Suggested Course Duration: The book is designed to be the basis for a 15 week long semester covering 45 contact hours. These assignments include exercises and projects using SAP and other common Analytics applications. Students are expected to spend between hours a week on the assignments. The reader will be able to learn and apply all the concepts in the book without excessive prerequisite knowledge or experience.
Be the first to review this product. Epistemy Press Store Search: Search. About Books Contact. Section 4 — Data Visualization 1. Some examples 7. Charts and Dashboards 1. Analytics methodology 7. Charting techniques to display large datasets 1. Roadmap of topics chapters 8. Advanced visualization 1.
Effective visual techniques Section 2 — Data Provisioning 8. Advanced chart types 2. Examples and opportunities 9. Data mining 2. Data Collection 9. Data representation for structured and unstructured data 9. Data storage 9. Data harmonization 9.
Mapping and consolidating data from multiple sources 9. Data mining process 3. Separating signal from noise Descriptive models for data mining 3. Dirty data handling and cleansing Unsupervised models 4. Data staging Model verification and validation 4. Predictive models for data mining 4. Supervised modeling 4. Data warehouses Multidimensional modeling- star schema Data mining models for predictive analysis 4. Multidimensional modeling — snowflake schema Big data analytics 4. Modeling cubes using snowflake schemas Cube optimization ETL — Extraction, transformation, loading Developments in big data technology Section 3 — Reporting and analysis Case studies in big data analytics 5.
Slicing and dicing Decision making 5. From data to insight to decisions to actions 5. Spreadsheets and pivot tables Responsibilities for the analyst 5. Not only does the data grow from patient volume but the type of data we store is also growing exponentially.
Practical Predictive Analytics and Decisioning. This is the same framework as used by Facebook, Google, and other social media analytics operations to derive business value from vast amounts of interconnected.
Practical Data Analysis is a book ideal for home and small business users who want. The book begins with an introduction to analytics, analytical tools, and SAS programming. This book introduces text analytics as a valuable method for deriving insights from text data.
Unlike other text analytics publications, Practical Text Analytics: Maximizing the Value of Text Data makes technical concepts accessible to those without extensive experience in the field. Using text analytics, organizations can derive insights from content.
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