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DexLab Analytics is Providing Intensive Demo Sessions in March

Posted on March 14, 2019March 14, 2019 by Dexlab

DexLab Analytics is Providing Intensive Demo Sessions in March

The internet has spurred quite a revolution – in several sectors, including education. Interested candidates are at liberty today to learn a vast array of things and garner a humongous pool of knowledge. Online demo sessions further add to the effect. These demo sessions are state-of-the-art and in sync with the industry demands. They are one of the most effective methods of learning and upgrading skills, particularly for the professionals. They transform the learning process and for all the good reasons.

DexLab Analytics is a premier data science training institute that conducts demo sessions, online and offline regularly. These demo sessions are indeed helpful for the students. With an encompassing curriculum, a team of experts and a flexible timing, the realm of demo sessions has become quite interesting and information-laden.

Talking of online sessions, they are incredibly on-point and high on flexibility. With daring innovations in technology, no longer do you have to travel for hours to reach your tuition center. Instead, from the confines of your home sweet home, you can gain access to these intensive demo sessions and learn yourselves. Adding to that, the medium of learning is easy and user-friendly. The millennial generation is extremely tech-savvy that leaves no room for difficulties learning online.

Moreover, we boast of top-of-the-line faculty strength, well-versed in the art and science of data science and machine learning. With years of experience and expertise, the consultants working with us are extremely professional and knowledgeable in their respected field of study. Lastly, online demo sessions are great tools for career advancement. While working, you can easily upgrade your skills in your own time – boosting career endeavors further. The flexibility of learning is the greatest advantage.

This month, DexLab Analytics is organizing the following demo sessions; kindly take a note of the date and timing:

Demo session on Machine Learning, Deep Learning and Python – Saturday 16th March at 2 PM by industry professionals
Demo session on Data Visualization and Reporting – Saturday 23rd March at 11 AM by industry professionals
Demo session on Credit Risk Modelling – Saturday 16th March at 2 PM by industry professionals

For more information on big data Hadoop training in Delhi, follow DexLab Analytics.

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6 Questions Organizations Should Ask About Big Data Architecture

Posted on May 24, 2017October 4, 2018 by Dexlab

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.

Black Money in India Can be Traced With Ease by Applying Big Data Analytics

Posted on December 1, 2016December 3, 2016 by Dexlab

The economy took a hit with the recent demonetization of the INR 500 and 1000 currency notes. The jury of economists around the world are still debating whether the move was good or not, but it has definitely caused a huge inconvenience for the public. Moreover, exchanging such a large amount of old currency notes is nothing shy of a mammoth Herculean task, as almost 85 percent of the economy is in the form of high denomination currency.

Black Money in India Can be Traced With Ease by Applying Big Data Analytics — Black Money in India Can be Traced With Ease by Applying Big Data Analytics

These measures have been taken by the government to curb the flow of Black Money in India and get rid of corruption from its roots. While there is still a mixed reaction from the common people about this move about it being good or bad, technological experts have a different viewpoint about preventing the flow of Black Money in the country. They say that with use modern technologies like Big Data Analytics it will be possible to trace Black Money painlessly and with much ease.

Continue reading “Black Money in India Can be Traced With Ease by Applying Big Data Analytics”

Why Getting a Big Data Certification Will Benefit Your Small Business

Posted on October 25, 2016December 19, 2017 by Dexlab

Do you know how much data is currently produced globally every year?

As per the reports published by IBM, the figures are 2.5 QB (Quintillion Bytes). The numeric representation of the same looks as: 2,500,000,000,000,000,000. And we thought that our mobile devices with 64GB memory space are capable of storing huge data.

Increasing reliance on Big Data

As technology is expanding at the speed next to light, more companies are planning to invest in Big Data platforms for getting the best out of it. Gartner Inc. had conducted a research recently among 437 global organisations across different industries and figured out that more than 75% of them are looking forward to the benefits they can derive from Big Data. The purpose for using Big Data varied to some instance across these organisations, however most of the companies were found to use data analytics for enhancing their customer service segments. Recently, security breach has hit the headline more often than global warming and that has been a factor of worry for many data driven companies. Thus, they are opting for Big Data tools in order to strengthen their online security. Continue reading “Why Getting a Big Data Certification Will Benefit Your Small Business”

How do you Like your Coffee? Strong with a Lot of Numbers!?

