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Category: Analytics

Should You Start With Big Data Training Or Learn Data Analytics?

Posted on by Imarticus Learning

While the difference between Big Data and Data Analytics isn’t huge, it’s an important one. Career streams in Data Science can diverge into two separate branches based on which one of the above you choose. 

Both Big Data and Data Analytics focus on the same thing – processing large chunks of data for valuable insights. But the methodologies and tools used for the same are different. Therefore, you can choose to specialize in one career stream and continue progressing in it, or you can learn both. 

In order to make a clear distinction, let’s define both the streams separately. 

What is Big Data?

Big Data refers to the management and operations performed on extremely large data sets (one data set is often greater than 100GB) in order to extract patterns, trends, and insights that can power business decisions. Therefore, you have to cultivate expertise working with a large amount of data, and meet specific challenges that it presents. 

 

  • What all you learn in Big Data Analytics Training?

 

  • Data optimization techniques. 
  • Finding relationships and patterns. 
  • Big Data tools such as Hadoop, MapReduce, etc.
  • Compiling, sorting, and processing data using Python, R, etc.

What is Data Analytics?

Data analytics deals with obtaining relevant and very specific information out of smaller data sets. Where a Big Data analyst will sort through millions of rows of data, a Data Analyst will work on finding the statistical parameters in a given range. It involves reporting of elementary but well-defined parameters than can be analyzed for business value. 

  • What is taught when you learn Data Analytics? 
  • Applying statistical principles to data sets. 
  • Compiling and disseminating information from data sets. 
  • Generating goal-oriented reports for decision-makers. 
  • Working with analytical techniques for smaller data sets. 

Which one to learn first?

  • Direct Path 

At Imarticus, we have made both the options available to the students. They can dive straight into Big Data Analytics Training which will prepare them for Big Data from grassroots levels.

Learning Big Data directly ensures that the students are absorbing the concepts they’ll use in professional places directly. They’ll be trained on various tools used in Big Data so that they can come to speed with the industry scenario. 

Same is the case with Data Analytics. Students can learn Data Analytics directly and make it as their career objective. In case they don’t want to get into Big Data, this is an excellent Data Science alternative for them. 

  • Progressive Path

The second option available to students is to specialize in both Big Data and Data Analytics but in a progressive way. They’ll study Data Analytics first and then shift their focus to Big Data. 

Although there is a significant difference between the two career streams, the underlying concepts remain the same. Therefore, someone with the knowledge of Data Analytics will not find it difficult to make the switch. Rather, they’ll be able to advance their career at will. 

Conclusion

As Data Science flourishes as a career, Big Data and Data Analytics continue to be the two best career streams in the market. Our courses cater to both the streams exclusively, as well as, in an integrated way for students to plan their career logically.  

What is the Career Growth if I Start as A Business Analyst Today?

Posted on by Imarticus Learning

Data is everywhere. And it is hard to make sense of so much data without proper analysis. Businesses depend entirely on their data for their planning, performance, and future strategies. They need professionals who are experts in handling business analysis to track their sales, launch new products, and make data-driven decisions. This is where Business Analysts come into the picture.

Business Analysts are the link between all the internal departments by gathering, circulating, and managing the data of the company. Business Analysts are also known as Management Analysts and they work for all kinds of businesses, government agencies, and non-profit organizations. The job role of business analysts can vary depending on the company and requirements. Business Analysts study the different business processes and formulate policies based on the results to improve the overall performance of the business. They work to eliminate the inefficiencies and enhance the position of the company.

Business Analysts Demand

Business Analysts are in-house staff members in many organizations that monitor the business processes and implement performance-enhancing solutions. Many business analysts also work as consultants who analyze and provide recommendations to businesses on a contractual basis. Business Analysts who work as a consultant specialize in specific industries like manufacturing, health care, information systems, etc. Business Analysts are also hired for different business projects like expansion in the international market, the introduction of a new product, the development of an e-commerce strategy to name a few.

Business Analysts Qualifications

Business Analysts’ qualifications include a bachelor’s degree or a business analyst course from a reputed institution. Candidates with a degree in business disciplines can also apply for the job role of a business analyst. A fundamental understanding of subjects like business analytics, business administration, business information systems, etc. is required to kick-start your business analyst career. Students with degrees in accounting, human resources, finance, operations management, and logistics can also apply for jobs in this field. Many online certificate business analyst course is also helpful to gain the basic knowledge and skills to build a career in business analysis.

