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Category: Analytics

What Are The Major Fields Of Robotics?

Posted on by Imarticus Learning

Before we jump into the major fields of robotics, we should have a clear idea about the term robotics. It is a diverse field related to construction, engineering, and operation of robots in many commercial industries and consumer fields. Robotics involves the study of a physically constructed technology system and its performance or role in any interface or new technology.

The fields of robotics

There are five major fields of robotics namely: 

    • Operator interface – This refers to the Human-Robot Interface that explains how the human and the robot communicate. The robot works based on the commands conducted by the human. The best example of this is a child playing a video game. The joystick here acts as the interface between the human and the machine.
    • Locomotion based robots – In this case, robots perform tasks based on locomotion. Here, you will at times find human-like robots using legs for moving. Some flying robots and drones use the propellers for movement. Some may even use wheels depending on the environment they are i.e. air, water or land.
    • Component-based robots – In this case, it is the component within the robot that makes it do the specified job. Based on the situation, these human-like robots may use mechanized arms and fingers, claws or pushers to conduct the job. This is especially required in industries that are into heavy lifting and moving of things.
    • Ways of delivering a message to the robot – Commands need to be given to the robots with the help of different means. Today there are over one thousand programming languages and each robot interprets the given instructions in their own way. Some robots are even modified so that they are able to adapt to their changing environment.
    • Based on how robots to sense and to perceive – This denotes how the machines identify things in and around their environment and react. For example, when a robot comes in contact with an obstacle, what direction should it take. This component is fed into the robot that helps them make the right decision.

Role of machine learning in robotics

Robot learning is a field of research that connects both machine learning and robotics. Machine learning training helps robots deal with dynamic interaction and adapt them to situations where they can avoid obstacles and maintain productivity. Machine learning is often used by computer vision algorithms when it comes to robotics and these applications are normally not referred to as “robot learning”.

The future of robotics

This rapid advancement in the robotics sector in India will ultimately affect the job market and become a major cause of unemployment. Due to the huge possibility of robotics in warehousing, the Indian warehouse automation market is anticipated to grow at a CAGR of 10-12% in the period 2015-2020.

The future of the professionals working in fields like automation is also uncertain, with the new technological development. It will also lead to layoffs and downsizing of the employee strength. Some areas will not be affected immediately, but in the near future, they will be impacted in some way or the other. Robotics is also expected to play a key role in the “Make in India” initiative and attract global manufacturers to invest in the country. Hence, we can see that the future of robotics has both positive and negative impacts.

Conclusion

In order to build a fully functional robot, all these major fields of robotics should be carefully incorporated to ensure maximum effectiveness. Proper incorporation guarantees that the robot would work at an optimum level without any glitches.

How Much Do AI Researchers Make?

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What is Artificial Intelligence?

Also known as Machine Intelligence, Artificial Intelligence deals with the automation of machines so that they can perform human-like activities. Artificial Intelligence is being used in a lot of data-oriented industries such as insurance, healthcare, retail, technology, automotive, etc.

It also finds use in the finance sectors where various credit frauds and identity thefts need to be traced. Artificial Intelligence makes use of various machine learning algorithms to perform a specific set of tasks. Artificial Intelligence training is shaping our new world. It aims at achieving a specific goal by rationalizing the processes involved and then taking actions accordingly.

Who are AI-Researchers?

AI Researchers are those who apply the fundamentals of artificial intelligence to various data sets to draw out conclusions and various business insights. The job of AI Researchers includes language processing, algorithm building, development of data sorting mechanisms, etc. Also, it includes the movement and manipulation of data from various channels so that an efficient and effective transformation of data takes place with the utmost ease and efficiency.

AI researchers usually have a great understanding of various computer programming languages, hence making them proficient in what they do. Their expertise lies in building machines which reduces the human effort to a great extent. Artificial Intelligence researchers are in great demand owning it to the fast-paced technological environment and this ever-growing need for constant change. Thus, making AI Research a lucrative career path. People with less experience are also trying their hands in this area, given the financial attractiveness of this field.

How much do AI Researchers make?

