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Let us Revise Regression Analysis

Let us revise regression analysis

By now every business owner and manager is aware of the latest megatrend related to data analysis and knows that they should make data-driven decisions only at work. Gut feeling and winging it are now practices outdates as they have proved time and again that they fail. But the problem still remains in the know-how of parsing through all the layers of data streaming into your systems. Do you have the requisite know how?

Luckily for you, you may not be the one to crunch all the numbers (phew!). You pay other robotic data analytics personnel to do that for you. But you must correctly understand the analysis report that is handed over to you for interpretation after your colleagues are done with all the heavy lifting. A common practice is the realm of data analysis is regression analysis. Continue reading “Let us Revise Regression Analysis”

How Non-profits Can Benefit From Modern Data Analytics Tools

Data analytics solutions in the past took several years to be planned and deployed, but those days are long gone. By adopting a few data analytics processes in their operations, non-profit organizations can streamline and improve their operations and outreach for achieving better results. Earlier this was not possible as data analytics did not adapt well with the existing technologies and processes. Also there was a lack of well-equipped personnel as the data analysis tools were so complex in nature, that it required the IT personnel to take care of analysis tasks for clients.

 

How non-profits cans benefit from modern data analytics tools Continue reading “How Non-profits Can Benefit From Modern Data Analytics Tools”

The Most Important Algorithms Every Data Scientist Must Know

Algorithms are now like the air we breathe; it has become an inevitable part of our daily lives and is also included in all types of businesses. Experts like Gartner has called this age as the algorithm business which is the key driving force that is overthrowing the traditional ways in which we do our business and manage operations.

The most important algorithms of machine learning

In fact the algorithm boom with uber diversification has reached a new high, so much so that now each function in a business has its own algorithm and one can buy their own from the algorithm marketplace. This was developed by algorithm developers at Algorithmia to save the precious time and money of business operators and other fellow developers and offers a plethora of more than 800 algorithms in the fields of machine learning, audio and visual processing and computer vision.

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But we as data enthusiasts in the same field with an undying love for algorithm would like to suggest that not all the algorithms from the Algorithmia marketplace may be suitable for your needs. Business needs are highly subjective and environment based. And things as dynamic as algorithms can produce different types of results even in the slightly different situations. Also the use of algorithms depends on a number of factors on how they can be applied and what results one can expect from their application. The variables on which the application of algorithms depends are as follows: type and volume of the data sets, the function the algorithm will be applied for and the industry in which the algorithm will be applied.

Hence, not always reaching for the easy option of buying a readymade algorithm off the shelf and simply tweaking it to fit into your model may not always be the most cost-effective or time saving way to go. So, it is highly recommended for data scientists to educate themselves well on the most important algorithms that must be known by them, as well as the back of their hands. A data scientist must also know how each algorithm is developed and also which purpose calls for which algorithm to be applied.

So, our experts associated with DexLab Analytics developed an infographic to let big data analysts know the 12 most essential algorithms that must still be included in the repertoire of a skilled data scientist. To know more about data science courses drop DexLab Analytics and find your true data-based calling.

 

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Are You a Student of Statistics? – You must know these 3 things

Are you a student of statistics?

We a premiere statistical and data analysis training institute offering courses on Big Data Hadoop, Business intelligence and Ai. We asked our faculty to tell us the three most important things that every student of elementary statistics should know.

So, let us get on with it:

  1. The notion that statistics is about numbers, is in the context only: statistics involves a rich treasure trove of numeric and graphical representation of displaying data to quantify them also it is very important to be capable of generating graphs along with numbers. But that is not the half part of statistics and the main interesting aspect is related to making the big leap from numbers and graphs to the realistic worldly interpretations. Uncannily statistics also poses to be a fascinating philosophical tension raising the question and healthy skepticism about we believe in and what we do not.
  2. The analysis part is not the most crucial part of a statistical study, the most important part lies with the when, where and how of gathering the data. We must not forget when we enter each number or data, calculate and plot the strategies we build on our understanding, but many a times at the time of interpretation that each every graph, data or number is a product of a fallible machine, be it organic or mechanical. If we are able to take proper care at the stage of sampling and observation we will be able to obtain great dividends at the final stage of interpretation and analysis of all our statistical efforts.
  3. All statistical functions off all kinds of mathematical sciences are based on a two-way communication system. This communication system should be between the statistician and non-statistician end. The main aim of statistical analysis is to put forward important social, public and scientific questions. A good statistician knows how to communicate with the public especially with those who are by and large not statisticians. Also the public here plays an important role and must possess simple idea of statistical conclusions to grasp what the statisticians have to say to them. This is an important criterion to be incorporated in the K-12 and college curriculum for elementary statistical students.

