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The Worst Techniques To Build A Predictive Model

While some of these techniques may be a little out of date and most of them have evolved over time greatly, for the past 10 years rendering most of these tools completely different and much more efficient to use. But here are few bad techniques in predictive modelling that are still widely in use in the industry:

 

Predictive Model

 

1. Using traditional decision trees: usually too large decision trees are usually really complex to handle and almost impossible to analyze for even the most knowledgeable data scientist. They are also prone to over-fitting which is why they are best avoided. Instead we recommend that you combine multiple small decision trees into one than using a single large decision tree to avoid unnecessary complexity.

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Infographic: How Big Data Analytics Can Help To Boost Company Sales?

Infographic: How Big Data Analytics Can Help To Boost Company Sales?

Following a massive explosion in the world of data has made the slow paced statisticians into the most in-demand people in the job market right now. But why are all companies whether big or small out for data analysts and scientists?

Companies are collecting data from all possible sources, through PCs, smart phones, RFID sensors, gaming devices and even automotive sensors. However, just the volume of data is not the main factor that needs to be tackled efficiently, because that is not the only factor that is changing the business environment, but there is the velocity as well as variety of data as well which is increasing at light speed and must be managed with efficacy.

Why data is the new frontier to boost your sales figures?

Earlier the sales personnel were the only people from whom the customers gathered data about the products but today there are various sources from where customers can gather data so people are no longer that heavily reliant on the availability of data.

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Using Hadoop Analyse Retail Wifi Log File

Since a long time we are providing Big Data Hadoop training in Gurgaon to aspirant seeking a career in this domain.So, here our Hadoop experts are going to share a big data Hadoop case study.Think of the wider perspective, as various sensors produce data. Considering a real store we listed out these sensors- free WiFi access points, customer frequency counters located at the doors, smells, the cashier system, temperature, background music and video capturing etc.

 

big data hadoop

 

While many of the sensors required hardware and software, a few sensor options are around for the same. Our experts found out that WiFi points provide the most amazing sensor data that do not need any additional software or hardware. Many visitors have Wi- Fi-enabled smart phones. With these Wifi log files, we can easily find out the following-

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5 Online Sources to Get Basic Hadoop Introduction

Basic Hadoop Courses

Big data Hadoop courses are hitting it big in the world of business whether it is healthcare, manufacturing, media or marketing. Data is generated everywhere, and Hadoop is a readily available open source Apache software program that can be utilized to crunch and store Big Data sets.

As per reports from the Transparency Market Research the forecast shows a promising growth opportunity from the existing USD 1.5 million back in 2012 to USD 20.8 million within 2018. These promising growth numbers suggest that there will be an increased need for human resources to manage, develop and oversee all the Hadoop implementations.

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Many experts believe that one can learn any new subject by simple self-study if only you invest enough time and sincere predisposition towards a topic. After all self-study is actually what a person does to acquire knowledge about any given topic. Be it how to fix a leaky faucet or learn a new language or learn strum a guitar. Studying is on one’s own in any case. But to be an expert in a given field, you have to study on your own while you also need to invest your energy in the right direction. And to know the right direction, you need a mentor or a guide to lead the way.

But if you want to test the waters, and tinker with Hadoop to understand its basics, you can go through the wide range of documents available at the Apache Hadoop website for your perusal. Also try downloading the Hadoop open source release to get the feel of the program while tinkering with different features.

Here are 5 online sources where you can seek some basic introduction to Hadoop for big data:

  1. IBM’s open sources, Hadoop Big Data for the Impatient is a good option to go through the basics of Hadoop. It also offers a free download of Hadoop image (you might need Cloudera) to help you work with examples of Hadoop-based problems. You will also be able to get an idea of Hive, Oozie, Pig and Sqoop. The course is available in Vietnamese, Chinese, Spanish and Portuguese.
  2. Cloudera offers a Cloudera essentials course for Apache Hadoop. Apache Hadoop chapter wise video tutorials are available with Cloudera essentials. But this course is mainly targeted at administrators and those who are well-acquainted with data science, to update their skills on the subject.
  3. YouTube also offers a long list of videos on Hadoop topics for beginners. Some are good while others may not be so helpful for the Hadoop virgins. Simply type Hadoop and you will find a never-ending list of videos related to Hadoop. Some are quite useful for clarifying simple doubts related to Hadoop.
  4. Udemy is another site where you can get some free videos as well as a few for a fee. Simply put Hadoop free on the search bar at their homepage and see what comes up.
  5. Udacity was developed by Silicon Valley giants like FaceBook, Cadence, Twitter and the likes. They offer a 14-day free trial with free course materials. But you will need to pay for the course if you do not finish the course within 14 days.