Artificial Intelligence has become one of the most important elements of this data-oriented world. Companies are moving towards automation, making the best use of artificial intelligence and its ability to analyze, compile and assess huge data within a given time frame. The application of artificial intelligence requires expertise, thus demanding a pedestal in the corporate world. And this pedestal converts to a huge salary when converted into financial terms.

Artificial Intelligence is an area with a high demand for skilled personnel and less supply of the same, making it a really expensive affair for the companies looking forward to hiring AI Researchers and experts. The pay is the sweet spot for AI Researchers as the job role comes with huge money. The average salary for an AI Researcher in Silicon Valley is somewhere around $100,000 and $150,000 as it involves a lot of brainstorming and this pay is further increasing with the increase in applications of artificial intelligence.

Also, with increasing applications of Artificial Intelligence, the complexity of the operations is increasing, thus making the job of an AI Researcher a hotspot.

AI training and commands have such huge salaries as it provides a practical approach and puts the theoretical knowledge to some good use. Also, one can teach oneself and be ready for the market but understanding AI takes sufficient time and is not a cakewalk.

It is rapidly growing and the ones who are catching up with this growth are being rewarded in the form of really high salaries which keep them motivated to stay on the path as this growth graph of Artificial Intelligence is constantly going up and will not become flat anytime soon. As per Glassdoor, the average base pay of an AI Researcher is $111,118 per year which is pretty high when compared to other sectors of the economy.

Conclusion

Artificial Intelligence has made the world more dynamic than it was ever before. It is evolving at a very fast pace thus giving rise to a huge demand for AI professionals and the salary associated with the field is making it even more attractive for the generations to come.

We at Imarticus Learning offer Analytics and Artificial Intelligence courses at our centers in Mumbai, Thane, Pune, Bangalore, Chennai, Delhi, Gurgaon, Noida, Ahmedabad, and Jaipur.

Job Opportunities Around Big Data

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It is said that in today’s world, data is the new oil. With the digital world that is intrinsically joined with the internet in almost every aspect, corporations and even governments have realized that Big Data is a great asset that should be used judicially. The age of information technology shows that one can get greater insights from Big Data in almost all fields. One just needs to know where and how to look.

What is Big Data?

Big Data can be simply explained as a field that uses many statistical and analytical methods to process data sets that are impossible to process manually. By analyzing large chunks of numbers and figures, big data reveals patterns and trends that reveal information such as consumer behavior and frequent interaction patterns.

This information is then used by corporations and other entities to conclude the results of their findings and use this information to plan strategies, marketing campaigns, plan improvements, or execute awareness drives, etc. John Mashey, a computer scientist who was very interested in Big Data is credited with popularising this word in the early 1990s. Before John Mashey, this term was unfamiliar from the technical field but soon became an important and frequent part of the technological lexicon around the world.

 What are some of the jobs available in Big Data?

Some of the most popular jobs associated with Big Data are:

  • Big Data Engineer: Big Data Engineers are the most important and most frequently hired people in the world of Big Data. These are the people who are responsible for making designs that decode the numbers and make patterns emerge out of them. Their main job is to check and maintain big data solutions intact and running around the clock. People who are good at math and software building can pursue this job after a short period of training. The pay scale for Big Data Engineers is more than average.
  • Big Data Analyst: Big Data Analyst is a very sought after job in the information technology field. Just like its name suggests, Big Data Analyst is responsible for extracting insights from the large data sets by using mathematical and statistical methods to categorize and sort numbers in definable patterns. A Big Data Analyst should have knowledge of technologies like Hive, Pig, and Hadoop amongst other languages as it is necessary for their job.
  • Data Visualization Developer: As the name suggests, a Data Visualization Developer maintains the design and aesthetic of Big Data software and statistical practices. Data Visualization Developer is responsible for maintaining the standardization of design that goes behind every analytical or construction job in this field. A Data Visualization Developer needs to have a great sense of conceptualization, design and should be skilled in technologies like Pig, Hive, etc.
  • Business Analytics Specialist: The person entrusted with this job is responsible for the creation of the scripts as well as testing them by regularly performing test runs around the year. Business Analytics Specialists are also deemed responsible for undertaking research that helps one find cost-effective alternatives to existing practices.

