September 2021 – Page 3 – Imarticus Blog

Hands-on Linear Regression Using Sklearn!

Posted on September 28, 2021March 21, 2024 by Imarticus Learning

Last Updated on 2 years ago by Imarticus Learning

Linear regression in Machine Learning is a kind of analysis where there are both dependent and independent variables. Their relationship is depicted here by a linear line from the provided data. This technique is mainly used for predicting the results.

The data and the values from the linear regression are used to find the Mean Squared Error (MSE) to change the values of the variables so that they have the least value possible and get the most accurate results.

While there are several tools that are usually used for linear regression Sklearn is one of the popular tools.

What is Sklearn?

Sklearn is the short form for Scikit Learn which in Python Language, an open-source ML library to help unify the various tools of algorithms in machine learning.

Sklearn precisely works as a one-stop solution that helps with importing, preprocessing, plotting, and predicting data.

Its versatility, powerful nature, ample documentation, and active nature make it one of the popular toolkits in ML.

Linear Regression in machine learning

Linear regression comes at the last stage of the ML processing. Though there are several tools for the same, Sklearn is used more because of its versatility. When there is a log of consistent data it is easier to predict future patterns. The various steps towards the implementation of linear regression are,

Loading the data or object from its source
Training the model results in dividing the data into dependent and independent variables.
Later it is split the data into train and test sets.
It is the test set that will be used for the predictions.
Explore the results to find the suitability of the data for linear regression. A low accuracy data shows the non-compatibility of data to this.
Narrow down the data to a smaller portion and repeat the same steps above.
Now the data should be able to show the MSE which can be used for better predictions.

Importance of Linear regression

Linear regression is important as it determines the accuracy of the prediction from the data given and also finds out the predictors that are significant for better results.

It is useful in determining the power between the dependent and independent variables such as sales and expenditure, or in the case of health care dose and its effects on the body, etc.

Analyzing the regression model explains how much the variables do change with respect to each other. Forecasting weather or its impact and changes is a fine example of this application. In the case of sales and marketing, it explains how much increase is possible in income with a specific amount of expenditure.

Finally, it is one of the main tools for predicting future trends and possibilities. It can be used for a future prediction of any time span. Linear regression using Sklearn provides a near-accurate prediction of the values.

In addition, there are different types of linear regression – simple or multiple linear regression. Each type is suitable for a different purpose or type of data available.

Conclusion

Its possibility is too vast that it requires proper Linear Regression Training to use the best data for getting the most accurate output. Once learned it is easier to predict data and values in the fields of sales, marketing, financial assessments, insights into consumer behavior, risks in finance or loans, etc.

As already mentioned, the accuracy of the prediction highly depends on how compatible the data is for the linear regression model. The Sklearn method is a far better option to determine the same and can provide the closest values to help with a better prediction model for the business.

How Can I Start Learning Finance and Accounting

Posted on September 27, 2021October 15, 2021 by Imarticus Learning

Last Updated on 4 years ago by Imarticus Learning

Understanding Finance & Accounting

Financial and accounting are the two pillars on which the banking and finance industry operates. All major financial work carried out can be grouped under these two disciplines. People are often confused about the purview of finance and accounting. It is often used interchangeably by people who are not familiar with the scope of each discipline. So what is finance and accounting and how do they differ?
If you are looking for a simple answer you can follow this approach; the finance discipline deals with the future monetary prospects whereas accounting deals with the financial transactions that have already taken place in the past. Accounting is more focused on recording and maintaining financial data whereas finance deals with raising funds and projecting future income from a business or project is undertaken.
Accounting is necessary for any business entity as it helps to maintain vital financial transaction-related data and allows introspection into the company’s finances. It helps to identify irregular and fraudulent transactional data. Moreover maintaining proper accounting records are mandatory for most of the businesses as per the laws. Finance, on the other hand, has a much broader scope and entails long term management of asset and liabilities in the business.

Learning Finance & Accounting

Now that we have a fair understanding of finance and accounting, let’s explore how we can learn finance and accounting effectively and efficiently. One of the most basic things to keep in mind before learning finance and accounting is to know the purpose of your learning. Why do you want to learn finance and accounting? This is an important question that you need to address before learning finance and accounting as it will help to guide you better on your educational journey.

The approach you take to learn finance and accounting is majorly influenced by your purpose of learning. If you just want to add some knowledge then you can pursue a different route than if you want to learn it for getting a job in the financial domain. Secondly, it depends on what stage of life or career you are at. If you are just in school then you have the advantage to build your foundation from the beginning.
If you have completed your high school and aiming for graduation then you can choose a relevant undergrad degree such as Bachelors in Commerce. This will give you a fair advantage if you want to get a job just after completing yours under graduation. You can supplement this knowledge by enrolling for finance and accounting course online, this will help to provide you with an edge over the competition and also help to clear your concepts on a deeper level.

Coming to the third scenario where someone is already doing a job and want to boost their prospects for finance or accounting job. In this case, the best thing to do is to opt for an online finance and accounting course. So why is this best option? Well, the first reason is that it allows you to learn at your own pace and convenience. Secondly, you won’t have much time to spare if you are already working at a job and this course will give you much more flexibility as it is very precise and don’t beat around the bush.

