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10 best skills required to become a Java Developer!

Posted on by Imarticus Learning

Skills required to become Java Developer?

While there are must-know technologies for Java Developer, the technology of choice may differ from developer to developer. According to a survey that was recently conducted by Java Tutorial Network, the most wanted technology/framework among Java developers this year is Java 9, followed by Artificial Intelligence and Machine Learning. Blockchain takes third place, after which comes Microservices. Spring Framework also seems to be highly favored among developers.

As you can see, not all of these are Java frameworks and technologies. You can see some front-end frameworks along with trending technologies that have emerged in the IT sector. These are some frameworks and technologies that Java developers seem to hold in high regard as it provides them with the ability to provide better solutions on a larger scale.

At the same time, Java developers are required to have extensive knowledge of the basics at all costs. This would include programming using Java and working on Unix OS. Additionally, you would also be required to familiarize yourself with essentials such as the RDBMS program, JEE architecture, framework, etc. To learn more join this course.

What are various Technologies used in learning Java?

You can’t possibly know all the Java technologies out there because no company will give you a chance to. One company will swear by the Spring framework while other companies like LinkedIn have moved on and are into the PlayFramework.

However, let me give you a list of 10 technologies that will always pitch you ahead of your competition regardless of the company.

10 Skills that will make you a great Java Developer:

  1. At least one MVC framework like JSF, Playframework, Struts, or Spring framework
  2. Hibernate or JPA for databases
  3. Dependency Injection (@Resource)
  4. SOAP-based Web Services (JAX-WS)
  5. Some build tools (Ant, Maven, etc.)
  6. JUnit (or other Unit Testing framework)
  7. Version Control mostly Git. Get comfortable writing Java code using the latest API changes. If you are already good at Java, it is suggested to learn the latest packages/API changes. You may come to know that an older version of 10 lines of code can be simplified by just 1 or 2 lines using the latest classes/methods.
  8. JSTL
  9. The application server/container configuration management and application deployment (whether it is WebSphere, Tomcat, JBoss, etc. you need to know where your application runs and how to improve its execution)
  10. AJAX

If one wants to be a web developer, one should know

  • JSP
  • Markup Languages like HTML, XML and JSON.
  • Servlets
  • JNDI
  • MVC
  • Frameworks like Struts / Spring
  • Services
  • Web Technologies like CSS, Javascript, and JQuery

If one wants to be a UI developer, one should know

  • Applets
  • Frameworks like Swing, SWT, AWT

How to Prepare for a Data Scientist Interview?

Posted on by Imarticus Learning

Appearing for any sort of interviews could increase your adrenaline level. Cracking interviews need massive amounts of preparation and research. More so in the given scenario of appearing for a data scientists position, as only appropriate preparation and practice will get you cracking and performing well on the big day.

If you are an aspiring data scientist, you are expected to have a working knowledge, or understanding, or the capability to perform over multiple firms, with a bag full of skills.

Continue reading to understand a quick step-by-step approach on specific areas of Aptitude, Technical know-how, and skill sets required to not only clear the interview but to also excel in the field of Big Data and Machine Learning.

The thing about data science is that its application, and hence expectation across industries varies to a large degree. The role is interpreted differently across companies, some might call a PhD Statistician as a data scientist, to others, it means proficiency in excel, while to some it may mean a generalist in Artificial intelligence and Machine Learning.

  • Step #1: read the Job Profile, specifically for Skills, Tools, and Techniques. If the job description is not self-explanatory or in detail, then some research on the company is non-negotiable. Be clear as to what type of a data scientist position you are applying for. The interview is usually a combination of an Aptitude Analysis, Technical Knowledge, Attitude Analysis. Most organizations in recent times test the applicant on fundamental topics to gauge their fit in the company, attributes like Language Comprehension, Analytical Reasoning, Quantitative Aptitude, etc…, can be easily cleared by reading up on the same to brush your skills.
  • Step #2 –Brush up on important and relevant concepts like these before the interview. To test your technical understanding on the subject, most probably there will either be a technical round or an assessment, case study, which will essentially gauge your knowledge in statistics, programming, machine learning etc…, ensure you are fluent in relevant languages like R, Python, SQL, Scala and Tableau.
  • Step #3: will be to brush up on elementary topics like….
  • Probability – Random variables, Bayes Theorem, the Probability distribution
  • Statistical Models – Algorithms, Linear Regression, Non- Parametric Models, Time Series 
  • Machine Learning, Neural Networks.