Conclusion

When Big Data was first used in the early 1990s, people thought that it would be used by niche companies and analytics departments of state-owned departments. Soon corporates and even small businesses started showing interest in the power of big data to reveal results thus increasing the career scope in Big Data tremendously. Today there are many interdisciplinary careers in the field of Big Data. While this field is still dominated by people who work in mathematics and technology, Big Data Analytics Training will your career a much-needed boost.

What is the Step by Step Approach To Learn Data Analysis Using Python?

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If you have decided to take up a data analytics course and learn Python without any prior experience in Python, you might get confused about where to start. There is a necessity to understand Python if you wish to learn data analytics. If carefully planned, you can learn python very easily. You need to be clear about your goal before getting started. Do you wish to become a Python developer, or you want to do data analytics with Python? If you wish to perform data analytics with python and not to become a python developer, you don’t need to spend time learning how to develop software in python.

Selecting Resources

If you want to read good books on Python, go for good books and try to avoid books intended for the common man. Some books on Python programming are more mathematically oriented. Those kinds of books discuss only very little about data analysis. Setting up a programming environment is essential to proceed further; so learn how to use the IPython notebook.

Process – Where to Start?

A good starting point would be to explore a reputable online resource and complete the exercises given by them. If you spend 3-4 hours a day, you should be able to finish those exercises in some days. However, your goal is to use Python for data analysis, not to build software. Once you finish the code academy you can proceed and read the IPython notebook and have a look at Python Essentials Tutorials. It has other exercises that are not offered by the code academy. It might take only a couple of hours to complete this tutorial.

Pandas

Pandas comprise of high-level data structures. It also has good manipulation tools for fast and easy data analysis in Python. You need to work with series, data frames, working with values that are missing, drop of entries from an axis, etc.

Numpy

Learning Numpy allows you to move closer to your dream career. This is because Numpy helps you understand the fundamentals of scientific computing with Python. Besides, you need to know Numpy to use tools like Pandas effectively.

Matplotlib

This 2D plotting library for Python is available in 4 parts.

    • The first part is an introduction to the fundamentals and basic functions of Python.
    • In the second part, you will get to know about line forms, structures, patterns, size (thickness), markers and about using color maps.
    • The third part discusses controlling the range of axis, compositing, coordinating both the system as well as the aspect ratio.
  • The fourth part deals with how to work with complicated figures

Conclusion

Taking up a data analytics course and understanding Python can be very easy if you take the right approach and access good resources. The most common mistake a person makes is to attempt to access many libraries simultaneously.

This could cause you to switch between the different concepts, and eventually end up in confusion. So as discussed plan your approach – learn the basics of Python and then learn Numpy, Pandas and then Matplotlib. So to learn data analytics with Python, adopt a methodical approach and concentrate on grasping one portion at a time. In this manner, you will understand the ins and outs of Python that is valuable for data analysis.

Is Python Good for Robotics?

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What is Python?

 Python is a high-level programming language with features that make it ideal for web and app designing and development. It is easy to learn when compared to the other high-level programming languages. This language absorbs low maintenance costs thus making itself desirable by developers with all types of budgets. Also, Python programming supports the module approach where a code once is written can be reused in other applications if needed.

Also, it gives free features of standard library and interpreter thus, making it an affordable program. Python is a language that can be accessed by anyone. Learning Python can open a lot of doors for understanding websites and their development process.

What is Robotics?

Robotics refers to the science of robots. It revolves around the elements of automation which are brought in by robots such as their conceptualization, design, operation, manufacturing, etc. Robotics makes use of artificial intelligence and is programmed to perform a specific set of tasks. Robots are programmable machines that are programmed in such a way that they can operate on their own when exposed to a certain number of situations.

Use of Python in Robotics

Python has been found out to have great uses when it comes to programming. This language is also being comfortably used in the field of robotics as it is simple to learn and understand. Python is one of the most important languages when it comes to Robotics as it is an interpretive language like Java.

Also, the ease of using Python makes it quite popular in Robotics culture. It comes with a lot of unusual features that save a lot of time and the module-based approach facilitates the use of the same codes which have proved their utility in the past. Also, it makes the casting and defining variable types easy thus, saving a lot of time.