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If you agree with our views and would like to discuss further on statistics and its application on data analysis then feel free drop by DexLab Analytics and stay updated on the latest trends in data management and mining.

 

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Credit Risk Managers Must use Big Data in These Three Ways

Credit risk managers must use Big Data in these three ways

While the developed nations are slowly recovering from the financial chaos of post depression, the credit risk managers are facing growing default rates as household debts are increasing with almost no relief in sight. As per the reports of the International Finance which stated at the end of 2015 that household debts have risen to by USD 7.7 trillion since the year 2007. It now stands at the heart stopping amount of a massive USD 44 trillion and the amount of debts increased in the emerging markets is of USD 6.2 trillion. The household loans of emerging economies calculating as per adult rose by 120 percent over the period and are now summed up to USD 3000.

To thrive in this market of increasing debts, credit risk managers must consider innovative methods to keep accuracy in check and decrease default rates. A good solution to this can be applying the data analytics to Big Data. Continue reading “Credit Risk Managers Must use Big Data in These Three Ways”

Trending Data Job Role: Chief Data Officer

Trending data job role: Chief Data Officer

Financial firms are going berserk in order to employ the best Chief Data Officers from around the world. This is the new hype in the C-suite world who wants to manage risks associated with data and also grasp its opportunities for conducting better business.

These days all financial firms are sincerely focused on maintaining their data and governing them to comply with the latest rules and regulations. They want to comply with customer demands to maintain their competitive edge and stay on top of the game. And in order to maintain this, the financial services teams are on a hyper drive in hiring the C-suite role of a Chief Data Officer i.e. CDO.

Recent developments in the regulatory mandates of Volcker Rule of the Dodd-Frank Act in relation to capital planning have made it difficult for financial organizations to aggregate and manage their data. In a recent stress test a large number of major US corporate banks and other financial institutions have failed as the quality of their data was not up to scratch.

But expert data analyst and scientists state that only regulatory compliance is not the main issue at hand. Effective risk management goes hand-in-hand with efficient data management. And firms are lacking that front as they do not manage their data effectively and are simply gambling with chances of a hug penalty at the risk of losing customers and acquiring a bad name in the business.

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The opportunities in this position of Chief Data Officer:

While the aspects of regulatory compliance and risk management are becoming more and more complex every day, but that is not the only reason to move up information management positions and invite them into the boardroom. That is why as most financial organizations know that good governance requires strong data management skills with good understanding of architecture and analytics. Companies have come to realize that this kind of information can prove to be effective and provide them with competitive advantage in terms of reaching out to customers and protecting them with the offering of innovative products and services.

According to latest research, experts predicted that 25 percent of every financial organization will have employed a Chief Data Officer by the end of 2015. The job responsibility of this role is still clouded and most organizations are trying to refine and boil it down, but as of now three main roles have been identified – data governance, data analysis and data architecture and technology. While according to this survey 77 percent of the CDOs will remain focused in governance focused but their responsibilities are likely to grow into other areas as well. The main objective behind data architecture is to oversee how data is sourced, integrated and then consumed in the global organizations. The way to lead efficiencies in this respect is to consider this aspect in depth. Thus, it can be concluded that data analytics has the most potential.

For more details on Online Certificate in Business Analytics, visit DexLab Analytics. Their online courses in data science are up to the mark as per industry standards. Check out the course module today.

DexLab Analytics Presents #BigDataIngestion

DexLab Analytics has started a new admission drive for prospective students interested in big data and data science certification. Enroll in #BigDataIngestion and enjoy 10% off on in-demand courses, including data science, machine learning, hadoop and business analytics.

 

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What we mean by the phrase “being data driven”?

It is only understandable that most corporations these days are submerged with data that forms the invaluable asset for them, which can lead them to insights worth the big bucks. It may be a good idea to think about the name Big Data which has been named so for being big in volume, but can also be correlated to Big Money when used wisely. But do all data open gates to gaining insights? Not entirely true, as data usually opens gates for obtaining more data and thus, creating opportunities to gain more insights. The main point of differentiation with data driven organizations is to put data at their focal point and use the insights derived from them to amplify their business gains with effective strategies and business decisions. Data in these companies are actually treated as valuable assets for the company, and they know how to use and protect them well. Intelligence obtained from data is embedded into their core business processes.

