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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.

 

Interested in a career in Data Analyst?

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Secrets To Clinch Victory in Global Data Science Competitions

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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

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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.

 

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.
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India Will Lead in Analytics Services:

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Today is a time when each day is witnessing the field of analytics gets more and more pervasive. It is helping other fields and sectors to achieve more. At a time like this our nation is expected to maintain its ground over other major offshore destinations such as Philippines, China, Eastern Europe and Latin America as per a recent survey.

 

A host of factors will drive the demand for this service from India. They are – availability of talent pool, industry maturity and a wide spectrum of services which was reported by the survey that was conducted by Avendus Capital which is a financial services company. Continue reading “India Will Lead in Analytics Services:”

Understanding the Difference Between Factor and Cluster Analysis

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Understanding the Difference Between Factor and Cluster Analysis

Cluster analysis and factor analysis are two different statistical methods in data analytics which are used heavily in analytical methods of subjects like natural sciences and behavioural sciences. The names of these analytical methods are so because both these methods allow the users to divide the data into either clusters or into factors.

Most newly established data analysts have this common confusion that both these methods are almost similar. But while these two methods may look similar on the surface but they differ in several ways including their applications and objectives.

Difference in objectives between cluster analysis and factor analysis:

One key difference between cluster analysis and factor analysis is the fact that they have distinguished objectives. For factor analysis the usual objective is to explain the correlation with a data set and understand how the variables relate to each other. But on the other hand the objective of cluster analysis is to address the heterogeneity in the individual data sets.

Put in simpler words the spirit of cluster analysis is to help in categorization but that of factor analysis are a form of simplification.

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Difference is solutions:

This is not an easy section for drawing a line of separation in between cluster and factor analysis. That is because the results or solutions obtainable from both these analysis is subjective to their application. But still one could say that with factor analysis provides in a way the ‘best’ solutions to the researcher. This best solution is in the sense that the researcher can optimize a certain aspect of the solution this is known as orthogonality which offers ease of interpretation for the analysts.

But in case of cluster analysis this is not the case. The reasons behind that being all algorithms which can yield the best solutions for cluster analysis are usually computationally incompetent. Thus, researchers cannot trust this method of cluster analysis as it does not guarantee an optimal solution.

Difference in applications:

Cluster analysis and factor analysis differ in how they are applied to data, especially when it comes to applying them to real data. This is because factor analysis can reduce the unwieldy variables sets and boil them down to a smaller set of factors. This makes it suitable for simplifying otherwise complex models of analysis. Moreover, factor analysis also comes with a sort of confirmatory use researchers can use this method to develop a set of hypotheses based on how the variables in the data set are related.  After that the researcher can run a factor analysis to further confirm these hypotheses.

But cluster analysis on the other hand is suitable only for categorizing objects as per certain predetermined criteria. In cluster analysis a researcher can measure selected aspects of say a group of newly discovered plants and then place these plants into categories of species grouped by employing cluster analysis.

Here is an infographic to better explain the difference between cluster analysis and factor analysis: 

 

Interested in a career in Data Analyst?

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The Beginners’ Guide to Data Science Jargon

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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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Trending Data Job Role: Chief Data Officer

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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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How Data Scientists Take Their Coffee Every Morning

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How Data Scientists Have Their Coffee

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.

Keep Pace with Automation: Emerging Data Science Jobs in India – @Dexlabanalytics.

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?

To learn more about Data Analyst with Advanced excel course – Enrol Now.
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To learn more about Machine Learning Using Python and Spark – Enrol Now.
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The Rise of the AI in Big Data

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The Rise of the AI in Big Data

The researchers working at the MIT “Computer Science and Artificial Intelligence Laboratory” or abbreviated simply as CSAIL are all set to make human intuition out of the analysis of big data equation by enabling computers to choose from the set of features that are put into use in order to identify patterns in the data that may be considered to be predictive. This is dubbed as the “Data Science Machine” and as things have progressed so far the software prototype has managed to beat 615 of 908 competing teams vying for the same ability across no less than three competitions of data science.

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Big Data may be considered as a complex and huge ecosystem that combines innovative processes from fields as diverse as storage, data analysis, curation, networking as well as search in addition to other functions and processes. As things stand much of analysis of big data is already algorithmic and automated but at the end of the day it is business users and data scientists who are needed in order to determine the particular dataset and analysis features which are required for visualization in the end and take action on the communicated data.

To put it simply at the end of the whole process humans are needed in order to make choices about data point combinations to chart out the relevant information.

The Data Science Machine is intended to naturally complement human intelligence and to make the most of the Big Data that is available for us waiting to be used.

The analysis of Big Data and Engineering of Features

As mentioned earlier actionable information lies at the hands of the big data scientist who is writing the code for analysis. It is this code that guides the analysis of the big data engine. In essence the advancement made by the MIT researchers is that not only does it serve to provide answers to questions regarding the data but also suggests additional questions accordingly.

This may be put into varied uses like to estimate the capacity of wind farms to generate power or making predictions about students who are likely to drop out of online courses.

5 Hottest Online Applications Inspired by Artificial Intelligence – @Dexlabanalytics.

The ultimate destination for all your data-related queries and assistance is DexLab Analytics. Being a premier Data Science training institute Gurgaon, DexLab Analytics takes pride in offering excellent data analytics courses for aspiring candidates.

 

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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