With the availability of several free libraries, Python makes the application of common and basic functions easy. Also, Python allows integration with other popular programming languages such as C and C++ which makes binding of programs convenient, thus avoiding any losses based on performance.

Python is gaining ground in the field of Robotics due to easy availability and also because it is easily accessible. It is a straight language without any complications. Machine Learning Training can give you the required insights when it comes to using Python as a language for driving robotics and the changes associated with it.

Learning Python is a cakewalk for people who know other programming languages. After gaining a good knowledge of Python, learning languages like C & C++ becomes quite easy. Python uses machine learning and this makes it a popular language. Python is the language used in Raspberry Pi which is used to control a robot.

Python is a cross-platform which also works on operating systems like Linux, Windows, and OSX depending on the libraries which are brought to use. It uses simple commands and syntax and allows dynamic typing. It is also one of the core languages of the Robot Operating System, thus provides great utility when it comes to Robotics.

Python is slow when compared to languages like C and C++ but it acts as a foundation of robotics and paves a way for better understanding of the other languages which are commonly used.

Conclusion

 Python is one of the core languages driving the Robotics Industry as it makes use of Machine learning which has gained a lot of popularity in recent years. Python, though easy to understand, has found its importance in the field due to easy accessibility and the availability of multiple free libraries which makes it an affordable and convenient tool for programming Robots.

Top Reasons Why Big Data Analytics Is One of the Best Career Moves!

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What is Big Data?

We are living in an era where we consume data more than anything, be it food or water or even electricity. If you use technology to help yourself in your day to day chores or if surfing the internet to find information for your purpose is an on-going thing, you are one of the beneficiaries of the big data.

So what exactly is the big data? In the most basic sense, big data can be understood as the collection of data that is extremely large in size and is accumulated on a continuous basis.

Big Data AnalyticsIf we go by the general definition of it, the big data is an industry that deals with systematic extraction and storage of information in the form of data from various data points; it also helps to perform complex analysis of extremely large data sets.

The data stored is used to perform analysis to identify patterns, trends or establish association and relationship among different variables especially relating to human behavior and interactions. Examples of some big data sets include data generated by the stock exchanges, social media sites, etc.

Many big corporations are using big data today to gain customer insights and design their policies and price their products accordingly to gain maximum output from their investment on customer acquisition and other areas.

Big data analytics as a career option

In the contemporary world data is the fuel for exponential growth. Corporations today use data in conjunction with other progressive technologies like the machine learning and artificial intelligence to help process large piles of information and gain valuable insights from the same. The new technologies not only help to do too much in too little time but it also minimizes the chance for random human error, which is very likely when dealing with an extremely large amount of data set.

Since the big data is a fairly new field, very little is known about the career options and the work required to build a career in the domain. If we were to compare it with traditional occupations that are held in high regard, data analysts have a similar role to play as the doctors in our lives. Basically, they are the doctors for data, who help to analyze and scrutinize data and find any anomalies if they exist.

Big Data Analytics CareerThe most important factor why big data analytics is a good career option can be understood through the lens of general economics.

At present the demand for data analytics professionals in the industry is higher than the labor supply for the same, this means that it’s not only a growing field but also the one with higher perks and remuneration.

According to IBM reports the jobs in the data industry in the US alone will increase to 2720000 by 2020, higher than the current supply of professionals in the field. A career in big data is the most sought after especially in the developed economies.

According to sources, the current size of the analytics market is around one-tenth of the global IT market and is assumed to grow and become one-third of the global IT market in the coming years, another reason why it’s a good career move.

Finance and Analytics online coursesThe number of job posting on some of the reputed job portals has also shown a significant increase from the previous year indicating high growth in the field.

Big data analytics adoption is growing with the minute, another important indicator that favors the career move in the data industry is the increase in companies that are adopting big data analytics to improve their day to day functioning and cut their costs on futile marketing and advertisement.

The use of big data helps to make things more contextual for the customers and optimize the output and price for the brand in the process. The big data course is designed to help develop a comprehensive understanding of the subject and is beneficial for those who are eying for a career in this field.

