School of Data Analytics & Business Intelligence

https://blog.datasciencedojo.com/azure-ml-tutorial/ https://www.toptal.com/machine-learning/predicting-gas-prices-using-azure-machine-learning-studio https://docs.microsoft.com/en-us/azure/machine-learning/studio/create-experiment

https://www.analyticsvidhya.com/blog/2018/05/19-data-science-tools-for-people-dont-understand-coding/

https://www.analyticsvidhya.com/blog/2015/09/perfect-build-predictive-model-10-minutes/

Evolution of Analytics Let’s begin by taking a look at the evolution of analytics, which spans numerous areas—from data mining and data monitoring to forecasting and machine learning. Analytics is “the scientific process of transforming data into insight for making better decisions.” In the security world, this definition can be expanded to mean the collection and interpretation of security event data from multiple sources and in different formats for the purpose of identifying threat characteristics and improving protection, detection, and correction. The science of analytics has undergone a transformation in a relatively short period of time: Analytics 1.0: In the early stages, data statisticians spent their time dissecting internally sourced structured data sets, most often in reaction to a specific problem. This type of analytics was descriptive and diagnostic, answering the questions “What happened?” and “Why did it happen?” Most vendors are extremely competent in th

What are the differences between data science, data mining, machine learning, statistics, operations research, and so on? Here I compare several analytic disciplines that overlap, to explain the differences and common denominators. Sometimes differences exist for nothing else other than historical reasons. Sometimes the differences are real and subtle. I also provide typical job titles, types of analyses, and industries traditionally attached to each discipline. Underlined domains are main sub-domains. Data Science First, let's start by describing data science, the new discipline.  Job titles include data scientist, chief scientist, senior analyst, director of analytics  and many more . It covers all industries and fields, but especially digital analytics, search technology, marketing, fraud detection, astronomy, energy, healhcare, social networks, finance, forensics, security (NSA), mobile, telecommunications, weather forecasts, and fraud detection. Projects inclu

DB Migration from SQL DB to NOSQL (Cassandra) is possible and it is in practice in the real-time. Migration tools (like DataStax Enterprise) are readily available in the market to migrate data from SQL to NOSQL DBs A big reason why many businesses are moving to NoSQL-based solutions is because Legacy RDBMS data model is not flexible enough to handle big data use cases that contain a mixture of structured, semi-structured, and unstructured data. If they are planning new applications that are big data in nature and need something “more” than traditional RDBMS for their database platform. Though I understand the driving factors for this DB migration plan is cost, maintainability and performance Need to ask the business user if any other factors would impact them as below Is RDBMS currently preventing development of new features? Is NOT providing acceptable uptime? Is NOT scalable for incoming data traffic? If the decision to

General Info: Below are technologies and techniques used in data stream, will analyse any form of data to extract meaningful insights. BI  - Business Intelligence DA- Data Analytics DS - Data Science Evolution of BI: Data analysis has been evolved through different stages from Business Intelligence to Data science. Model we develop should determine the level of intelligence that we can analyse the data. In order to excel in analytics one must have below skills. Knowledge on statistics tool like R, python, SAS (must have) Analytics Techniques using statistics tools (must have atleast few below list) Domain Exposure (nice to have) Data Visualization tools like Tableau, Qlikview etc., (nice to have) Analytics Techniques: Advance Statistics Data Mining Predictive Modeling Time Series Forecasting Machine Learning Optimization Techniques Domain Exposure: Market and Retail Analytics Web and Social Media Analytics Finance and Risk Analytics Suppy Cha