUse the `quickbooks-client` mops package whenever the user asks the canister to create/read/update invoices, customers, payments, items, or chart-of-account entries in QuickBooks Online (QBO), email an invoice, or run a QuickBooks query. The package wraps the QuickBooks Online Accounting API v3 at `https://quickbooks.api.intuit.com` via outbound HTTPS calls.
- Connector TmdbcaffeinelabsMANDATORY recipe for every Caffeine build that reads movie, TV or people data from a canister. The supported path is the `tmdb-client` mops package (The Movie Database Web API v3) over outbound HTTPS, authenticated with a v3 API key or a v4 read-access token.
- Csv Over Json TablesalivirgoRepresent rectangular datasets as CSV or TSV when repeated JSON field names add unnecessary payload size.
- Data ChangeDimkoxUse for SQL schema changes, migrations, indexes, backfills, Elasticsearch/OpenSearch mappings, ClickHouse pipelines, and data-quality work.
- Data Cleanhongtongzhou-design数据清洗规范。当用户要求清洗/整理数据集、制定清洗规则、去重与缺失值处理、或产出清洗报告时使用。先画像再定规，规则先写进 rules.md 再动手，脚本只读原始数据绝不原地修改，处理后数据写入产物目录（不进 git），产出含行号/计数证据的清洗报告。
- Data Edahongtongzhou-design探索性数据分析与绘图规范。当用户要求对数据集做 EDA、分布/相关/异常分析、或绘制可复现图表时使用。分析前先做数据档案（样本量/缺失模式/变量类型）并列数据警告；分布、相关、异常按适用性覆盖并披露选择；图表英文标注、由可复现脚本生成并写入 figures/；每条发现区分描述性事实、探索模式和有明确验证依据的确证结论并可回溯出处；统计结论先过 stats-check 口径。
- Data Science ToolsmikkelkrogsholmDocumentation of available data science libraries (scipy, numpy, pandas, sklearn) and best practices for statistical analysis, regression modeling, and organizing analysis scripts. **CRITICAL:** All analysis scripts MUST be placed in reports/{topic}/scripts/, NOT in root scripts/ directory.
- Db OpsSagargupta16Database work on the user's apps with a read-only-first posture. Inspect schema and live data (Neon or another hosted Postgres via its MCP server or CLI, local SQLite or Postgres via the repo's own CLI), answer what tables and columns exist and whether prod and local point at the same database, then on ask author and run migrations (Alembic for Python backends, Drizzle for TypeScript ones)…
- Deal Champion MappingmbfinottiBuilds an evidence-graded stakeholder map for one active deal from titles, call notes, email and meeting activity, and CRM contact roles, naming the likely champion, economic buyer, and blockers, each with a confidence level tied to observed behaviour rather than title. Covers B2B buying committees and high-consideration B2C households.