Data Analytics Changing the Structure of Media and Entertainment Industry

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Data Analytics Changing the Structure of the Media and Entertainment Industry

Big Data Analytics Course is a highly searched course on Search Engines. Also termed as Data analytics, it is among the indomitable tools that allow businesses to compete in the market. Around 73% of the businesses are involved with Data Analytics in some ways. There is hardly any sector that remains unaffected by Big Data.

The media and entertainment industry are one of the primary adopters of data analytics. The industry generates a huge amount of data, which is basically in digital form. The data also comes with the capability of changing the research space regarding the consumers.

Media and entertainment companies are increasingly transforming and executing Big Data analytics and machine learning in various areas.

Here are some of the primary areas. 

  • Improvised Ad Targeting;

According to the Big Data Analytics Courses, advertising is an important aspect. The concept comes with features like advanced segments, detailed view of customers, hyper-targeted ads, and much more. Working on advanced analytics includes improvised ad targeting to help the correct viewers visualize the ads. Along with the standard advertisements, using video marketing campaigns, social media platforms are also helpful in improving the ad target to obtain data in bulk.

  • Optimization of Media Scheduling;

Data analytics consists of collecting data from various sources to derive efficient predictions regarding the actions of the users. The external sources of data collection are much essential. The detailed predictions would also be more accurate for the complete optimization of the audience for obtaining more views. The companies can also look for personalized advertisers for using the demographic data obtained before.

  • Getting new sources of revenue 

This is among the prime chapters of the Big Data Analytics courses. With the help of data analytics, it becomes easier to get new sources of revenue in terms of media and entertainment. In today’s competitive market, identifying the innovative resources of revenues, apart from the traditional advertising campaigns and partnerships, is considered to be one of the valuable assets for the company. The companies can also go with the digital conversion of the micro-segmented customers for advertising the exchanges and networks.

  • Social Media Analysis;

In the digital market, nearly all companies use social media on a regular basis for proper analysis of the data collected. Be it Facebook, Instagram, Twitter, or any other social media platform, it generates data in bulk, which is helpful in real-time analysis. This is also a cost-effective way of data processing with a large amount of data. For obtaining actual feedback, the usage of multiple sensors for the theaters or smartphones is also an effective way of social media analysis.

Some other applications used for detailed data analytics of the media and entertainment industry include data visualization, inference engines, cross-sell, and many more. Through all these techniques, the companies can easily analyze the services and the data obtained from them, taking all the requirements into consideration.

Big Data is a boon for the media and entertainment industry. Media comes with improved access to the data of the consumers compared to other sectors. By analyzing the data from the content consumed, the users would have a clear insight into the effective formats, consumption patterns and viewing provided. Using the analytical data is also helpful for the media companies in working over various issues regarding the channels and the formats that would attract consumers.

So, are you looking for effective Big Data Analysis Courses? Get in touch with Imarticus Learning for all your needs.

How Analytics Can Help You Prevent Customer Problems Before They Arise?

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How Analytics Can Help You Prevent Customer Problems Before They Arise?