Posted on October 19, 2016 by Dexlab

In the health conscious world today, people are often engaging in hot debates as to which drinks have the most and least amounts of caffeine in them. But as data analysts we do not like to engage in any critical discussion without having a few graphs to show for our arguments! So, the moment someone put the kettle on, we turned on our computers to sip a cuppa and make a few caffeinated graphs.

The moment we head on to a cafe or a breakfast joint these days, we are bombarded with a long list of beverages which include an assortment of coffees, tea and other sugary drinks that promise to refresh our Monday-blues thirst with a shot of energizing elixir! It is not only spoiling to have so many options, but in fact confusing to say the least when the waiter/waitress keenly asks us how we would like our coffee.

But coffee, that is dubbed as the Devil’s juice (which I believe, further adds to its charm) is known to be bad for our health when drunk too much. So, we analysed and researched and downloaded some of the data of caffeine in various drinks like tea, soda and different blends of coffee pasted them on an Excel sheet and then used Proc Import to feed and read the data into SAS datasets.

The problem we faced next was that each category still had a few too many drinks to get a quick mental grasp on the matter (i.e. typically 100+). So, we took a two stage approach. The first thing we did was to plot just a handful of drinks in each of the categories which we then recognized as we have or want to drink, and then we plotted the entire list. The images of the small subset graphs are attached below:

Here’s a graph of caffeine content in the most popular coffees:

Here is the complete list of data for all coffees:

