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Category: Big data courses

The Tides Of Change Is Here: Accenture’s Bhaskar Ghosh Talks About AI, IoT and Big Data

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With the Fourth Industrial Revolution looming ahead, many would think that we are already in a digital economy era. Well, somewhat it holds true even. There are countless new apps and software programmes that help people hail a cab, make reservations in a hotel or mop floors by using robotic technology. Smart machines have become really smart to do a plethora of highly adept jobs, which would have been a little bit difficult on the part of humans to perform.

 
The Tides Of Change Is Here: Accenture’s Bhaskar Ghosh Talks About AI, IoT and Big Data
 

“While technology has long been developed to serve specific business needs, we are now in an era where people are central to the design and development of technologies,” stated Bhaskar Ghosh, group chief executive, Accenture Technology Services. In a recent interview with a leading financial magazine, he talked over Accenture’s Technology Vision 2017 and gave snippets about the latest trends and innovations that have become a pre-requisite to achieve success in the more-than-ever digitised economy.

Continue reading “The Tides Of Change Is Here: Accenture’s Bhaskar Ghosh Talks About AI, IoT and Big Data”

Celebrate #InternationalYogaDay: Get the Most Out of Yoga from Big Data

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Celebrate #InternationalYogaDay: Get the Most Out of Yoga from Big Data

 

Bored of the same old fitness routine?

 

This International Yoga Day, dust off your yoga mat and take a holistic approach to health and wellbeing. Continue reading “Celebrate #InternationalYogaDay: Get the Most Out of Yoga from Big Data”

Big Data Salary Report 2017: A Gateway to a Great Career in Analytics

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Big Data Salary Report 2017
 

In the US, big data engineer salaries are predicted to range between $135000 and 196000 in 2017, an increase of 5.8% from 2016 salary structure.  

 

In India, big data professionals are predicted to earn salaries in the range of 9.8L INR to 13.10L INR, increasing 6.4% over 2016 salary level.

Continue reading “Big Data Salary Report 2017: A Gateway to a Great Career in Analytics”

Speaking with Tanmoy Ganguli, the expert Data Analyst Bringing Cutting Edge Technology to DexLab Analytics

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Speaking with Tanmoy Ganguli, the expert Data Analyst Bringing Cutting Edge Technology to DexLab Analytics

 

DexLab Analytics is proud to announce that Tanmoy Ganguli, a proficient Data Analyst who has a long standing experience in Credit Risk Modelling, SAS and regression models is joining our Gurgaon institute as Program Director. Here are some excerpts from an interview we conducted, where he talks about the various challenges he faced in his career and the rapid development of Data Analytics.

Continue reading “Speaking with Tanmoy Ganguli, the expert Data Analyst Bringing Cutting Edge Technology to DexLab Analytics”

Skills required during Interviews for a Data Scientist @ Facebook, Intel, Ebay. Square etc.

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Skills required during Interviews for a Data Scientist @ Facebook, Intel, Ebay. Square etc.

Basic Programming Languages: You should know a statistical programming language, like R or Python (along with Numpy and Pandas Libraries), and a database querying language like SQL

Statistics: You should be able to explain phrases like null hypothesis, P-value, maximum likelihood estimators and confidence intervals. Statistics is important to crunch data and to pick out the most important figures out of a huge dataset. This is critical in the decision-making process and to design experiments.

Machine Learning: You should be able to explain K-nearest neighbors, random forests, and ensemble methods. These techniques typically are implemented in R or Python.  These algorithms show to employers that you have exposure to how data science can be used in more practical manners.

Data Wrangling: You should be able to clean up data. This basically means understanding that “California” and “CA” are the same thing – a negative number cannot exist in a dataset that describes population. It is all about identifying corrupt (or impure) data and and correcting/deleting them.