 

Seeking a good and reliable Hadoop training in Delhi? When DexLab Analytics is here, why look further! Being a recognized Big Data Hadoop institute in Gurgaon, the courses are truly interesting.

 

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The Pros and Cons of HIVE Partitioning

The Pros and Cons of HIVE Partitioning Hive organizes data using Partitions. By use of Partition, data of a table is organized into related parts based on values of partitioned columns such as Country, Department. It becomes easier to query certain portions of data using partition.

Partitions are defined using command PARTITIONED BY at the time of the table creation.

We can create partitions on more than one column of the table. For Example, We can create partitions on Country and State.

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Syntax:

CREATE [EXTERNAL] TABLE table_name (col_name_1 data_type_1, ….)

PARTITIONED BY (col_name_n data_type_n , …);

Following are features of Partitioning:

  • It’s used for distributing execution load horizontally.
  • Query response is faster as query is processed on a small dataset instead of entire dataset.
  • If we selected records for US, records would be fetched from directory ‘Country=US’ from all directories.

Limitations:

  • Having large number of partitions create number of files/ directories in HDFS, which creates overhead for NameNode as it maintains metadata.
  • It may optimize certain queries based on where clause, but may cause slow response for queries based on grouping clause.

It can be used for log analysis, we can segregate the records based on timestamp or date value to see the results day wise / month wise.

Another use case can be, Sales records by Product –type , Country and month.

 

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The Rise of the AI in Big Data

The Rise of the AI in Big Data

The researchers working at the MIT “Computer Science and Artificial Intelligence Laboratory” or abbreviated simply as CSAIL are all set to make human intuition out of the analysis of big data equation by enabling computers to choose from the set of features that are put into use in order to identify patterns in the data that may be considered to be predictive. This is dubbed as the “Data Science Machine” and as things have progressed so far the software prototype has managed to beat 615 of 908 competing teams vying for the same ability across no less than three competitions of data science.

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Big Data may be considered as a complex and huge ecosystem that combines innovative processes from fields as diverse as storage, data analysis, curation, networking as well as search in addition to other functions and processes. As things stand much of analysis of big data is already algorithmic and automated but at the end of the day it is business users and data scientists who are needed in order to determine the particular dataset and analysis features which are required for visualization in the end and take action on the communicated data.

To put it simply at the end of the whole process humans are needed in order to make choices about data point combinations to chart out the relevant information.

The Data Science Machine is intended to naturally complement human intelligence and to make the most of the Big Data that is available for us waiting to be used.

The analysis of Big Data and Engineering of Features

As mentioned earlier actionable information lies at the hands of the big data scientist who is writing the code for analysis. It is this code that guides the analysis of the big data engine. In essence the advancement made by the MIT researchers is that not only does it serve to provide answers to questions regarding the data but also suggests additional questions accordingly.

This may be put into varied uses like to estimate the capacity of wind farms to generate power or making predictions about students who are likely to drop out of online courses.

5 Hottest Online Applications Inspired by Artificial Intelligence – @Dexlabanalytics.

The ultimate destination for all your data-related queries and assistance is DexLab Analytics. Being a premier Data Science training institute Gurgaon, DexLab Analytics takes pride in offering excellent data analytics courses for aspiring candidates.

 

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The Possibilities of Big Data

It is no secret that Big Data has some wonderful applications that may change the way we interact with businesses, and even more how they interacts with us through other facets of this rapidly growing field. But, what can it do concretely? This blog post shares insights of this question.
 
The Possibilities of Big Data

Endless Possibilities of Big Data

 It can tell you what may most probably happen

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Top 10 Best Hadoop EBooks That You Should Start Reading Now

Top 10 Best Hadoop EBooks That You Should Start Reading Now

Based on Java, Hadoop is a free open source framework for programming where dealings with huge amounts of processed data in a computing environment is said to be distributed. None other than the Apache Software Foundation is sponsoring it. If you are looking for information about Hadoop, you will like to get in-depth information about the framework and its associated functions. To get you up to the mark with the concepts, the eBooks listed below will prove to be of invaluable help.

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MapReduce

If you are looking forward to get started with Hadoop, and maximize your knowledge about Hadoop clusters, this book is of right fit. The book is loaded with information on how t o effectively use the framework to scale apps of the tools provided by Hadoop. This ebook lets you get acquainted with the intricacies of Hadoop with instructions provided on a step-by-step basis and guides you from being a Hadoop newbie to efficiently run and tackle complex Hadoop apps across a large number of machine clusters.