With these degrees or courses, one can get entry-level jobs in this field. To become a senior business analyst, one can pursue an MBA (Master in Business Administration) or any other relevant degree. You can do it after gaining some experience in the field or you can choose to pursue your master’s degree immediately after bachelors. However, it is not necessary to do a master’s degree to take your career ahead in this field. Good work experience, skills, and talent can take you far on its own in this field.

Business Analysts Career Path

After gaining eligibility for the business analyst role, you can apply for internships or entry-level roles like business analyst intern, junior business analyst, etc. These jobs will equip you with the necessary understanding of business analyst roles. Once you gain some work experience, you can move to senior roles which have greater influence and responsibilities. There are various job titles under which you can find business analyst roles. Some of these are:

● IT business analyst
● Data business analyst
● Data analysis scientist
● Quantitative analyst
● Business analyst manager

A senior business analyst might be the head of the business analysts team in a big organization. Top-quality analysts can also move up the corporate ladder and get high-level management roles. The major career pathways for a business analyst are Lead business analyst and Project manager. One can get these roles easily with a good working experience paired with a business analyst course. The salaries in the business analyst job role are also quite lucrative with an average salary of INR 6,07,210. With experience, the salary keeps on increasing. There is also the option of becoming self-employed by opening your own business analysis consultancy.

Overall, the career pathway of a business analyst is very exciting, meaningful, and valuable. This field has a good scope and one can easily make a substantial career in it with interest, knowledge, and passion.

How AI and ML Affects Cybersecurity?

Posted on by Imarticus Learning

The digital world has been shaken up many a time by cyber-attacks which only continue to get sophisticated and more complex. True to the fact that this era is being referred to as the ‘digital dark age’, data fraud and cyber attacks are two of the top 5 global risks in the world today, not far behind natural disasters and abject weather situations

However, AI and ML are being leveraged to take the battle against cyber attacks up a notch. The future of artificial intelligence will see cybersecurity being taken off the hands of human resources and automated to achieve more efficient results in real-time.

How AI and Machine Learning Affects Cybersecurity

Detection of Anomalies

Before prevention comes detection– and that was one of the many failings of a human-based security force that couldn’t keep up with increasingly complex digital threats. Deep learning and access to databases spanning decades have made AI and ML capable of detecting anomalies in existing systems and tracing sources, whether internal or external.

Pre-emption of Strikes

AI and ML are crucial in the continuous battle against cyberattacks. That said, they’re also instruments used by hackers to conduct strikes. In a case of fighting fire with fire, AI and ML can be leveraged to pre-empt such strikes to identify vulnerabilities and identify threats in services from as basic as emails to as confidential as financial transactions.

Prediction of Threats

As any Machine Learning course would teach, pattern prediction is a perk of AI and ML that can be used in achieving cybersecurity targets and maintaining defenses against breaches. Emerging technologies such as these can successfully predict the likelihood and type of future threats as well as identifying the source to take preventative measures. The same logic can be implemented internally, to analyze internal systems and close up loopholes and weak links.

Improvement of Biometric Authentication

Gone are the days when passwords and swipe patterns were the most innovative authentication technology could get. Biometric authentication is the new norm– think face ID, fingerprint technology– but inaccuracies and failings were always a concern. Today, developers are leveraging AI and machine learning to rid biometric authentication of its imperfections to make it more stable, reliable and more difficult to hack. This is crucial because biometric authentication affects so much more than cellphones and email addresses– it’s used for ID verification, financial authentication and more. Therefore, the stakes are much higher.

Management of Vulnerabilities

In the days when security was largely relegated to antivirus software and human resources, vulnerabilities would be manipulated and turned into a threat or an outright attack before measures were taken. In contrast, AI and ML allow firms to identify and manage their vulnerabilities well in advance so that the approach is preventative rather than scrambling for a cure. AI and ML use a plethora of combinations and tactics to identify these vulnerabilities, such as:

  • Dark web leads
  • Hacker discussions or threats
  • Threat patterns
  • Frequently targeted systems or divisions
  • Risks and losses at hand

By effectively leveraging emerging technologies, firms can meet cyber-threats head-on, even strike pre-emptively, instead of dealing with thousands of dollars’ worth of losses and bills in the cleanup.

Does Companies Hire Data Analyst Freshers? What Are The Criteria For Selection?

Posted on by Imarticus Learning

Data Analysis is a science of collection of data and converting it into useful information in order to develop a better business strategy. Data Analyst training involves the learning of the required tools and language, a well-developed analytical sense and statistical knowledge.

