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Category: Analytics

Top 5 Data Science Trends in 2018!

Posted on by Imarticus Learning

Data Science in today’s world is a combination of various functions – AI, Deep Learning (real and hyped progress), Quantum Computing, Big Data, IoT, and many more such applications which are used together as a network. 2017 was dominated by advances in the AI space which had taken over from Big Data. Data has become popular due to the open-source regime which is slowly chipping away at the market and technology shares of established names like Oracle and  Microsoft. With the ever-increasing popularity of newer and scalable programs, let us see the top trends to expect in 2018.

Also Read: How to Become A Data Scientist?

Regulation

The awaited impact-event will be GDPR (European General Data Protection Regulation) which will become enforceable on May 25, 2018. This regulation will affect data science practice in three areas – limits to be applied on data processing and consumer profiling, “automated decision making” and the right to an explanation for that, and feeding in biases and discrimination in automated decisions.

The measures under this act were approved by the European Parliament on April 27, 2016, and will go into effect on May 25, 2018. The law will focus on the new rules on the collection and management of personally identifiable information (PII) of EU citizens. Implementing these rules will bring broad changes in the big data modeling and in creating predictive models.

Artificial Intelligence

According to Garter’s list of Top 10 tech trends in Big Data, it is laying the foundation of AI across organizations. It will remain a major challenge and work plan to follow through till at least 2020 as significant investment in skills, processes and tools will be required to exploit these techniques.

Intelligent Apps

These will be created and used with an aim to enhance human activity and effort and mostly not replace it. Augmented analytics is a strategic growth area in which machine learning will be widely used to automate data preparation, insight discovery, and sharing for a large range of business users, operational workers, and citizen data scientists.

Virtual Representations of Real-World Objects or Systems

Digital representations of the real-life objects will be a common reality and their inter-linkages will help in checking the cause and effect changes for improving the operations and value. It is predicted that over time digital twins of every physical reality will be available and infused with AI capabilities to enable simulation, operation, and analysis. This will particularly help in fields of city planning, digital marketing, healthcare, and industrial planning.

Cloud to the Edge

Edge computing works to maintain the closeness of processing, content collection and delivery close to the source of information. This helps in reducing issues to latency, bandwidth, connectivity. Garter predicts that pairing this strategy with cloud computing will give the best of both the worlds to create a service-oriented model and a centralized model and coordination structure.

While many trends will take a long while to cultivate from its conceptual stage to a working philosophy, these trends will lead the way for future innovations.
Related Articles:

Bring on Blockchain

Posted on by Imarticus Learning

The blockchain and Internet of Things (IoT) are two topics which are causing a great deal of hype and excitement, not just in the technology circle but in the wider business world, too.
In principle, it makes a lot of sense. The blockchain is an encrypted, distributed computer filing system designed to allow the creation of tamper-proof, real-time records. IoT is a term used to describe the ongoing proliferation of always-online, data-gathering devices into our work and personal lives.

What are the benefits of a Business Analyst Certification?

Posted on by Imarticus Learning

Usually, a business analyst is a professional who has a certification in the said field that confirms his professional competency and credibility and at the same time ensures to his employers that he or she is definitely worth his/her salt. These certifications are offered by the International Institute for Business Analysis or IIBA as it known popularly since its inception, about more than a decade ago. This organization is essentially a non-profit body which aims to work towards fields similar to business analysis.
Getting certified by this institute does not just establish one as a credible candidate, but also ensures a global recognition for the same. Here are a few reasons why you must get certified in the business analysis if you are a business analyst enthusiast.

