Contact Us
×

Category: Analytics

Reasons Why You Should be Studying Hadoop and Python

Posted on by Imarticus Learning

With big data analytics continuing to achieve prominence at a number of software services companies, its popularity is growing to unprecedented levels. As big data analytics happens to offer many perks as well as great packages, it makes for a great choice for anyone looking to pursue a fulfilling career.
Armed with certifications from Imarticus Learning, as a candidate you have millions of opportunities opened up for you. So if you are looking for a serious career change, or have plans of starting off in the data science industry, you would have a few questions. One of them being which data analytics tool should you master? The answer to this question would most certainly be either Hadoop or Python or both.
Why do you ask? Let’s talk about the reason why you must be getting certified in these two data analytics tools.
Python 
Data Science PythonLet’s begin with Python. Due to its great similarities with tools like Java, C or Perl, the basics here are the easiest to master for the novices. As this tool happens to be very beginner friendly, a freshly graduate programmer would find it be a perfect fit. With having to write less code and other features like code readability, simple syntax, and easy implementation, it happens to be one tool that every data analytics aspirant must study.
For any programmer, the most difficult task is finding out bugs and squashing them, but in Python, its unique design lends itself really well to all of its users. Writing less code basically means that the programmers would find debugging easier and will also be prone to fewer issues as compared to other programming environments.
In addition to all the above winsome attributes, Python also happens to be an object oriented language. This would make it easier for the user to migrate to any other object oriented language, just by learning the syntax of the new language. But one of the most important reasons why you should learn Python is that it can power Google’s search engine, YouTube, Dropbox, Mozilla, NASA and IBM too.

Big data and Hadoop
Hadoop
Moving on to Hadoop, this is an open sourced software framework, which is very easily able to store and process great amounts of data. Many professionals have been blown away by its sheer horsepower. It has the ability to make a huge difference to many organizations by assisting them in their marketing needs.
It so happens that Hadoop and NoSQL are believed to be the fastest growing technological networks in the market of Data Science. There have been reports which state that Hadoop will possibly grow in the Big Data Market and reach up to $13.9 billion by the year 2017.
As Hadoop has quite a lot of implementations, professionals who have expertise in this programming language have a wide range of career roles to choose from. All one needs to have is the deep rooted understanding of how the framework of Hadoop works. As it is one of the most sought after skills, it also pays really well
Thus, here we have the most touted reasons, as to why one must study Hadoop and Python.

Loved this blog? Read these as well:
Master Machine Learning With Python in Simple Steps
The Potential Of Big Data and Analytics
What Are The Best Data Science Courses At Imarticus?

What is Meant By Data-Driven Innovation?

Posted on by Imarticus Learning

What is meant by data-driven innovation?

Do you know why almost 40 per cent of business models fail every year? The reason behind them is not using data analytics or data-driven innovation. 

Let’s understand what data-driven innovation is.

It is a concept where we use data and analytics to achieve the organisational goal by revamping the process, product and business module etc.  As per Gartner hype cycle 2021, data-driven innovation is the most crucial technique for business growth. Not just for organisations, but data-driven innovation is helpful in the education, social media, healthcare sectors etc. 

What is the role of data analytics in data-driven innovation?

Without analysing the data, data innovation is not possible. So, both data analytics and data innovation go hand in hand. Data-driven innovation can be done after we club and analyse the data.

There are a few types of data innovation. We will name a few here.

Business model data innovation

If your business is not doing up to the mark or you want to grow your organisation, business model data innovation can play a pivotal role. This technique collects the data and analyses it. Businesses then take the necessary information and innovate the business model to improve and raise their growth bars.

Data-driven innovation for products 

Companies can revamp their products after analysing the data from the past. Data analytics is used to interpret the information about the merits or demerits of the products, and then changes can be made accordingly. Specifically, product-based industries or solution design companies can experience the wonders of data-driven innovation. 

Process innovation

Sometimes, everything in the product is good, but the profits are not looking appreciable in the reports. That’s when data is analysed, and data-driven innovation improves the process. These scenarios are prevalent in consumer-centric industries. Their customers like the products but sales were not reaching the targeted numbers. Data-driven innovation comes into the picture here. Data helps to know the leaders where the process is lagging and creating hurdles for the profits. Then, necessary actions can be taken to add or delete the steps in the process, and the problem can be addressed effectively. 

