Info
- Utilizzare la checkbox di selezione a fianco di ciascun documento per attivare le funzionalità di stampa, invio email, download nei formati disponibili del (i) record.
Data Analytics [[electronic resource] ] : Effective Methods for Presenting Results
| Data Analytics [[electronic resource] ] : Effective Methods for Presenting Results |
| Autore | Samaddar Subhashish |
| Pubbl/distr/stampa | Milton, : Auerbach Publications, 2019 |
| Descrizione fisica | 1 online resource (175 pages) : illustrations |
| Disciplina | 658.4/52 |
| Altri autori (Persone) | NargundkarSatish |
| Collana | Data analytics applications |
| Soggetto topico |
Business - Data processing
Business requirements analysis Business analysts |
| ISBN |
1-351-97341-X
1-315-26755-1 1-351-97340-1 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Know your audience -- Presenting results from commonly used modeling techniques -- Visualization to improve analytics -- Marketing models - Demonstrating effectiveness to clients -- Restaurant management : Convincing management to change -- Project presentations in the armed forces -- Inventory management - Customizing presentations for management layers -- Executive communication in process improvement -- Internal auditing - Seeking action from top management to mitigate risk -- Consumer lending : Winning presentations to investors -- As you can see. |
| Record Nr. | UNINA-9910793203103321 |
Samaddar Subhashish
|
||
| Milton, : Auerbach Publications, 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Data Analytics [[electronic resource] ] : Effective Methods for Presenting Results
| Data Analytics [[electronic resource] ] : Effective Methods for Presenting Results |
| Autore | Samaddar Subhashish |
| Pubbl/distr/stampa | Milton, : Auerbach Publications, 2019 |
| Descrizione fisica | 1 online resource (175 pages) : illustrations |
| Disciplina | 658.4/52 |
| Altri autori (Persone) | NargundkarSatish |
| Collana | Data analytics applications |
| Soggetto topico |
Business - Data processing
Business requirements analysis Business analysts |
| ISBN |
1-351-97341-X
1-315-26755-1 1-351-97340-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Know your audience -- Presenting results from commonly used modeling techniques -- Visualization to improve analytics -- Marketing models - Demonstrating effectiveness to clients -- Restaurant management : Convincing management to change -- Project presentations in the armed forces -- Inventory management - Customizing presentations for management layers -- Executive communication in process improvement -- Internal auditing - Seeking action from top management to mitigate risk -- Consumer lending : Winning presentations to investors -- As you can see. |
| Record Nr. | UNINA-9910814460203321 |
|
Samaddar Subhashish
|
||
| Milton, : Auerbach Publications, 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Data science and analytics for SMEs : consulting, tools, practical use cases / / Afolabi Ibukun Tolulope
| Data science and analytics for SMEs : consulting, tools, practical use cases / / Afolabi Ibukun Tolulope |
| Autore | Tolulope Afolabi Ibukun |
| Pubbl/distr/stampa | New York, NY : , : Apress, , [2022] |
| Descrizione fisica | 1 online resource (341 pages) |
| Disciplina | 658.4038 |
| Soggetto topico |
Business requirements analysis
Knowledge management Small business |
| ISBN | 1-4842-8670-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Table of Contents -- About the Author -- About the Technical Reviewer -- Acknowledgments -- Preface -- Chapter 1: Introduction -- 1.1 Data Science -- 1.2 Data Science for Business -- 1.3 Business Analytics Journey -- Events in Real Life and Description -- Capturing the Data -- Accessible Location and Storage -- Extracting Data for Analysis -- Data Analytics -- Summarize and Interpret Results -- Presentation -- Recommendations, Strategies, and Plan -- Implementation -- 1.4 Small and Medium Enterprises (SME) -- 1.5 Business Analytics in Small Business -- 1.6 Types of Analytics Problems in SME -- 1.7 Analytics Tools for SMES -- 1.8 Road Map to This Book -- Using RapidMiner Studio -- Using Gephi -- 1.9 Problems -- 1.10 References -- Chapter 2: Data for Analysis in Small Business -- 2.1 Source of Data -- Data Privacy -- 2.2 Data Quality and Integrity -- 2.3 Data Governance -- 2.4 Data Preparation -- Summary Statistics -- Example 2.1 -- Missing Data -- Data Cleaning - Outliers -- Normalization and Categorical Variables -- Handling Categorical Variables -- 2.5 Data Visualization -- 2.6 Problems -- 2.7 References -- Chapter 3: Business Analytics Consulting -- 3.1 Business Analytics Consulting -- 3.2 Managing Analytics Project -- 3.3 Success Metrics in Analytics Project -- 3.4 Billing the Analytics Project -- 3.5 References -- Chapter 4: Business Analytics Consulting Phases -- 4.1 Proposal and Initial Analysis -- 4.2 Pre-engagement Phase -- 4.3 Engagement Phase -- 4.4 Post-Engagement Phase -- 4.5 Problems -- 4.6 References -- Chapter 5: Descriptive Analytics Tools -- 5.1 Introduction -- 5.2 Bar Chart -- 5.3 Histogram -- 5.4 Line Graphs -- 5.5 Boxplots -- 5.6 Scatter Plots -- 5.7 Packed Bubble Charts -- 5.8 Treemaps -- 5.9 Heat Maps -- 5.10 Geographical Maps -- 5.11 A Practical Business Problem I (Simple Descriptive Analytics) -- 5.12 Problems.
5.13 References -- Chapter 6: Predicting Numerical Outcomes -- 6.1 Introduction -- 6.2 Evaluating Prediction Models -- 6.3 Practical Business Problem II (Sales Prediction) -- 6.4 Multiple Linear Regression -- 6.5 Regression Trees -- 6.6 Neural Network (Prediction) -- 6.7 Conclusion on Sales Prediction -- 6.8 Problems -- 6.9 References -- Chapter 7: Classification Techniques -- 7.1 Classification Models and Evaluation -- 7.2 Practical Business Problem III (Customer Loyalty) -- 7.3 Neural Network -- 7.4 Classification Tree -- 7.5 Random Forest and Boosted Trees -- 7.6 K-Nearest Neighbor -- 7.7 Logistic Regression -- 7.8 Problems -- 7.9 References -- Chapter 8: Advanced Descriptive Analytics -- 8.1 Clustering -- 8.2 K-Means -- 8.3 Practical Business Problem IV (Customer Segmentation) -- 8.4 Association Analysis -- 8.5 Network Analysis -- 8.6 Practical Business Problem V (Staff Efficiency) -- 8.7 Problems -- 8.8 References -- Chapter 9: Case Study Part I -- 9.1 SME Ecommerce -- 9.2 Introduction to SME Case Study -- 9.3 Initial Analysis -- 9.4 Analytics Approach -- 9.5 Pre-engagement -- 9.6 References -- Chapter 10: Case Study Part II -- 10.1 Goal 1: Increase Website Traffic -- 10.2 Goal 2: Increase Website Sales Revenue -- 10.3 Problems -- 10.4 References -- Data Files -- Index. |
| Record Nr. | UNINA-9910616397403321 |
|
Tolulope Afolabi Ibukun
|
||
| New York, NY : , : Apress, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