  1. Getting certified in this field, especially on the professional level broadens your perspective. This is because while you are in the preparatory stage of this certification, you will have to study a varied number of approaches, in order to tackle a particular situation or a problem. Learning different problem-solving techniques would make you think outside of the box and prepare you to be an effective professional once you enter the real playing field.
  2. You must understand that obtaining a certification, especially one that has global connotations is never a bed of roses. Rather than being a cake walk, it is quite difficult mainly because it challenges you on many levels. There is a lot of hard work and perseverance that goes in the process of preparation. Which is why when you finally succeed in acquiring this certification, your noteworthy efforts are acknowledged and applauded by the industry. Your prospective employers would never mistake you for some run off the mill, ordinary professional with a Business Analyst Certification.
  3. Like any other field, even business analysis requires a professional to be quite proficient in the jargon of the industry. It is important for you to understand the various nuances and concepts that interplay the role of making a company successful. For this, the most important thing required is to have a certain amount of clarity of concepts. This kind of clarity definitely comes to you once acquire the certification of business analysis as it helps you get a strong hold over the way things function in the industry, thereby increasing your chances of success.
  4. The IIBA organization recently released an article relating to the study that claimed that the Business Analyst Certification when compared to the other certifications in the market, results in 10% more of the monetary benefits. Apart from this, the professionals who do get certified have a concrete chance at getting better and more lucrative projects as compared to their peers working in related industries. This certification is known to add great value to the CV of a professional.

Thus, the above benefits seem to do quite a convincing job, when it comes to getting certified in the field of business analysis. This is why many professionals today opt for the training of the same from institutes like Imarticus Learning which offer to help you gain that certification.

Related Article: Skills Required for Business Analyst

Top Careers to Explore In Big Data Analytics

Posted on by Imarticus Learning

In a really short period of time, it seems like the field of big data analytics has frog leap to its current status of the most lucrative career option. Within itself as well, there are quite a number of interesting options for those looking for an adventurous yet exciting career ride in their future.
Of these options one can opt for positions like a Data Scientist, A Data Engineer, A Big Data Engineer (yes, the two differ from each other), Machine Learning Scientist, Business Analytics Specialist, Data Visualization Developer, Business Intelligence Engineer, BI Solution Architect, BI Specialist, Analytics Manager, Machine Learning Engineer, Statistician and so on.

Also Read: Salary Trends in Big Data Industry
Professionals who usually work at these high profile positions, tend to generally rake in impressive salary packages. Someone who works as a Big Data Engineer generally tends to earn comparatively more than their counterparts who are in involved in jobs such as data management and administration. In terms of job responsibilities, this professional has to oversee the various analytical programs that are used by their company. At the same time, they have to closely work with data architects, various analysts and other professionals in order to obtain valuable insights.Data Analytics Banner
Someone who works as a Data Architect often earns quite equal to the Big Data Engineers. These professionals need to have a mixture of both business and technical skills as they are required to fully understand what kind of information is required by business leaders from the data that they seem to have collected and then go on to both design and deploy their databases, data warehouses and data lakes and so on. A bachelor’s degree is the least minimum requirement needed for getting into this field, having some knowledge about how the software works beforehand is one other benefit for the job.

The position of a Data Scientist is time and again touted as one of the ‘sexiest’ positions by many of the various companies as well as magazines and publications out there. While the demand for data scientists was skyrocketing when this career was introduced for the first time, today it seems to have waned off a little. But at the same time, the salaries have consistently remained very high. Data Scientists are popularly known as the masters of statistics. They are supposed to not just get data, but they are also supposed to clean it, transform it, build data models, apply algorithms and as well as create visualizations that help the business leaders get the perfect kind of insights from them.
One very common prerequisite for all of these job descriptions is just that you need to learn programming languages. These languages could range from R Programming, SAS Programming, Big Data Hadoop, SQL, Python, Pig and so on. While some of these can be learned on your own but then again there are some which need to be taught under proper guidance. This is why the majority of candidates opt for professional training classes which prepare them for the industry, like the ones offered by Imarticus Learning.
Related Article: Impact of Big Data on the World

What are The Skills You Need to Become a Machine Learning Engineer?