Either company can hire professionals who are good at data analytics to interpret the data and do data innovation, or existing employees can opt for data science and artificial intelligence courses. 

Data-driven innovation is doing wonders in the education sector too. The most popular data-driven strategy is used to add value to the students in the education sector.  

Let’s look at the benefits of data innovation in the education sector.

Customised learning

Students can benefit from artificial intelligence and data science by getting customised learning courses. There are education tech companies that design courses as per the learning needs of the students. Students can take different courses per their learning needs and opt for their desired careers. 

Support teachers and growth of students

Student’s detail can be captured efficiently. Data like the number of lectures, attendance, lesson plan etc., can be recorded and shared with the students and other stakeholders. Students can work on the lagging areas and know their strengths and weaknesses. Data innovation will increase the student’s interest in studies and raise the performance graph.

So, we have understood what data-driven innovation is. How it benefits various sectors, including the educational domain. Therefore our courses in Data Science and Machine learning might interest you. You can learn data science and drill complete information from the data. Remember, data is the new fuel; you can only fill this fuel in the growth by interpreting it to the fullest. 

The Promises of Artificial Intelligence: Introduction

Posted on by Imarticus Learning

The field of Artificial Intelligence seems to working on a winning streak. In the year 2005, the U. S Defence Advance Research Project Agency, held the DARPA Grand Challenge, which was supposedly held to spur development of autonomous vehicles, basically in order to make self-driven, smart cars. This challenge was taken up and successfully completed by 5 teams. In the year 2011, in a great competition of Jeopardy, the IBM Watson system, was successfully able to beat two long time, human champions of the same legendary game. Another great win of technology over the human race would be in the year 2016, when Google DeepMind’s AlphaGo system was able to successfully defeat the world champion of Go Player, who was reportedly the world champion for 18 consecutive times.
While these feats of technology over the human brain are extremely commendable, today the long surviving dream of humans, which basically revolved around developing technology to control their surroundings, has finally come to fruition. This has resulted in the form of Google’s Google Assistant, Microsoft’s Cortana, Apple’s Siri and Amazon’s Alexa. As a result of all of these AI (Artificial Intelligence) powered virtual assistants, people are able to make greater use of technology in order to live better lives.
Artificial Intelligence is considered to be a field of computer science, which is entirely devoted to the creation of computing machines and systems, all of which are able to perform operations that are similar to human learning and decision making. According to the Association for the Advancement of Artificial Intelligence, AI is, “the scientific understanding of the mechanisms underlying thought and intelligent behaviour and their embodiment in machines.” While these intelligence levels can never be compared to those of the humans, but they can certainly vary in terms of various technologies.
Artificial Intelligence includes a number of functions, which include learning, which primarily includes a number of approaches such as deep learning, transfer learning, human learning and especially decision making. All of these functionalities can later help in the execution of various fields such as cardiology, accounting, law, deductive reasoning, quantitative reasoning, and mainly interactions with people, in order to not only perform tasks, but also to learn from the environment.
While the recent changes may be extremely mind blowing, the promise of AI has always been existing since era of electromechanical computing, this began in the time period, after the World War 2. The first conference of Artificial Intelligence was held at the college of Dartmouth in the year 1956 and at that time, it was said that AI could be achieved within the time period of summer. Later on, in the 1960’s there were scientists, who claimed that in the next decade, it would be possible to see various machines controlling human lives. But it was in the year 1965, when the Nobel Laureate, Herbert Simon, who is supposed to have predicted the words, which would have some substance and which were, “In the next 20 years, it would be possible that machines would be able to do any work of labour that man can”.
With Artificial Intelligence, going in full fervour, the field which it has affected most in the field of Data Science. And as there are many who believe that there is a great to achieve in this field, have begun to get trained in the same by approaching professional training institute – Imarticus Learning.

