I am Data! Edition II: If you don’t want to learn Data but want to know What Data is, then Read ME!
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Comprehensive guide simplifying complex data concepts.
If the world of data seems daunting, "I am Data! Edition II" acts as your navigational beacon. Mr. Mustafa Qizilbash breaks down intricate data topics into digestible pieces, making it ideal for anyone eager to understand the field without getting bogged down in technical jargon. Whether you're a professional looking to brush up on terminology or a curious mind delving into the data realm, this book serves as a clarifying resource.
Note: While we do our best to ensure the accuracy of cover images, ISBNs may at times be reused for different editions of the same title which may hence appear as a different cover.
I am Data! Edition II: If you don’t want to learn Data but want to know What Data is, then Read ME!
Regular price
- Unit price
- / per
ISBN: 9798374837889
Authors:
Mr. Mustafa Qizilbash
Publisher: February 17, 2023
Date of Publication: 2023-02-17
Format: Paperback
Related Topics:
Technology,
Data Science,
Business Intelligence,
Data Visualization,
Finance,
Society,
Sociology
Goodreads rating: 5.0
(rated by 1 readers)
Description
It contains 201 data topics including 72 topics from previous edition.Original Decision Support System and KPI(s), What is Data?, Types of Data, Types of Architecture, Data Architecture, Medallion Architecture, Lambda Architecture, Kappa Architecture, Unified Data Architecture, Zero Trust Architecture, On-Premises vs Cloud, IaaS, CaaS, PaaS, FaaS, SaaS, DaaS, AAaaS, Data Modeling, Granularity, CDM, The Chasm and Fan Trap, Data Cardinality, Cartesian Data, Data Governance, Data Quality, MDM and RDM, Data Deduplication, Metadata Management, Data or Business Glossary, Data Dictionary, Data Catalog, Data Observability, Data Lineage, Data Provenance, Data Classification, Categorization and Data Clustering, Data Categorization vs Classification, Data Segmentation, Data Labelling, Data Annotation, Data Entropy, Data Taxonomy, Data Ontology, Comparing Taxonomy and Ontology, Data Epistemology, Data Hierarchy, Data Anonymization/ Data Pseudonymization/ Data De-Identification, Data Identification, Data Generalization/ Blurring and Specialization, Data Perturbation/ Data Swapping/ Data Shuffling/ Data Scrambling/ Data Obfuscation, Static Data Masking, Dynamic Data Masking, Data Tokenization, Data Redaction, Data Pipelines, ETL, ELT and ECL, Reverse ETL, Data Conversion, Data Parsing or Formatting, CDC and Real-Time, ESP, Data Security and Data Privacy, DLP, Data Integrity, Data Compliance, Data Preservation, Data Sovereignty, Data Virtualization, Data Federation, Data Consolidation, Data Encryption and Decryption, Data Encoding and Decoding, Data Subsetting, Data or Web Scraping, Database and OLTP, Data Warehouse or Data Marts, Star Schema, Snowflake Schema, Galaxy Schema, OLAP - Cubes, Immutable Data Warehouse, Logical Data Warehouse, Big Data vs Data Lake, SQL and NoSQL Databases, Delta Lake, Data Lakehouse, Data Mesh, Data Vault and Business Vault, Data Swamp, Data Hub, Data Fabric, HTAP, Data Room, Data Locality, Object, File and Block Storages, Hadoop Architecture, Hadoop, HDFS and Hive, Data Sprawl, Dark Data and Dormant Data, Data Detritus, Data Dividend, Data Assets, Data Liabilities, Data Citizens, Data Spread, Data Intuition, Big Data File Formats, Query Optimization, Index, Partitioning, Sharding, ACID, BaSE, DevOps, CI/CD, DevSecOps, DataOps, Difference between DevOps and DataOps, MLOps, DLOps, ModelOps, ITOps, AIOps, Data Science vs Data Mining, Machine Learning vs Deep Learning, Supervised vs Unsupervised Learning, AI vs Data Science, Data Algorithms, Data Splitting for Data Science, Feature Table in ML Modeling, Data Scrubbing, Cleansing and Cleaning, Data Dredging, Snooping, p-hacking, and Fishing, Data Wrangling and Data Munging, Data Enrichment, Data Democratization, Data Liberalization, Data Literacy, Data Driven Organization, Data Entitlement vs Authorization, Authentication vs Authorization, Data Self-Service, Business Intelligence and Business Analytics, Data Visualization, Data Blending and Integration, Data Mashup, Data Harmonization, Data Discovery, Heat Map, Data vs Information vs Knowledge vs Wisdom, Data Monetization, Hub-and-Spoke and Point-to-Point, CDE, Data Ethics, Data Anomalies, Data Surfing, Semantic Layer, Augmented Analytics, Data Island, Data Silos, Data Synthetic or Mockup Data, SISD, SIMD, Data Vectorization, Data Due Diligence, Data Maturity, Data Filtering, Data Validation, Data Inventory, Data Curation, Data Syndication, Data Supply Chain, DLM, Data Usability, Data Sharing, Data Aggregation, Data Profiling, Data Standardization, Geocoding, Data Matching and Linking, Data Serving or Serving Layer, Consumption Layer, MPP, Canned Data, Adhoc Reports, Data Drift, Concept Drift, Scope Creep, Data Discrepancies, Data Skew Issue, Data Coupling, Data Imputation, Data Disambiguation, Entity Recognition, Data Fusion, Integration Hub, NLP (Natural Language Processing), and Modern Data Stack.
Condition guide
Book Condition Guide
Books in Like New Condition
This book may contain very minor cosmetic defects, but it is in pristine condition. There should be no yellowing, no foxing, no water damage, and no annotations of any kind on the cover and the pages. For paperbacks, there should not be any crease marks on the spine. This book is good as new — lucky you!
*Books may be missing bundle media (e.g. CD, e-book code), if included.
Books in Very Good Condition
This book has been used, but it is still in a clean condition. There should be no foxing or annotations of any kind on the inner pages, but a sparse amount may be present on the cover, title pages, or outside edges of the book. There should be no water damage of any kind. For paperbacks, there may be light crease marks on the spine.
*Books may be missing bundle media (e.g. CD, e-book code), if included.
Books in Good Condition
We do our best to ensure the quality of our books, but there is no escaping the wear and tear that comes with time. Slight foxing and some annotations may be present on the pages and the cover. There should be no water damage of any kind. For paperbacks, there may be several crease marks on the spine.
*Books may be missing bundle media (e.g. CD, e-book code), if included.
Books in Well Read Condition
This is a well-read book, which means that the previous owner probably really enjoyed it! The cover and pages may include moderate foxing and annotations, but the text is not obscured and still readable. Moderate cosmetic defects and minor water damage may be present on the edges of the book. For paperbacks, there may be multiple crease marks on the spine.
*Books may be missing bundle media (e.g. CD, e-book code), if included.
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Comprehensive guide simplifying complex data concepts.
If the world of data seems daunting, "I am Data! Edition II" acts as your navigational beacon. Mr. Mustafa Qizilbash breaks down intricate data topics into digestible pieces, making it ideal for anyone eager to understand the field without getting bogged down in technical jargon. Whether you're a professional looking to brush up on terminology or a curious mind delving into the data realm, this book serves as a clarifying resource.
Note: While we do our best to ensure the accuracy of cover images, ISBNs may at times be reused for different editions of the same title which may hence appear as a different cover.
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