So here, essentially you will be tested by the medium of a case study or discussion, on your problem-solving capabilities. It will help if you are able to define the problem for them on the presented scenario, and link the same to the suggested solution and its impact on business. While doing so, cite examples of case studies, or research papers for supporting the suggested solution.

  • Step #4: while you may come with the required skill sets and qualities, ensure through-out the interview you show the willingness to learn and flexibility in adapting to the current organisation, as Data Science and its applications are unique.
  • Step #5: to have a tight resume and pre-empt on ways you will link your experience with the given position during the course of the interview.
  • Step #6: is to carry out data science projects specifically if you are a fresher, there are many public domains available for the same. In addition, it is also advisable to take up MOOCs – Massive Open Online Courses to gain exposure to different as well as focused applications.

Remember, in recent times the role of a data scientist is viewed as someone who can bridge the gap between multiple features of a business. So it is not expected or required of you to be a specialist in all the aspects, but you should be able to link the features, idea and provide solutions across domains. To stand apart in an interview you should not only show your individual strength and domain expertise but come across as a person with enough management skills, along with good communication and technical skills who can blend and get to the crux of a problem.

What Job Opportunities Are Available For Apache Hadoop Experts?

Posted on by Imarticus Learning

Everyone talks about Apache Hadoop but no one talks about the scope of employment in the field. As you must have already learned, Hadoop as an application software aids a variety of processes across business sectors in the world. Its development tools are primarily used to store and process Big Data.

In that regard, there are several different types of job roles you can take up. As an Apache Hadoop expert, you can either join a software company that develops the tools or an application company that takes advantage of those tools.

The following are some of the most common types of jobs you can do once you learn Hadoop and master it.

Job Opportunities for Apache Hadoop Experts

A quick look at some of the career paths available in the field.

Apache Hadoop Developer

This is the most common job you can get once you finish your Hadoop training and gain some experience. Your role will basically entail the building of data storage and processing infrastructures. Since different companies follow different processes and have different products and services to sell, building a unique one for each of them is important.

For example, a Hadoop developer working at a bank will need to focus on extra security. Hadoop Spark and Hive are some of the technologies you will need to be skilled at.

Data Analyst

If you are going to deal with Big Data, you might as well be an analyst. Don’t see this role as an entry-level job. Data analysts with Hadoop training are in high demand these days as they can oversee the architecture and its output.

You have to be proficient in SQL. Huge to be able to work on SQL engines like Hive. If you are still studying, make sure you carve out a specialization as part of your Hadoop training.

Tester

Most software application jobs have this role of a tester who detects bugs in systems and helps developers with solutions. Testers are important in a Hadoop environment too as they can help detect issues in a newly built infrastructure. Some companies even have an entire team of expert testers who provide continuous suggestions and testing results to better an ongoing infrastructure build.

The good part about being a Hadoop System Tester is that you can switch to this role from any field. Are you a software tester at TCS? Learn Hadoop, get trained, and become a Hadoop tester.

Data Modeller

In this job, you will be a supporting member of the Hadoop development team in a company. A modeler’s responsibilities include system architecture and network designing so that a company’s processes align with the newly created infrastructure for Big Data.

Years of experience in this field can open gates for employment in large corporations. Here you can participate in decision-making rounds.

Senior IT Professionals

The Hadoop environment doesn’t just need people with technical Hadoop skills. It also needs innovators and world analyzers who can provide wise suggestions in the entire process involving a Hadoop setup. It could be in the development phase, processing phase, or output phase.

These professionals have decades of experience in research and development as well as a fair understanding of Apache Hadoop. If you are a senior IT professional who realizes the significance and relevance of the field in the modern world, you can learn Hadoop and slightly shift your career path.