Data Visualization: Data scientist is useless on his or her own. They need to communicate their findings to Product Managers in order to make sure those data are manifesting into real applications. Thus, familiarity with data visualization tools like ggplot is very important (so you can SHOW data, not just talk about them)

Software Engineering: You should know algorithms and data structures, as they are often necessary in creating efficient algorithms for machine learning. Know the use cases and run time of these data structures: Queues, Arrays, Lists, Stacks, Trees, etc.

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What they look for? @ Mu-Sigma, Fractal Analytics

    • Most of the analytics and data science companies, including third party analytics companies such as Mu-sigma and Fractal hire fresher’s in big numbers (some time in hundreds every year).
    • You see one of the main reasons why they are able to survive in this industry is the “Cost Arbitrage” benefit between the US and other developed countries vs India.
    • Generally speaking, they normally pay significantly lower for India talent in India compared to the same talent in the USA. Furthermore, hiring fresh talent from the campuses is one of the key strategies for them to maintain the low cost structure.
    • If they are visiting your campuses for interview process, you should apply. In case if they are not visiting your campus, drop your resume to them using their corporate email id that you can find on their websites.
    • Better will be to find someone in your network (such as seniors) who are working for these companies and ask them to refer you. This is normally the most effective approach after the campus placements.

Key Skills that look for are-

  • Love for numbers and quantitative stuff
  • Grit to keep on learning
  • Some programming experience (preferred)
  • Structured thinking approach
  • Passion for solving problems
  • Willingness to learn statistical concepts

Technical Skills

  • Math (e.g. linear algebra, calculus and probability)
  • Statistics (e.g. hypothesis testing and summary statistics)
  • Machine learning tools and techniques (e.g. k-nearest neighbors, random forests, ensemble methods, etc.)
  • Software engineering skills (e.g. distributed computing, algorithms and data structures)
  • Data mining
  • Data cleaning and munging
  • Data visualization (e.g. ggplot and d3.js) and reporting techniques
  • Unstructured data techniques
  • Python / R and/or SAS languages
  • SQL databases and database querying languages
  • Python (most common), C/C++ Java, Perl
  • Big data platforms like Hadoop, Hive & Pig

Business Skills

  • Analytic Problem-Solving: Approaching high-level challenges with a clear eye on what is important; employing the right approach/methods to make the maximum use of time and human resources.
  • Effective Communication: Detailing your techniques and discoveries to technical and non-technical audiences in a language they can understand.
  • Intellectual Curiosity: Exploring new territories and finding creative and unusual ways to solve problems.
  • Industry Knowledge: Understanding the way your chosen industryfunctions and how data are collected, analyzed and utilized.

 

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6 Questions Organizations Should Ask About Big Data Architecture

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6 Questions Organizations Should Ask About Big Data Architecture

Big data come with big promises, but businesses often face tough challenges to determine how to take big advantage of big data and deploy the effective architecture seamlessly into their system.

From descriptive statistics to AI to SAS predictive analytics – every single thing is spurred by big data innovation. At the 2017 Dell EMC World conference, which took place on Monday, the chief systems engineer for data analytics at Dell EMC, Cory Minton – gave a presentation simplifying the biggest decisions an organisation need to make when employing big data.

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

Let’s get started with 6 questions that every organization should ponder over before stepping into the tech space:

Buy or build?

Do you want to buy a successful data system or build one right from the scratch? Minton said, though buying offers simplicity and a shorter time to value, it comes at a hefty price. The building idea is good and provides huge scale and variety, but it is very complicated, and interoperability is one of the biggest issues faced by admins, who take this route.

Teradata, SAS, SAP, and Splunk can be bought, while Hortonworks, Cloudera, Databricks and Apache Flink are used to build big data systems.

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

Batch or streaming data?

Products like Oracle, Hadoop MapReduce and Apache Spark offers batch data – they are descriptive and can manage large chunks of data. On the other hand, Products like Apache Kafka, Splunk, and Flink creates potential predictive models, coupled with immense scale and variety.

