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Hi Everyone,


I just wanted to post a quick note to inform you all that we’ve begun writing on the Practical Analytics book.  My co-author, Nancy Jones, and I are trying to create a resource that covers all of the key general analytics concepts and illustrates the hands-on application of those concepts using the most popular SAP Analytics technologies.  We intend to make the book appropriate for both university classroom use and professional readers who are new to the topic.  We will have the book ready in Spring 2015.


Here’s the overview: 

Practical Analytics covers analytics concepts and activities in a way that provides real-world skill building while reinforcing fundamental concepts. This book provides a much-needed approach to analytics through theory, applications, and hands-on experience using the leading industry tools from SAP. Although many books have been written on statistical data analysis, data mining, predictive analytics and business intelligence, these books are often too technical or theoretical for a business user. The goal of this book is to provide a comprehensive and self-contained overview of analytics for students and practitioners. The reader will be able to learn and apply all the concepts in the book without excessive prerequisite courses or experience.  The table of contents is available here for more details on the concepts that will be covered.


For updates on the book as we progress or to get early access to chapters for review, please sign up for the mailing list and we’ll keep you in the loop.


Nitin Kale


Table of Contents

                                                                    

Section 1 – Basics

6.      Reporting

1.      Introduction to data analytics

6.1.    Where   are reports used?

1.1.    What   is Analytics?

6.2.      Authoring reports

1.2.    Why   study analytics?

Section 4 – Data Visualization

1.3.    Who   and how they benefit? Some examples

7.      Charts   and Dashboards

1.4.      Analytics methodology

7.1.      Charting techniques to display large datasets

1.5.      Roadmap of topics (chapters)

8.      Advanced visualization

1.6.   Introduction to model company – Global Bike   Inc.

8.1.       Effective visual techniques

Section 2 – Data Provisioning

8.2.       Advanced chart types

2.      Data acquisition

Section 5 – Knowledge discovery, prediction &   decision making

2.1.    Source   systems. Examples and opportunities

9.      Data mining

2.2.    Data   Collection

9.1.    What   is data mining?

2.3.    Data   representation for structured and unstructured data

9.2.    Why is   it needed?

2.4.    Data   storage

9.3.   Predictive vs. descriptive analytics

3.      Data harmonization

9.4.      Supervised vs. unsupervised models

3.1.      Mapping and consolidating data from multiple sources

9.5.    Data   mining process

3.2.      Separating signal from noise

10.   Descriptive models for data mining

3.3.    Dirty   data handling and cleansing

10.1.    Unsupervised models

4.      Data staging

10.2.  Model   verification and validation

4.1.      Transactional systems vs. informational systems

11.   Predictive models for data mining

4.2.      Normalized vs. denormalized models

11.1.    Supervised modeling

4.3.    Data   warehouses

11.2.  Model   verification and validation

4.4.      Multidimensional modeling- star schema

11.3.  Data   mining models for predictive analysis

4.5.      Multidimensional modeling – snowflake schema

12.   Big data analytics

4.6.      Modeling cubes using snowflake schemas

12.1.  What is   big data?

4.7.    Cube   optimization

12.2.    Structured vs. unstructured data

4.8.    ETL –   Extraction, transformation, loading

12.3.    Developments in big data technology

Section 3 – Reporting and analysis

12.4.  Case   studies in big data analytics

5.      Slicing and dicing

13.   Decision making

5.1.    What   is slicing and dicing?

13.1.  From   data to insight to decisions to actions

5.2.      Spreadsheets and pivot tables

13.2.    Responsibilities for the analyst

5.3.      Slicing and dicing

13.3.  Using a   combination of analytical tools

5.4.      Consumers to CEOs, everyone slices and dices

13.4.  Expert   Systems

5.5.    Tools   used for slicing and dicing (using MS Excel)

13.5.    Evaluating analytical process in terms of outcomes

5.6.      Multidimensional analysis

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6 Comments

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  1. Jared Hansen

    Is this going to be available on Amazon for ordering or only directly from the publisher? Also, is it in print or only the eBook?

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  2. Gregory Dawson

    Very interested in adopting this book but it appears from the instructions that it requires a Windows-based machine for the instructor and students. I am assuming that since all of the instructions are for that. Can you confirm/deny? The issue is that lots of my students have Macs (as do I) and I don’t want to force them to do a dual boot. Help?

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    1. Nitin Kale Post author

      Gregory,

      We provide instructions for professors and lab managers on software installation for Windows machines. The client side apps are Windows only. What we suggest is to either set up your lab with the software or set up a remote desktop environment that students can connect to. The second option has worked successfully at my University for years.

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      1. Nitin Kale Post author

        Also, Thank you for considering the book for adoption. Feel free to contact me if you need any help or have questions about the books or supplemental materials.

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