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Hitesh Mistry

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#eBookConversion Services Set To Change Face Of Publication Industry. Read more at https://goo.gl/xcncE6
Today, the need and popularity of digital publishing has grown manifolds. One of the primary reasons of this skyrocketin…
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Have you noticed how many people are suddenly calling themselves data scientists? Your neighbour, that gal you met at a cocktail party — even your accountant has had his business cards changed!
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Over the past few weeks, we’ve had several conversations in our data lab regarding data engineering problems and day to day problems we face with unsupervised data scientists who find it difficult to deploy their code into production.
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Kaeter Joe

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A data analyst knows everything about SQL. He wants to run adhoc analysis on data . In your opinion , which of the following is a data warehousing software built on top of ‪#‎ApacheHadoop‬ that defines a simple SQL-like query language well-suited for this kind of user?

A Pig
B Hue
C Hive
D Sqoop
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I think you are looking for Hive. 
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Managing Data Intensive Programs and Projects
From the Introduction:  Digital and cloud-based services are changing how government IT resources are procured and managed. Of personal and professional interest to me is how data intensive programs are governed given growing interest in big data and open data. I’ve created this special compendium of posts that are relevant to planning and managing data related programs and projects. There are four groups:
1. NOAA “Big Data Project”
2. Other Federal Programs
3. Program and Project Management
4. Metrics and Performance Measurement
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Hitesh Mistry

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#DataMining Services - How Real Estate Industry Can Benefit By Identifying Customer. Read more at http://goo.gl/WnkVvm
Data mining services help businesses analyse and understand buyer requirements and preferences and hence plan their business strategy and device marketing tactics based on this information. Customers are the most important entities, and catering to their requirements is the most important activity that can drive business growth and boost sales.
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Exosite's profile photoMahendranath Reddy's profile photo
Exosite
 
Very useful post thanks for sharing such information.
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This could have been written 20 years ago
Why is that? My response: you have to be able to ask the right questions, no matter what kinds of tools are available.
Are you still not fully utilizing data? Columnist David Booth outlines five things you need to do to help you and your organization better dive into data.
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Below is the updated list of available projects, for participants in our data science apprenticeship (DSA) program. It includes four business / applied data science and two data science research projects. In addition to these projects, we strongly encourage you to participate in our data science challenges.
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Eakta Gautam

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This is a complement of Dzone Refcardz #43 and #103, which provides introductions to high performance computational scalability and high-volume data handling techniques, including MapReduce.  
This Refcard presents a basic blueprint for applying MapReduce to solving large-scale, unstructured data processing problems by showing how to deploy and use an Apache Hadoop computational cluster.
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Key Takeaways from the Open Data Science Conference 2015
The article by Madalin Mihailescu is a succinct report on the  Open Data Science Conference (ODSC) in Boston. I pulled out some interesting quotes:

“No one wins competitions on Kaggle using SAS or SPSS.”

"One key insight that is becoming recognized is that 80 percent of data science is grunt work around data loading, cleaning and transforming, while only 20 percent involves deep thinking."

"Josh has an interesting definition of what a data scientist is: a person who’s better at statistics than any software engineer and better at software engineering than any statistician."

"More time should be spent defining the right business problems, and problem parameters, including tolerated accuracy measures, e.g., false positive and false negative rates."
A couple of weeks ago I attended the Open Data Science Conference (ODSC) in Boston. Held over two days, ODSC brought together practitioners and thought leaders in the open source and data science fields.
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About this community

It's a home for anybody data-curious, whether your data is big, small, square or scruffy. If you think you can decide better, do better, or be better through data, you belong here! Before posting, please check out our guidelines below.
 
Guest blog post by Kirk Borne. Over the past 2 to 3 years, I have attended many Big Data and Data Science conferences (where I have given numerous talks, participated on many panels, and talked with countless data professionals). During this same period, I have also been receiving numerous contacts each month from hiring managers and recruiters who are seeking to hire someone like me. Recently, I have been detecting increasing dismay and agitation among recruiters and managers who are trying to hire data scientists.
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Ashley Blake

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Anyone willing to share any good resources / beginner's guide to python & programming?
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Hamza Khan's profile photoSimon Bynoe's profile photo
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Besides Google communities, I recommend Bucky Roberts at www.thenewboston.com 
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Data Entry Outsourced​ is telling you how to build a Marketing Contact List. Read it at - http://www.dataentryoutsourced.com/blog/how-to-build-a-marketing-contact-list/
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These are external resources. Starred articles were potential candidates for our picture of the week published in our weekly digest. Enjoy our new selection of articles and resources (R, data science, Python, machine learning etc.) Comments are from Vincent Granville. For a full list of all resources featured so far, click here. 
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Tanisha Prakash

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A good data scientist = data hacker + programmer+ analyst+ coach+ story teller+ artist.
A study by McKinsey Global Institute claims ‪#‎datascience‬ is the hottest career yet. Abir Barua tells you how to get your hands dirty with data science.
http://www.thehindu.com/…/on-the-number-…/article7336889.ece
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Read along when we give you the Dos and Don’ts in Data Entry job applications - http://www.dataentryoutsourced.com/blog/dos-and-donts-in-data-entry-job-applications/
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Just curious about the following: model fitting for polynomial regression, and least squares outlier issues (over-fitting). In which contexts does polynomial regression make sense? And how to make it robust? Is it better to first transform the data before doing any kind of regression? And how to choose the best transformation?
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In an earlier blog post on Making the Business Case for Text Analytics , I had spoken of the importance of Social Media Analytics and specifically Text Analytics within the context of Social Media.for big and small business. Social Media plays a critical role in today's world  in  understanding, measuring and influencing the real time perception of your company and/or brand. 
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If we look at traditional ways of using data warehouses, this has revolved around storing internal transaction data linked to internal master data. With the raise of big data there will be a swift to encompassing more and more external data. One kind of external data is reference data, being data that typically is born outside a given organization and data that has many different purposes of use.
TechTarget has recently published a definition of the term data lake. In the explanation it is mentioned that the term data lake is being accepted as a way to describe any large data pool in which ...
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