Drink	Fluid ounces	Caffeine	mg Caffeine
		(mg)	per fluid oz
Nescafe Ice Java	0.9	100	117.6
Stok Black Coffee Shots	0.4	40	90.9
Coffee Crave Fearless Black	12	844	70.3
Chameleon Cold Brew Coffee	32	2160	67.5
Black Insomnia Coffee	12	702	58.5
Death Wish Coffee	12	660	55
Coffee (Espresso)	1.5	77	51.3
Killer Coffee	8.5	430	50.8
Biggby Espresso	2	100	50
Gourmesso Coffee Pods	1.4	65	48.1
Bizzy Cold Brew	16	750	46.9
Peet’s Coffee Espresso	1.5	70	46.7
Nespresso Coffee Capsules	1.4	60	44.4
Robusta Coffee	8	265	33.1
Gloria Jean’s Coffee	2	67	33
Shock Coffee Triple Latte	8	231	28.9
Chameleon Cold Brew RTD Coffee	10	270	27
Stumptown Cold Brew Coffee	10.5	279	26.6
Long Black	6	154	25.7
Greek Coffee (Metrios)	2	50	25
Turkish Coffee	2	50	25
Illy Issimo Cafe	6.8	155	22.8
Seattle’s Best Brewed Coffee	12	260	21.7
Stumptown Cold Brew Chocolate + Milk	16	340	21.2
Coffee Bean & Tea leaf Coffee	16	333	20.8
Starbucks Grande Coffee	16	330	20.6
Coffee (Brewed)	8	163	20.4
High Brew Coffee	8	163	20.4
Stumptown Cold Brew + Milk	16	319	19.9
Starbucks Doubleshot	6.5	125	19.2
Caribou Brewed Coffee	16	305	19.1
Black Medicine Iced Coffee	11	206	18.7
TrueStart Performance Coffee	5.1	95	18.6
Costa Coffee	15.2	277	18.2
Bulletproof Coffee	8	145	18.1
Don Tomas Estate Coffee	8	145	18.1
Starbucks Via Ready Brew	8	135	16.9
Peet’s Brewed Coffee	16	267	16.7
Gold Peak Coffee	8	126	15.8
Cafe Viva Probiotic Coffee	8	125	15.6
Dunkin’ Donuts Brewed Coffee	14	210	15
K-Cup Coffee	8	120	15
Keurig Vue Pack	12	180	15
Starbucks Grande Caffe Americano	16	225	14.1
Starbucks Iced Espresso	16	225	14.1
Starbucks Latte Macchiato	16	225	14.1
Barista Bros Iced Coffee	16.9	219	13
Einstein Bros Coffee	16	206	12.9
Americano Coffee	12	154	12.8
Cappuccino	12	154	12.8
Dutch Bros. Coffee	20	256	12.8
Caffe Mocha	12	152	12.7
Starbucks Protein and Coffee	11	140	12.7
Biggby Brewed Coffee	16	200	12.5
Starbucks Cold Brew Coffee	16	200	12.5
Dunkin’ Donuts Iced Coffee	24	297	12.4
Biggby Iced Coffee	16	192	12
Panera Bread Coffee	16	189	11.8
Pronto Coffee	6	67	11.2
Muscle Milk Coffee House	11	120	10.9
Starbucks Grande Caffe Mocha	16	175	10.9
Dunkin’ Donuts Latte	14	151	10.8
Dunkin’ Donuts Mocha	14	151	10.8
Vita Coco Cafe	11.1	120	10.8
Starbucks Discoveries Caffe Mocha	50.7	539	10.6
Starbucks Bottled Iced Coffee	11	115	10.5
McDonalds (McCafe) Mocha	16	167	10.4
Peet’s Caffe Mocha	16	165	10.3
Peet’s Iced Mocha	16	165	10.3
Tim Hortons Large Brewed Coffee	20	200	10
Baskin Robbins Cappuccino Blast	24	234	9.8
Starbucks Doubleshot Energy + Coffee	15	145	9.7
Latte	16	154	9.6
Dare Iced Coffee	16.9	160	9.5
Starbucks Bottled Frappuccino	9.5	90	9.5
Peet’s Iced Coffee	16	150	9.4
Starbucks Grande Caffe Latte	16	150	9.4
Starbucks Grande Cappuccino	16	150	9.4
Flat White	8.5	77	9.1
McDonalds Coffee	16	145	9.1
McDonalds Iced Coffee	22	200	9.1
V Double Espresso Iced Coffee	16.1	147	9.1
Bean and Body Coffee	8	72	9
McDonalds (McCafe) Latte	16	142	8.9
Peet’s Caffe Americano	16	140	8.8
Peet’s Caffe Latte	16	140	8.8
Peet’s Cappuccino	16	140	8.8
Peet’s Iced Latte	16	140	8.8
Skinny Cow Iced Coffee	8	70	8.8
International Delight Iced Coffee	8	65	8.1
Starbucks Verismo Coffee Pods	8	60	7.5
Coffee (Instant)	8	57	7.1
Zola Coconut Water Espresso	17.5	125	7.1
Real Beanz Iced Coffee	9.5	66	6.9
Caffe Nero Coffee	12	80	6.7
Chick-fil-A Iced Coffee	14	94	6.7
Peet’s Decaf Espresso	1.5	10	6.7
Dunkin’ Donuts Iced Latte	24	151	6.3
Biggby Creamy Lattes	16	100	6.2
Biggby Frozen Lattes	16	100	6.2
Big Train Java Chip Ice Coffee	8	49	6.1
CoolBrew Coffee	10	60	6
Tim Hortons Small English Toffee Coffee	10	60	6
Tim Hortons Small French Vanilla Coffee	10	60	6
Dunkin’ Donuts Dunkaccino	14	83	5.9
Svelte Cappuccino Protein Shake	11	65	5.9
SlimFast Cappuccino Delight Shake	10	40	4
Choffy (roasted cacao)	6	23	3.8
Starbucks Refreshers	16	50	3.1
Indulgio Cappuccino	8	20	2.5
Starbucks Decaf Coffee	16	25	1.6
Coffee Leaf Tea	8	12	1.5
Dunkin’ Donuts Coolatta	24	18	0.8
Nescafe’ Ricoffy	8	6	0.8
Arby’s Jamocha Shake	16	12	0.7
Coffee (Decaf, Brewed)	8	6	0.7
Coffee (Decaf, Instant)	8	3	0.3