Posted on by Imarticus Learning

Machine Learning (ML) is a subset of Artificial Intelligence, which enables the computers to perform certain tasks such as Recognition, Diagnosis, Planning, Robotics Control, Prediction etc., without specific programming. Machine Learning focuses on developing algorithms with the capability of teaching itself to grow and adapt when exposed to new sets of data. As a result, there is a massive interest in the field of machine learning, in individuals who wish to pursue their career in this field, as well as organizations who wish to reap the benefits by its application.
As a Machine Learning engineer, it is very important that you understand not only the specific skill set, but also that you have a fair understanding of the environment, for which you are designing.

Let us understand this with the help of an example, assume that you are working for a retail store. And let us say the company wants to design a reward system, through which coupons are issued based on facts like, purchase history, with the intent that the issued coupons will actually be used. Now traditional data analysis approach would be to study the historical data, and figure out trends, and subsequently propose a strategy. But in the Machine Learning approach an engineer would need to create an automated coupon generation system, however, you will only be successful, if you understand the peripheral functions of the environment like the inventory, Catalogue, Pricing, Purchase Orders, Invoice Generation, CRM Software etc.,

So the skill requirement is not only restricted to the application of machine learning algorithms and the understanding of what to apply when, it is also equally important to understand the Interconnected Relationships of these Functions so that you can then successfully create a software which integrates interface, for an effective output.
Now for the real deal, the actual technical skills you need to kick-start your career as a machine learning engineer. You need to have a good and detailed understanding of the ML Algorithms, Mathematics, Skills in Problem Solving and Analytical thinking, and above all an innate sense of Curiosity. In addition to this, the below mentioned Skill….
Programming Languages like C++ can help in speeding code up, R, Python & Java works wonders for statistics.
Theories like Naïve Bayes, Hidden Markov Model, would require you to have a good understanding of Probability and Statistics so that you can comprehend these models.
A firm understanding of Applied Math and Algorithm theory, along with the knowledge of how the algorithms works, will help you discriminate models.
You will also need to skill yourself on Distributed Computing, as a machine learning role would require you to work on large datasets, which cannot be processed using a single machine, but you will be required to distribute it across an entire cluster

Data Modelling and Evaluation

Data Modelling is the process of estimating the underlying structure of any given dataset, with the intent of finding a pattern that is useful or picks up predictions of previously unseen trends. This process will be futile if the appropriate evaluation is not done to access the effectiveness of the model. So that you can choose an appropriate error measure, and apply an evaluation strategy, it is important that you understand these measures, even while applying standard algorithms.

Software Engineering and System Design

These are considered as the typical output of any ML engineer’s deliverables. It is that small component that becomes a part of the larger ecosystem. Like said earlier you need to make the puzzle, keeping in mind the various components, ensure they work with the help of proper communication of the system with the interface, and finally carefully design the system such, that any bottlenecks are avoided and the algorithms successfully scale along with the volume of data.
It is hence without a doubt that the demand for machine learning Engineers will rise exponentially, as the challenges of the world are complex and only complex systems will be able to solve them. Machine Learning Engineers are building these complex systems, therefore you become the future!
Also Read : Skills Required to Learn Machine Learning

Everything You Need To Know About Machine Learning and Deep Learning

Posted on by Imarticus Learning

Artificial Intelligence (AI), Machine learning (ML) and Deep Learning (DL), can be imagined as the three bears from Goldilocks staying together in a house, where each member has a specific use but yet conceptually they are interconnected. Artificial Intelligence is the big umbrella under which resides the Machine Learning concepts, and Deep Learning can be referred to as a sub-set of Machine Learning. So while Deep Learning and subsequently Machine learning comprises of Artificial Intelligence, the other way around is not necessarily true.
The field of data science is buzzing with these terminologies, and in all the noise it is very easily misinterpreted, and often Machine Learning and Deep Learning are used interchangeably for one another. One thing is certain that Deep Learning is a technique for implementing Machine Learning.
Also Read: Future of Machine Learning in India
Let us understand this by breaking down the puzzle….

