Benefits of Big Data Analytics Training and Certification Program

It’s no longer a question of whether an association needs a Big Data technique. It’s an issue of how soon they grasp it. IT experts are scrambling to get certified in Big Data or Hadoop, which is relied upon to end up basically the most heated tech expertise in the following couple of years. Huge Data is gradually receiving noticeable interest known everywhere throughout the world, as organisations’ overall verticals like utilities, retail, media, pharmaceuticals, vitality, and others are grasping the most current IT idea. This is also the reason why Big Data training and certifications in big data have become so popular in recent years.

According to some current techniques, numerous associations did not have the capacity to meet the requests of the client because of the unpredictability of information broken down and prepare the information. To dodge these sorts of issues, Organisations are actualizing Big Data innovations. Overall areas of the world, 53% of the 1,217 organisations had embraced no less than one Big Data activity.
Why Big Data Certification?
The truth of the matter is, organisations are attempting to get Hadoop ability. Ventures embracing Hadoop need to be guaranteed that individuals they contract can deal with the petabytes of Big Data. The testament is proof in such manner, making a man in charge of the information.
The following happen to be the benefits of big data training, especially with Hadoop:

• Taking after a portion of the regular points of interest Big Data affirmation offers.
• HR managers and HR groups are chasing for aspirants having big data and Hadoop certifications. It’s an unequivocally preferred standpoint over those having no affirmation.
• Huge information accreditation gives an edge over different experts, in regards to the compensation bundle.
• Hadoop, as well as big data certification, helps an individual quicken vocation development amid the inside employment posting process.
• One of the real points of interest Big Data certification gives is that it is useful for those attempting to change over to Hadoop from other specialised foundations.
• Hadoop certification underwrites hands-on understanding of working with Big Data.
• Confirms that an expert knows about the most recent Hadoop highlights.
• The big data certification helps in talking all the more unquestionably about the innovation to the organisation while organising with others.

While the above are the general benefits of anyone who happens to have pursued a big data training or certification. The biggest benefit of all would most definitely be the salary packages that some of the expert Data scientists receive these days. As the world becomes increasingly data-driven, organisations of various stature have begun to depend on these big data magicians, to create their magic with numbers and help their respective firm’s progress.
Now for the important question. Where does a data aspirant go to get a certification in Big Data? Imarticus Learning happens to be the best in class, especially when it comes to certification in Big data courses that are thoroughly industry-endorsed.
The program includes comprehensive coverage of Big Data trends, HDFS architecture, MapReduce concepts, Query tools like Hive and Pig, data loading tools and several advanced Hadoop concepts, all taught by experienced industry professionals who have 15+ years of experience in this domain.
Our program is aligned to meet the needs of the industry and the focus is always on job readiness rather than being excessively academic. The curriculum and learning methodology is designed and vetted by our Analytics Advisory Council which features senior management from top Analytics firms to ensure effective learning. You will also have periodic guest lectures with industry professionals to help gain new perspectives and broaden your horizon.

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The Emergence of Data Engineers & Data Scientists

Data engineering and data scientist are job titles which might be new to us in recent times, however, these roles have been around for a while. Traditionally, anyone who would analyse data would be called a Data Analyst, and the person responsible for creating platforms to support the analysis is a Business Developer.
In the world of IT, the data scientist gets more visibility and praise, as they are the ones, extracting vital intelligence from big data and helping organisations make critical decisions with regard to their business swiftly. But it is important to note that the data scientist does not work in isolation, they are not capable of generating valuable information independently, and they need the constant support of Data Engineers. The engineers are the ones designing and maintaining software and platforms that operate the big data pipeline. They set the stage and keep it running.
A Data Engineer is essentially a software engineer with a decent understanding of math and statistics, he should be skilled in data warehousing, designing, data collection transformation and programming. Have the ability to transform and load operations, build data warehousing solutions, and test database architecture. He should be hands on with tools like Hadoop, Spark, Javascript etc.…,
A Data Scientist is someone who is an excellent statistician, with above-average software engineering skills. Should be primarily inquisitive, and have the skills of data visualisation and storytelling along with programming skills. His tasks would essentially be to identify the question and find answers through data, find a correlation between dissimilar data, to be able to tell the findings, hence storytelling ability, and lastly should be hands-on with tools like Julia, Python Programming, data visualisation tools like Qlik view or Tableau.
The description of Data Engineers and Data scientists can be quite obscure, there is an overlap. While these roles still maintain to be distinct data science job roles, they require different skills and experience. Some data scientists can do data engineering, while some data engineers can do data analysis and visualisation as well.
The emergence of big data has opened space for new titles and roles to come into existence. Over the past couple of years, businesses have applied all means to get individuals who have the skills to turn data into gold.
A lot has changed in the way businesses function, earlier a lot of companies were functioning in the physical world, nowadays most businesses function on the digital platform. When a company is mostly functioning online, there is a huge accumulation of data. Data about who is visiting your website, if they are choosing your competitor’s website as opposed to yours, and what could be the reason, you also get data about the statistics of the competitor’s target audience, So the possibility of the data accumulation is too big and very fast. The data are screaming information and is noisy beyond comprehension.