Finance and accounting course can teach you the specifics that you are unaware of, you can choose the topics that you want to learn as per your requirements and skip through the others. It has a more personalised approach to teaching the subject and focuses on individual requirements. You can also learn finance and accounting by following YouTube channels related to the field if you are looking for a quick and cost-effective solution. If you want professional qualification then you can opt for specializations like CFA, CA, FRA, etc.

Will an MBA Help You Become an Investment Banker?

Posted on September 27, 2021March 22, 2024 by Imarticus Learning

Last Updated on 2 years ago by Imarticus Learning

Investment Banking as a Career

The investment banking industry offers one of the most lucrative and rewarding career prospects for individuals aspiring to make a mark in the finance industry. So what’s so lucrative about the job that so many people want to join it?

Well, at first glance, the remuneration certainly seems like the cherry on top and that makes it far more lucrative for those who want to make a fortune for themselves.

Most of the people who aspire to become investment bankers are unaware of the work it takes to survive in this industry, let alone thriving. If you are dedicated and hardworking with good financial acumen and social skills, you will certainly go a long way in this sector.

Those of you who are only charmed by the high-profile deals and monetary benefits and not the work won’t survive in the long run in the investment banking sector. The role of an investment banker is very dynamic and involves overviewing a lot of things.

From networking with your clients and industry experts to researching and conducting complex financial analysis, there’s a lot that you need to do for being a successful investment banker.

It is certainly one of the best choices if you are passionate about finance and investing and won’t mind working 60-70 hours per week. One of the most mainstream questions is how to become an investment banker and will an MBA degree help or not.

Let’s delve deeper into the subject to obtain a comprehensive understanding of the matter.

Will an MBA Degree Help?

So coming to the big question, will an MBA degree help you break into the investment banking industry? Is it worth getting an MBA in investment banking? Well, at first glance, yes, an MBA degree surely improves your chances of getting into the investment banking industry but it’s not a necessity in many cases. If we go deeper into addressing this question, the answer is very subjective and highly varies from person to person.

There are a lot of factors that count when getting into the investment banking segment. First and foremost is your educational background, it has a huge role to play as it helps to establish your foundations. So, let’s take an example, there are two people named A & B. A has always been driven towards business and commerce and has an educational background in the same domain. B, on the other hand, has a technical background and pursued engineering.

Let’s say A pursued an undergraduate program in Business Administration or Commerce. The chances of A getting a job as an investment banker straight after completion of this under graduation program is higher as he is already aware of the basics of investment banking.

Comparing it with B who has a technical background, A will surely beat B if they both are to apply for an investment banking job in most cases.

B can now improve his/her chances of getting a job in an investment banking firm by completing an MBA degree in Finance as it will fill up the knowledge gap, A too can opt for an MBA and improve his chances further for getting a high profile investment banking job.

So, there are two aspects to this whole scenario, first, what type of job are you looking for? Is it an entry-level or a high-profile one? Secondly, what is your educational background? Have you completed any investment banking courses?

So if you are looking for an entry-level job and you have a relevant educational background then you don’t need an MBA degree to break into investment banking. However, if you are looking for a high-profile job then an MBA degree will surely help as it adds more credibility to your profile.

In case you don’t have a relevant academic background and you are looking to switch from a different industry, an MBA degree will surely help your cause. If you are short on time and monetary investment then an investment banking course will help you land a decent job in this industry.

Credit Risk In Finance

Posted on September 27, 2021October 15, 2021 by Imarticus Learning

Last Updated on 4 years ago by Imarticus Learning

Understanding Credit Risk

Credit risk can be defined as the probability of the occurrence of loss on account of the failure of any entity to abide by the contractual obligations. If that was a little technical for your understanding let’s simplify it a bit here; to put it simply credit risk is the risk of loss that might occur if the borrower fails to pay the loan amount. The risk involved here includes lost principal and interest amount, major disruptions to the institution’s cash flows, the huge cost of collection. A borrower failing to repay the loan is just one of the many aspects of credit risks. There are different types of credit risks, such as concentration risk, country risk, etc. Let’s get some clarity into these two different types of credit risk.

Concentration Risk

Concentration risk can be understood as the risk of loss that might occur when a financial institution or business has limited its operations to some specific industries or businesses. Any economic downturn for that particular industry or business might lead to heavy losses for the financial institutions. It is advised to keep a diversified portfolio to hedge this risk by offering services to businesses across multiple industries.

Country Risk

Country risk is the risk of loss that the financial institution or the lending party might incur when they have operations in a specific country that might not have institutional stability. Country risks can be due to various macroeconomic factors specific to the nation such as political instability or feeble institutions & incompetent regulatory bodies, etc. The changes in the business environment can lead to a reduction in profits for the lending party.

How to calculate Credit risk?