Artificial Intelligence orchestrates the capacity of a machine to comprehend complex human tasks like, decision making, understanding spoken language, detecting fraud, in short, it enables a machine to imitate intelligent human behaviour.
Machine Learning is surely a part of AI, where it enables a computer to act in a specific way without the need for explicit programming. This is imperative as the volume of data is ever increasing and to keep up, the machine should have the capability of implementing effective algorithms which can effectively and efficiently make predictions by recognising patterns. To perform the same, data scientists have a number of existing ML methods or Algorithms which can be easily applied to any data problem, at the same time it can be applied to a number of real-life use cases, for example, recommendation engines, or applying Natural Language Processing (NLP) in chat logs.

Deep Learning is a subset of ML and when data scientist refers to the term deep learning they most often mean Deep Artificial Neural Networks or alternatively Deep Reinforcement Learning.
Deep Artificial Neural Networks are essentially a set of Algorithms which are popular in recent times, for setting new records in accuracy while dealing with complex problems like, Image Recognition, Sound Recognition, Recommendations Systems, etc…, To add on, one can easily define DL in a similar manner to ML, so it can be safely said that DL also enables a computer to act in a specific manner without the need of explicit programming, with the addition that DL ensures it produces results with higher accuracy.
DL is often more complex as compared to ML, the prerequisites to DL would be a high-performance computer and huge volumes of Labelled data to give reliable results.
ML can be used with a small volume data set and has a shorter training time, while DL will be effective with large volume data set and often requires a longer training time. In ML you can use your own features, so for example, if you need a computer to be trained on recognising an image of a cat, you will first need to key in all the relevant features of a cat into it. In DL you will need to feed the computer with large volumes of data with cat images, and the system will choose the features on itself which characterises a cat. Hence the training time is longer, while the chances of accurate information are higher due to the complexity of reaching the conclusion, in some cases, it is more accurate than humans.
Driverless Cars, Movie Recommendations and preventive health care are all possibilities enabled by Deep Learning. DL in the future could be responsible for machine assistance in possibly all aspects of life. Deep Learning has the promise of taking the application of AI from fantasy to reality.
Related Article: Skills Required to Learn Machine Learning

What is Big Data and Business Analytics?

Posted on by Imarticus Learning

Big Data has the potential to dramatically change the way organizations are making decisions, with the use of information and insights from it to enhance customer experience and build effective business models. No wonder then that Big Data is known as the most important technology trend of recent times. Does this mean that Big Data is this marvelous invention of today? Well not really.

It is true that most organizations now understand the potential of capturing data that generally streams into their business so that they can apply analytics and get significant value from it, but even as long ago as the 1950s, way before the term big data was even whispered, its concepts were in use on a smaller scale.

Businesses were using business analytics, in its basic form, over spreadsheets manually created, to uncover insights and trends.

So why is this buzz around big data now? Quite simply because of the speed and efficiency it offers. While years ago analytics could be done over the accumulated data, the method would only allow making a future prediction for their business. Today businesses can identify insights for immediate consumption. It is the pure ability to work quickly and stay responsive, is that gives the organizations the competitive edge which was lacking before.

Big Data is not a single component, as accumulating information is easy in today’s time and technology. Big Data is a combination of Data Management Technologies that have evolved over time. When organizations talk about Big Data they mean, To Store, Manage, and Manipulate, humungous amounts of structured and unstructured data at the right speed and the right time to get the right insights.

And thus Business Analytics or Big Data Analytics gains importance. It is business analytics that makes it possible for organizations to connect to their data, and use it effectively to identify new opportunities. This further leads to a better business environment, where you can make informed decisions, create an efficient operations environment, gain higher profits, and acquire happy customers.

Companies using Big Data Analytics gain value by…….