In order to find a way in this data, one needs to sort this in two ways,
Firstly, to create a database to process the data and to store it and the second would be the need of people to comprehend the data and know-how to ask relevant questions and research the data in a method that the concerned business can take informed pointers from it. This stored data needs people who know statistics and know how to write code, in order to get insightful information.
Data scientists and Data Engineers are these people; they are the need of the hour. To know how to process data using various platforms, and more importantly, we need them to be around, These people also know how to make sense of the information, and how to analyse it. They don’t only plot graphs from data collected from a spreadsheet but also create statistical models that over a period of time affect the business and products with effective ways to increase the revenue.
The data available could be stand but smart and appropriately skilled people are the ones who help find that needle in the haystack.

Read more…
Having Technical Knowledge Is Not Enough For Data Scientists
Five Reasons Why Big Data is The Right Career Move for you…
The Future of India in the Field of Big Data Analytics

Why Learning Financial Modelling is a Great Decision?

Do you ever wonder what does a Financial Modeller do? Some of you may aspire to succeed as Financial Modellers. So today, let us learn about Financial Modelling and Valuation. 

The global economy is ever-evolving, so it’s good to be on top of your game, a step ahead of the others. Get a career boost by learning financial modelling. It will jumpstart your career in incredible ways.

Learn the concepts and tools required to get an edge in the ultra-competitive job market. Financial modelling is one of the most sought-after skills in today’s corporate world.

What is Financial Modelling?

Financial modelling analyses the company’s performance on relevant economic factors.

The analyst intends to forecast an organisation’s capability in potential earnings.

 Various theories exist. A financial model is a spreadsheet created in Microsoft Excel. It projects the future financial performance of a company. It usually captures all variables for a particular event. Financial analysts use financial models to understand the company’s performance. This helps them to predict its future.

Building a financial model is a prerequisite for jobs in investment banking.

One might be a business school graduate, and one could understand financial modelling. But there is a gap in learning financial modelling and application. Most of the learning in B-schools is not relevant to the latest developments. 

 So if you are having experience, you might know what to do; yet, you would not know how to do it right with the best technique. For example, you might know about cash, debt paydown, and ways a company can raise revenue. But what big companies want you to know is to determine the fair stock price of a company given all their financial statements.

Learn Financial Modelling the Right Way

When you understand financial modelling you will understand the company’s fundamentals. 

In recent years financial modelling has become integral for career advancement. Also, most corporate finance roles need knowledge of financial modelling. This means if you know financial modelling, it also opens many career choices for you.

The reason it is so multipurpose is that it assists in any job role involving analysing. Few people know how to build a financial model. Hence, a specialised course will give you an advantage over others.

A financial modelling course is for anyone pursuing an MBA. Also, those who have done their CA, CFA, or plan to, as it will add to the theoretical learning in a practical way. Additionally, working professionals will get an in-depth understanding and an edge over others. 

Imarticus Learning has created a Financial Modelling program for careers in Corporate Finance. It helps develop a fast-paced career path, which is rewarding. 

Contact us if you have any questions about the batches or investments needed to enrol in this course. They would be more than happy to guide you at each step. We hope you found this blog to be quite enlightening. Are you prepared to develop your abilities and become a Certified Financial Modeller?

