Taking the technological progression into consideration, none of the industries is left untouched across the globe. In the recent few years, the industrialization has successfully marked its space into the area of banking. A lot of countries have embedded Machine Learning and Artificial Intelligence into their operations. The trend has impacted, massively, the Indian banking industry, wherein, the investment banking segment is witnessing a significant upsurge in concern to getting automated.
The Investment Banking in India is a much broader concept as it majorly revolves around the establishing and retaining essential relationships with the potential clients. The Indian banks are actively ensuring the successful implementation of automation for attaining desirable results. Though the human intervention is a necessary facet of investment banking, however, the automation is undoubtedly taking over investment bankers and initiating much-systematized operations. An Indian investment banker involves more of Sales and Marketing as compared to applying finance in their routine responsibilities. The efficacious execution of technology-driven software’s in Investment Banking Course in India has eased the complexities and are proving an added advantage for contemplating financial analysis.
The automation in Investment Banking is turning out be a hugely profitable deal. The banking transactions have become smooth followed with error-free execution, in turn, circumventing the possible associated risks. On the other hand, the financial analysis has become proper due to the figures being extracted from the automated system technology. The automation has indeed, fetched stronger systems that effectively reduce costs for the human intervention. The scenario of traditional investment banking in India involved a range of significant workforce. However, with the emergence of automation within the business, the added costs incurred in the process of hiring and training of the new employees have witnessed a reduction. Also, the values have been saved to a great extent in bringing the office equipment followed by paying massive wages to the workforce.
Taking a major throwback to the Investment Banking in India, the routine operations – right from essential transactions to financial analysis, the processes were surrounded with tremendous challenges. The workforce suffered from workplace disturbances which in turn, impacted their productivity adversely. Such aspect also, made their performance weak and raised competition as well. Nevertheless, the implementation of Machine Learning and Artificial Intelligence ensured a better execution and reducing the paperwork within the investment banks. The abridged processes have undoubtedly increased the productivity of the investment bankers and have provoked them to deliver the profitable results.
Long gone are the days where banks took extensive time and energy to solve the customer queries. The automation has successfully led to a substantial increase in the conversion rate with a significant boost into the responsiveness from the customer’s perspective. The automation has provided the investment banks with a personalized platform that drives better and timely execution of decisions. Following such tremendous automation approach in the investment banking, the operations have efficiently become flexible. The flexibility in the procedures have improved the functionalities and enabled the banks to expand their services overseas.
Automation is an intense wave which is hitting the banking across the globe. The successful implementation is ensuring better retention of customers and doing extreme benefit to the banks. However, the automation within the Investment Banking in India is demanding a considerable awareness due to the technology being overlooked by those situated in the developing cities. Automation possesses a strong tendency to change the face of the economy and enable efficient delivery of strategized decisions for the betterment of the sector.
The banks are automating with time, in turn, leading to improved and appropriate changes in the back-office segment of the banks.
Day: February 12, 2019
Which Skills are Required for Machine Learning Jobs?
Machine language is generally related artificial intelligence, which provides the machine or computers with the ability to complete certain tasks like diagnosis, planning, prediction, recognition or robot control. It consists of different algorithms, which you can use to teach the machines to change and grow when exposed to new data.
The process of implementing machine language is somewhat similar to data mining because the process looks through data and searches for the same pattern. Now that you have an idea of what machine learning is, let’s have a look at the skills that are required to get a machine learning job.
Also Read: Future of Machine Learning in India
Computer Science & Programming Skills
Some of the fundamentals of computer science are essential when you are looking to learn machine learning. Concepts like data structure, algorithms, complexity and computability, along with computer architecture are essential for artificial intelligence. In fact, you should also have knowledge of programming languages like C, C++, Java, Python and R, among others. A little bit knowledge of assembly language doesn’t hurt either.