It has been proven time and again that establishing customer loyalty is a necessity for companies these days for the simple reason that it needs one-seventh of the resources needed to acquire a new customer. Although the agenda is clear, it’s not that easy these days. With an abundance of choices among consumers in today’s markets, it has become really hard to stop them from switching to a competitor and maintaining loyalty to your company.
Foreseeing a difficult situation before it arises is imperative for any business to get successful and with such an abundance of data in the modern age, organizations can work to address problems and minimize customer problems even before most of them arise. Data analytics, more importantly, Predictive Data Analytics helps companies do that.
A major industry where this can be seen in abundance is the mobile retail industry where the majority of those who come for mobile replacement claim the device is not working properly, while mostly they don’t always know how to use it properly.
Preparing for the unseen
Many people say that you can’t count the number of chicks before hatching, well Predictive Data Analytics can do it for you.
Predictive Data Analytics can be used to predict customer behaviour by utilizing the purchasing and visiting patterns. With modern technology so advanced businesses can use the data prediction tools to get an advanced idea of buyer demographics and plan out their orders and strategies accordingly. A major industrial example of the same is Price Optimizer Software. Companies these days use price optimizer software to determine how consumers will respond to a particular price for their product.
Three of the data predicting models have been mentioned here, these are:-
Data from Visiting
Collecting data on parameters such as the number of visits on the sites, duration of these visits, locations visited and duration since the last visit, businesses can accurately predict the purchasing patterns of a customer concerning the future visits and purchases. This important data gives these businesses an edge which allows them to classify and plan accordingly so that they can ensure regular visits as well as the loyalty of customers.
Predicting the Particulars
Predictive Analytics can also make available the location as well as online as well as offline data to the retail marketers and armed with knowing who the buyers are and by analyzing their website and shop visits retail marketers can successfully predict the buyer’s visit with accuracy up to a really small timeframe.
Certainty Establishment
Retailers can use Predictive Data Analytics to personalize the services provided to an individual or group. A business must have proper marketing techniques and strategies which cover every group and individual alike. With regards to the purchasing decisions, there are many traits to a customer’s overall behavior and when these traits are separated and smartly implemented by companies and businesses, they’ll be becoming better in identifying and segmenting different customers according to the wants and needs.
Conclusion
Several companies these days are trying to leverage the power of Predictive Data Analytics to outgrow their bottom lines and grow their businesses while effectively combating problems before they even arise. With excellent data analysts at their disposal, these companies can effectively predict customer behaviour and get better in predicting customer problems.

Imarticus Learning is adamant about providing the data science enthusiasts with an opportunity to grow their skills in data analytics and benefit from the ever-increasing need of ever-increasing data analysts. The Data Analytics PG course available at Imarticus Learning can be useful for both companies as well as employees to get their data science skills up to date and benefit.

Introduction To Python Set and Frozen-set Methods

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Introduction To Python Set and Frozen-set Methods

Python is a widely-used programming language. No doubt that learning Python will bring you better job prospects. The advantage of Python is that it is generally smaller than other languages like Java. As a programmer, you will need to type only fewer indentations, which makes them highly readable highly. Many global market leaders like Google, Facebook, Amazon, Instagram, etc. are using Python. Learning Python will help you find a career in machine learning as well. A program widely used in GUI applications, image processing, multimedia, text processing and many more, Learning Python is highly advisable if you are looking at a career in IT.

Sets in Python

Sets in Python represent a collection of data. This unordered collection is mutable and iterable. It doesn’t have duplicate elements. The Python set is similar to the mathematical set. Using a set is recommended over a list because the set has several advantages. Based on the hash table, it is very easy to check if a particular element is included in the set. This is not possible if you use a list.

Properties of a Python set()

There are no parameters to create an empty set. If you create a dictionary using a set, the values will not remain after conversion. However, the keys will remain.

Sets have many methods:

  1. Method of addition (x): This method is used to add one or more items to a set if currently it is not included in the set.
  2. Method of union (s): This method is used to unite two sets. The operator use is ‘|’, which is similar to set1.union(set2)
  3. Method of intersection (s): This method is used for the intersection between two sets. You can also use the ‘&’ sign as the operator here.
  4. Method of difference (s): This method is used to return a set that contains replicates the elements present in the invoking set, but not present in the second set. The operator used here is ‘-‘.
  5. Method of the clear set (): This method is used to empty the whole set.

Operators for sets and frozen sets

Operator Function
key in s containment check
key not in s non-containment check
s1 == s2 s1 is equal to s2
s1 != s2  S1 is not equal to s2
s1 <= s2 s1is subset of s2
s1 < s2 s1 is a proper subset of s2
s1 >= s2 s1is superset of s2
s1 > s2 s1 is a proper superset of s2
s1 | s2 the union of s1 and s2
s1 & s2 the intersection of s1 and s2
s1 – s2 the set of elements in s1 but not s2
s1 ˆ s2 the set of elements in precisely one of s1 or s2

Frozen sets

Frozen sets, unlike the set, are immutable. Once created, the frozen set remains the same. It cannot be changed. This is the reason we use a frozen set as a key in Dictionary. It accepts an iterable object as its input and makes them unchangeable. However, it is not guaranteed that the order of the element will be preserved. In a frozenset, if no parameters are passed to the () function, an empty frozen set is returned. If you change the frozenset object by mistake, you will get an error saying that the item assignment is not supported by frozenset object.

