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What Makes Artificial Intelligence So Incredibly Powerful?

What Makes Artificial Intelligence So Incredibly Powerful?

Do you also feel that Artificial Intelligence (AI) is getting eerily powerful day-by-day? That is because the structures of Artificial Intelligence exploit the very fundamental laws of physics and of the universe as per latest research.

These new findings help to answer a long-awaited mystery about a category of AI that employs an interesting strategy called deep learning. These are programs based on deep neural networks hence, the name deep learning. The way this works is that they have multi-layered algorithms in which the lower-level calculations feed into the higher level ones within the hierarchy. These deep neural networks often perform surprisingly well when it comes to solving problems which are highly complex, like beating the world’s best player of a strategic board game called Go or categorising cat photos, however no one truly knows why… Continue reading “What Makes Artificial Intelligence So Incredibly Powerful?”

Are you taking care of your digital self?

Whether you like the idea or not, we all have a digital self, a facade that we put on to engage and participate in the technological world! As per psychoanalysts and physicians, a theory proposed by them says that there is a ‘true self’ that is the instinctive core of our personality, it must be realized and nurtured. And there is also a ‘false self’ that is built to protect this true self. From what you ask? From the dangers of insults and vulnerabilities!

Dexlab blog for 12th Oct

Our true selves are usually complex and fragile but it ultimately remains to be our essence. In trying to share that self with the world, we send out our decoy selves to take on the day-to-day vulnerabilities, challenges, and anxieties that come forth.

Continue reading “Are you taking care of your digital self?”

High Demand for Data Scientist profiles in LinkedIn

High Demand for Data Scientist profiles in LinkedIn

Currently, Data Science experts are the most sought candidates in the world. According to a research report published by DJ Metrics, the number of ‘Data Scientist’ profiles in LinkedIn has nearly doubled over the last few years. At present, there are more than 11,400 data scientists on the professional networking website, out of which, 52% have added the particular job description (read Data Scientist) during the period between 2012 and 2015.

About the Research

DJ Metrics have taken into account 60,200 LinkedIn profiles of professional experts, while 27,700 records of Educational data and 254,000 records of skills sets were also used to conduct an analysis. Additionally, they have analysed the database of 6200 companies that have provided employment to the Data Scientists. The names of the Companies were collected by analysing the profiles of the Data professionals, since they have listed the names of their employers.

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Great Career Opportunities

Great Career Opportunities

Researchers are forecasting that there will be a steady rise in the demand for trained Data Scientists, because of the increased adoption of Big Data and Business Intelligence by the leading global companies. High-end business organisations like Microsoft and Facebook are going through a continuous recruitment phase, as these companies had accelerated their hiring process by 151% and 39% respectively in 2014, as compared to what they had done in 2013.

According to the research report, about 65% of the total recruitments were carried out by the following industries:

  • Information Technology and Services, Internet and Computer Software Sector: 9%
  • Education: 3%
  • Banking and Finance: 2%
  • Marketing and Advertising: 2%

Big Data demands Bigger Skills

Big Data demands Bigger Skills

 DJ Metrics has analysed the database of 254,000 skills in order to figure out the growth in the number of skilful Data Science professionals. The results are significant, as apart from the general ‘power’ skills; namely, Data Analysis, Analytics and Data Mining, the top skills found among the vast number of profiles included R, Python, Machine Learning, MATLAB, JAVA, Statistics and SQL. Surprisingly, the Chief Data Scientists are found to have the least technical skills, as only 27% of the profiles had listed Python, while 26% listed R as their technical skill sets. On the other hand, 52% and 53% Junior Data Scientists have listed Python and R, respectively.

Top Recruiters

Top Recruiters

If you see the chart above, you will see that Microsoft and Facebook are the top recruiters over the given period. Surprisingly, Google has not made it to the top 10, although it has recruited quite a number of Data Science professionals. The reason may be that the Data Scientists at Google are called ‘Quantitative Analysts’, which is probably used by their employees while listing their designation on LinkedIn. Since, LinkedIn has researched about the general Data Scientists; they may not have detected the alternate titles.

Countries with highest Data Scientist population

Countries with highest Data Scientist population

Almost 55% of the total Data Scientists in the world are currently located in the United States of America (USA), which makes the top of the list. The second country with maximum numbers of Data Science professionals is United Kingdom (UK), while the third position is occupied by India.  

