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Day: June 29, 2021

Why Indian Businesses are adopting Fast Oracles Self-driving Database?

Posted on by Imarticus Learning

Last Updated on 3 years ago by Imarticus Learning

The era of automation and cloud is here. In order to drive success and delight customers, Indian companies are looking to Fast Oracle Self driving databases. 

Data drives the world. Be it consumer insights such as shopping behavior, music preference or payment preferences. Today companies are leveraging the power of technology to crunch large amounts of data. Digital transformation has become an integral factor behind a company’s growth. When we take a closer look at how large amounts of data is processed and stored, we know that the possibilities are endless thanks to automation and cloud.

Companies such as Oracle have been leaders in the space for decades are now combining technology pillars such as machine learning, automation and cloud to provide solutions such as ‘self-drive database’. In simple terms, what it means with minimal human intervention, the power of data, businesses are able to achieve high performance at a lower cost.

Take for example, a clothing brand that wants to improve their point of sale interactions to enhance customer experience. How can they do it? By using the ‘Autonomous Cloud Service’ by Oracle, the brand is able to extract and manage relevant data which can support the end customer experience. All this can be done in an Agile business courses enterprise by unlocking deep insights from large amounts of data.

Furthermore, services such as Autonomous Database not only automate the whole process but also take into consideration data privacy, protection against cyber-attacks, data thefts and storage. Organisations that undergo Agile business training can unlock the true potential of autonomous services.

Some of the key attributes which can help companies invest in such services are:

Automation of Management Processes

Oracle Cloud Infrastructure and Autonomous Database provide companies with integrated solutions such as data management, repair, tuning and upgrade to ensure business continuity and growth. 

Reduces Cost of Operations

Due to minimal or zero human intervention, businesses can focus on leveraging key customer insights derived from data thereby reducing operational costs. 

Data Privacy

Data Privacy has become a top priority for most organisations today and implementing a software database storage solution provides them with the opportunity to safeguard data on cloud against cyber-crimes.

Take Strategic Decisions Fast

In an agile world, a key factor that comes into play is when businesses are provided with an opportunity to take quick, decisions. From a strategy perspective, Autonomous Data Warehouses offer this as a part of the solution.
Conclusion
Using a traditional database is time-consuming, and Indian businesses are catching on. Due to the exponential value that self drive databases provide Indian companies are adopting this in order to accelerate growth.

Spark Vs MapReduce

Posted on by Imarticus Learning

Last Updated on 2 years ago by Imarticus Learning

Spark and Hadoop MapReduce are both open-source frameworks from the Apache stable of Software. Since 2013 when Spark was released it has literally overtaken and acquired more than twice the number of Hadoop’s customers. And this lead is growing.

However, big-data frameworks are directly linked to the customer’s need for a particular framework and its uses. Therefore a literal comparison is difficult and we need to discuss what Spark and MapReduce are used for and their differences to evaluate their performance.

The performance differences between Spark and MapReduce:

The main differences between the two are that is that MapReduce processing involves, reading from data and then writing it to the disk, whereas Spark process data within its memory. This feature makes Spark very fast at processing data.

However, MapReduce has a far greater potential for processing data compared to Spark. Spark is faster by a 100-fold increase in speed and its ability to process data within the memory has scored with its customers preferring it over MapReduce.

Where MapReduce is useful:

As pointed out above the potential for data processing is high in MapReduce. It  is useful in applications using:

  • Large data sets linear-processing:

Hadoop-MapReduce enables very large data sets to be processed in a parallel fashion. It uses the simple technique of dividing the data into smaller sets processed on different nodes while gathering the results from these multi-nodes to produce a single set of results. When the resultant data set produced is bigger than the RAM capacity Spark will falter whereas MapReduce performance is better.

  • The solution is not for speedy processing: 

Where processing speed is not critically important Hadoop MapReduce is a viable and economical answer. Ex: If data can be processed at nights.

Where Spark is useful:

  • Rapid processing of data: 

Spark’s processing speeds are within the memory and about 10 fold better in terms of storage data and a 100 fold in terms of RAM data.

  • Repetitive data processing:

Spark’s RDDs allow it to map all operations with the memory. MapReduce will read and write the resultant set to the disk.

  • Instantaneous processing:

Spark enables such processing if instantaneous decision-making is required.

  • Processing of Graphs:

Spark scores in repetitive iterative tasks as in graphs because of its inbuilt API GraphX.

  • Machine learning:

Unlike MapReduce, Spark has an inbuilt ML library. MapReduce needs an ML library to be provided by an outside source to execute the same task. The library has many innovative algorithms that both Spark and MapReduce use while computing.

  • Combining datasets:

Spark is speedier and can combine data sets at high speeds. In comparison, MapReduce is better at combining very big data sets albeit slower than Spark.

Conclusion:

Spark outperforms Hadoop with real-time iterative data processing in memory in

  • Segmentation of customers demonstrating similar patterns of behavior thus providing better customer experiences.
  • Management of risks in decision-making processes.
  • Detection of fraud in real-time is possible due to its ML library of algorithms being trained on data that is historical and inbuilt. 
  • Analysis of industrial big-data analysis in machinery breakdown is a plus feature of Spark.
  • It is compatible with Hive, RDDs and other Hadoop features.  