Apart from these five job opportunities, there are several roles that you can take up if you have some qualifications in the field. So, start your Hadoop training and get a job today!

How COVID-19 is Revolutionizing the Online Education Industry!

Posted on by Imarticus Learning

When the initial cases of COVID-19 were documented in the Indian subcontinent in late January, few could have anticipated its impending course and impact on every aspect of human life over the following months. Yet here we are more than five months later, and our world has been transformed dramatically. Everything from board meetings to grocery shopping is now being conducted online via laptops or smartphones, exposing our heavy dependency on stable internet availability like never before.

Business Analyst Certification course in IndiaConsidering these massive shifts in the status quo, it is clear that technology—especially the Internet—has been central to our evolution and adaptability in the COVID-19 era. However, it is common knowledge that a tech-driven transformation was underway long before the pandemic hit us.

Take the online education industry for instance. The online education sector in India was not only valued at an all-time high of INR 19,300 crore in 2018 but it was also poised to reach INR 36,030 crore by 2024. Fuelling this growth was the rising Internet penetration, as well as simplified access to innovative technology.

More than anything else though, the online education industry was witnessing growth at a breakneck pace due to professionals and students looking to upskill themselves in order to thrive in the new-age business landscape, while balancing their careers with their learning endeavors. Simultaneously, students who attended classes regularly still leveraged online learning to augment their education.

The COVID-19 Impact: Causing Ripples in the e-Learning Ecosphere

While we have established that the online education sector was rapidly growing even before the COVID-19 pandemic, it is safe to say that the contagion has accelerated this growth at an unimaginable rate. Through online learning, being physically present in classrooms has given way to innovative new methods of education.

As students refrain from being physically present in the same room as their teachers and classmates, online education is inevitably the only way of learning during this age of quarantines and social distancing.

As a result, the scope of online learning has also expanded during this challenging period. From preschools to top-tier universities, most institutes of learning now offer online education to varying degrees. Schools and colleges are closed indefinitely, which means millions of students are now dependent on online learning platforms to further their education and make the most of this unprecedented situation. The e-learning space, therefore, is bound to skyrocket over the next few months.

 

5 Ways to Understand the Importance of Big Data

Posted on by Imarticus Learning

Modern times handle Big-data and the amount of data just keeps growing by the moment. Today enterprises not only use the data generated by them but also cull the data from internet services, audio clips, video s, social posts, blogs and other sources.

Understanding Importance Of Big Data

Big data analytics deals with data primarily and the predictions or forecasts from analyzing databases that help with informed decision making in all processes related to business. All of us generate data and the volume of data has now become incredibly large. Keeping pace with the generation of data has been the need for cutting edge tools to clean, format, group, store and draw inferences from databases not only our own but across verticals and fields. Some of the interesting fields spawned and co-existing with the use of big data analytics are in machine learning, artificial intelligence, virtual reality, and robotics.

In modern times the value of Big Data, its forecasts and insights are invaluable to companies. However, it is not easy to clean the data, match and format the various types of data, prepare the data to be available in an easily understandable form and then use the data for analytics. It requires discipline, patience, lots of practice and asking the right question to the right database to be able to produce those predictive insights.

Importance of Big Data is so encompassing in a world ruled and constantly generating large amounts of data every moment that analysts, engineers, scientists and others making a career in the Big Data field is sure to have an unending scope. The more the data, the better the evolving technologies get and so also follows the demand for personnel who can understand and handle it.

Yet, the 4 V parameters can be used to understand Big data. They are
• Variety – This defines the type of data source and whether it is generated by a machine or people.
• Volume – This parameter has moved from Gigabytes to terra bytes and beyond and denotes the amount of data generated. The sources have increased as also the speeds of data generation. The definition of volume should be very large Big Big Data many times over by now.
• Velocity – This parameter defines the generational speed of data. This grows by the moment and entails huge volumes.
• Veracity – This parameter defines the data quality and at times is out of the analyst’s control.
Technology has also evolved and has taught us that it is not sufficient to just gather data but use it effectively to improve organizational performance. Big-Data has immense applications across all industrial verticals, in personal and industrial scenarios and has successfully advanced not just organizational productivity but the economy as a whole. This development in data and its technology-enabled predictive analytics to make use of forecasts and gainful insights to improve the various processes and applications.

