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Data Science and Data Analytics Glossary

Data Prep and Analytics

Business Analytics

ビジネスアナリティクスとは、より良いビジネス成果を導く意思決定を行うために統計的・定量的手法を用いてデータを分析するプロセスです。

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Business Intelligence

ビジネスインテリジェンスは、組織のデータ、ソフトウェア、インフラ、ビジネスプロセス、人間の直感などの累積的な成果であり、実用的なインサイトの提供を可能にします。

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Data Analytics

データアナリティクスとは、データを調査、変換、分析して、重要なインサイトを明らかにし、意思決定の効率性を高めるためのプロセスです。

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Data Blending

データブレンディングは、さまざまなソースから取得したデータを 1 つの有用なデータセットにまとめるプロセスであり、より深く、より複雑な分析を可能にします。

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Data Catalog

How a Data Catalog Helps an Organization Make the Best Use of Data Assets

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Data Cleansing

Data cleansing, also known as data cleaning or scrubbing, identifies and fixes errors, duplicates, and irrelevant data from a raw dataset.

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Data Exploration

Data exploration is a way to get to know data before working with it. Through survey and investigation, large datasets are readied for deeper, more structured analysis.

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Data Lineage

Track where an organization’s data comes from, the journey it takes through the system, and keep business data compliant and accurate.

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Data Preparation

データ準備は、ビジネス分析に使用する生データのクレンジングと統合を行う工程です。その重要性と仕組みをご紹介します。

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Data Profiling

Data profiling helps discover, understand, and organize data by identifying its characteristics and assessing its quality.

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Data Wrangling

Data wrangling is the act of transforming, cleansing, and enriching data to make it more applicable, consumable, and useful to make smarter business decisions.

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Descriptive Analytics

Descriptive analytics answers the question “What happened?” by drawing conclusions from large, raw datasets. The findings are then visualized into accessible line graphs, tables, pie and bar charts, and generated narratives.

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ETL

ETL is the process used to copy, combine, and convert data from different sources and formats and load it into a new destination such as a data warehouse or data lake.

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Spatial Analytics

Spatial analysis models problems geographically, allowing a company to analyze the locations, relationships, attributes, and proximities in geospatial data to answer questions and develop insights.

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Data Science and Machine Learning

Advanced Analytics

Advanced analytics uses sophisticated techniques to uncover insights, identify patterns, predict outcomes, and generate recommendations.

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AutoML

Automated machine learning, or AutoML, makes ML accessible to non-experts by enabling them to build, validate, iterate, and explore ML models through an automated experience.

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Data Science vs Machine Learning

Data science and machine learning are buzzwords in the technology world. Both enhance AI operations across the business and industry spectrum. But which is best?

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Feature Engineering

With feature engineering, organizations can make sense of their data and turn it into something beneficial.

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Machine Learning

機械学習 (machine learning) とは、コンピューターが特定の制約を持つデータセットを反復的に処理することで、内在するパターンを見つけ出すプロセスです。

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Prescriptive Analytics

Prescriptive analytics answers the question “What should/can be done?” by using machine learning, graph analysis, simulation, heuristics, and other methods.

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Supervised vs Unsupervised Learning

Supervised and unsupervised learning models work in unique ways to help businesses better engage with their consumers.

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データサイエンス

データサイエンスとは、応用統計学の一種で、コンピュータサイエンスと数学の要素を組み込み、定量的・定性的なデータからインサイトを抽出します。

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予測分析

予測分析は、機械学習や統計アルゴリズムなどの手法を用いて、将来何が起こるかを予測するデータ分析の一種です。

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機械学習オペレーション (MLOps、Machine Leaning Operations)

MLOps は、データサイエンスの運用における、部門横断的、協調的、反復的なプロセスであり、機械学習 (Machine Learning、ML) やその他のタイプのモデルを、反復可能なプロセスを介してデプロイし、継続的に監視できる再利用可能なソフトウェア成果物として管理します。

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