Detailed data on all kinds of popular sodas:

Drink	Fluid ounces	Caffeine	mg Caffeine
Drink	Fluid ounces	(mg)	per fluid oz
Bawls Exxtra	16	150	9.4
Blink Energy Water	16.9	150	8.9
Flatt Cola	8	65	8.1
Afri Cola	12	89	7.4
Fritz Kola	11.2	83	7.4
Premium Cola	11.2	83	7.4
Hansen’s Diet Red	8.3	57	6.9
Bawls	10	64	6.4
Bawls Orange	10	64	6.4
Bawls Cherry	16	100	6.2
Bawls Root Beer	16	100	6.2
Mountain Dew Game Fuel	20	121	6
Monster Mutant	20	115	5.8
Pepsi Max	12	69	5.8
Ski Soda	12	69	5.8
Sun Drop Soda	12	64	5.3
Mountain Dew Black Label	16	83	5.2
DOC 360	20	100	5
Mountain Dew Voltage	12	55	4.6
Diet Mountain Dew	12	54	4.5
Mountain Dew	12	54	4.5
Mountain Dew Baja Blast	16	72	4.5
Mountain Dew Live Wire	12	54	4.5
Pepsi One	12	54	4.5
Cult Cola	16.9	75	4.4
Surge Citrus Soda	16	69	4.3
Dr Pepper 10	12	51	4.2
Mello Yello	12	51	4.2
Mello Yello Zero	12	51	4.2
Starbucks Refreshers Canned	12	50	4.2
Cheerwine	12	48	4
Diet Cheerwine	12	48	4
Diet RC Cola	12	47	3.9
Diet Coke	12	46	3.8
Diet Coke Plus	12	45	3.8
Diet Coke with Lemon	12	45	3.8
Diet Coke with Lime	12	46	3.8
Diet Vanilla Coke	12	45	3.8
Hint Caffeine Kick Water	16	60	3.8
Soda Stream	16	60	3.8
TAB Diet Cola	12	45	3.8
Zevia Cola	12	45	3.8
RC Cola	12	43	3.6
RC Cola, Cherry	12	43	3.6
Shasta Cola	12	43	3.6
Diet Pepsi UK, AU, NZ	12	43	3.5
Diet Sunkist Orange Soda	12	42	3.5
Faygo Cola	12	42	3.5
Pepsi Max UK, NZ, AU	12	43	3.5
Diet Dr Pepper	12	41	3.4
Dr Pepper	12	41	3.4
Sunkist Orange Soda	12	41	3.4
Sunkist Sparkling Lemonade	12	41	3.4
Sunkist Ten	20	68	3.4
Diet Mr. Pibb	12	40	3.3
Honest Professor Fizz	12	40	3.3
Kickapoo Soda: Joy Juice & Fruit Shine	12	40	3.3
Pibb Xtra	12	40	3.3
Pibb Zero	12	40	3.3
Diet Dr Pepper Cherry	12	39	3.2
Diet Dr Pepper Cherry Vanilla	12	39	3.2
Diet Ruby Red Squirt	12	39	3.2
Diet Wild Cherry Pepsi	12	38	3.2
Dr Pepper Cherry	12	39	3.2
Dr Pepper Cherry Vanilla	12	39	3.2
Jazz Caramel Cream	12	38	3.2
Pepsi Cola	12	38	3.2
Pepsi Diet Lemon	12	38	3.2
Pepsi Diet Lime	12	38	3.2
Pepsi Diet Vanilla	12	38	3.2
Pepsi Throwback	12	38	3.2
Pepsi True	7.5	24	3.2
Ruby Red Squirt	12	39	3.2
TK Diet Cola	12	38	3.2
Wild Cherry Pepsi	12	38	3.2
Ale 8 1	12	37	3.1
Crystal Pepsi	20	63	3.1
Inca Kola	16	50	3.1
Red Flash	12	37	3.1
Co-Operative Diet Cola	16.9	50	3
Double Cola	12	36	3
1893 Cola	12	34	2.8
Big Red Soda	12	34	2.8
Caffeinated Club Soda	12	34	2.8
Cherry Coke	12	34	2.8
Cherry Coke Zero	12	34	2.8
Coca-Cola Classic	12	34	2.8
Coke Zero	12	34	2.8
Diet Cherry Coca-Cola	12	34	2.8
Diet Coke with Splenda	12	34	2.8
Diet Pepsi	12	34	2.8
Vanilla Coke	12	34	2.8
Pepsi Next	12	32	2.7
Slurpee	16	40	2.5
A&W Cream Soda	12	29	2.4
Coca-Cola Life	12	28	2.3
Red Rock Cola	12	26	2.2
Barq’s Root Beer	12	22	1.8
Diet A&W Cream Soda	12	22	1.8
Pepsi Slurpee	8	14	1.8
Faygo Moon Mist	12	20	1.6
PC Cola Diet	12	13	1.1
PC Cola	12	12	1
Ritz Cola	12	10	0.9
Canada Dry Green Tea Ginger Ale	12	9	0.8
Boost Nutritional Drink	8	5	0.6
7-Up	12	0	0
A&W Root Beer	12	0	0
Barq’s Red Creme Soda	12	0	0
Coca-Cola caffeine free	12	0	0
Diet Barq’s Root Beer	12	0	0
Fanta	12	0	0
Fresca	12	0	0
Ginger Ale or Ginger Beer	12	0	0
IBC Root Beer	12	0	0
Kinley Soda	12	0	0
Mug Root Beer	12	0	0
Orange Crush	20	0	0
Pepsi Caffeine Free	12	0	0
Sprite	12	0	0
Squirt Soda	12	0	0
Tonic Water	11.9	0	0
Tropicana Twister Soda	20	0	0
Vernors Ginger Ale	12	0	0