The Three Stages of Data

All data may not be in the same format and may be in different formats and made available from various sources. Labelled data is very different from real-time unlabeled data. Thus all data passes through three stages which are performed as loops and repeated many times in a fraction of a second.
• Managing the data: Here the data is extracted from various sources and the relevant data is extracted from it.
• Analyze and perform data analytics on it: In this stage, ML algorithms are applied and data processed to gain foresight, insights and make predictions.
• Make the correct decision with data: The all-important stage of applying the data to a relevant decision-making process is executed to provide the desired outcome. When the results are not the desired outcome the process is automatically repeated to narrow the differences between output and the desired result.
With traditional tools, one can work with relatively smaller databases that are less than a terabyte size-wise. However, modern data tends to be unstructured and comes in the form of videos, audio clips, blog posts, reviews, and more which are challenging to clean, organize and include huge volumes of data. The tools and techniques involved in the capture, storage and cleaning of data need necessarily to be updated. One also would need faster software that can compare databases across platforms, operating systems, programming languages and such complexities of technology.

The Five Organizational Benefits of Big Data

Big Data brings in great process benefits to the enterprise. The top five are

  •  Understand market trends: Using big data, enterprises are enabled to forecast market trends, predict customer preferences, evaluate product effectiveness, customer preferences, and gain foresight into customer behaviour. The insights can help understand purchasing patterns, when to and which product to launch and suggest to clients product preferences based on buying patterns. Such prior information helps bring in effective planning, management and leverages the Big Data analytics to fend off competition.
  •  Understand customer needs better: Through effective analysis of big-data the company can plan better for customer satisfaction and thus make alterations needed to ensure loyalty and customer trust. Better customer experience definitely impacts growth. Complaint resolution, 24×7 customer service, interactive websites and consistent gathering of feedback from the customer are some of the new measures that have made big-data analytics very popular and helpful to companies.
  • Work on bettering company reputation: Sentiments and their analysis can help correct false rumours, better service customer needs and maintain company image through online presence which eventually helps the company reputation using Big Data tools that can analyze emotions both negative and positive.
  • Promotes cost-saving measures: Though the initial costs of deploying Big Data analytics are high, the returns and gainful insights more than pay for themselves. This also enables constant monitoring, better risk-management and the IT infrastructure personnel can be freed up. This translates into reduced personnel required. Besides this, the tools in Big Data can be used to store data more effectively. Thus the costs are outweighed by the savings.
  •  Makes data available: Modern tools in Big Data can in actual-time present required portions of data anytime in a structured and easily readable format.

If you are keen to take up data analytics as a career then doing Big data training with a reputed institute like Imarticus is certainly advantageous to you. The courses augment your knowledge, bring you up to speed with the latest tools and technologies and even include real-time, live projects that enable the transformation of theory into confidence-based practical applications of learning in the data analytics field. Why wait?

What Are the Career Options after Graduation?

Posted on by Imarticus Learning

Once you have completed your graduation, it is time to build a successful career that is also very rewarding in a preferred industry. One can choose a traditional career after graduation like civil engineering services, medical, etc., or can opt for trending and new-age jobs that are in high demand, based on their academic background and interest.

 

For example, the global data science industry is predicted to grow with a CAGR of 26.9% by 2027. You can choose data science courses to build a successful career as a data scientist. Read on to know some of the best rewarding and trending career options after graduation. 

 

Investment banking 

 

Investment banking is concerned with financial advisory services and raising capital for clients. As an investment banker, you may work with a corporate or a governmental organization. Investment bankers act as a mediator between the client and shareholders/investors.

In the investment banking sector, you can start your career as an analyst and will have to work on databases and visualizations. After a few years as an analyst in the investment banking sector, you can upscale to become an associate. 

Financial Online classes 

If you have completed your graduation in finance, economics, or mathematics, it is a good choice to join the investment banking industry.