  • Cost Reduction, Cloud-Based Analytics, Hadoop, and similar technologies can not only bring down the cost of storing large amounts of data but at the same time they can bring about more ways of doing business.
  • Quicker Decision making, with the availability of in-memory analytics, and to add to that the ability to analyze the new sources of data, businesses can analyze real-time data, and make immediate decisions based on what they have learned.
  • Segmented Products and Services, the power of analytics is that it can gauge customer needs so that organizations can create products catering to the specific needs of the said customers.

The only pitfall that organizations applying big data analytics initiatives need to be alert to, is the lack of internal analytics skills and the high cost of hiring experienced data scientists and data engineers to bridge the gap. If you are a fresher looking at career opportunities, with an interest in data analytics, then getting yourself trained in techniques of Big Data Analytics, will be a fantastic career choice.

 

What Are The Benefits Of Business Analytics?

Posted on by Imarticus Learning

For any business enterprise, the end goal is to gain lots of profit. Any capital intensive business would want to stay at least two steps ahead of its competition, mainly in order to offer both unique and highly satisfying products and services. Now in the earlier days, to achieve this coveted status of being in lead, in the proverbial race made a number of companies adopt, quite inefficient methods. Many of the models that these companies ended up adopting did not really reach the goals, or encountered a lot of errors or consumed quite a great amount of time.
But today, with the huge leaps that the field of technology has taken, there has been a new concept of Big Data and Data Science on the horizon of the industry. With this coming into the picture, there is a great amount of data that is available for all of these companies to actually run their various models with proper and efficient results for the same. This way, the models that the companies employ to bring in proper results and ensure that the business and profits keep moving in the right direction, which is most positively upward.

The above is a very generic and commonplace definition of what we mean exactly when we talk about the concept of Business Analytics. It is quite a common opinion these days that analytics would most definitely let your business grow in precisely the way you want it to. H

Benefits of Business Analytics

  • Business Analytics helps you quantify your business values: What many companies fail to accomplish is quantifying their business values, they either end up training their freshly recruited new employees or re-training their old ones instead. Rather than doing the long haul work, these companies could take the help of business analytics by actually being able to translate these very business values into numbers. Thus when numbers are involved the company mission statements are also quantified and this can thereby end up helping companies focus on their operating processes.
  • Analytics brings in smart decision making: Now it is obvious that when a company is able to get a hold of proper and good data, it would most definitely be able to take accurate decisions, thereby aiding it in leveraging businesses. Now we all know that there is quite a lot of unity in strength, but are we all also aware that there is also a lot of strategic benefits in strength. Analytics provides useful data that can be further analyzed by a major group of people and thus be analyzed for maximum benefit.

While these two may seem like benefits which are not really enough, but business analytics offers quite a number of other benefits too. With analytics, your business is able to get clearer insights mainly through a process known as data visualization, as well as be on top of things by being updated all the time. But more importantly, analytics is more beneficial for companies if they employ a proper, trained professionals to help them along the way.

How to Get a Data Science Job After A Career Gap?