Probability & Statistics
Conditional probability, and its characteristics and the techniques derived from it plays a key role in the machine learning algorithms. Moreover, you should also know about the different terms of statistics like mean, median and mode along with variance and standard deviation. These are all necessary to not only observe the pattern but also validate the data that is received through different means. Some machine learning algorithms are in essence an extension of the common statistical operation procedures.
Applied Mathematics & Algorithms
You need to know not only how to solve a problem but also how to implement it in short executable steps when it comes to machine learning. Algorithms help you to understand how to break down a problem into executable steps, and that is why this is important. In addition, you also need to know about gradient, convex optimization and its application in daily life, so that you can implement it in machine learning.
Operating Systems
When it comes to machine learning, most of the coding is done in Linux or some version of it. So, you need to be versatile with Unix or a version of Linux, which is in use presently. You also need to know about the Linux tools, which will make your life easier in the long run. Some examples include grep, find, sort and tr.
Software Engineering & Designing of Systems
When you are designing a machine learning tool, you are also designing an advanced software. So, at the end of the day, you need to know how to design system, and how you can implement your ideas in that. You also need to understand how different algorithms interact with your system, and how you can speed up the process without compromising on the resource space.
Now that you know about the skills required for machine learning jobs, it is time to get started on acquiring these skills. In case you have some of these skills, make sure you hone them so that you can implement it and build a great system when the time comes. Good luck!
Related Article: What is The Easiest Way To Learn Machine Learning?
Tips that Will Help Elevate Your Career as a Data Scientist
For a data scientist, appearing smart should be the topmost priority. It will not only elevate your self-esteem among others but will also keep you self-motivated. It’s not just for the data scientists but also for the individuals employed in different sectors as well. Even if you have a great knowledge and depth in your field, may not be able to reach the heights of your potential.
In this case, having the right attitude, personality, and communication skill helps you to achieve your targets. Also, the job market for the data scientists is getting tougher day by day. Thus, in order to bag the right job for you, only if you learn data science, that may not be enough. That is why in this article, let’s explore four tips that will make you stand above the rest in your data scientists career.
Have a Sporting Attitude towards Competitiveness
There is no doubt today, wherever you go, you will face competition. Even if you are looking for a job or in a company, you will face tough competition from others. If you feel scared or if you just back off from that competition thinking that you are beyond it or don’t have the ability to compete then I am afraid you will never going to make it. If you want to have a successful career as data scientist after completing data science courses then you have to embrace the competition that you have in front of you. Only having the knowledge of statistics for data science will not going to help your cause because, at this age, your attitude plays a key role.
Look Beyond the Job Title
Data science salary and job titles may vary from company to company. By just hearing the name of the position, it will be hard for you to make out whether the position you are getting in that company is the position you wanted for yourself after completing the data science courses. Asking about what would your responsibilities be in the company and what methodologies you will be using to fulfill your responsibilities will help you to get an idea of your job responsibility. However, if you are looking to have a good career as a data scientist, then you should be selecting your company solely based on the job title.
Having Multiple Skills is a Plus Point
This is an age where companies look for specialists. However, if those specialists have other skills that might be helpful for the company then that is a plus point for the candidate. There are companies looking for other skills as well apart from the knowledge you have in data science. Sometimes you may miss out on grabbing the job opportunity even after knowing statistics for data science just because you don’t have the required skill set the company is looking for. Also, having an extra skill might have an impact on your data science salary.
Be Prepared to Face the Tests
Anyone can put data scientist on their resume. But, that does not mean that they have the required skill set to handle the work of data scientists. That is why even if you have put data scientist on your resume, the companies will test how much of that is actually true. They will test whether the candidate knows his or her R or Python or you have a vague idea of these terms. That is why you need to learn data science in a robust manner so that whichever test companies put in front of you, you will have the confidence of passing them.
Final Thoughts
The career as a data scientist is very lucrative and everyone knows that. However, in this competitive world, many fail to establish themselves as data scientists simply because they don’t have the X-factor that will elevate their career. Following the aforementioned tips will surely help you to succeed as data scientists.