Kappa or lambda architecture?

Twitter is the best example of lambda architecture. This kind of architecture works best as it gives the organisation access to batch and streaming insights along with balances lossy streams, as said by Minton. While, kappa architecture is hardware efficient and Minton recommends it for any newbie organisation starting fresh with data analytics.

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

Private or public cloud?

Ask your employees, about what kind of security platform they are comfortable working, and then decide.

Physical or virtual?

Minton said – a decade ago, the debate surrounding virtual or physical infrastructure used to gain more momentum. Now, things have changed. Virtualization has become so competitive that sometimes it outdoes physical hardware. Today, it stresses more on what works for our infrastructure rather than individual preferences.

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

DAS or NAS?

Minton said Direct-attached storage (DAS) is the only way to initiate a Hadoop cluster. Today, the tides are changing; with increasing bandwidth in IP networks, the Network-attached storage (NAS) option is becoming more feasible for big data implementation.

DAS is easily initiated and the model works well within software-defined concepts. NAS is efficient in handling multi-protocol needs, offers functionality at scale and addresses security and compliance issues.

For more big data related news, check out our blog section in DexLab Analytics. We are a pioneering data analyst training institute, offering excellent Big data hadoop certification training in Delhi.

 

Interested in a career in Data Analyst?

To learn more about Data Analyst with Advanced excel course – Enrol Now.
To learn more about Data Analyst with R Course – Enrol Now.
To learn more about Big Data Course – Enrol Now.

To learn more about Machine Learning Using Python and Spark – Enrol Now.
To learn more about Data Analyst with SAS Course – Enrol Now.
To learn more about Data Analyst with Apache Spark Course – Enrol Now.
To learn more about Data Analyst with Market Risk Analytics and Modelling Course – Enrol Now.

What to Expect: Top 4 Hadoop Big Data Trends 2018 Reigning the Healthcare Industry

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What to Expect: Top 4 Hadoop Big Data Trends 2018 Reigning the Healthcare Industry

Of late, we have been scrounging through plenty of news about healthcare challenges and gruelling choices confronted by hospital authorities, administrators, researchers, pharmaceutical in-charges and clinicians. Coupled with that, consumers are battling increased costs without corresponding enhancement in health security or in the authenticity of clinical consequences.

However, just as every dark cloud has a silver lining – the healthcare industry is now at the threshold of a major transformation using the stroke of luck Big Data and Hadoop.

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In this blog, we are going to brash about 4 above the rest big data trends in 2018, and trust me they are mind blowing:

The patient is the king (well not literally!)

A supreme objective of modern healthcare facilities is to offer value-based and patient-centric service with the use of veritable health information technology in order to:

  • Improve healthcare coordination and quality
  • Lessen healthcare costs
  • Offer support for reclaimed payment structures

By leveraging information technology and concentrating on healthcare systems on patient results, a spectrum of doctors, health insurance, care and hospitals need to correspond with each other to customize care that is price effective, efficient in quality, transparent in delivery and billing and based on patient satisfaction level.

 

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IOT is omnipresent, so why leave healthcare

If reports are right, over $120 billion has been spent on healthcare IOT over 4 years, ONLY. Most of the data derived by the healthcare IOT is unstructured, thus helming ways for the use of Hadoop and advanced big data analytics relying on Hadoop framework.  

healthcare-Internet-of-things

Advanced monitoring devices interacting with other patient devices could possibly reduce the chances of direct doctor’s intervention, and might substitute it with a phone call from the nurse. Moreover, other smart devices installed can detect if medicines have been consumed regularly at home from smart dispensers. In the event of failure, the device will instantly initiate a call to help patients take medications, properly. From this, you can understand the costs will fall drastically, while improving the patient care.