Also read: Big Data Analytics and its Impact on Manufacturing Sector

Programming Pig

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If you are looking for a reference from which you may learn more about Apache Pig, which happens to be the engine powering executions of parallel flows of data on the Hadoop framework which also is open source, the Programming Pig is meant for you. Not only does it serve the interests of new users but also provides advanced users coverage on the most important functions like the “Pig Latin” scripting language, the “Grunt” shell and the functions defined by users for extending Pig even further. After reading this book, analyzing terabytes of data is a far less tedious task.

Also read: What Sets Apart Data Science from Big Data and Data Analytics

Professional Hadoop Solutions

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This book covers a gamut of topics such as that how to store data with Hbase and HDFS, processing the data with the help of MapReduce and data processing automation with Oozie. Not limiting to that the book further covers the security features of Hadoop, how it goes along with Amazon Web Services, the best related practices and how to automate in real time the Hadoop processes. It provides code examples in XML and Java and refers to them in-depth along with what has been added to the Hadoop ecosystem of late. The eBook positions itself as comprehensive resource with API coverage and exposition of the deeper intricacies, which allow developers and architects to better customize and leverage them.

Also read: How To Stop Big Data Projects From Failing?

Apache Sqoop cookbook

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This guide allows the user to use Sqoop from Apache with emphasis on application of parameters that are enabled by the Command Line Interface when dealing with cases that are used commonly. The authors offer Oracle, MySQL as well as PostgreSQL examples of databases on GitHub that lend themselves to be easily adapted for Netezza, SQL Server, Teradata etc relational systems.

Also read: Why Getting a Big Data Certification Will Benefit Your Small Business

Hadoop MapReduce Cookbook

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The preface of the book claims that the book enables readers to know how to process complex and large datasets. The book starts simple but still gives detailed knowledge about Hadoop. Further, the book claims to be a simple guide on getting things done in one place. It consists of 90 recipes that are offered simply and in a straightforward manner, coupled with systematic instructions and examples from the real world.

Also read: How to Code Colour Values Within SAS Enterprise Guide

Hadoop: The Definitive Guide, 2nd Ed

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If you want to know how to maintain and build distributed systems that are both scalable and reliable within the framework of Hadoop then this book is for you. It is intended for – programmers who want to analyze datasets, irrespective of size; and – administrators, who seek to know the setting up and running of Hadoop Clusters, alike. New features like Sqoop, Hive as well as Avro are dealt with in the new second edition. Case studies are also included that may help you out with specific problems.

Also read: How to Use PUT and %PUT Statements in SAS: 6 Tips

MapReduce Design Pattern

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If one is to go by the book’s preface, the book is a blend of familiarity and uniqueness. The book is dedicated to design patterns by which we refer to the general guides or templates for solving problems. It is however more open-ended in nature than a “cookbook” as problems are not specified. You have to delve more in the subject matter than mere copying and pasting, but a pattern will get you covered about 90% of the whole way regardless of the challenge at hand.

Also read: SAS Still Dominates the Market After Decades of its Inception

Hadoop Operations

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This book is necessary for those who seek to maintain complex and large clusters of Hadoop. Map Reduce, HDFS, Hadoop Cluster Planning. Hadoop Installation as well as Configuration, Authorization and authentication, Identity, Maintenance of clusters and management of resources are all dealt in it.

Also read: Things to judge in SAS training centres

Programming Hive

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Knowledge on programming in Hive provides an SQL dialect in order to query data, which is stored in HDFS, which makes it an indispensable tool at the hands of Hadoop experts. It also works to integrate with other file systems, which may be associated with Hadoop. Examples of such file systems may be MapR-FS and the S3 from Amazon as well as Cassandra and HBase.

Hadoop Real World Solutions CookBook

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The preface of this eBook illustrates its use. It lets developers get acquainted and become proficient at problem solving in the Hadoop space. The reader will also get acquainted with varied tools related to Hadoop and the best practices to be followed while implementing them. The tools included in this cookbook are inclusive of Pig, Hive, MapReduce, Giraph, Mahout, Accumulo, HDFS, Ganglia and Redis. This book intends to teach readers what they need to know to apply Hadoop knowledge to solve their own set of problems.

 

So, happy reading!

 

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Besides, feeding knowledge through eBooks, it is vital to be enrolled for an excellent Big data hadoop certification in Gurgaon. DexLab Analytics is here for you; it offers a gamut of high-end big data hadoop training in Delhi, courses that will surely hone your data skills.

 

Interested in a career in Data Analyst?

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