This is quite crucial in the fast-paced world we live in today. Here the role of a data analyst is vital, as various business plans are structured based on the findings. Defining a certain range of products, the customers’ needs and current demand, various trends in market strategy and the areas of improvements required. 

While the field of data analysis might appear to be quite sophisticated, surprisingly, there is a wide chance for the freshers to acquire a job in the relevant company. Having a high GPA from a data analysis program is quite helpful for a basic-level job as a data analyst.

For the others, a basic degree in Statistics, Mathematics, Economics from a well-known University is also acceptable for a Data Analyst career in the beginning. For a fresher to land a job in data analysis, a bachelor’s degree is mandatory. As you proceed up the hierarchy of the job ladder, you will be paid in better terms. For this, you might want to get a master’s or a doctoral degree in Data Science or Business analytics. 

So, once you have understood that being a fresher is not a hindrance in your quest for a data analyst career, there are a few skills to develop as well:

 

  • High-Level Skills in Mathematical Ability

 

At the entry-level job, good maths skills can be quite impressive. Statistics and grip over formulae are necessary for translating the data analyzed into a real-world value system. In short, Maths helps you interpret your results to a more common language. This holds true, for a data analyst as well. When it comes to calculating compound Interest, statistical measurement, and depreciation. Also, as a fresher, the college-level Algebra is of huge help in making the visualizations more attractive.

 

  • Learning Programming Languages

 

For a better chance of a data analyst career, it is important to be well-versed in at least one programming language. These include Python, R, C++, PHP, MATLAB, JAVA.

However, the more programming languages you know, the better are your chances at a good job. 

 

  • DATA Manipulation and Management

 

One of the major skills as a successful data analyst is to be able to build relevant queries in order to extract the required data. For this language such as R, HIVE, SQL are essential. Also, having the skills to develop relevant reports after data analysis is a crucial aspect of the job. Data analyst training at various reputed institutes like Imarticus Learnings helps the freshers with such tools, languages, and programs.

 

  • Communication Skills

 

Just like all reputed job profiles, excellent communication skills are important and required for a stint towards a data analyst career. When dealing with clients, executives and experts, you should be able to ‘communicate’ your ideas the right way. 

Being a fresher is not a problem. In fact, it is an asset, as you are brimming with ideas and are enthusiastic about your goals. With the right kind of skills, there is no stopping your race to excellence.

How Python Is Used in Hadoop?

Posted on by Imarticus Learning

Perks of using Python

A lot of unstructured data is produced each day, the companies and firms use big data and its applications to extract meaningful information from the raw data. A distributed file system is used for parallel processing of data and to enhance fault tolerance. The Hadoop ecosystem offers a Hadoop distributed file system (HDFS) which is widely used by companies and firms.

Hadoop is a database framework that allows users to process big data. While the Hadoop framework is originally written in java then why companies are willing to hire candidates fluent in python? Let us find out the importance of python in Hadoop in this article.

It is possible to write the codes for the Hadoop framework in python and it is compatible with the Hadoop distributed file system. All the analysis applications can be performed with the Hadoop framework coded in python. Python is easy to learn and use and yet is powerful in performing big data applications.

It has a big library of in-built functions which can be used as and when required. Python is a predictive language that has less syntax and semantic constraints as compared to other languages.

A lot less time is wasted in coding in Python due to its predictive nature and that’s why companies and firms are looking for candidates fluent in python, individuals who can solve big data problems with the help of python in a more efficient way. Python has a lot of remarkable applications such as Instagram, Google, Quora, etc. Facebook uses python with HDFS for data extraction and its parallel processing.

The libraries of python fit right in the slot for big data analytics. It makes coding convenient and fast. Users choose among various python frameworks available in the market for working with Hadoop such as Hadoop streaming API, Dumbo. Pydoop, etc.

These frameworks help to enable Hadoop with the help of python and using its services. Real-time computation can be done through python. Python has lists, tuples, dictionaries, etc. as data structures. These data structures can be used for high-end evaluation of big data.

The codes written in python are scalable and scalability is one of the main features of big data. Python is used a lot nowadays for application and web development. Python has an in-built mechanism and algorithm to deal with unstructured data and for doing the processing of that unstructured data. For example, NumPy is an in-built function in the python library that supports complex operations and scientific computing.

