AnalyticsTraining.Guru

There's an internship opportunity in Hadoop & Cloud (Big Data Analytics - Hive/Pig, Sqoop, Spark, AWS EMR). Interested freshers please send CV to [email protected]. You got to be a quick learner, someone with a hacking mindset, and a go-figurer.

Our parent company (Winnow) is looking for Data Miners with 2-4 years experience.
If you are good at predictive modeling, please drop at note along with your resume to [email protected]

https://www.linkedin.com/pulse/analytics-advantage-kiran-bm

Analytics Advantage What are the options for companies that want to take the Analytics Advantage and start Competing on Analytics:This is for those companies that are tired of doing business the old-school way. This is for those many mid- and small-size companies out there that can't afford a in-house analytics team. A…

We're hiring!
Role: Marketing Analytics Professional
Skills: Data Mining, R, Lead Scoring, Digital Marketing Analytics, Campaign Analysis, Web Analytics
Contact: [email protected]

Experience Details:
1. Experience analyzing digital marketing campaigns

2. Has experience developing revenue attribution models. Had developed net-lift, propensity, or segmentation schemes for marketing targeting models

3. Has experience with R, SQL, Tableau. Developed lead scoring models or CRM analytic models. This includes segmentation, LTV, RFM, and attrition models

4. In-depth understanding of Web Analytics and Online Demand Generation vehicles

5. Working experience in Google Analytics and/or Adobe Site Catalyst

If you are looking for a start on analytics as a career path, first decide whether you want to be on the IT side of it or the BA/Statistician side of it. The latter is a KPO route while the former is an upgraded path for a 'Data & Reporting Analyst'. More, by choosing the KPO route one can become a Data Scientist one day after a decade or so experience, while the IT side leads to a high-end data management expert with Hadoop and like in the skill set. This said, the KPO side is not everyone's cup of tea. But if you had a flair for math back in school then you will have a natural bent towards it. Here I mean 'math' not necessarily 'probability and statistics' MA201 course in your college..

Questions like whether to learn SAS or R sound silly after 2-3 years into practicing this 'sexy' profession. What matters then is the problem-solving skill and the tool is just a means. Imagine a civil engineer asking which software to use to design a 3000-meter span cable-stayed bridge! Relax...

Start with some easy to understand books like "Discovering Knowledge in Data - An Introduction to Data Mining by DANIEL T. LAROSE" and get the concepts right first, then use a tool to apply the concept and build your first 'hello world' model.

What is Geospatial Analytics?
Geospatial Analytics is an approach to applying statistical analysis and other informational techniques to data that has a geographical or geospatial aspect.

What Geospatial data do Telcos have about customers?
Cellular network & movement data associated with each mobile number. For e.g., how many cellular towers does the customer hook up to in a given day, and time spent on each tower.

Now, what are the possibilities?
The data can be used to profile the customers using their geospatial movements. E.g., distinguish between a house maker & a working person, travelling sales person & stationery employee.

How? If the device spends most of the time in a day on one tower the owner is likely to be a house maker or a retired (pun intended) person. Working persons spend most of time on two towers (home & work).

By overlaying GPS information and location-specific intelligence such as rural/urban/suburban, large establishments viz. hospitals, colleges, tech parks, the profiles can be qualified further.

Also, externally this data has high economic value for City Planners & Marketers. Telcos can share it in "anonymous & aggregated" forms and collaborate with 3rd parties for additional revenue streams.

http://articles.economictimes.indiatimes.com/2014-09-19/news/54108999_1_data-scientists-amod-malviya-paytm

Data scientist among the sexiest jobs of the century NEW DELHI: The title has been around for less than a decade, but already 'data scientist' is considered to be among the sexiest jobs of the 21st century. In India it is no different. Data...

https://econsultancy.com/blog/64743-predictive-analytics-machine-learning-and-the-future-of-personalization .1vug4vw10pycza

Predictive analytics, machine learning and the future of personalization Predictive analytics has been around for a while, as has machine learning, but it's only now with the profusion of cloud-based software in marketing that this form of data analysis has started to take off.

http://blog.revolutionanalytics.com/2014/05/companies-using-r-in-2014.html

Companies using R in 2014 I'm honoured to have been invited to give a keynote presentation at the 7th China R Users Group conference in Beijing tomorrow — I'm told around 1000 R users are expected to be there. (This easily makes it the largest R users meeting in the world.) For my presentation, I decided to curate a list of…

http://www.eweek.com/innovation/ieee-predicts-top-technologies-for-2022.html

IEEE Predicts Top Technologies for 2022 Machine learning, computational biology, bioinformatics, nanotechnology and the Internet of Things are on IEEE's list of top technologies for 2022.

"Possibility Thinking & Solutioning using Analytics"

-one of the takeaways from 'The Foundation Course in Analytics'.

First, the course helps develop a clear understanding of the frequently used data mining methodologies. Next, knowing that these techniques exist and understanding which one to use when would expand one’s thinking space. Mix this with your domain expertise and you will find yourself taking a scientific approach to solving business problems.

The four data mining techniques that you will learn in ‘The Foundation Course in Analytics’ are:

1. Decision Trees
Classify things – say, your customers, products, or partners – on whether they are good, bad or ugly.

2. Association Rules
Discover affinity groups or association between things. Determine which things go together – say a customer has bought/done these and these things, what other things you could sell her or expect her to do next?

3. Linear Regression
Estimate something or identify the drivers of some business phenomenon. Example, what drives the repeat customers to come back again, or what is the optimal marketing mix for generating maximum leads?

4. Logistic Regression
Predict the occurrence of something, or the likelihood of occurrence of an event. Example: In a telecom, what is the probability that the customer is likely to churn?