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Call for cleansing – Curb waste, abuse and fraud

After suffering from spiralling healthcare costs for years, big data is a solace to our finances. By initiating predictive modelling structured on the Hadoop big data platform, identification of erroneous claims in a systematic and repeatable is possible, resulting in a generation of 2200% return on advanced big data technology.

 

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Now, the healthcare organisations can inspect and evaluate patient records and billing anomalies and identify frauds. It has been made possible by going back in time to analyse unstructured datasets and implement machine learning algorithms to detect the inconsistencies.

Predict better outcomes with predictive analytics

Predictive Modelling is being used worldwide, by deriving data from EHRs (Electronic Health Records) to reduce mortality rates from diseases like congestive heart failure and sepsis. As you all know, Congestive Heart Failure (CHF) is one of the costliest health problems and needs huge healthcare spending. So, the earlier it is diagnosed the better it is, without getting into the expensive complications.

Predictive analytics combined with machine learning on large sample sizes, containing more patients’ data can expose all the nuances and sequences that couldn’t be uncovered previously.

 

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Concisely, the more healthcare organizations adopt Hadoop and advanced big data technology, the more profound will be the data dissemination across teams and partners, which will further boost patients’ easy cure and reduction in costs.

Promote your analytic skills with Big Data Hadoop certification in Gurgaon, offered by DexLab Analytics. Embrace and enrol for Hadoop certification in Delhi today, as the future is going to be ruled by Big Data.

 

Interested in a career in Data Analyst?

To learn more about Machine Learning Using Python and Spark – click here.
To learn more about Data Analyst with Advanced excel course – click here.
To learn more about Data Analyst with SAS Course – click here.
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To learn more about Big Data Course – click here.

Cyber Security Today: Curing Big Mobile Security Holes with Small Steps

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Cyber Security Today: Curing Big Mobile Security Holes with Small Steps

You have employees? And they bring smartphones to work? Is everything right? Or wrong?

Period.

The moment an employee carries a personal mobile device, be it a smartphone or a tablet, to work, a merger of personal and professional is bound to happen. And this could definitely give a rough time to the employer. If not handled properly.

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

Of late, there has been a lot of furore, thanks to our effervescent, ever-efficient media about messaging apps. But the headlines took e negative bend when a London- based banker was fired and fined by FCA for exposing crucial confidential data through WhatsApp. Though he defended himself by stating that he simply wanted to MAKE AN IMPRESSION on his friend, he was booked under cybercrime sections.

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Over the past few decades, the communication forms have undergone a magnanimous evolution. Once a mail-driven society is now a bustling centre of myriad high-on-function communication apps, the apps includes personal, social and enterprise-oriented apps.  However, with new technologies materializes new challenges. The best way to manage such personal apps is by ensuring safe and secure mode of communication, instead of banning them completely. Embrace the BYOD culture but with due protective measures.

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

Let’s talk about Data Mining

Mobile Device Management (MDM) is the key

MDM is the best way to ensure productivity from the employees, while administering their mobile devices. It allows the employees to access data and meaningful information without posing any threat to company data. By implementing MDM, companies can keep a tab on corporate data segregation, corporate policies, secure emails and confidential documents, and integrate and manage mobile devices. Sometimes, a company can go a step higher by restricting users from using WhatsApp on their company provided device, and in its place give them some secure and safe team messaging solution.

Launch a secure team messaging app

For safekeeping of confidential company data, make sure you provide your employees an efficient messaging app. Choose an app that ensures better control over the information that is to be accessed or shared by the users.

The app should be used by the team admin to keep an eye on the team’s activities and the content that they are sharing. They are the ones responsible to control who can or cannot join the team, along with blocking external domains.

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

It is advisable to select a tool that provides its users advanced controls, from basic channel level. Flock is developed on these mechanisms and empowers the channel admin to delete any content, and add/remove members from the team. These ways are good to go in restricting the leakage of confidential data through company professionals.