While we are not too big a fan of the dancing bologna to be added into serious graphs, but in this we figured we would add a few minimalistic pictures of the drinks to make it easier to be distinguished.

Feel free to share how well your favourite drink fared in this very hotly debated list. And for more interesting data analysis news and Big Data courses stay tuned with DexLab Analytics.

The 1st Clinical Trial Predictive Model By Pfizer is Here

Posted on July 21, 2016July 21, 2016 by Dexlab

Joining the data analytics bandwagon, the pharmaceutical giant Pfizer has launched their first clinical trial predictive modelling system which is aimed at reducing study risk during protocol design and to better study execution phases. In a recent interview Jonathan Rowe, the Executive Director and Head of Clinical Development Quality Performance and Risk Management of Pfizer shed some light on these predictive modelling systems.

When asked in the interview about the purpose of their predictive model and what it is meant to achieve, Rowe responded as follows…

It is true that there are quite a few models in the realm of GCP quality performance which we have developed and continue to refine. A relatively straightforward one is the correlation model where we correlate our clinical trial process performance to select the results of the GCP as is defined in the ICH E6. Continue reading “The 1st Clinical Trial Predictive Model By Pfizer is Here”

Credit Risk Managers Must use Big Data in These Three Ways

Posted on April 19, 2016May 31, 2019 by Dexlab

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”

The evolution of Big Data in business decision making

Posted on March 19, 2016May 3, 2019 by Dexlab

Big Data is big. We have all established that, and now we know that all the noise about Big Data is not just hype but is reality. The data generated on earth is doubling in every 1.2 years and the mountainous heap of data keep streaming in from different sources with the increase in technology.

Let us look at some data to really understand how big, Big Data is growing:

The population of the world is 7 billion, and out of these 7 billion, 5.1 billion people use a smart phone device
On an average every day almost 11 billion texts are sent across the globe
8 million videos are watched on YouTube alone
The global number of Google searches everyday is 5 billion

But the balance has long been tipped off as we have only been creating data but not consuming it enough for proper use. What we fail to realize is the fact that we are data agents, as we generate more than 25 quintillion bytes of data everyday through our daily online activities. The behaviors that add more numbers to this monstrous hill of data are – online communications, consumer transactions, online behavior, video streaming services and much more.

