The huge demand of predictive analytics has led to various trainings been sprung up to impart or at least promising to impart the kind of skills required for developing an analytics model. But more often than not it forgets the primary ingredient. Geert Verstraeten, managing Partner and professional trainer of Python Predictions, feels that understanding the main project phases is the most important thing. He explains these phases with an interesting analogy- developing analytics model is as simple as making soup in a soup bar. Here are 5 phases of model building: 

• Taking the order- Project Definition 

• Mise en place- Data Preparation 

• Cooking the soup- Model building 

• Tasting the soup- Model Validation

• Serving the soup- Model Usage

This makes learning more engaging. To keep enjoying the analogy, click the link http://www.predictiveanalyticsworld.com/patimes/how-to-manage-projects-in-predictive-analytics0710151/

With IoT becoming the norm rather than the exception, there is an overwhelming amount of customer data at the disposal of companies. But with huge data comes huge challenges. The challenge of combining, cleaning and shaping data before the analysis. Cari Jaquet, vice president of marketing at Paxata, in a special guest feature in Inside Bigdata talks about why data preparation solutions are important to make data more consumable. According to a recent Gartner report, it has been advised to use simple data preparation tools to first, save analysts time and second simplify the task of data preparation from diverse sources. For a deeper insight into data preparation follow the link  http://insidebigdata.com/2015/06/19/data-preparation-the-key-to-unravelling-the-big-data-opportunity/