There are many other functions that support data analytics. When used in Hadoop, python increases efficiency and fault tolerance. Python boasts a strong user base throughout the world, there is an active community of people working on python which will help you by giving their approach to any particular problem.

A lot of research material and learning guide can be found on python as it is a globally used language. Big data and its applications are also being used by firms to enhance their business and predict trends and solutions. For this Hadoop training is being used and if we are getting such scalable language with an advanced library and is also easy to use, we are bound to use it!

Conclusion

So, each day new languages are coming but that doesn’t mean you have to learn them all. If you are working on the Hadoop platform then python is by far the most suited language for it. You can code much faster in python as compared to other programming languages and also with the chance of getting fewer errors and warnings due to its interactive and predictive nature. Hadoop and python have shown a lot of compatibility in big data use cases across the globe by firms and companies. This article highlights the importance of python in Hadoop.

How To Get Into Data Science From a Non-technical Background?

Posted on by Imarticus Learning

Whatever you wanted to learn can change with time, and it seems like you have decided to dive into the field of data science. It is a vast field that is growing every day. In today’s modern era, every person can be defined with data, every person is data, data is strength.

Every day uncountable MBs of data are produced and so there is a demand of data scientists. If you have mathematics/statistics in your backdrop then it becomes easy to aspire for a data scientist. But, if you are from a non-technical background, more hard work would be required from you, and you can join the field of data science.

Without big data analysis firms wander into the world like a man in the woods. There is a demand for data analysts nowadays, so if you enhance your skills to the anticipated level, jobs would be hunting you. If you don’t have the pre-requisite of statistics or programming, the first and foremost thing is to enroll yourself in such courses. Udacity, KDnuggets, Dataquest, etc. are some platforms that can provide you online courses in data science.

They also provide certification which proves to be helpful when you toil for a job. But remember, education should also come. If you keep your focus maintained then data science is a very interesting field. Certification is secondary, if you have the knowledge, your value will automatically increase in the market. All the talks of big data and analysis, not many people understand it. It is a trendy field so many talk about it just for the sake of talking. Real knowledgeable people are valued when we are talking about data science.

Once you have enrolled yourself in a course, you can find new ways to brush up your knowledge. You can dive into real-life data analysis projects. There is much free data sets out there for various kinds of projects like criminal records, census reports, cause of death count, etc. They are available on the internet and you can use them for better interpretation and analysis.

Indulging yourself in a project will enhance your statistical skills and practical knowledge will help you when you will be seeking for a job. Also, you can join various data science communities and learn what is best suited for you. You can also follow data scientists on different social media sites to learn their perspectives on data science.

The field of data science is vast, but you have to gain knowledge. Without knowledge, nothing ever happened and nothing will. Since you are from a non-technical background, there is no substitute for hard work. If you have a mentor in this field, then it is even greater. Because sometimes, learning a new technology is not going to be easy.

Proper guidance will help you in investing your time for the right thing. You will also want to learn programming languages as an analysis of such large sets of data is done with the help of machines. Hadoop and R languages are widely used for data science and analysis.

They help in parallel usage of data at multi-points. Keep yourself updated with news and blogs so that you know which thing is in demand nowadays. The statistical approach to data science will also require a lot from you including real-time computation. And at last but the least, keep trying.

Once you have the knowledge and the skills, keep looking for the job until you find it, yes, it is going to be hectic but that’s how everyone starts! And who knows, one day you can provide jobs in data science if you keep learning and keeping your focus towards your goal.

How Machine Learning is a Boon For License Plate Recognition?

Posted on by Imarticus Learning

Machine Learning has weathered some tough days, pulling through to become a powerful technological force capable of leading and creating real-world change.

A prime example of this is the use of Machine Learning in automated surveillance systems on roads in busy metropolises. License plate recognition, for example, has transformed from a pipe dream to current reality, thanks to image processing and recognition capabilities of AI and ML systems.

Out of all the solutions posited towards vehicle movement and management, machine learning solutions are the most accurate because:

  • They derive crucial information from vehicles on the move
  • They are real-time and efficient
  • They are self-taught and don’t need human resource support

What is license plate recognition?

It is the process of detecting and identifying license plates, through Optical Character Recognition, to be run against an existing database. The most basic recognition system consists of three steps:

  • Detecting the actual license plate
  • Segmenting characters into individual images
  • ML algorithms recognize each character

License plate recognition is a boon for many government and private entities, especially when used in tandem with an existing robust database.

Where is license plate recognition thought to be useful?

