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Showing posts from October, 2018

Azure ML Tutorial - Build a Predictive Model

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


19 Data Science and Machine Learning Tools for people who Don’t Know Programming

Perfect way to build a Predictive Model in less than 10 minutes

Evolution of Analytics

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 this area, appl…

Data science overlap with other analytical disciplines

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 include taxonomy creation …

Migration to NoSql

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 migrate to NOSQL only bcos of Cost…

Info about BI | DA | DS

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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 StatisticsData MiningPredictive ModelingTime Series ForecastingMachine LearningOptimization TechniquesDomain Exposure:

Market and Retail AnalyticsWeb and Social Media AnalyticsFinance and Risk AnalyticsSuppy Chain and Logistics Analytics













Each business process or subject a…