Learn Machine Learning With Python in Simple Steps

Posted on by Imarticus Learning

Learn Machine Learning With Python in Simple Steps

There exist a number of free and accessible Python Machine Learning resources in the market today. While it may be true that anyone can begin their learning process, in a hassle free way but, the amount of variety poses a threat of confusion. Many data aspirants undergo a number of apprehensions like deciding which course to take, how to proceed and most importantly, where exactly to begin.
In order to reduce your apprehensions, we have got here a complete guide to being efficient at understanding and mastering, machine learning with Python. Let’s begin with tackling one of the most important questions, which is ‘Where to Begin?’ While everyone, regardless of the field of study they belong to, faces this question but, it would be agreed upon that to begin somewhere, is the hardest step to take. Couple that with having to make a choice from among the multiple options and you land up confused and staggering.
There are a number of professionals who code but have sufficient working knowledge about computer science. Similarly, if you are looking to get trained in Machine Learning with Python, you don’t need to have a through theoretical knowledge, the practical side more than makes up for it. There exist a number of source libraries, which help with the machine learning aspect, while working with Python. A few of them, those that are known as scientific Python libraries, can be distinguished by the names, nymph (used for N-dimensional array objects), pandas (python data analysis library), matplotlib 2D plotting library) and so on. If you are well aware of the variety of topics of machine learning, which make it easy to work with Python and with the help of professional training courses, it would be a cake walk.
Assuming that the reader is a novice at Machine Learning, Python and any data analysis resources, scientific computing or any other related resource. Let’s begin from the basics, to begin with, you are required to mandatoraly have a certain amount of foundational knowledge about Python, in order to make use of it in Machine Learning. When it comes down to it, your level of experience and comfort in the usage of this data analytics tool, would help you choose the proper starting point. To begin with, you have to first install the Python software, using one with industrial strength implementation for operating services like Linux, Windows is always better.
As most of the work of a Data Scientist revolves around Machine Learning algorithms, it as a whole reflects the field of Data Science. For an aspirant, it is not very important to thoroughly understand kernel methods, as opposed to being well versed with the practical usage of the same. Like they say, practical application of any particular tool, is entirely relative to the theoretical understanding. Machine Learning, in particular, is a concept which very few can learn on their own. This is why most people tend to opt for professional training institutes. Institutes like Imarticus Learning, usually focus on teaching various data analytics tools and machine learning, with a more practical approach coupled with case studies and mentoring from the industry experts.

The Potential Of Big Data and Analytics

Posted on by Imarticus Learning

Since the time its popularity hit the roofs, there’s one statement about Big Data that’s remained a constant. “It isn’t about what you know, it is mainly all about what you do, with what you now.” While this may seem as a bit of gibberish to some, industry experts claim that it happens to be a valuable lesson, that companies across the globe will soon end up learning, in the coming years, especially when it comes to the field of Big Data.
Innumerable industry experts among us claim, that 2017 will be the ‘It’ year. The year when data science and big data are bound to go mainstream. Did you know that there are a number of teenagers out there, who are entirely dependent on Google analytics to monitor their brands, regardless of their size. There are a number of parallels drawn between, the thriving start-up culture on one hand, and the increasing developments in the field of predictive analytics and target marketing.
As we are well into the year of 2017, it can be noted, that there are a number of changes in store for Data Science. There are signs of a meaningful shift, gradually taking place when it comes to business and big data. It probably would be the first time, when data analytics, would be the driver of a number of business operations. This change will be a very rewarding proposition for all of those working in the data science industry. While on the other hand, those companies who are lagging behind in this race of technology, could be in for some serious liabilities.
According to Harvard Business Review, “A majority of business outfits today, are nowhere close to recognizing the value and benefits, that data analytics can bring to their firms.” Industry experts believe there happen to be a number of reasons for the same. From lack of communication to absolute absence of a proper, well-designed plan could result into businesses, being entirely oblivious to kind of benefit data analytics can bring. While this news may lead you to panic, there are still a number of things that you, as a business entrepreneur can do and you, as a data science professional can be well aware of.
When it comes to gathering the generated data, almost every single person in the company must buy into the value analytics. If your firm fails to do so, it runs the risk of your company ending up with data, that is either worthless, or enormous amounts of data insights, which will rarely be put to use. Every company and firm out there, needs to make a proper action plan, especially when it comes to the professionals, who are responsible for managing data, reporting it, gathering information, inputting the data and most importantly, who analyzes this data. If these processes aren’t outlined properly, your data will almost never pay.
As the whole world comes to terms with the potential of Big Data and data analytics, there is an increasing need for trained professionals, who are adept in working with data analytics tools. A number of data enthusiasts have begun to look for institutes like Imarticus Learning, which will offer them industry endorsed training programs, in various data analytics tools like R, SAS, Python, Hadoop and so on.