As the number of vehicles increases across metros and crime also shoots up, many law enforcement bodies find it increasingly difficult to track offensive vehicles and levy fines, book drivers or conduct searches in good time.

Identification of traffic defaulters: ML-based license plate recognition systems allow bodies like the police and traffic control to identify vehicles that break rules like driving over the speed limit, not wearing seatbelts or having broken headlights. Depending on the business of the junction or road, the technology can be used in tandem with human traffic police to ensure accuracy and efficiency.

Recognition of abandoned wanted or stolen vehicles: By integrating ML-based systems on mobile devices, police or other law enforcement bodies can recognize and identify vehicles whether wanted, stolen or abandoned. This reduces a lot of time wasted on identifying vehicles or organizing paperwork. It also allows the enforcers to contact the person in question or make arrests were necessary.

Automating of toll collection: By adding LP systems to toll booths, law enforcement authorities can conveniently collect tolls without the need for actual service personnel manning the station.

A good LPR system is set apart from the rest by the following salient features–

Functions in all environments: Using humans to identify license plates in extreme weather leaves plenty of room for errors. Good LPR systems work regardless of the environment– stormy, cloudy, foggy or dusty– thereby heightening the accuracy and increasing real-time problem solving or escalation. They also function on low-resolution images, when the image is at an angle or where the image is blurry and of a fast-moving vehicle.

Identifies plates on all vehicles: Depending on the vehicle, indeed even the country it’s in, the license plate can differ in color, font, and sequence. It could also be placed on different parts of the vehicle body, which means a good LPR tool needs to be able to identify the right markings in the right spot and identify the sequence correctly.

The final word

Detecting and recognizing license plates is a task that grows increasingly cumbersome as the number of vehicles and registration requirements increase. To get a head start on this emerging field, students, freshers, and industry professionals must engage in a Machine Learning Course and open up new avenues for themselves. Using a license plate recognition system that is dynamic, ML-based and scalable is a positive step towards managing the chaos and getting real-time, positive results.

Apache Spark or Hadoop: Which one is Better?

Posted on by Imarticus Learning

With the advent of the internet, data and its distribution have been in the prime focus. With millions of interconnected devices capable of distributing data anywhere in the world at any time, data and its usage is likely to grow in geometric progression. Such large sets of data, big data, has to be analyzed to learn about patterns and trends associated with it.

Data analysis has taken the business world to the next level and now the focus is on creating tools that could process the data faster and better. Apache Spark and Hadoop are two technological frameworks introduced to the data world for better data analysis. Though Spark and Hadoop share some similarities, they have unique characteristics that make them suitable for a certain kind of analysis. When you learn data analytics, you will learn about these two technologies.

Hadoop

Apache Hadoop is a Java-based framework. It is an open-source framework that allows us to store and analyze big data with simple programming. It can be used for data analysis across many clusters of systems and the result is generated by a combined effort of several modules like Hadoop Common, Hadoop Distributed File System (HDFS), Hadoop YARN and Hadoop MapReduce.

Hadoop: Advantages and Disadvantages

Advantages Disadvantages
Stores data on distributed file and hence, data processing is faster and hassle-free It is more suitable for bigger files. It cannot support small files effectively.
It is flexible and allows data collection from different sources such as e-mails and social media. It features a chain form of data processing. So it is not a choice for machine learning or other solutions based on Iterative learning.
It is highly scalable The security model is low/disabled. Data can be easily accessed/stolen
It does not need any specialized system to work, so it is inexpensive It is based on the highly exploited language – Java; so easier for hackers to access sensitive data.
It replicates every block and stores it and hence, data can be recovered easily. It supports only batch processing.

Spark

This framework is based on distributed data. Its major features include in-memory computation and cluster computing. Thus, the collection of data is better and faster. Spark is capable of hybrid processing, which is a combination of various methods of data processing.

Spark: Advantages and Disadvantages

Advantages Disadvantages
Dynamic data processing capable of managing parallel apps It does not have a file management system.
It has many built-in libraries for graph analytics and machine learning algorithms. Very high memory consumption, so it is expensive

 
It is capable of performing advanced analytics that supports ‘MAP’ and ‘Reduces’, graph algorithms, SQL queries, etc. It has less number of algorithms
Can be used to run ad-hoc queries and reused for batch-processing It requires manual optimization
Enables real-time data processing It supports only time-based window criteria, not record based window criteria
Supports many languages like Python, Java, and Scala Not capable of handling data backpressure.