What Is The Difference Between B. Com Banking And Finance And BBA Banking And Finance & Which One Should You Choose?

Posted on by Imarticus Learning

Last Updated on 2 years ago by Imarticus Learning

With so many professional alternatives to choose from these days, it can be challenging for students to make the best decision. However, based on student preferences, Commerce appears to be the topic of choice in the current situation.

However, there are many disputes about which of the two approaches has a better chance of succeeding. So, in this in-depth piece, we’ll look through the differences between BBA and BCom as undergraduate courses.

What Is the Difference Between B. Com Banking and Finance and BBA Banking and Finance?

  1. Com Banking and Finance is a 3-year degree in commercial banking that covers a wide range of topics in the banking and insurance industries, including accounting, banking law, insurance law, risk management, and regulations.

MBA online CoursesBBA Banking and Finance program focuses on the management of many disciplines in the banking and insurance industry, such as Treasury Operations, Risk Management, Investment Banking, Project and Infrastructure Management, and so on.

BBA Banking and Finance involved with the management of many fields included in the banking sector, whereas B. Com Banking and Finance deals with studying basic principles involved in the banking business.

Which Is Better: BBA or B. Com?

BCom is a wonderful choice for those who want to pursue a career in Commerce and want to learn more about various areas of the sector while also being interested in numbers. If you solely want to pursue a job in banking and management to become a New Age Banking and Finance Specialist, the necessary abilities to do so, a BBA degree may be a better option.

BBA is a professional course with more practical workshops and case studies in the curriculum. On the other hand, BCom gives students an in-depth understanding of the topic and prepares them to pursue a range of career routes within it.

How To Begin a Career in BBA Banking And Finance?

The widespread adoption of Financial Technology has dramatically changed the dynamics of the global economy, resulting in a surge in the need for qualified Finance Specialists.

Imarticus with JAIN online, new age banking and finance program are explored through training using cutting-edge technologies such as, Cloud Computing, AI, Machine Learning, Blockchain, IoT, and Big Data.

The immersive learning experience allows you to apply what you’ve learned in class to real-life situations. If you dream to advance your career by becoming a New Age Banking and Finance Specialist, you can embark on a fantastic professional learning adventure by Imarticus Online.

Data analytics courseYou’ll work on real-world projects, simulations, and assignments while gaining a solid practical understanding of finance and general management, preparing you to join the New Age career in Banking and Finance workforce.

JAIN Online Highlights      

  • Online Degree Programs Approved by the UGC
  • NAAC ‘A’ Graded University
  • A Four-quadrant approach to program delivery is used, recommended by UGC
  • Studying hours and credentials are comparable to those found in regular classroom programs.
  • Top Ranked institute in India

The Bottom Line

You will learn from a comprehensive curriculum spanning in-demand New Age career in Banking and Finance skills like Financial Modelling, Equity Research, and FinTech as part of Imarticus BBA in Banking and Finance Program.

How To Skyrocket Your Investment Banking Career Prospects By Transforming Into A New Age Investment Banking Expert?

Posted on by Imarticus Learning

Last Updated on 2 years ago by Imarticus Learning

Banking and finance are changing at a breakneck pace, fueled mainly through disruptive, cutting-edge technologies. As a result, the intricacies of New Age Investment Banking have evolved to mirror these shifts, creating a greater demand for tech-savvy New Age Investment Banking professionals.

Who Is An Investment Banker?

Individuals who work as investment bankers assist corporations, organizations, and clients in managing and growing their financial assets.

They assist businesses in making sensible judgments when it comes to investing their assets to increase their holdings’ value. Investment bankers must have a thorough understanding of market trading operations and successful securities.

Career In Investment Banking

Investment banking trainingInvestment Banking course with placement in India is a lucrative business that offers substantial profit margins, and the worldwide investment banking business is expanding at a rapid pace.

Making a profession in investment banking is a huge possibility for a better life. However, people who want to work in this field must grasp what it takes and achieve it as a professional.

To work successfully at an investment bank, one must have the right skill set, including analytical thinking abilities such as problem-solving ability and strong mathematical skills, such as familiarity with numbers and statistics.

Getting Into Investment Banking with Imarticus

Online Distance MBA Program

Suppose your full-time employment experience isn’t in finance, or you become interested in investment banking after graduating. In that case, you’ll almost certainly need to apply to top MBA programs, get accepted, and use one of them to break into the field.

If you get into one of the top programs, you stand a strong chance of receiving a great offer if you put in a lot of effort and begin planning early.

Program Highlights

Imarticus has partnered with JAIN Online to provide their strong online MBA course in Investment Banking program, which covers every New Age Banking Operations protocol. The course is aimed to give you in-depth knowledge of essential aspects of the Investment Banking industry.

This Investment Banking online distance MBA program provides every instruction from industry professionals in every important facet of investment banking. You can apply your learnings to actual business strategies and difficulties.