The numbers of 2012 suggest that world generated more than 2 Zetabytes of data. In simpler terms that is equal to 2 trillion gigabytes. What’s more alarming is the fact that by the year 2020, we will generate 35 trillions of data. To manage this growing amount of data we will need 10 times the servers we use now by 2020 and at least 50 times more data management systems and 75 times the files to manage it all.

The industry still is not prepared to handle such an explosion of data as 80 percent of this data is mainly unstructured data. Traditional statistical tools cannot handle this amount of data, as it is not only too big, but is also too complicated and unorganized to be analyzed with the limited functions offered by traditional statistical analysis tools.

In the realm of data analysts there are only 500 thousand computer scientists, but less than 3000 mathematicians. Thus, the talent pool required to effectively manage Big Data will fall short by at least 100 thousand minds prepared to untangle the complex knots of intertwined data hiding useful information.

But to truly harness the complete potential of Big Data we need more human resource and more tools. For finding value we need to mine all this data.

Then what is the solution to this even bigger problem of tackling Big Data? We need Big Data Analytics. This is more than just a new technological avenue, but on the contrary this is fresh new way of thinking about the company objectives and the strategies created to achieve them. True understanding of Big data will help organizations understand their customers. Big Data analytics is the answer behind where the hidden opportunities lie.

A few advanced tools that are currently in use in the data analysis industry are: SAS, R Programming, Hadoop, Pig, Spark and Hive. SAS is slowly emerging to be an increasingly popular tool to handle data analysis problems, which is why SAS experts are highly in-demand in the job market presently. To learn more about Big Data training institutes follow our latest posts in DexLab Analytics.

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How Data Scientists Take Their Coffee Every Morning

Posted on January 25, 2016July 10, 2018 by Dexlab

To a data scientist we are all sources of data, from the very moment we wake up in the morning to visit our local Starbucks (or any other local café) to get our morning coffee and swipe the screen of our tablets/iPads or smart phones to go through the big headlines for the day. With these few apparently simple regular exercises we are actually giving the data scientists more data which in-turn allows them to offer tailor-made news articles about things that interest us, and also prepares our favorite coffee blend ready for us to pick up every morning at the café.

The world of data science came to exist due to the growing need of drawing valuable information from data that is being collected every other day around the world. But is data science? Why is it necessary? A certified data scientist can be best described as a breed of experts who have in-depth knowledge in statistics, mathematics and computer science and use these skills to gather valuable insights form data. They often require innovative new solutions to address the various data problems.

Data Science: Is It the Right Answer? – @Dexlabanalytics.

As per estimates from the various job portals it is expected that around 3 million job positions are needed to be fulfilled by 2018 with individuals who have in-depth knowledge and expertise in the field of data analytics and can handle big data. Those who have already boarded the data analytics train are finding exciting new career prospects in this field with fast-paced growth opportunities. So, more and more individuals are looking to enhance their employability by acquiring a data science certification from a reputable institution. Age old programs are now being fast replaced by new comers in the field of data mining with software like R, SAS etc. Although SAS has been around in the world of data science for almost 40 years now, but it took time for it to really make a big splash in the industry. However, it is slowly emerging to be one the most in-demand programming languages these days.What a data science certification covers?

Tracing Success in the New Age of Data Science – @Dexlabanalytics.

This course covers the topics that enable students to implement advanced analytics to big data. Usually a student after completion of this course acquires an understanding of model deployment, machine language, automation and analytical modeling. Moreover, a well-equipped course in data science helps students to fine-tune their communication skills as well.

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Things a data scientist must know:

All data scientists must have good mathematical skills in topics like: linear algebra, multivariable calculus, Python and linear algebra. For those with strong backgrounds in linear algebra and multivariable calculus it will be easy to understand all probability, machine learning and statistics in no time, which is a requisite for the job.

More and more data-hungry professionals are seeking excellent Data Science training in Delhi. If you are one of them, kindly drop by DexLab Analytics: we are a pioneering Data Science training institute. Peruse through our course details for better future.

Interested in a career in Data Analyst?
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