Spark vs Hadoop

Feature Spark Hadoop
Speed fast slow
Memory needs more memory needs less memory
Ease of use Has user-friendly APIs for languages like Python, Scala, Java, and Spark SQL Have to write a MapReduce program in Java
Graph Processing good Better than Spark
Data processing supports iterative, interactive, graph, stream and batch processing Batch processing only

Conclusion

Both Spark and Hadoop have their strength and weaknesses. Though appears to be similar, they are suitable for different functions. Choosing Spark or Hadoop Training depends on your requirement – if you are looking for a big data framework that has better compatibility, ease-of-use, and performance, go for Spark. In terms of security, architecture, and cost-effectiveness, Hadoop is better than Spark.

What is The Best Way to Jump Into Data Analytics Within The Finance Industry?

Posted on by Imarticus Learning

Mayanka Chowkar talks about getting placed at a leading Data Analytics finance firm after her Imarticus stint.

I completed my B.Com, and M.Com as well, and was on the lookout for a platform to jump into the finance industry.

I felt Imarticus would be a great way to get into the industry.

Some of my friends have done this Big Data Analytics Training course already and landed good placements. Seeing that, I thought it would be an excellent opportunity for me to also get into the industry through this course and through Imarticus.

I’d rate my experience with Imarticus as a solid 4. I loved most of the concepts and information that are given during the course. The faculty is also excellent.

The Imarticus Learning placement system worked wonders for me.

The staff members and faculty on the course and in Imarticus, in general, have helped me by giving me placement opportunities even before the course ended! By doing this course, I got a once-in-a-lifetime opportunity to learn about the industry and was placed in a good company, too. I’m really excited and happy about this.

I have been placed as an associate and have been tasked with handling the derivative operations. I will be joining my new workplace soon, and I’m so excited about it, I don’t have words to express enough!

Want to learn in-demand skills in the data analytics industry? Talk to an Imarticus counselor today.

How Big Data is Implemented in Business?

Posted on by Imarticus Learning

Big data is everywhere, and behind every organized solution, you face on the daily. The term refers to massive sets of data that inundate businesses during day-to-day operations– but it’s not the data dump itself that matters to businesses, but the goldmine of insights it reveals once it’s sifted through, analyzed and put into plain and simple words.

The amount of data an average business sees in a day is torrential. Big data scientists find themselves having to deal with the ‘three V’s’ as they’re called:

  • Volume: tonnes of data from a dozen different sources including social media and daily transactions
  • Variety: structured and unstructured data; numeric or stock; video or audio
  • Velocity: Breakneck speeds at which data flows in from all channels into the dump

Big data is highly complex and interrelated, which means sifting through and making sense of it can be quite the herculean task. However, the insights gathered through the process of going through the dump can enable reductions in costs, effort and time. It can also open up new revenue streams, enable the development of new products and bolster analytical and strategic business decision-making.

How is big data implemented in business?

The traditional method of storing data is by using relational database software, built for Structured Query Language (SQL). However, the future of big data began looking too complex for businesses to be able to control, which led to the introduction of NoSQL.

NoSQL is customizable and scalable, making them ideal solutions for businesses both big and small. It’s made specifically for big data, and stores data in the following ways:

  • Document storage
  • Graph storage
  • Key-value storage
  • Column family storage

NoSQL provides real-time, super-quick access to data, without the need for schemas and columns. This allows the running of real-time programs towards furthering business processes. Without the schema middleman, data scientists can directly interact with tonnes of data, which in turn saves any business a lot of effort, time and money.

Why is big data important in business?

Industry professionals and students alike are looking to learn big data analytics and science because of the plethora of job options it opens up in the world of business.

Access to information

Bug data opens up new avenues for businesses to explore, be it in terms of generating revenue, introducing new products or strengthening marketing. It enables real-time data monitoring and allows for A/B testing where necessary without too much of an impact on ‘business as usual’ if the strategy doesn’t work out.

Faster decision-making

Hadoop and other in-memory analytics software allow businesses to conduct analyses on information immediately, further enabling them to come to crucial decisions faster and based on data instead of speculation. Big data can also be leveraged to lookup more updated and dynamic data, allowing decision-making to be accurate as well.

Conclusion
As a good data analytics course will show you, big data is in use across several burgeoning industries, each with their own means and end goals. Be it manufacturing, pharmaceuticals, retail or even governments, there is no place big data can’t be implemented– which means there is no place big data specialists can’t go.