R Programming in Business Analytics

Posted on by Imarticus Learning

Data Science as a concept has existed for quite some time, but it’s come into the limelight in very recent times. The whole world is witness to the kind of magic and power, that data analytics generally exudes, as a result of which, it is imperative for every business out there to be able to acknowledge this phenomenon. Regardless of the size, manner, focus area or revenue of a firm, it is essential for it, to understand the dynamics, behind the enormous amount of data, that it generates due to its clients and the maintenance of the same.
While there are field where spreadsheets still hold the place of power, but they have long become redundant and obsolete, all because of the emergence of data analytics tools. These data analytics tools are essentially the very important cogs of the proverbial machine, which help data scientist accomplish absolute feats with predictive analytics. So when it comes to the go to tools of data analytics, there ensues an intense debate, so as to which one could happen to be the best or the most efficient aid.
While many believe that SAS programming (mainly due to its time honored presence in the industry and its huge client base), is the tool to go for, lately the younger generation has been differing opinions. Many believe that the best programming language right now is the R Programming language, one of the main reasons cited here, is the fact that R, is an open sourced programming language, which means that it is easily accessible as well as free to be downloaded.
Being free of cost, over time, R has generated its own community of users, which includes numerous data scientists, who have all the liberty to develop updated beta versions and to fix the bugs. It has become the hot favorite of all those data analysts and data scientists, working to analyze huge amounts of information and being able to formulate new breakthroughs, in various business fields.
Apart from being a great tool for use in data analytics, R programming comes to be of major use when it comes to business analytics. This programming language basically, makes it very easy for any business to go through its entire data, in the most hassle free manner. It primarily scales all the information, so that numerous parallel processors, are able to work at the same time. As many computers don’t have sufficient memory, to handle and deal with enormous amounts of data, R programming offers ScaleR, which is a part of the application that does the job of trying to re-purpose great amounts, into smaller chunks of information, so that it can be processed on a number of servers, at the very same time.
As R allows the users to analyse statistical information in the most sophisticated of manner and in literally a matter of minutes, which most of the other languages cannot really accomplish; this makes R a force to reckon with in the world of business analysis. Rising popularity of R has led to quite a number of people opting to get professional trained in this language, for which majority of them look for institutes offering certification courses like Imarticus Learning.

Top Reasons To Learn Python

Posted on by Imarticus Learning

Top Reasons To Learn Python

While a college education is of the utmost importance, how many times have we seen a self-taught entrepreneur or an innovator pass us by, while we were busy looking for job vacancies? The major difference between that professional and you is that they were able to successfully learn all those industry-relevant tools, while you were busy studying out of generic books. This trend of acquiring skills in order to fit in, with Industry requirements is catching on really fast. Institutes like Imarticus Learning, which offer a number of comprehensive courses in the field of Analytics and Finance, are successful in training their students, in keeping with all the industry-relevant talents, the HR managers are looking for.
Continuing in the same vein, in the field of Data Science, it is very advantageous to be adept in the data analytics tool, called Python. If you are a data enthusiast, wondering why should you be learning this tool, here’s a list of reasons, just for your benefit;

It’s a Very Easy-to-learn Programming Tool

While we do agree, that learning a programming language is in no way as exciting as learning how to race cars, Python happens to be one of those tools, which was specifically designed for the newbies. Reading this would be as easy, as doing a first-grade math assignment, as this programming language is entirely easy and comfortable for someone from a non-technical background. Another reason why this language is so economical to learn is that it requires much less amount of code, for instance, any Python code is about three to five times shorter than the Java code and about 5-20 times shorter, than the C++ code.

Better for Your Progress

Python can very well be called your stepping stone, to a better career in the field of analytics. HR managers are always on the lookout for well-rounded programmers, and having the knowledge of Python will surely get you there. Like other key programming languages, Python is also an object-oriented language. This will surely help you adapt very easily in any type of environment.