Awareness and compliance helps

Security Business People Team Teamwork Success Strategy Concept

Make your employees, your strength and not weakness. They are the best defence against any attempt of breaching crucial data. So, ensure compliance by conducting frequent safety awareness audits and workshops. Also, make sure that not every employee has access to sensitive company data, as it enhances the risks of becoming a victim of cybercrime.

Still wondering, what have you done to secure your company’s confidential data?

For more tips and advices, keep updated via DexLab Analytics. The prime Big Data training institute feels honoured to offer a wide spectrum of intensive courses on Data Science Online training in Gurgaon for aspiring students and industry professionals.

 

Interested in a career in Data Analyst?

To learn more about Machine Learning Using Python and Spark – click here.
To learn more about Data Analyst with Advanced excel course – click here.
To learn more about Data Analyst with SAS Course – click here.
To learn more about Data Analyst with R Course – click here.
To learn more about Big Data Course – click here.

Get Introduced to Big Data Analytic Techniques and Fly High

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Big data is the big word, NOW. Data sets are becoming more and more large and complex, making it extremely troublesome to coordinate activities using on-hand database management tools.

Get-Introduced-to-Big-Data-Analytic-Techniques-and-Fly-High
The flourishing growth in IT industry has triggered numerous complimentary conditions. One of the conditions is the emergence of Big Data. This two-word seven-letter catch phrase deals with a humongous amount of data, which is of prime importance in the eyes of the company in question. And the resultant effect leads to another branch of science, which is Data Analytics.

What is A/B Testing?

A/B Testing is a powerful assessment tool to determine which version of an app or a webpage helps an individual or his business meet future goals effectively and positively. The decision is not abrupt; it is taken after carefully comparing various versions to reveal out the best of the lot.

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

A/B Testing forms an integral part in web development and big data industry. It ensures that the alterations happening on a webpage or any page component are data-driven and not opinion-based.

What do you mean by Association Rule Learning

This comprises of a set of techniques to find out interesting relationships, i.e. ‘association rules’ amidst variables in massive databases. The methods include an assortment of algorithms to initiate and test possible rules.

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

The following flowchart, a market basket analysis is being focused. Here, a retailer ascertains which products are high in demand and eventually use this data for successful marketing.

How to understand Classification Tree Analysis?

Statistical Classification is implemented to:

  • Classify organisms into groups
  • Automatically allocate documents to categories
  • Create profiles of students who enrol for online courses

It is a method of recognizing categories, in which the new observation falls into. It needs a training set of appropriately identified observations, aka historical data.

Why should you take a sneak peek into the world of Data Fusion and Data Integration?

Well, this is a complex multi-level process involving correlation, association, combination of information and data from one and many sources, to attain a superior position, determine estimates and finish timely assessments of projects. By combining data from multiple sensors, data integration and fusion helps in improving overall accuracy and direct more specific inferences, which would have otherwise been impossible from a single sensor alone. 

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

Let’s talk about Data Mining

Identify patterns and strike relationships, with Data Mining. It is nothing but the collective data extraction techniques to be performed on a large chunk of data. Some of the common data mining parameters are Association, Classification, Clustering, Sequence Analysis and Forecasting.

Generally, applications involve mining customer data to deduce segments and understand market basket analyses. It helps understanding the purchase behaviour of customers.

Neural Networks – Resembling biological neural networks

Non-linear predictive models are mostly used for pattern recognition and optimization. Some of the applications ask for supervised learning, whereas some invites unsupervised learning.

To know more about Big Data certification, why don’t you check our extensive Machine Learning Certification courses in Gurgaon! We, at DexLab Analytics have all sorts of courses suiting your professional work skill.

 

Interested in a career in Data Analyst?

To learn more about Machine Learning Using Python and Spark – click here.
To learn more about Data Analyst with Advanced excel course – click here.
To learn more about Data Analyst with SAS Course – click here.
To learn more about Data Analyst with R Course – click here.
To learn more about Big Data Course – click here.

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