There are many online courses for investment banking that can help you hone your skills. Imarticus Learning is a reputed source that can offer you industry-oriented courses for investment banking. MBA investment banking courses are also offered by Imarticus Learning.

 

Data science 

 

Firms are preferring candidates that have done a data analytics course over other candidates. With more and more businesses going online, the demand for data scientists is more than ever. If you have completed your computer science engineering with a specialization in data science, this is the right time to join the industry. A degree in other streams like statistics, applied mathematics, economics, etc., can also help you in getting into the data science industry. One can opt for an online data analytics course to know about the industry processes. 

 

Imarticus will offer you data science courses in India led by industry experts. It provides data science courses in India for professionals as well as for recent graduates. Not only will you learn the basic of data analytics, but will also receive placement support through Imarticus Learning’s courses. You can opt for various job roles in the data science industry like data analyst, data engineer, marketing analyst, etc.

 

Digital marketing 

 

Consumers have shifted from traditional TV sets to online platforms and, firms need reliable digital marketers to engage with them. There are not many institutions/colleges that offer a classroom course for digital marketing. How to learn digital marketing online if there are no classroom courses?

 

Financial Analysis courseWell, Imarticus provides the best digital marketing courses in India without compromising on the learning experience. You do not have to search for ‘how to learn digital marketing online’ as Imarticus offers a PG program and a pro-degree in digital marketing.


Digital marketing is a vast industry and, you can choose from various fields like mobile marketing, content creation, web design, SEO, SEM, social media management, and many more. 

 

Machine learning 

 

Machine learning is another new-age technology that has a successful future. You can opt for an online course in machine learning and can learn about the key aspects of this field. The machine learning industry has various job roles like machine learning engineer, data scientist, NLP scientist, and many more. 

 

Conclusion 

 

You can find data science courses or investment banking courses via Imarticus that can help you get your dream job. They will also offer the best digital marketing course in India with industry-oriented training. You can do a certification course and can straightway start working after graduation. Choose the right career path after graduation with Imarticus Learning!

 

Big Data to Now Help Fight Illegal Fishing!

Posted on by Imarticus Learning

In The News

Time and again we have all come across news of the inability of the law enforcers to implement clear and efficient laws in the regard of oceans. This is exactly where we give a chance to the illegal elements to play their card. Illegal fishing has become very common today, both in India and the West. Records state that as much as a third amount of fish, which is sold in America is a result of illegal fishing. Not only is it illegal, but it also has a grave ecological impact on the ocean ecosystem.

Data Science, with a new development of data technology, seems to have come to the rescue. The basic aim here is to stop these illegal happenings by ensuring the protection of the high seas. This technology basically uses the satellite signals of the ships, in order to detect any kind of transshipment. This will take place whenever two vessels would meet at sea to exchange their cargo.

Transshipment basically refers to the method, with the help of which great amounts of illegal fish is able to make into the main (legal) supply chain. Once this has taken place, there is close to no way of finding out which is illegal and which isn’t. This is why the recognition would provide major help in stopping the practice.

Global Fishing Watch has reportedly analysed about 21 billion satellite signals, which have been broadcasted by various ships, over the period of 2012 to 2016. This company majorly uses an artificial intelligence system, which was created by the professionals here and helps in identifying all the ships that have refrigerated cargo vessels. These are also known as reefers. Once the information is gathered, it is further verified with fish registries and other related sources, which rounds up the number of reefers to about 749.

This is about 90% of the entire world’s total number of such vessels. With this technology, this company was able to track all of those scenarios when reefers were acting like potentially illegal and likely transhipments as well as times when a ship and a reefer were moving at a close proximity.

This development has led to a lot of speculations in terms of the development in the field of Data Science. Thus, we see a number of data aspirants looking to get professionally trained in this field by pursuing courses from Imarticus Learning, which happen to offer courses in data analytics.

Loved this blog? Read the below as well!
Best Books To Read In Data Science And Machine Learning
What Are The Best Data Science Courses At Imarticus?
The Future of India in the Field of Big Data Analytics

What is Google Trends Data Mining Using R Programming?