There’s a Micro-computer, Specially Made for Python Language

Raspberry Pi is a card-sized, extremely inexpensive micro-computer, which usually specializes in video game consoles, remote-controlled cars, and the like. Python is considered to be the main programming language here and it’s so easy and comfortable to work with, that even the kids are using it to build arcade machines and pet feeders and the like.

Handsome Rewards

Reports state, that Python has had the largest job demand growth, in the past three years. In the year 2014, while the hiring demand for IT professionals dipped down, on the other hand, the demand for Python programmers increased, and that too by about 8.7%. Prospective employers are Google, Yahoo!, Disney, Nokia, IBM and so on.

It Functions Online as Well

Web Development is all the rage these days and all thanks to Python, you will now be able to take a huge bite of that as well. Django is an open-sourced application framework, which is entirely written in Python and is also the foundation for a number of popular sites like Pinterest, The New York Times, The Guardian, Bit Bucket and so on.
There you go, five extremely appealing reasons why you must learn Python.

How Does Facebook Identify Where You Are From Your Profile Photo?

Posted on by Imarticus Learning

We all know that Mark Zuckerberg of Facebook is strongly passionate about Machine Learning and Artificial Intelligence, but how has that impacted our everyday online social life?
You may think you’re just uploading a photo, but facebook knows how many people are there, whether you’re outside or inside, and if you’re smiling.
The technology that Facebook uses, Artificial Intelligence, is a rigorous science that focuses on designing systems that make use of algorithms that are much similar to that of our human brain. AI learns to recognize patterns from large amounts of data and come up with a comprehensive conclusion.

What does that have to do with how Facebook knows if I’m smiling or not?

Facebook is constantly teaching their machines to work better. By using deep learning, they train AI to structure through various processing layers and understanding an abstract representation of what the data could be. By using their system called “convolutional neural network”, the computer is able to go through layers of units and understand whether there is a dog in a photo.

Follow Us on Facebook

Facebook works through layers. In the first layer, it is able to identify the edges of objects. In the second layer, it is able to detect combinations and identify it to be an eye in a face or a window in a plane. The next layer combines these further and identifies them to be either an entire face or a wing on a plane. The final layer is able to further detect these combinations and identifies if it is a person or a plane.
The network needs to be able to read the labels on the database and identify which of these are labeled as humans or plants. The system learns to associate the input with the label. The way facebook works is that it is able to now identify not only that there are humans in a photo, but how many humans, whether they are indoors or outdoors, and their actions, i.e. if they are sitting or standing.
However, a photograph that has been uploaded may need to be completely zoomed in for Facebook’s AI to understand intricacies if a person is smiling or not.

It may not always be perfect in its recognition, but it’s getting there.
A lot of information can be extracted from a photograph. Facebook is only going to get better with its AI and making use of big data.

Artificial Intelligence and Machine Learning is a concept that will be looked at in Imarticus’s Data Science Prodegree. This course is a cutting-edge program designed and delivered in collaboration with Genpact, a leader in Analytics solutions. Students get their hands-on learning with 6 industry projects and work with industry mentors.

Written by Tenaz Shanice Cardoz, Marketing & Communications.

Loved this blog? You might like these as well-
Machine Learning Demystified
Importance of R Programming As a Tool in the Machine Learning Spaces
Best Books To Read In Data Science And Machine Learning

The Most Important Concepts to Keep In Mind When It Comes To Big Data in HR

Posted on by Imarticus Learning

The phenomenon that is well known as Data Science has literally gone on to spread all over the glob in a similar fashion as that to a raging wildfire. The world of business and commerce has remained no stranger to this concept and field and in fact has embraced it more than any other. Big Data has been the catalyst in some of the most remarkable discoveries, especially in the field of HR. While the technical aspect dictates that the various valuable insights provided by big data have made for amazing growth and development of a number of firms, it has also helped a number of HR managers in targeted recruitment as well as employee enhancement. As a professional in the HR industry, regardless of the position, there are a certain number of things and concepts that one has to abide by. As surprising as it may sound, it has been proven recently that these few concepts still hold great value and importance, especially when it comes to big data in HR.