Posted on by Imarticus Learning
Deepti recounts her journey from a career gap to be a stay-at-home mom to a landing a data science job after Imarticus’ data science course. It’s always challenging – getting back into the job market after an extended break. A business venture that went bust, prolonged illness, family responsibilities, the possible reasons are many. In my case, I wanted to take a career break for a couple of years to focus on raising my child who is now 9 years old. My name is Deepti, and I am a mathematics graduate with 6 years of experience in the IT industry, specifically Oracle applications and data warehousing. After my break, I experienced first-hand the challenges involved in restarting my IT career. The jobs I wanted were out of reach; the available jobs didn’t pay enough, and the jobs that paid decently pronounced me as overqualified. Imarticus’ data science course fit my needs perfectly!  In the midst of all this, I came across data science and realized that this industry is growing and, considering my background in IT and mathematics, would be the perfect fit for me. I began researching institutes with the right curriculum, faculty, infrastructure, and other pre-requisites you’d generally look for. I zeroed in on Imarticus’ data science course because it was the perfect blend of theoretical learning and hands-on industry-relevant practical experience. Big Data Analytics Courses One worry at the back of my mind was ‘how do I juggle between a full-time data science course and taking care of my 9-year old son?’ A quick call with Swapnil, the head of Imarticus’ Thane center, put my mind to rest. I spoke to him about my concern, and without a second thought, he offered to let my son accompany me to class. With my son in the room beside me, I was able to concentrate entirely on my lectures and complete the course successfully. The course itself is outstanding and warrants a 5-star rating. What I liked most about the course was a learning management portal – Learntron – that I was given 24/7 access to. I remember going back home at the end of a day of classes to put in some time working on the day’s assignments. I could always log on to the online platform, re-watch the lectures & videos, refer to the notes and clear any doubts & queries that arose. The learner was a great aid to my entire learning journey. I also want to make special mention of the faculty, Ms. Ritu Singh and Ms. Prachi Samant, who are amazing trainers. Being data science professionals themselves, they are incredibly proficient in every aspect of the industry and were able to help me relate the projects and case studies I worked on to real-world data science business problems. The final step was crucial: building my resume and preparing for interviews. Because of my career break, my resume was outdated and needed a complete overhaul. Imarticus’ career services team helped me hone my resume to reflect my new skills and turn it into a job-magnet. They also helped me run through the most probable data science interview questions through mock interviews. The professional transformation I’ve undergone is a testament to Imarticus’ data science program, and I am thrilled that I took up the course. Do you want to kick-start your data science career? Click Here to inquire about Imarticus’ Data Science course and speak to a career counselor today.

Why R Programming is better than SAS?

Posted on by Imarticus Learning

R Programming and SAS Programming have over the years, assumed the role of programming giants in the big data industry. And since the time of R programming’s inception, a debate between which tool is the best has been quite commonplace. For us to finally determine why one tool is better than the other, it would be very imperative to know what exactly the two ecosystems comprise of.
SAS by far has been the hands-down leader in the industry, mainly because of its long list of renowned clients and its time-honored existence in the industry. This tool or software environment offers its users with an enormous amount of varied statistical functions and has a great Enterprise Guide and Miner for its clients. This ensures that users learn faster with a continuous flow of technical support.
R, on the other hand, has come to be a crowd favorite, mainly because of its open-sourced nature. This means that it can be downloaded free of cost as opposed to its licensed counterpart SAS. This free mature and an overactive community, R programming have to offer quite a number of latest statistical functions and a number of solutions as well.
Related Article: R Programming in Business Analytics
It may seem that R has come to lead the race in this commercial analytics space and rightly so. Here we present, in a nutshell, a few very strong reasons which tip the scale in R Programming’s favor.

  1. The Cost

SAS programming, since its inception, has been a licensed software environment. While it has a plethora of private companies under its belt, very few individuals are able to get access to it on their own. This is the fundamental difference that gives R the cutting edge. Being free and easily available to download makes R a novice’s dream.

  1. Application

R programming wins here with quite a large margin too, mainly owing to its community. All the latest features and new abilities have already reached the users, both faster and more frequently when compared to SAS. On the other hand, SAS has a team, which upgrades its features in variation roll outs, which have major intervals of time between them.

  1. Transparency 

R happens to have the largest community available for any kind of open-sourced programming tool out there. As this proves a greater advantage for all those looking for small tricks and tips to solve any sort of bugs in their programs. Fast and free help is always appreciated by many of us in the society today. This is not quite the case with SAS though.
These three qualities unearth stark differences between the two tools. At the same time, it is also true that many professionals today don’t just work with either one of these tools. Many reasons are touted for this, it could be the amazing customer support that SAS offers, the extensive amount of statistical facilities, and the fact that SAS has only reviewed features in it and so on. But at the same time, there has been a popular trend among data science aspirants of opting for courses, which give them the benefit of the best of both worlds. Institutes like Imarticus Learning thus become their go-to places for the same.

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