Posted on by Imarticus Learning

Mostly misunderstood as a keyword research tool, Google trends are much more than that. Google trends were not merely built to give a match to monthly keyword volume, Google trends were built with an advanced level of sophistication, to generate insights which are visual and dynamic in nature. Google trends are capable of presenting an entire life cycle of a keyword phrase, past, present and some kind of future as we may predict. So, what are Google trends exactly? It is essentially a service that brings together the relative frequency of Google searches over a period of time.

Google trends tool opens the possibilities to obtain incredible amounts of information from one of the world’s largest search engines. The google trends tool is derived from Google search data. ‘Trends’ to simply put it is numeric and also a historic representation of the search data. This feature differentiates google trends from google keyword planner, as in google trends, an index is created to represent the ‘trending’ instead of the definite volume. Therefore, the data presented by google trends can actually depict actionable insights which the keyword planner function cannot present.

Google trends thus adapt a multi-dimensional approach of comparing queries against required options. It is a fairly simple tool to use. To start one needs to put a search term in the query box, and then you can proceed to select from the various filtering options. Like…

  • Region – search definition can be Geo-specific
  • Time Frame – you can select a variety of predefined time frames. Like ‘last seven days’, ‘one month, etc…, you can go back in time up to the year 2004.
  • Categories – one can select and limit the terms and focus only on a certain category. This way you will be able to study specific trends with the possibility of discovering new searches or themes.
  • Engines– through this option you can choose between news, youtube, shopping search, thus offering increased flexibility and further allowing to choose focus on the right to intend.

All the results are presented as separate graphs,
(a) Interest over time, which offers a historical trending,
(b) Regional Behavior, offering on how localized behavior was during that time.

One can use ‘R’ to extract the data from google trends using ‘gtrends’. Using Google trends one can perform the simultaneous search on five terms, more than five terms are not possible, also it does not provide data in API format. These issues can be dodged using R especially by using ‘gtrends’ package. There are various functions in R that can be used to build automated solutions, which can be further applied to build end to end solutions.

Google trends thus become a powerful tool especially for a data scientist or even a marketing analyst’s inventory.
For the marketing department of any company or brand, google trends are like a goldmine of information that could perhaps supersede findings from focus groups, on other metrics like brand health by the region, or brand topics of discussion over a period of time. Once you understand what the consumers for a particular brand are searching for, you can start building your messages around those areas of opportunity and interest.

As with any data-driven insights, the flexibility and the opportunity that Google trend offers with tools like gtrendsR, the possibilities are fathomless. Learning the applicability of data mining using R on google trends will surely be very valuable in the long run.

Cybersecurity for Wealth Management Firms: Are You Tailoring Security for Your Specific Risks?

Posted on by Imarticus Learning

Over the past few years, there are various reports of banks and financial firms experiencing a huge number of security breaches. On average, a company that deals with financial services faces 85 to 90 attacks every year, and at least one out of three succeeds.

A Cybersecurity breach is a serious problem faced by every organization. Even though financial firms and companies try their best to keep these attacks at a minimum, success rates of cyber-criminals have begun to increase, and this has become a threat to the world of finances.

This is when the importance of new-age banking training arises. It is crucial for everyone to be well-versed with techniques to avoid such incidents. However, because of a lack of proper training, bank and financial firm employees are unaware of different ways to deal with such situations.

What Are the Biggest Threats Faced by Wealth Management Firms?
There are plenty of different ways cyber criminals try to lure their targets or breach cybersecurity in an organization. Here are a few ways you should know-

  1. Phishing Emails, And Phone Calls

Cybercriminals use the tactic of sending phishing emails and phone calls to people to get information. This is one of the most commonly used strategies with the highest success rates. Any individual may get emails from such people that may look legit and fall into their trap by replying to the same with confidential information.

The cybercriminals also act as individuals willing to take services from wealth management firms and make phone calls to gather information. Such phishing calls can lead to huge data extortion and a breach of cybersecurity.

  1. Malware and Viruses 

Using malware and viruses, cybercriminals attempt to get into a firm’s information drive and steal data. These viruses are sent in links through emails or documents. When someone clicks on those links and opens them, the malware gets activated.

