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A Data Analyst’s Guide to Choosing The Right Halloween Costume

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There are a handful of decisions that seldom wrap people in mind-boggling conundrum – whether to take the job or not, to get married or not and to choose between Batman and Superman for Halloween costume!

 
A Data Analyst’s Guide to Choosing The Right Halloween Costume
 

For a data analyst, there is an easy way out all we need is a bigger boat and when we say boat, we mean efficient analytical software. Let us dive right into the data of it all and determine which superhero to smart for our Halloween shindig. Continue reading “A Data Analyst’s Guide to Choosing The Right Halloween Costume”

Data Analytics is Helping Computers Create Rembrandt Masterpieces

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Ever imagine what would happen if master painters like Rembrandt were alive and painting in the time of Prisma?

 

They would probably have a brain aneurysm looking at perfect photographic images to be transformed into almost accurate paintings on the hands of puny commoners with so much as a smart phone!

 

Data Analytics is Helping Computers Create Rembrandt Masterpieces

 

But how is that, computers these days can replicate the master paintings of famous legendary artists like Van Gogh or Rembrandt for that matter within a matter of few seconds? While Prisma transforms real photographs into paintings by playing around with the graphic information. But what if we told you…that you can create a whole new painting using your computer or to be precise with this ingenious algorithm! Continue reading “Data Analytics is Helping Computers Create Rembrandt Masterpieces”

Know The Answer To These Interview Questions To Get A Job As Data Analyst

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List of Interview Questions for Data Analysts

With this Data analyst interview guide you will know what to expect in an interview round for a position of data analyst.

A good data analyst or scientist must be capable of drawing actionable insights from the data that a company generates. They must possess a good sense of what data they must collect and should have a solid process for carrying it out effectively using processes of data analysis and building predictive models.

A data analyst must possess a strong foundation in the following topics: operations research, statistics, machine learning along with some database skills, such as SQL or SAS in order to clean, retrieve and process the data from different sources. One can lead to this role from different pathways thus candidates can expect to be bombarded with questions relevant to statistics ort mathematics and even computer programming or engineering.

Data scientists are also often required to script programs using R or Python or Matlab and the role will typically not place emphasis on the programming skills or practices and the general software engineering skills which is necessary for working with production quality software.

Here is a list of common data analyst interview questions:

Operational questions:

  1.  Describe the steps that you follow when creating a design a data-driven model to manage a business problem. For example you may try and automatically classify customer support mails, by either sentiment or topic. Another task may be to predict a company’s employee churn.
  2. What models would you classify as simple models and which are the ones that are complex according to you? What are the comparative strengths and weaknesses of choosing a more complex model over a simplistic one?
  3. What are the possible ways in which you can combine models to create an ensemble model and what are the main advantages of doing this?
  4. Tell us about certain pre-processing steps that you may carry out on data before using them to train a model and describe the conditions under which they may be applied.

Role specific questions:

About basic ideas in probability, statistics and machine learning:

  1. Define what is confidence interval and why do you think it is useful?
  2. What is the main difference between correlation and independence?
  3. What is Bayes Theorem? What is conditional probability? What is its use in practice?
  4. When and how do you understand that you have collected ample data for building a model?
  5. Tell us the difference between classification and regression.

Hope this list of common data science interview questions will prepare you for a job at a reputable data analysis company. For more such data science news, tutorials and articles with emphasis on programming and analytics view our regular updates from DexLab Analytics.

 

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Big Data Analytics Is Helping To Curb Cancer For More Than 40 Years Now

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The times now are such that we can name at least one of our friends, relatives or peers who have fought the dreadful battle with cancer. But luckily for us there are several people who along with their loved ones have not only fought this battle with courage but have triumphed to achieve sweet survival. But such glorious accomplishments would not have occurred if it were 40 years ago.