Are you interested in coveted data science online courses to upgrade your data science skill-set? Look no further than DexLab Analytics. They offer cutting edge Data Science training in Gurgaon for aspiring candidates.

 

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

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.

 

Interested in a career in Data Analyst?

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Secrets To Clinch Victory in Global Data Science Competitions

Data scientists are often perceived as crazy IT nerds who would use formulas and algorithms to even determine how many teaspoons of sugar to put in their tea! Well, we would not argue about this, as much as stereotypical this may sound, but a data scientist feels a rush when he solves a problem with calculations, analysis and logic; a rush that incomparable to anything else.

 
Secrets To Clinch Victory in Global Data Science Competitions

 

Just as an avid gamer who plays COD (Call of Duty) or CS (Counter Strike) waits for WCG (World Cyber Games). A data analyst waits for – Datahack. People who are just crazy about machine learning wait for the whole year to participate in Datahack. For them this is Olympics of Data Analysis. Continue reading “Secrets To Clinch Victory in Global Data Science Competitions”

Prepare For Your Data Science Job Interview With Answers to These Puzzles

Prepare For Your Data Science Job Interview With Answers to These Puzzles

You may have passed your data science certification course with flying colours, but getting your first break in an analytical job role can be quite difficult. Did you know that more than 30 percent of top tier analytical firms evaluate and select their candidates on their ability to solving puzzles? After all this is the best way to determine that they are logical, with ample creative thinking abilities and are definitely pros at dealing with numbers (a skill must have for data personnel).

The companies are keen on hiring people who have the ability to bring a unique perspective in solving business problems. Such individuals are capable of to offer their hiring firms with a huge advantage over other candidates. But to garner such capabilities an individual must practice regularly with consistent efforts.

As fellow data analysts, we recommend that you develop a daily habit of solving puzzles. They are mental exercises which on disciplined training will help you to get better with time. When employed in a job role that involves having to deal with complex problems everyday such a skill will prove to be an asset.

Are you ready to work out your grey matter cells? Here are the most common puzzles asked at interviews for data science positions:

These questions have been asked to candidates at companies like Amazon, Google, Goldman Sachs, and JP Morgan etc.

Note: Try solving these problems on your own before checking the solution, and feel free to share your logic behind the solutions in the comments below. We are all ears eyes to see how unique someone’s mind can be!

Puzzle #1:

Blind game challenge:

You have been placed in a dark room, there is a table kept in the room. The table has 50 coins atop its surface, out of these 50 coins 10 coins have their tails side up and 40 coins have their heads side up. Your task is to divide this set of 50 coins into 2 groups (not necessarily of equal size) so that both the groups have equal numbers of coins with the tails side up.

Solution #1:

The coins should be divided into two groups one with 40 coins and one with 10 coins, then flip all the coins in the group with 10 coins.

Puzzle #2:

Bag of coins problem:

You have been given 10 bags full of coins; each bag comes with an infinite number of coins. But there is a twist, one of the bags is full of forged coins but sadly you do not remember which one it is. But you do know that the weight of the real coins are 1 gram and those which are forged are 1.1 gram. Your task is to identify the bags in minimum readings with a digital weighing machine that has been provided with you.

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Solutions #2:

You need to take 1 coin from the first bag, 2 coins from the second bag, and 3 coins from the third bag and so on and so forth. Eventually you will end up with 55 coins in total (1+2+3+4+…10). The next step is to weigh all the 55 coins together. You can identify which bag has the forged coins based on the final reading of the weighing machine. For instance, if the reading ends with 0.4 then it is the fourth bag with forged coins. And if it comes 0.7 then it is the 7th bag with the forgeries.

Puzzle #3:

The Sand timer trouble:

You have two hourglasses or sand timers one of which can show 4 minutes and the next one can show 7 minutes respectively. Your job is to use both the sand times (either one at a time or simultaneously or in any other combination) and measure a time of 9 minutes.

