Imarticus Learning
India’s leading professional education institute, offering certified industry-endorsed training in Financial Services, Investment Banking, Business Analysis, IT, Business Analytics & Wealth Management
When a professional begins his or her work in the field of data science, there happen to be a varied amount of roles that they need to follow and to succeed in. These various roles are most often overlapping with data science and various other disciplines like machine learning, deep learning, AI, Statistics, IoT, operations research, applied mathematics and so on. It is a widely accepted fact that data science is a broad discipline and there are a number of functions a data scientist would come across as a part of a data science setting. Read on to know the intricate difference between such very functions of a data scientist. First of all, there is no one type of data scientist, there are actually as many as nine types and could most definitely be more. There is the classic data scientist, then there is the data architect, the data engineer, the statistician, the analyst and then there are those who although work with data but don’t really have the job specific titles, like the founders of the companies and top professionals and so on. Moving ahead lets discuss the various roles of a data scientist in particular two disciplines, machine learning and deep learning. While the former refers to a set of algorithms that are supposed to train on a data, in order to predict or take actions which will happen in accordance with optimization of systems. For some the concept of deep learning usually refers to a set of neural networks. This basically means that any function which takes place within this discipline would be a kind of a machine learning technique but on a much deeper level and with a much deeper layer as well. While machine learning could be referred to as the whole, deep learning is most often referred to as a subset of the same. The latter has seemingly become quite popular as opposed to the former as it involves a particular mathematical model that is similar to a set of blocks, where a little adjustment will give out quick results. When it comes to the different roles that are supposed to be performed in machine learning and data science, the former usually refers to a kind of a learning. Here in many types of processes are involved for instance regression, naïve Bayes and clustering and so on. On the other hand when we talk about the roles that are supposed to be followed in the field of data science, then here it is usually a statistical technique that is put to use and the aim is to detect clusters and cluster structures without any sort of prior knowledge of an algorithm set to help the classification process. Some techniques used here are hybrid, whereas some are semi supervised and so on. While these many roles and functions of a professional data scientist may be different, but all of them have to undergo the same educational procedure. Where some choose to do individualistic study of data science, whereas others choose the route of professional training institutes like Imarticus Learning.
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These days almost every single person, regardless of them being associated with the field of information technology or not, are well aware of the concept of big data and data science. As is true for any other field, this field too keeps on continuously evolving as time passes by. So for all of those data enthusiasts who are obsessed with keeping tabs on every new development in this field, here we bring to you the newest trends in the industry.
What is meant by the term ‘predictive analytics’? Many a data aspirants must have wondered quite a lot of times in their journey in this field. When we talk about predictive analytics we basically mean to talk about the usage of various data, statistical algorithms and varied machine learning techniques that help a person to not just identify but also make optimal use of all of the data in order to realize the best outcomes in keeping with its history. The main aim herein is to actually ensure what will happen while progressing expertly from what has happened in the present stage.
For many who think that predictive analytics is a by-product of the contemporary times, they are sadly living under some pleasant misconceptions. This field was always in the existence since decades. The only difference here is that today this field has emerged as quite a force to reckon with. This new age and contemporary development and coming to the fore of this field is due to a huge number of reasons. In a nutshell, we could talk about the various reasons like growing amounts of volumes of data, while more interest seems to be growing in generation and collection of this data. Apart from this there is the availability of cheaper and faster computers and computing systems all across the globe lately, even in the most remote parts of the most remote villages. Data generation and analytics is in a lot of ways an easier to use software which adds to the ever growing competition and tougher economic conditions which make companies strive harder to become their very best versions. With the emergence of new and interactive technologies, have resulted into a huge shift in the technology and the way it works especially in the field of predictive analytics. Today it no longer remains the field of mathematicians or statisticians, but rather has soon transformed into the field of business experts and business analysts. Predictive analytics is used widely in various different types of verticals in the industry. These include detecting fraud, wherein multiple analytical systems can actually devise methods that help in improvement of the pattern of detection and help in the prevention of crime and criminalistics behavior. It can also be used in optimizing marketing campaigns. These very systems then help one in promote cross sell opportunities in order to help businesses grow and attract more and more customers as well as retain and grow the most profitable customers. These modules help prevent any kind of reducing risks and helping the companies increase their credit scores. This is why many financial firms usually follow the route of predictive analytics in terms of protecting the firms from any sort of financial debacle. Developing predictive models is a firmly strong practice followed by many. There are many professional training institutes in India as well abroad which offer a number of courses in various data analytical tools as well as predictive analytics like Imarticus Learning. Credit Analysis in simple terms is basically referred to as a type of analysis procedure which is most often opted by an investment banker or a portfolio manager. These professionals, when perform this procedure, they usually deal with examining the ability of various financial institutions in keeping with meeting the demands of its debt obligations. The various entities that this procedure usually involves is being able to identify the appropriate level of default risk, which is associated with the investments that are potentially going to go into that particular entity.