 

What we mean by the phrase “being data driven”?

 

What differentiates the data driven companies are that they are able to achieve a sustainable competitive advantage and can deliver greater customer satisfaction to their clients. The main pillars of work is a cohesive business model with a data aware culture that inspires its customer base as well employees to look through newer perspectives, challenge the status quo of their business environment, take greater risks and optimize their usage of data.

Continue reading “What we mean by the phrase “being data driven”?”

The Limitation of R Programming

R Programming is sort of the darling of the academia and researchers as well due to the cutting edge tools of data science and analytics that it offers. Not only does its open source nature ensure that contributors to the project are able to come out with packages that facilitates in making R Programming be able to sport the latest advances in its field but also make it a option that may be implemented with burning a hole in ones pockets.

 

The Limitation of R Programming

The Disadvantages of R

In spite of all its flexibility, R is found want in a number of specific situations. R cannot scale properly with large sets of data. There have been a number of efforts to overcome this significant disadvantage of R, but these efforts have not met with much success and the bottleneck remains an issue which needs to be dealt with seriously.

Continue reading “The Limitation of R Programming”

Data analysis resources to keep you updated

Data analysis resources to keep you updated

One should always be proactive about building upon what they already know and have learnt, and with explosion of the web such resources can be obtained fairly easily. The problem is not the availability of resources but the abundance from it. Due to the availability of too many choices it often becomes difficult to gauge if the sources are actually authentic.

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So, here is a list of books, websites and other resources which we think are authentic:

To stay on top of the latest trends and analyses reports and what’s new in the realm of analytics here are the best latest blogs:

  • FiveThirtyEight: the main man behind this blog is Nate Silver, a data whiz kid, this blog is the place to find out data analysis and visualizations of political, economic and cultural issues. The content in his blogs are usually light-hearted and interactive yet pointed with illustrative examples of data can be used in day-to-day activities.
  • Flowing Data: this is an interesting blog where Dr. Nathan Yau, PhD reveals how the data personnel – like designers, analysts, scientists and statisticians can analyze and visualize data to gather a better understanding of the world around us. It is especially fun to read as Yau offers a funny approach about the regular challenges faced by a data professional in this field. One can also find job recommendations, tutorials and other resources in this blog.
  • Simply statistics: this is another blog that is managed by expert professors each from Ivy League colleges like Johns Hopkins University, Harvard University and the Dana Ferber Cancer Institute. These professors also talk about how data is being used or misused around the world in different industries.
  • Hunch: this blog has been created by John Langford from Microsoft Research, he is the doctor of learning there and his blog talks about machine learning basics of what we know and how we use what we know. This is a good read for those who are new in the field of machine learning and do not yet know how things work in machine learning as it provides an in-depth view of new ideas and events going on in this industry.

To connect to other fellow data scientists and analysts to inquire about questions that may arise while you try the tread the treacherous roads of the data world, these are few communities of data analysts you can follow.

    1. Kaggle competitions: this is a popular community that all data scientists are likely to come across. This is a platform where one can find data prediction competitors. This is a platform where one can search for upcoming competitions in data analysis the website also features a forum where a visitor can ask any question or find a partner for the competition, share resources and ask for support to make a good career in data science.
    2. Metaoptimize: this is a question and answer community for people who are into machine learning, natural language processing, data mining and more. Badges are awarded as per votes on questions are awarded. Thus, making it becomes simpler for the visitors to discover the most popular helpful answers to the questions.
    3. Datatau: this website is best described as hacker news for data scientists and it lives up to this description to the last word. People share career advice with each other; interesting articles are shared amongst the users and then commented upon also the people here share useful information to those new to the world of data analytics.
    4. DexLab Analytics blogs: while DexLab Analytics is one of the leading data analytics training institute in Gurgaon, but they maintain regular blogs about the latest developments in the field of data science and provide India-specific as well global data related news. For students pursuing or aspiring to pursue a career in data science must follow the daily posts from this institute.

In conclusion we would like to add that while there are several resources from where one can obtain valuable information about data analysis. Thus, keeping this list as a starting point you can find several other experts out there to help you learn more about data analytics.

 

Interested in a career in Data Analyst?

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