Solution #3:

Step 1: start the 7 minute sand timer along with the 4 minute sand timer

Step 2: when the 4 minute sand timer ends turn it upside down instantaneously

Step 3: when the 7 minute sand time ends also turn it down at that instant

Step 4: when the 4 minute sand timer ends turn the 7 minute sand timer upside down and it will have 1 minute worth of sand in it

Thus, effectively 8 + 1 = 9

In closing thoughts:

Hope these questions were enough to get your brain rolling, while a lot of these questions may seem challenging to most of the people, but with a little out-of-the-box analytical thinking you will soon discover that they are not too difficult to solve.

If these questions were simple enough for you, we have plenty more with increasing difficulty. And if all these brain picking has left you overwhelmed to the peak and all you want is to solve real-world data problems, then follow our regular social media uploads advertising latest job openings in the field of data science.

DexLab Analytics is a premier data science training institute in Gurgaon that offers program centric courses. Their online certification course on data science is stellar, come check out the course itinerary now.

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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The Beginners’ Guide to Data Science Jargon

The Beginners’ Guide to Data Science Jargon

Are you poised to join the ranks of c-suite data operatives working with the Big Word – Big Data? But before you set foot in the industry with the big players, you must train yourself how to talk the talk before you can walk the walk. Data science and analysis is a complex field as it is with mind-numbing numbers and lengthy algorithms and on top of that there is the jargon that is stranger than fiction in this field. So, to help you prepare your tongue right, here is our brief list of the most commonly used terminology in the data science industry. After you have the enlightenment of knowledge about these words, you will not need to be hesitant when hearing these words and silently think to yourself that, “This sounds data-related”.

Here is a list of data-related jargon to clear your doubts about from all over the Big Data spectrum:

Analytics: the process of drawing conclusions from raw information or data that is actionable. With the help of analysis raw data can be transformed into meaningful information that was otherwise useless to the company. The main emphasis of analytics remains on the inference rather than the systematic operations or even the software in use.

 

  • Predictive Analysis: after analysing the events that happened in the future and the historical data of a company or organization and then being able to make probable predictions about the company’s future. This may also involve proposing counteractive plans and strategies to prevent an incoming disaster or loss.
  • Descriptive Analysis: narrowing down or in other words boiling down huge numbers into small pieces of usable information. Instead of listing a lot of numbers and complex details these use a general narrative and thrust in the report. 

 

Prescriptive Analysis: this is the course of action that analytics personnel propose after landing upon a definite approach after days of analysis on a problem. Data is turned into actions and real world problems find solutions with the right decisions.

 

Algorithms: the mathematical formulas statistical procedures used to analyse data by analytical personnel. These are usually used in software processes and analyze any data that have been input.

 

Cloud: this is not the same stuff that the weather report talks about when speaking of an overcast day. But that being said, this cloud is also basically everywhere. This is the process of storing or accessing data, files and software over the World Wide Web, instead of the old system of hard drive storage.

 

R:  maybe not a very descriptive name for a programming language but nevertheless, this is a very commonly used programming language used in data science that uses statistical computing. This is also one of the easiest and most popularly used languages in data science.

 

SAS: Statistical Analysis System is a software suite developed by the SAS institute and is also a very commonly used data analysis language. It was developed in the North Carolina State University.

 

Machine Learning: a method considered equivalent to machine wizardry where data analysis is automated by teaching machines to use models, algorithms and other processes for analytics.  

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Hadoop: better known as Apache Hadoop which is an open source software framework, it principally works by storing files and processing data, which is why it is still mostly used as a data warehousing system.

 

IoT: this is a proposed system wherein devices will be able to talk to each other. This is like a network of objects like, your phone, car, and smart wearables etc. which are embedded with network connectivity. The best examples are driverless vehicles.

 

These were the most commonly used data analytics jargon; for more such news and articles about data analytics stay hooked to daily uploads from Dexlab Analytics, creating an easier world with data backed decisions.

 

Interested in a career in Data Analyst?

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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.
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To learn more about Data Analyst with Apache Spark Course – Enrol Now.
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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.

 

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.

Quantitative Analysis 1 – Five Number Summary

To be a successful analyst or be a part of great analytics team, there are 3 important dimensions one would aspire to be or have. They are technical, business and tools. Hence, we would begin with one of the sub dimension of the technical skills, i.e. being quantified self or developing quantitative skills.

 Quantitative Analysis 1 – Five Number Summary

As per the Informs, the definition of Analytics shall be:

  Continue reading “Quantitative Analysis 1 – Five Number Summary”

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