Data Analytics and Big Data:
Cover -- Half-Title Page -- Dedication -- Title Page -- Copyright Page -- Contents -- Acknowledgments -- Preface -- Introduction -- Why this book? -- Whom is this book for? -- Organization of the book -- Glossary -- PART 1 Towards an Understanding of Big Data:Are You Ready? -- 1. From Data to Big Da...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
London
ISTE Ltd
2018
|
| Schriftenreihe: | Informations systems, web and pervasive computing series
|
| Schlagworte: | |
| Zusammenfassung: | Cover -- Half-Title Page -- Dedication -- Title Page -- Copyright Page -- Contents -- Acknowledgments -- Preface -- Introduction -- Why this book? -- Whom is this book for? -- Organization of the book -- Glossary -- PART 1 Towards an Understanding of Big Data:Are You Ready? -- 1. From Data to Big Data: You Must Walk Before You Can Run -- 1.1. Introduction -- 1.2. No analytics without data -- 1.2.1. Databases -- 1.2.2. Raw data -- 1.2.3. Text -- 1.2.4. Images, audios and videos -- 1.2.5. The Internet of Things -- 1.3. From bytes to yottabytes: the data revolution -- 1.4. Big data: definition -- 1.5. The 3Vs model -- 1.6. Why now and what does it bring? -- 1.7. Conclusions -- 2. Big Data: A Revolution that Changes the Game -- 2.1. Introduction -- 2.2. Beyond the 3Vs -- 2.3. From understanding data to knowledge -- 2.4. Improving decision-making -- 2.5. Things to take into account -- 2.5.1. Data complexity -- 2.5.2. Data quality: look out! Not all data are the right data -- 2.5.3. What else?...Data security -- 2.6. Big data and businesses -- 2.6.1. Opportunities -- 2.6.2. Challenges -- 2.7. Conclusions -- PART 2 Big Data Analytics: A Compilation of Advanced Analytics Techniques that Covers a Wide Range of Data -- 3. Building an Understanding of Big Data Analytics -- 3.1. Introduction -- 3.2. Before breaking down the process... What is data analytics? -- 3.3. Before and after big data analytics -- 3.4. Traditional versus advanced analytics: What is the difference? -- 3.5. Advanced analytics: new paradigm -- 3.6. New statistical and computational paradigm within the big data context -- 3.7. Conclusions -- 4. Why Data Analytics and When Can We Use It? -- 4.1. Introduction -- 4.2. Understanding the changes in context -- 4.3. When real time makes the difference -- 4.4. What should data analytics address? -- 4.5. Analytics culture within companies 4.6. Big data analytics application: examples -- 4.7. Conclusions -- 5. Data Analytics Process: There's Great Work Behind the Scenes -- 5.1. Introduction -- 5.2. More data, more questions for better answers -- 5.2.1. We can never say it enough: "there is no good wind for those who don't know where they are going" -- 5.2.2. Understanding the basics: identify what we already know and what we have yet to find out -- 5.2.3. Defining the tasks to be accomplished -- 5.2.4. Which technology to adopt? -- 5.2.5. Understanding data analytics is good but knowing how to use it is better! (What skills do you need?) -- 5.2.6. What does the data project cost and how will it pay off in time? -- 5.2.7. What will it mean to you once you find out? -- 5.3. Next steps: do you have an idea about a "secret sauce"? -- 5.3.1. First phase: find the data (data collection) -- 5.3.2. Second phase: construct the data (data preparation) -- 5.3.3. Third phase: go to exploration and modeling (data analysis) -- 5.3.4. Fourth phase: evaluate and interpret the results (evaluation and interpretation) -- 5.3.5. Fifth phase: transform data into actionable knowledge (deploy the model) -- 5.4. Disciplines that support the big data analytics process -- 5.4.1. Statistics -- 5.4.2. Machine learning -- 5.4.3. Data mining -- 5.4.4. Text mining -- 5.4.5. Database management systems -- 5.4.6. Data streams management systems -- 5.5. Wait, it's not so simple: what to avoid when building a -- 5.5.1. Minimize the model error -- 5.5.2. Maximize the likelihood of the model -- 5.5.3. What about surveys? -- 5.6. Conclusions -- PART 3 Data Analytics and Machine Learning: the Relevance of Algorithms -- 6. Machine Learning: a Method of Data Analysis that Automates Analytical Model Building -- 6.1. Introduction 6.2. From simple descriptive analysis to predictive