 
Big Data Analytics Is Helping To Curb Cancer For More Than 40 Years Now

 

As per the reports, adults who were diagnosed with cancer back in 1975 only had a lowly 50/50 chance of survival after five years of being diagnosed. But today the relative five year after rate of survival across all types of cancer is as close as 70 percent. And for better, the cancer survival rate during the same time frame for child cancer patients within five years of diagnosis has improved from the previously existing 62 percent to 81 percent, which is a steep rise. Continue reading “Big Data Analytics Is Helping To Curb Cancer For More Than 40 Years Now”

Latest Open Source Tools in Data Analytics Beyond Apache Spark

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Latest Open Source Tools in Data Analytics Beyond Apache Spark

In the IT world change is always in the air, but especially in the realm of data analytics, profound change is coming up as open source tools are making a huge impact. Well you may already be familiar with most of the stars in the open source space like Hadoop and Spark. But with the growing demand for new analytical tools which will help to round up the data holistically within the analytical ecosystem. A noteworthy point about these tools is the fact that they can be customized to process streaming data.

With the emergence of the IoT (Internet of things) that is giving rise to numerous devices and sensors which will add to this stream of data production, this forms one of the key trends why we need more advanced data analytics tools. The use of streaming data analysis is used for enhanced drug discovery, and institutes like SETI and NASA are also collaborating with each other to analyze terabytes of data, that are highly complex and stream deep in space radio signals.

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The Apache Hadoop Spark software has made several headlines in the realm of data analytics that allowed billions of development funds to be showered at it by IBM along with other companies. But along with the big players several small open source projects are also on the rise. Here are the latest few that grabbed our attention:

Apache Drill:

This open source analytics tool has had quite good impact on the analytics realm, so much so that companies like MapR have even included it into their Hadoop distribution systems. This project is a top-level one at Apache and is being leveraged along with the star Apache Spark in many streaming data analytics scenarios.

Like at the New York Apache Drill meeting in January this year, the engineers at MapR system showed how Apache Spark and Drill could be used in tandem in a use cases that involve packet capture and almost real-time search and query.

But Drill is not ideal for streaming data application because it is a distributed schema free SQL engine. People like IT personnel and developers can use Drill to interactively explore data in Hadoop and NoSQL databases for things such as HBase and MongoDB. There is no need to explicitly describe the schemas or maintain them because the Drill has the ability to automatically leverage the structure which is embedded in the data. It is capable of streaming the data in memory between operators and minimizes the use of disks unless you need to complete a query.

Grappa:

Both big and small organizations are constantly working on new ways to cull actionable insights from their data streaming in constantly. Most of them are working with data that are generated in clusters and are relying on commodity hardware. This puts a premium label on affordable data centric work processes. This will do wonders to enhance the functionality and performance of tools such as MapReduce and even Spark. With the open source project Grappa that helps to scale the data intensive applications on commodity clusters and will provide a new type of abstraction which will trump the existing distributed shared memory (DSM) systems.

Grappa is available for free on the GitHub under a BSD license. And to use Grappa one can refer to its quick start guide that is available readily on the README file to build and execute it on a cluster.

These were the latest open source data analytics tools of 2017. For more such interesting news on Big Data analytics and information about analytics training institute follow our daily uploads from DexLab Analytics.

 

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.

Aspiring Data Analysts Must Know the Answer to These Interview Questions

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Aspiring data analysts must know the answer to these interview questions

You have recently completed Data analyst certification and are hunting vigorously for a job as a data scientist. But the prospect of sitting for such an important job role at a corporate firm in front of a room full of C-suite interviewers is an intimidating prospect. But fear not as we at DexLab Analytics have got you covered both inside the class room as well out.

This megatrend on Big Data analysts started first in 2013, when the leading universities of the world began to realize the gap in between the demand and supply of Big Data professionals. And soon several , Data analyst training institutes cropped up here and there and rooms transformed into classrooms with several students being keen to learn about the steps to handle Big Data  and to join the ranks of data scientists which is a highly sought after profession of these days. Continue reading “Aspiring Data Analysts Must Know the Answer to These Interview Questions”

A Few Key Business Analytics Tricks Every Manager Must Know

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The main objective behind using any analytics tool is to analyze data and gather commercially relevant and actionable insights to accelerate results and performances of any organization. But currently there are a variety of tools available so, it often becomes difficult for managers to know which ones to use and when. You may be considering an online certificate in business analytics so reviewing and understanding these key tools may be of great value.

 

A few key business analytics tricks every manager must know

 

So, we thought you may want to know a few of the key analytics tools in use today and how they can be helpful for different business organizations.

Continue reading “A Few Key Business Analytics Tricks Every Manager Must Know”

How Non-profits Can Benefit From Modern Data Analytics Tools

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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”

Data analysis resources to keep you updated

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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?

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.

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