The whole process of credit analysis involves a lot of varied type of proceedings, the first of which begins with identification of the firms that are supposedly going to experience a change in their debt rating. On the basis of this then, the financial analyst gets to decide what kind of changes if followed will result into the profits and dividends for the individual. This helps the analyst of the investment manager to help derive what bonds that professional should purchase in order to not just achieve profits but also to ensure no loss whatsoever right before the credit ratings of the same begin to change. This credit analysis makes up for a smaller process in the larger spectrum of a field of work known as credit market, this happens to be a virtual marketplace where companies, governments and financial institutions alike, issue ‘debt’ to all the investors who make up the society. These debt instruments refer from various investment grade bonds, junk bonds, short term commercial papers, promissory notes, treasury bills and so on. This is basically what makes up this credit market, which is also sometimes popularly known as the debt market. It also involves a lot of other offerings such as notes and securitized obligations which range from various types of mortgage pools, collateralized debt obligations, dwarfs and so on. These credit markets come to be of greater use usually when these corporations and governments are in need of raising money. In lieu of earning money thus, they offer bonds in the market for all the investors to buy. These investors, when they buy the bonds usually lend money to those who are the issuers of these bonds. And when these very bonds, which happen to be timed perfectly mature, this results in these investors selling them to those who have issued them at face value and thus ultimately achieving profit. This is the very process that the portfolio managers and some of the investment banking professionals carry out for their clients. These professions have become quite popular in the recent times with more and more candidates looking to apply herein and advancing their career through various professional training courses, the likes of which are offered by institutes like Imarticus Learning. Data Analytical languages or as they are popularly known, programming languages tend to be a little on the difficult side when it comes to learning them. Of all of them, it is believed that Python is one such language or tool, which is pretty easy to learn, especially when we compare it to the others. The syntax that this programming environment provides is not really that ceremonial and is quite easy to get a hang of. This helps all of those non-programmers work really efficiently in this software. When it comes to learning python or teaching it to someone, it is easier to do so with examples as opposed to teaching say Ruby or Perl mainly because of the lesser number of rules and special cases that Python has. Many might have heard this name ‘Python’ for the very first time in the past couple of years. But what is interesting to know that this programming language has existed in the industry for the past 27 years, which is a lot more time. What then makes this tool so relevant in spite of being so old? It is the fact that Python can be pretty much applied to any and every software development or operations scenario that you can find in the world today. You can make use of python if you are looking to manage local and cloud infrastructure, or developing websites or have to work with SQL or even if you are looking for a custom function in order to make do with Pig or Hive, then Python applies there as well, this is a major reason as to why professionals especially those working in the analytical fields must learn python. With python it is so easy that once you learn the language, you can very easily leverage the platform. It happens to be backed by PyPi which is pronounced as Pie Pie. Herein a user can make use of more than 85,000 modules as well as scripts. These modules are formulated in such a way that they are able to deliver pre-packaged functionality to any of the local python environments as well as solve a number of problems like the working of databases and the glitches therein, implementation of computer vision, and execution of advanced data analytics such as sentiment analysis or building of RESTful web services. These days it has become quite a norm that any job you happen to be looking for, you will most probably be in need of having a skillset that is defined by big data and analytics which is why it becomes quite important for one to thoroughly understand the working of Python. As this data analytical tool happens to be a strong presence in the various areas of coding as well as data analytics it is sure to rule the roost in the near future. This is why we see a lot of professionals opting to learn Python from various professional training institutes like Imarticus Learning. Any organization whatever it does, has to store a huge amount of information in the form of records. These records are those entities which come to be of great use especially when it comes to analysis and when the higher ups of that very organization need to reach a certain decision on the basis of all the information received in the past, so that it can facilitate them to take a proper decision for the future. This information that these organizations store is popularly known as Big Data. Now one might wonder as to why we call it big data, this is because it is actually very big and enormous. This information, in the most unstructured format is received from various millions of sources. This big data once received is stored by the various firms. Now retrospectively speaking, when this information or big data started pouring in, organizations had to buy expensive storage spaces in order to store it. But soon there happened to be one data analytical tool that came to their rescue. It is because of this data analytical tool because of which today all the organizations are store all the information that they generate at very affordable costs. This data analytical tool is very popularly known for its logo of an elephant and is called Hadoop. It is almost like it has taken over the form of the Sirus star in the cluster of other stars and shine brighter mainly because of the fact that it has that great an amount of fan following. Here’s what some of the top guns of the industry have to say about Hadoop. Mr John Roese, who is the CTO of EMC says, “When it comes to analytics, there is not a large talent pool. It thus becomes a huge challenge to find 1000 Hadoop experts.” Mr. Doug Cutting, another senior level official says, “I think there is still some doubt in people’s minds about whether Hadoop is a flash in the pan. And I think they are missing the point. I think that is going to be proven to the people in the next year.” Two top level employees, extolling the various benefits that Hadoop has to offer make for two reasons strong enough to persuade any professional to actually take up learning of Hadoop and ensuring their bright future in the field of Data Science. Hadoop can process zetabytes of data with astute perfection and at the same time has increasingly generated a lot of demand among the employers of various firms, making it one of the top data analytical tools skill sets that are being employed in this field. Hadoop is also one of the fastest growing technologies in the market which makes it a huge success with the candidates as well as the employers in the industry. There are a number of professionals who want to learn this amazing tool and are actually opting for professional training institutes like Imarticus Learning for the same. Hive or Apache Hive as it happens to be professionally known, is basically a data warehouse software project which is built as a superstructure on the base of Apache Hadoop. The main purpose for the conceptualization of Hive is in order to provide various data related services such as data summarization, query and analysis. Hive usually happens to give a very SQL type of an interface in order to query data that happens to be stored in various databases and file systems that happens to integrate with the data analytical programming tool of Hadoop Programming.