and prescriptive analyses: what are the different steps? -- 6.3. Artificial intelligence: algorithms and techniques -- 6.4. ML: what is it? -- 6.5. Why is it important? -- 6.6. How does ML work? -- 6.6.1. Definition the business need (problem statement) and its formalization -- 6.6.2. Collection and preparation of the useful data that will be used to meet this need -- 6.6.3. Test the performance of the obtained model -- 6.6.4. Optimization and production start -- 6.7. Data scientist: the new alchemist -- 6.8. Conclusion -- 7. Supervised versus Unsupervised Algorithms: a Guided Tour -- 7.1. Introduction -- 7.2. Supervised and unsupervised learning -- 7.2.1. Supervised learning: predict, predict and predict! -- 7.2.2. Unsupervised learning: go to profiles search! -- 7.3. Regression versus classification -- 7.3.1. Regression -- 7.3.2. Classification -- 7.4. Clustering gathers data -- 7.4.1. What good could it serve? -- 7.4.2. Principle of clustering algorithms -- 7.4.3. Partitioning your data by using the K-means algorithm -- 7.5. Conclusion -- 8. Applications and Examples -- 8.1. Introduction -- 8.2. Which algorithm to use? -- 8.2.1. Supervised or unsupervised algorithm: in which case do we use each one? -- 8.2.2. What about other ML algorithms? -- 8.3. The duo big data/ML: examples of use -- 8.3.1. Netflix: show me what you are looking at and I'll personalize what you like -- 8.3.2. Amazon: when AI comes into your everyday life -- 8.3.3. And more: proof that data are a source of creativity -- 8.4. Conclusions -- Bibliography -- Index -- Other titles from iSTE in Computer Engineering -- EULA |
| Beschreibung: | XXVII, 225 pages Diagramme |
| ISBN: | 9781786303264 1786303264 |
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| 520 | 3 | |a Cover -- Half-Title Page -- Dedication -- Title Page -- Copyright Page -- Contents -- Acknowledgments -- Preface -- Introduction -- Why this book? -- Whom is this book for? -- Organization of the book -- Glossary -- PART 1 Towards an Understanding of Big Data:Are You Ready? -- 1. From Data to Big Data: You Must Walk Before You Can Run -- 1.1. Introduction -- 1.2. No analytics without data -- 1.2.1. Databases -- 1.2.2. Raw data -- 1.2.3. Text -- 1.2.4. Images, audios and videos -- 1.2.5. The Internet of Things -- 1.3. From bytes to yottabytes: the data revolution -- 1.4. Big data: definition -- 1.5. The 3Vs model -- 1.6. Why now and what does it bring? -- 1.7. Conclusions -- 2. Big Data: A Revolution that Changes the Game -- 2.1. Introduction -- 2.2. Beyond the 3Vs -- 2.3. From understanding data to knowledge -- 2.4. Improving decision-making -- 2.5. Things to take into account -- 2.5.1. Data complexity -- 2.5.2. Data quality: look out! Not all data are the right data -- 2.5.3. What else?...Data security -- 2.6. Big data and businesses -- 2.6.1. Opportunities -- 2.6.2. Challenges -- 2.7. Conclusions -- PART 2 Big Data Analytics: A Compilation of Advanced Analytics Techniques that Covers a Wide Range of Data -- 3. Building an Understanding of Big Data Analytics -- 3.1. Introduction -- 3.2. Before breaking down the process... What is data analytics? -- 3.3. Before and after big data analytics -- 3.4. Traditional versus advanced analytics: What is the difference? -- 3.5. Advanced analytics: new paradigm -- 3.6. New statistical and computational paradigm within the big data context -- 3.7. Conclusions -- 4. Why Data Analytics and When Can We Use It? -- 4.1. Introduction -- 4.2. Understanding the changes in context -- 4.3. When real time makes the difference -- 4.4. What should data analytics address? -- 4.5. Analytics culture within companies | |
