Data Prep and Analytics
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。
Read MoreData Cleansing
Data cleansing, also known as data cleaning or scrubbing, identifies and fixes errors, duplicates, and irrelevant data from a raw dataset.
Read MoreData 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.
Read MoreData Lineage
Track where an organization’s data comes from, the journey it takes through the system, and keep business data compliant and accurate.
Read MoreData Profiling
Data profiling helps discover, understand, and organize data by identifying its characteristics and assessing its quality.
Read MoreData 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.
Read MoreDescriptive 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.
Read MoreETL
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.
Read MoreSpatial 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.
Read MoreData Science and Machine Learning
Advanced Analytics
Advanced analytics uses sophisticated techniques to uncover insights, identify patterns, predict outcomes, and generate recommendations.
Read MoreAutoML
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.
Read MoreData 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?
Read MoreFeature Engineering
With feature engineering, organizations can make sense of their data and turn it into something beneficial.
Read MoreMachine 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。
Read MorePrescriptive Analytics
Prescriptive analytics answers the question “What should/can be done?” by using machine learning, graph analysis, simulation, heuristics, and other methods.
Read MoreSupervised vs Unsupervised Learning
Supervised and unsupervised learning models work in unique ways to help businesses better engage with their consumers.
Read More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。
Read MoreReady to Empower Impressive Outcomes?
