School of Data Analytics & Business Intelligence

A Comprehensive Guide to Digital Marketing and Analytics Every Data Science Professional Must Read TAVISH SRIVASTAVA ,  DECEMBER 17, 2018 Introduction One of the biggest challenges of breaking into the field of digital analytics is that the landscape of digital marketing is extremely complex. It’s a hard task finding professionals who know the best of both worlds – digital marketing and data science. There is a serious shortage in the supply of adequate talent, while spending on digital marketing continues to rise unabated. This spending is quite prevalent in the developed economies. But here’s the good news – developing countries are starting to catch up, and are not far behind the curve. Check out the below chart and see for yourself the growth in the digital marketing spend share over time in India: Figure source : eMarketer I have seen positions open for years in the digital analytics space because of the shortage of such niche talent coupled with the im...

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 ...