| 520 | 3 | |a 4.6. Big data analytics application: examples -- 4.7. Conclusions -- 5. Data Analytics Process: There's Great Work Behind the Scenes -- 5.1. Introduction -- 5.2. More data, more questions for better answers -- 5.2.1. We can never say it enough: "there is no good wind for those who don't know where they are going" -- 5.2.2. Understanding the basics: identify what we already know and what we have yet to find out -- 5.2.3. Defining the tasks to be accomplished -- 5.2.4. Which technology to adopt? -- 5.2.5. Understanding data analytics is good but knowing how to use it is better! (What skills do you need?) -- 5.2.6. What does the data project cost and how will it pay off in time? -- 5.2.7. What will it mean to you once you find out? -- 5.3. Next steps: do you have an idea about a "secret sauce"? -- 5.3.1. First phase: find the data (data collection) -- 5.3.2. Second phase: construct the data (data preparation) -- 5.3.3. Third phase: go to exploration and modeling (data analysis) -- 5.3.4. Fourth phase: evaluate and interpret the results (evaluation and interpretation) -- 5.3.5. Fifth phase: transform data into actionable knowledge (deploy the model) -- 5.4. Disciplines that support the big data analytics process -- 5.4.1. Statistics -- 5.4.2. Machine learning -- 5.4.3. Data mining -- 5.4.4. Text mining -- 5.4.5. Database management systems -- 5.4.6. Data streams management systems -- 5.5. Wait, it's not so simple: what to avoid when building a -- 5.5.1. Minimize the model error -- 5.5.2. Maximize the likelihood of the model -- 5.5.3. What about surveys? -- 5.6. Conclusions -- PART 3 Data Analytics and Machine Learning: the Relevance of Algorithms -- 6. Machine Learning: a Method of Data Analysis that Automates Analytical Model Building -- 6.1. Introduction | |
| 520 | 3 | |a 6.2. From simple descriptive analysis to predictive and prescriptive analyses: what are the different steps? -- 6.3. Artificial intelligence: algorithms and techniques -- 6.4. ML: what is it? -- 6.5. Why is it important? -- 6.6. How does ML work? -- 6.6.1. Definition the business need (problem statement) and its formalization -- 6.6.2. Collection and preparation of the useful data that will be used to meet this need -- 6.6.3. Test the performance of the obtained model -- 6.6.4. Optimization and production start -- 6.7. Data scientist: the new alchemist -- 6.8. Conclusion -- 7. Supervised versus Unsupervised Algorithms: a Guided Tour -- 7.1. Introduction -- 7.2. Supervised and unsupervised learning -- 7.2.1. Supervised learning: predict, predict and predict! -- 7.2.2. Unsupervised learning: go to profiles search! -- 7.3. Regression versus classification -- 7.3.1. Regression -- 7.3.2. Classification -- 7.4. Clustering gathers data -- 7.4.1. What good could it serve? -- 7.4.2. Principle of clustering algorithms -- 7.4.3. Partitioning your data by using the K-means algorithm -- 7.5. Conclusion -- 8. Applications and Examples -- 8.1. Introduction -- 8.2. Which algorithm to use? -- 8.2.1. Supervised or unsupervised algorithm: in which case do we use each one? -- 8.2.2. What about other ML algorithms? -- 8.3. The duo big data/ML: examples of use -- 8.3.1. Netflix: show me what you are looking at and I'll personalize what you like -- 8.3.2. Amazon: when AI comes into your everyday life -- 8.3.3. And more: proof that data are a source of creativity -- 8.4. Conclusions -- Bibliography -- Index -- Other titles from iSTE in Computer Engineering -- EULA | |
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| id | DE-604.BV045103155 |
| illustrated | Not Illustrated |
| indexdate | 2024-08-01T10:39:27Z |
| institution | BVB |
| isbn | 9781786303264 1786303264 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-030493699 |
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| owner | DE-862 DE-BY-FWS DE-521 |
| owner_facet | DE-862 DE-BY-FWS DE-521 |
| physical | XXVII, 225 pages Diagramme |
| publishDate | 2018 |
| publishDateSearch | 2018 |
| publishDateSort | 2018 |
| publisher | ISTE Ltd |
| record_format | marc |
| series2 | Informations systems, web and pervasive computing series |
| spellingShingle | Sedkaoui, Soraya Data Analytics and Big Data Datenauswertung (DE-588)4131193-0 gnd Big Data (DE-588)4802620-7 gnd |
| subject_GND | (DE-588)4131193-0 (DE-588)4802620-7 |
| title | Data Analytics and Big Data |
| title_auth | Data Analytics and Big Data |
| title_exact_search | Data Analytics and Big Data |
| title_full | Data Analytics and Big Data Soraya Sedkaoui |
| title_fullStr | Data Analytics and Big Data Soraya Sedkaoui |
| title_full_unstemmed | Data Analytics and Big Data Soraya Sedkaoui |
| title_short | Data Analytics and Big Data |
| title_sort | data analytics and big data |
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