  • Industry Partnerships
  • Investment Banking Pedagogy
  • Extensive Career Support
  • JAIN Connect

Why Opt for This Online MBA Course?

Imarticus comprehensive best online MBA courses delve deeply into the various paradigms of New Age Investment Banking, providing you with a high-quality learning experience, training from professionals, tech-based projects on prominent New Age tools, and meaningful encounters with Investment Banking industry leaders and entrepreneurs will boost your professional Investment Banking expertise like never before. Those eager to learn can expect to be in high demand as New Age Investment Banking Professionals.

Take Away

Imarticus best online MBA courses are an excellent approach to update your qualifications and eligibility. It aids candidates in their pursuit of a career in investment banking operations.

Millennials with fewer than three years of experience may benefit from and find an investment banker course to be a good fit for their learning needs.

Which Are The Important Financial Modeling Techniques That Makes A Model Flexible?

Posted on by Imarticus Learning

Last Updated on 4 years ago by Imarticus Learning

Which Are The Important Financial Modeling Techniques That Makes A Model Flexible?

Flexibility or rather, variability and simulation of a scenario under different conditions is the end goal of a model. Here are some of the various techniques you can use to make a model more adaptable.

• Model assumptions clearly– the first step to creating a workable model is to always document the delta assumption. What does that mean? As discussed earlier, if you want to say that you forecast sales of firecrackers during Diwali to up by 15 percent from 2015, then you model in the assumption.

The origin value is, let’s say, 1000 crackers sold in 2015. The result would be (1000 *0.15) + 1000 which would equal 1150 crackers sold in 2016. But you have to document the 0.15 clearly so that if someone wanted to change that assumption to 20 %, then they would just need to key 20% in instead of 15 and the entire model would change.

• Created more detailed assumptions – While complex models are generally less robust due to higher chances of linkage issues etc, there needs to be some amount of complexity for a model to be useful. For instance, we want to forecast revenue from the sale of fireworks from 2015 to 2016.

The first would be to break the Rs 1000 up into the various products like sparklers, (30% of 1000) flowerpots, and the like.

Once that happens you need to break sales into its component. Sales equal price into quantity. So instead of saying, arbitrarily, that the total sales of sparklers go up from Rs 300 to Rs 345 (a jump of 15%) in 2016, you would say that the number of sparklers would go from 100 sparklers to 115 (model in the 15%) sparklers while the price of the sparkler (Rs 3 per piece ) did not increase at all. (the model in the 0%) The flexibility comes in when I change the cell that holes from 0% to 10%.

This would make the price of the sparkler go up from Rs 3 to Rs 3.30 which would lead to a total sales of Rs 379.5.

• Use a spin button– A spinner helps model variability especially as it relates to stepping costs. So let’s say that for every extra Rs 200 I make in sales, I need to add one extra salesperson. That is not a variable cost. That is a steep cost. So when my sales go up 15% from Rs 1000 to Rs 1150, I don’t need an extra salesperson.

But what if I want to sell 1250. I need to add one more salesperson. A spin button does the job for you. Every time increment sales go up by Rs 200, one extra person at a salary of Rs x a month will be added to that cell, thereby making your model more adaptable and robust.

AI and Machine Learning in Robotics!

Posted on by Imarticus Learning

Last Updated on 5 years ago by Imarticus Learning

Artificial intelligence (AI) has to transform almost every industry we can imagine, and industrial robotics is no different. The powerful combination of robotics and artificial intelligence or machine learning opens the door to completely new automation possibilities.

Artificial intelligence courses and machine learning are currently only used to a limited extent and are increasing the capabilities of industrial robot systems. We have not yet reached the full potential of robotics and machine learning, but the current applications are very promising.

4 Basics of Artificial Intelligence and Machine Learning in Robotics
There are four areas of robotic processes that influence AI and machine learning to make today’s applications more efficient and profitable. The scope of AI in robotics includes:

Vision – AI helps robots see elements they’ve never seen before and see objects in greater detail.
Reaching – Robots also capture objects they’ve never seen before using AI and machine learning to help them determine the best position and orientation to pick up objects.
Motion Control – Machine Learning helps robots interact dynamically and avoid obstacles to maintain productivity.
Data – AI and machine learning help robots understand physical and logistical data models in order to be active and act accordingly.
AI and machine learning are still in their early stages for robotic applications, but they are already having an important impact.
Two types of applications for industrial robots using artificial intelligence and machine learning
Supply chain and logistics applications see some of the first implementations of AI and machine learning in robotics.

In one example, a robotic arm is responsible for handling frozen food crates that are closed cold. Frost causes the shape of objects to change – the robot not only displays different parts from time to time, but also constantly displays parts of different shapes. AI helps robots to recognize and capture these objects even though they are different shapes.

Another great example of machine learning is selecting and storing over 90,000 types of parts. This number of types of parts would be useless without machine learning, but now engineers can regularly send images of new parts to the robot, and the robot can then successfully capture those parts.

AI and machine learning will have a transformative impact on industrial robots. While these technologies are still in their infancy, they will continue to push the boundaries of what is possible with industrial robotic automation for decades to come.

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