Five Reasons Why Big Data is The Right Career Move For You

You cannot achieve tomorrow’s results using yesterday’s methods, and this line is the need to understand and accept the impact of BIG DATA and the growing demands of the business.
Big data means access to big information, leading to abilities in doing things you could not do before.
Case in point – a small exercise…
Before you read ahead, check the posture you are sitting in, now look around and check the posture of others sitting beside you, observe… what do you see? Someone is slouching. Someone has their legs crossed, elbows resting on the armchair, palms pressed against their chins, constant moving of the limbs. Not everyone sitting around you has the same posture. What if we put many sensors on the chairs around you to help record and create an index, which is unique to you, which says person X sits in these particular postures during specific intervals of the day?
In addition, say what if this data was sold to car manufacturers as an anti-theft design to help recognise that the person sitting behind the wheel is not the same and say until he keys in a password the car even though entered would not start.
Now imagine what if every single car in the world had this technology, think of the benefits of aggregating this data. Maybe we would be able to predict which postures while driving would lead to an accident say in something as close as the next 7 seconds do, and alert the driver to change position or take a break from driving.
Now my dear reader THAT is the power of BIG DATA. It can help record, collect, understand, predict, and prevent events from a random collection of information.
So if that is so useful where lies the problem? Why is the world not already a better place?
Because there is a gap between opportunity and demand in skilled professionals to help comprehend and present valuable insights into specific relevant trends, big or small from the available data.
Big data is all around us and there is a calling need to preserve all the generated data for the fear of missing something that could be important. Hence, comes the need for big data Analytics, Big data is crucial to do better business, to take accurate decisions and to always be a step ahead of your competitors. Therefore, if you are a professional in the Analytics domain there is a sea of opportunities waiting for you to dive in.
Big data Analytics is Unfathomable and depending on the environment one can choose from
Prescriptive Analytics
Descriptive Analytics
Predictive Analytics
The Big Data Analytics market is predicted to surpass $125 billion between 2015 – 2020, which in turn in some sense means handsome pay brackets for the skilled individual.

Salary Aspect

To improve the performance of the organisation, most companies have either already implemented or are in the process of implementing Big Data Analytics, as they already have the data at their disposal.

All Organisations adopting some form of Data Analytics = Growing Market

There is a big gap in skilled professionals who are able to convert the data available. The ability to see small trends from the pool of big information. Which ultimately advances the organisation in the right direction. There are two types of talent deficits. Data consultants – who have the ability to not only, understand but also use the data at hand in the appropriate way and the other is Data Scientists who can perform analytics.

Skill Deficit

Big data analytics is not restricted to any specific Domain; it can be and in recent times is being used in Healthcare, Automobiles, Manufacturing and more, creating a massive global demand.

Global demand across industries

Analytics becomes a competitive resource for organisations, according to certain studies Analytics has already become the most important asset in current times. Because we are emerging from the undeveloped analytics trends to more advanced forms. It is undeniable that Analytics plays a vital role in decision-making and taking strategic initiatives for the business.

Importance of Analytics for Better Decision Making in Organisation

So in deduction to the above, Analytics no matter how advanced is human-dependent. These are exciting times for skilled people who can comprehend data and give valuable insights from the business point of view. A trained individual with the right Analytics insight can master an ocean of big data and become an indispensable asset to the organisation becoming a springboard to the business and their career.

The field of Artificial Intelligence is experiencing great advances, both rapidly and on a large scale. The various fields that have successful applications of AI to make them better are namely the accessibility sector, agriculture, business operations, consumer convenience, disaster prevention, response to disasters, education, energy, environment, health care, prevention and screening for any health discrepancies as well as, treatment and monitoring of the same, industrial operations, public safety, social good, transportation and so on. All of these fields have enhanced applications of AI presently and will have more in terms of the future as well. But the two fields here of greater importance would be health care and education as they happen to be determinants of the global development. This is why this article will be talking about the various applications in the above mentioned fields.
Let’s begin with education. One of the very primary applications of AI would be dealing with personalization of the maths. Let’s take for instance the IBM, which seems to have developed a new age tool known as the Teacher Advisor. Based on the Watson cognitive computing platform, this tool will reportedly help all the ninth grade math teachers. It would help in developing personalized lesson plans, by analysing common core education standards and student data. This way it will be able to assist the teachers in tailor making personalized instructional material, which would be based on the intelligence levels of the particular students. Other various applications of AI will possibly involve the method of predicting which students would be potential drop outs in particular years, or transforming teacher assistants in automated machines. Artificial Intelligence would seemingly make it easier for all the students to learn new languages as well as, would improve them by providing feedback in real time.

Let’s move on to health care, the various applications of AI here would include, prevention of loss of vision in Diabetes patients, analysing speech in order to predict Schizophrenia, successfully figuring out how to prevent pancreatic cancer, transforming a microscope in order to diagnose malaria, developing diagnosing techniques in order to diagnose voice disorders and so on. Other applications would be helping of all the diabetes patients in taking smart decisions, streamlining the discovery of drugs, developing robot surgeons in order to make stitches even safer, using artificial intelligence in order to speed the recovery stages of a patient, as well as to speed radiotherapy and lastly increasing participation of people in Clinical Trials, as it would be safer and side effect free.
AI has already begun its work especially in the health sector as a certain stat-up has developed an app, exclusively for patients with Type 2 diabetes. This app basically analyses all the medical records of the patient and then goes on to make personalized recommendations about alterations in their diet, which would help them deal better with their disease. Such developments would ensure a better, healthy and disease free tomorrow for our entire world. These developments have urged many to take up a career in this field. This is why we see a number of data aspirants pursuing professional training courses offered by Imarticus Learning.

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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 
Let’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.

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.

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