Follow us on Linkedin for Company Insights
The first basic thing, that all HR professionals need to remember, is the massive difference that there is between “story telling” and “story selling”. This is basically only in the context of how a professional perceives a certain data set. It is important for these professionals to be able to distinguish between a very neutral interpretation of data and on the other hand, a data set that is used to derive a certain expected solution. This skill becomes very important in a market, where every vendor and seller is out there to promote their business solutions, entirely on the basis of data and numbers. When faced with numerous such vendors, it holds in positive stead for you to be a little skeptic.
Another basic concept here is not confusing correlation to causation, which is the one of the primary attributes of statistical analysis. This simply put in layman terms, goes on to state that just because two things are related to each other, it does not mean that they are also the cause of each other. This is very important especially in the world of Human Resources, because HR services are most often related to positive business results. This would only be possible, when the professionals are able to realize the difference between certain variables, that can actually cause similar kind of impact, thus statistically stating the connection between the various HR activities and profitability. The most basic trick to know here, is when you would not require causation at all, when only correlation would be enough to provide the required results. While these days, the data is very much required by the HR professionals, in order to determine the correlation and causation, as well as, evaluation of results and making the decisions. It is also important to know when and how the sample size, taken into account is sufficient.
Thus we can infer that HR is soon becoming the latest avenue for all the data scientists out ther to test their abilities. There is no surprise hence, in the increasing popularity of various institutes. Imarticus Learning, provides excellent professional training in a number of data analytics tools like R, SAS, Hadoop and so on.

Loved this blog? Read similar articles here –
3 Skills to Master Before You Turn 30
The Emergence of Big Data In The Field of Agriculture
What’s Machine Learning All About?

4 Most Exciting Data Based Start Ups In India Today!

Posted on by Imarticus Learning

As the world rapidly becomes data driven today, the most exciting places which are rapidly developing as a result of this are all the startups out there. While data technologies have been around for quite a while now, various factors like the increasing speed of data generation, as well as the ability to store data, have resulted into the emergence of a number of advanced data run startups. Apart from being either involved with data analytics and being data driven, these startups are very unique in terms of their various founding members and the educational backgrounds they belong to, the industry that they work in and the opportunities available therein, the kind of investment and funds they receive or raise and so on. Apart from these deciding factors, there is also the growth factor, which makes for a great importance and good career in big data field in terms of how popular a startup goes on to be.
Here’s a list of four such amazing, Indian start-ups, which would be a dream for any Data Science aspirant, to work with.

Edge Networks

Taking the use of data science, to enhance the HR departments of every company out there, this startup was founded in the year 2002. With data science and artificial intelligence, as its core drivers, Edge Networks, in the bid to decrease the cumbersomeness, encountered by a number of job seekers out there, has come up with their product, HIREalchemy. Their solution basically deals with talent acquisition, internal workforce optimization and talent analytics.

Fluid AI

Gone are the days, when one would wish for a way to operate any and every electronic device, with the help of a touch screen. Now it is actually possible for anyone to control, any electronic device with a screen, just by their gestures. The best part? An Indian startup, Fluid AI is attempting to create a huge revolution, which would be really gainful for not only the government of India, but also for the Finance, Web and Marketing industries. Imagine how exciting it would be if any screen, could assist you with more efficiency than any human assistance would be able to provide.

Mad Street Den

While we all may joke around about how sometimes, online shopping can be entirely misleading. Especially when you want to buy the kind of dress that you’ve been dreaming of. You find the exact one and order it, but something entirely different is delivered to your place, which is absolutely not what you have been looking for. This is where this start-up’s flagship product, Vue.ai comes into the picture. It relies heavily on the concepts of Machine Learning and Artificial Intelligence and attempts to provide the customers, with exactly what they’ve been looking for by sending targeted emails and the likes.

SigTuple

India is a very large, populous nation and more often than not, a lot of doctors are faced with the herculean task of being able to provide diagnosis, as well as treatment very promptly. This is where this start up comes into the picture. It is being devised to assist various medical practitioners, in rapid diagnosis, with the help of Image Processing, Classification with AI and Machine Learning at its core.
These amazing startups make it seem like a dream, for any Data Scientist to work in the Indian Data Industry. With various institutes like Imarticus Learning, offering specialization courses in various data analytics tools like R, SAS and Hadoop, achieving the dream is very possible today.