Usually according to the earlier standards, all of the SQL type of data base queries were supposed to be implemented into the system of MapReduce Java and API in order to execute the operations and queries over the distributed data. This is what changed with the arrival of Hive on the programming scene. Herein the required SQL abstraction is provided by Hive itself in order to integrate these similar queries directly in to JAVA without the need of implementation of queries along the low level of JAVA API. Did you know that Hive was initially developed by the social networking giant called Facebook? What is more interesting is that hive was also used by various other companies when it came to programming like Netflix and also the Financial Industry Regulator Authority. Even the delivery giant Amazon makes use of Hive as a part of its software fork which basically includes a software environment called Amazon Elastic MapReduce on the web services provided by Amazon. The various feature of Hive in Hadoop include accelerating the index types and index compaction and many other indexes like bitmap index and many more types that have been planned of and will eventually be implemented. Under it there are a varied number of storage types such as plain text, RCfile, HBase, ORC and so on. It also helps in order to store Metadata and this helps in also reducing the significant amount of time that is required to perform semantic checks during query execution. In order to operate on compressed data that is stored in Hadoop, this Hive ecosystem comes to be of great use as it makes use of various algorithms which include DEFLATE, BWT, snappy and so on. As a part of Hive, the user will also get to access built in user defined functions which are commonly known as UDFs and are generally used in order to manipulate dates, strings and various other data mining tools. Hive supports the extension of this very UDF set to various other functions which are usually not supported by the essentially in built functions. SQl queries as a part of Hive are directly converted into MapReduce or Tez or even Spark Jobs. This very Hive has thus become insanely popular as a part of the Hadoop data analytical tool environment and is highly preferred by many data aspirants. Many of these aspirants thus tend to go ahead and get trained through professional training institutes like Imarticus learning that offer courses in Hive in Hadoop. These days you get to hear this term ‘global marketing managers’ quite a lot during tea break conversations, in the media, almost as if its reiterating its omnipresence. The professionals working in the field of Global marketing are very well known as Global Marketing Managers. They can be seen in various capacities such as product development, market research or even communication with clients overseas. So if you happen to be looking for an entry into this field, then this is the right place for you. Read on to know all about the career and the various inner and outer workings of the same. Essentially speaking, Global Marketing Managers generally have one specific job, which they are required to thoroughly do. This is to maintain their company’s shares and stocks on a global scale. And by maintaining, we don’t just mean merely maintain them, we also mean ensuring maximum benefit for the same. This field requires for an individual to literally always be on their toes so as to ensure that they are always aware of the various trends in the global markets and how they affect the various firms and industries. Another function that these managers have to accomplish is establishing competitive and profitable pricing strategies. So now that we know of what exactly the profession entails, let’s focus more on what exactly the educational requirements for the same are. Most of the professionals who belong to this industry generally have had their background in the field of business administration. The various subjects of their study focus mainly on marketing as well as a little bit of economics, management, finance and international business law. Many of them usually take up various lucrative internships which serve as valuable experience before they take a plunge in the career of global marketing. Global marketing managers more often than not are required to be creative idea generators. They usually possess a strong skill set of being competitive and extremely decisive. One very interesting fact about their skill set is that they are very multi lingual and some may even have the ability to do a few, varied accents. This is because these professionals usually deal with clients from various different countries and nationalities. This skill set in particular serves as a huge bonus in order to help gain those international clients by both wining and dining them and most importantly showing them how globally attuned a person is. Recent studies state that the employment rate of these global marketing managers is bound to go on increasing. The proposed rate is supposed to go up to 9% by the year 2024, this is what the U.S Bureau of Labor Statistics believes. Many experts and gurus of the industry are of the opinion that seeing how the world is literally going on ahead and coming together, it won’t really take long for the companies to actually go ahead and take their operations on an international level. And when they do, the career of global marketing is soon to be positively explosive. This is why many institutes like Imarticus Learning are already gearing up by providing courses in global marketing today. |
About ImarticusImarticus Learning is a education institute based in Mumbai. We offer certified industry-endorsed training in Financial Services, Investment Banking, Business Analysis, IT, Business Analytics & Wealth Management. Archives
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