For smaller organizations, the goal is to collect data. However, for a wealth management organization, the reward is huge.

What Can You Do to Educate Your Employees?
As an organization, to avoid such cyber-attacks, there are a few things you should do. Apart from asking your employees to take up new-age banking courses from reliable institutes like Imarticus Learning, you can also educate them in the office. Here’s how –

  1. Educate your employees about the importance of cybersecurity. Teach them the basics of cybersecurity through new-age banking training classes and workshops.
  2. Take help from a reliable IT service provider. Once you know your potential attack surface and its various risks, improve your network security.
  3. Keep an eye on your network activity.
  4. Use various policies like secure passwords and the use of VPN tools to minimize any mobile device risks and casualties.
  5. Enforce proper and practical policies in your network, users, and devices. With the help of a reliable IT team, you can configure them in a way that can impose automatic compliance.

Importance of New Age Banking Training 
The new-age banking training has become very important for anyone who wishes to take up a career in the world of finances. These training sessions will not only help you shape your career as an impeccable wealth management advisor but will also help you learn various ways to combat cybersecurity attacks.

With the help of institutes like Imarticus Learning, now you can take up such new-age banking courses and learn various important lessons. The institute is a prominent one in the market for providing varieties of courses in machine learning, data mining, as well as data science, among many others.

Preparing for your data science interview: Common R programming, SQL and Tableau questions

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Preparing for your data science interview: Common R programming, SQL and Tableau questions

This data science interview questions blog includes the most frequently asked data science questions. Here is the list of top R programming, SQL and Tableau questions.

R Programming Interview Questions

R finds application in various use cases, from statistical analysis to predictive modelling, data visualisation and data manipulation. Facebook, Twitter and Google use R-programming training to process the huge amount of data they collect.

Which are the R packages used for data imputation?

Missing data is a challenging problem to deal with. In such cases, you can impute the lost values with plausible values. Amelia, Hmisc, missForest, Mice and mi are the data imputation packages used by R. In R, missing values are represented by NA, which should be in capital letters. 

Define clustering. Explain how hierarchical clustering is different from K-means clustering.

A cluster, just like the literal meaning of the word, is a group of similar objects. K denotes the number of centroids needed in a data set. While performing data mining, k selects random centroids and optimises the positions through iterative calculations.

The optimisation process stops when the desired number of repetitive calculations have taken place or when the centroids stabilise after successful clustering. Hierarchical clustering starts by considering every single observation in the data as a cluster.  Then it works to discover two closely placed clusters and merges them.  This process continues until all the clusters merge to form just a single cluster. 

SQL Interview Questions

If you have completed your SQL training, the following questions will give you a taste of the technical questions you may face during the interview.

What is the difference between MySQL and SQL?

Standard Query Language (SQL) is an English-based query language, while MySQL is used for database management.

What do you mean by DBMS, and how many types of DBMS are there?

DBMS or the Database Management System is a software set that interacts with the user and the database to analyse the available data. Thus, it allows the user to access the data presented in different forms – images, strings, or numbers – modify them, retrieve them and even delete them.

There are two types of DBMS:

Relational: The data is placed in some relations (tables).

Non-Relational: Random data that are not placed in any relations or attributes.

Tableau Interview Questions

Tableau is becoming popular among the leading business houses. If you have just completed your Tableau training, then the interview questions listed below could be good examples.

What is Tableau? How is Tableau different from the traditional BI tools?

Tableau is a business intelligence software connecting users to their respective data. It also helps develop and visualise interactive dashboards and facilitates dashboard sharing. Traditional BI tools work on an old data architecture supported by complex technologies. Tableau is fast and dynamic and is supported by advanced technology. It supports in-memory computing. ‘Measures’ denote the measurable values of data. These values are stored in specific tables, and each dimension is associated with a specific key. Dimensions are the attributes that define the characteristics of data. For instance, a dimension table with a product key reference can be associated with attributes such as product name, colour, size, description, etc.

The above questions are examples to help you get a feel of the technical questions generally asked during the interviews.