Conclusion

Equivalent to the data set in mathematics, Python set is a data structure similar to the mathematical sets. It consists of several elements, but the order in which the elements should be deployed in the set is undefined.  You can perform addition, deletion, iterate, union, intersection, and differences on the sets.

FAQs during Business Analytics Interviews

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[vc_row type=”in_container” full_screen_row_position=”middle” scene_position=”center” text_color=”dark” text_align=”left” overlay_strength=”0.3″ shape_divider_position=”bottom”][vc_column column_padding=”no-extra-padding” column_padding_position=”all” background_color_opacity=”1″ background_hover_color_opacity=”1″ column_link_target=”_self” column_shadow=”none” column_border_radius=”none” width=”1/1″ tablet_width_inherit=”default” tablet_text_alignment=”default” phone_text_alignment=”default” column_border_width=”none” column_border_style=”solid”][vc_column_text]FAQs during Business Analytics Interviews

1. List some of the frequently used documents a Business Analyst always has at his disposal.
A business analyst associated with an organisation needs to have the following documents at his
disposal.
● Requirement Management Plan
● User stories
● Project vision Document
● Business Requirement Document
● Use cases
● Functional Specification Document (FSD) or Functional requirement specification (FRS)
● Requirement traceability matrix (RTM)
● Test case
● System Requirement Document (SRD) or System requirement specification (SRS)

2. What is the difference between a BRD and SRS?
A BRD or Business Requirement Specification explains the functional specifications of software
at a very high level. It is formal in nature and describes the client’s requirements.
An SRS or System Requirement Specification explains the technical and functional
speciations of software and is created by an in-house system architect instead of a business
analyst.

3. What is Gap analysis?
The process of evaluating the differences n performance between software applications and
business information systems to understand if required business needs are being met or not is
called Gap Analysis. The word Gap, here, refers to the present state of the organisation and the
targeted state.

4. How do you explain the importance of non-functional requirements?
Non-functional requirements are important in describing how a system needs to behave ad
what might be the possibility of constraints in functionality. Non-functional requirements make
the system easy to use and also prevent the system from being abandoned.

5. What do you mean by Personas?
Personas can be termed as detailed fictional profiles that follow the methodology of following a
User-Centred Design. A persona represents a certain segment of users that fall under a given
demographic and helps analysts within an organisation comprehend and study behavioural
patterns and attitudes of users.

6. Name the most important elements of an activity diagram.
The most important elements of an activity diagram are listed as follows:

● Action node
● Initial Node
● Activity Final Node
● Decision
● Activity Edge
● Join
● Fork
● Merge

7. Explain UML modelling.
UML or Unified Modeling Language is a modelling language that consists of a set of diagrams
that are integrated and developed in a manner that can help both system and software
developers specify, visualise, construct and document software systems and their artefacts. UML
has also been used for business modelling as well as modelling for other non-software systems, regardless
of whether the system is regular or larger and more complex. The advantage of using a UML is that
business analysts can help their internal project teams communicate among themselves
seamlessly.

8. List 5 common problems you may face in business analytics.
The most common problems in business analytics may include:
● Improper or a complete lack of communication during the process of implementation
● Lack of extensive support from the software provider which may hamper analysis results
● Organisation members may be unreceptive to provided or suggested solution
● Lack of industry knowledge on the part of software provider
● Less possibility of user adoption when the solution provided is noot mobile

9. Describe Business Model Analysis.
A Business Model Analysis helps to understand the working of an organisation, whether the
the venture is delivering and meetings its goals, how viable it is or may turn out to be and how
valuable it is.

10. Name 5 important tools used in Business Analytics.
Five of the most important tools in Business Analytics are:
R
SAS
Excel
QlikView
RapidMiner

11. What is risk management?
Every decision in an organisation, be it big or small, involves a certain amount of risk that comes
with it. Businesses try and manage these risks in a way so that losses that follow are minimised

and do not have an impact o the overall revenue or taint the company’s reputation. The concept
of controlling or effectively managing the risk taken is called risk management.[/vc_column_text][/vc_column][/vc_row]