Now that we have established what do we mean by credit risk and two different types of credit risks, let’s see how credit risk is actually calculated. One of the simpler calculations for credit risk is in the form of the expected loss. There are three components to the expected loss formula, the probability of default, exposure amount at default, recovery rate (one minus loss given the default). Let’s take an example to understand this calculation better.
Expected loss = probability of default* exposure at default* recovery rate (1-loss given default (percentage))

Example:

Mike borrows an amount of $100000 to start a business. In the next year of its operation due to some macroeconomic challenges, the business failed and Mike was unable to repay the bank. If the loss given default is given as 60% and the probability of default is given as 50% calculate the amount of expected loss.

Probability of default = 50%
Exposure at default = 100000
Recovery rate= 1 – .06 (loss given default) = .40Expected loss = 0.50*100000*0.40 = 20000

What is a Credit rating?

The process of credit rating can be explained as an assessment of the borrower’s profile while lending them credit to, credit rating helps in computing the ability of the borrower to pay back the debt and the probability of loss or degree of loss associated with any specific borrower. Credit ratings are provided to both individual and institutional borrowers. The credit rating agency does a thorough analysis of the borrower’s standing on the basis of the documents provided by the borrower and other relevant non-public information regarding the borrower obtained with the help of analysts.

Conclusion

Credit risk can be summed up as the loss incurred due to the borrower defaulting on his debt commitments to the lending party. There are multiple types of credit risks suck as country risk, concentration risk, etc. A simple way to calculate credit risk is through calculating expected loss which takes into account factors such as the probability of default, exposure amount and recovery rate.

What is the Financial Analysis and Planning Process?

Posted on September 27, 2021October 15, 2021 by Imarticus Learning

Last Updated on 4 years ago by Imarticus Learning

Companies are dynamic bodies. Companies also operate in dynamic work environments. Therefore, it is essential to keep a check on various aspects of a business, including the financial as well as the planning process. Financial analysis is an integral part of any business. It should be conducted with the utmost precision and care as it determines the viability of the company. Financial analysis, as well as planning, are considered to be fundamental tasks when it comes to running a business. You can also take up an economic analysis course to understand how the process is conducted and what things have to be taken care of. There are different steps involved in the financial analysis and planning process of a company. The planning is done to achieve both short term and long-term goals of a company. Also, profitability is the primary goal any company wants to achieve. The financial analysis and planning process is aimed at attaining the profit objective of the company.

Forecasting

It is a critical step in the financial analysis and planning process. Planning for the future is a vital element for any company. Forecasting is that tool which does planning for the future possible. A financial analysis course will help you understand how businesses forecast and make decisions. Forecasting helps in building or formulating a sales strategy which will take into consideration all the future possibilities that can arise at any point in time. Both internal and external conditions are taken into consideration while forecasting results and possible courses of action. Tools like Hyperion Planning can be used to conduct the forecasting process efficiently. Here, the past trends are also taken into consideration to come up with predictions.

Budgeting

Budgeting is a crucial step in the planning process. You need to take into consideration the planned expenditures before actually incurring those expenditures. Budgeting for fulfilling the short term and long-term objectives of a company is a fundamental process. Budgeting is done to assess what kind of spending is coming your way. Budgets are prepared after considering the past trends as well as taking future requirements into account. A yearly budget is ready, and then it is segregated to monthly charges were recurring, and one-time expenses are segregated and recorded. This is done to understand the financial requirements of the company.

Reporting

Reporting is another important aspect of the financial analysis and planning process. Recording every financial transaction is an essential step that needs to be followed by every company. Financial reporting is done keeping in mind the interests of both the internal and external shareholders. Internally, managers need to know the financial position and standing of the company. Managers also want to see the profitability of the company, and that makes reporting important. When it comes to external sources, financial reports are used for competitor analysis. Also, auditors require financial information to understand how the company is performing and even if the company has recorded every incident responsibly. Investors also want to go through the financial data of the company before investing in the company.

Analysis

The analysis of financial statements is a significant step in the economic analysis and planning process. Analysing the expenditure and investment patterns is very important for a company. If a company overspends, there is a strong need to understand why such a cost has occurred. A complete analysis of all factors is required to realise the controllable and uncontrollable elements that are driving such expenses for a company. The financial analysis also involves calculating ratios and understanding the overall position of the company

How to choose the best machine learning & AI course?

Posted on September 27, 2021March 21, 2024 by Imarticus Learning

Last Updated on 2 years ago by Imarticus Learning

The artificial intelligence and machine learning courses are becoming extremely popular, with many of the best universities offering degree programs in this field. It is not surprising that these courses are becoming more popular as they cover a wide range of topics.

For example, artificial intelligence includes computer science, engineering, and psychology, while machine learning covers artificial intelligence algorithms like neural networks. This blog post will help you understand what the artificial intelligence & ML course entails before taking one of these courses.

What are Machine Learning and Artificial Intelligence?

Machine learning and artificial intelligence are some of the most trending topics in this decade. Machine learning is a subset of artificial intelligence that focuses on studying artificial systems that can learn from data without being explicitly programmed. On the other hand,

Artificial Intelligence is a branch of computer science that deals with making computers do things that would typically require human intelligence, for example, playing chess or driving cars. So why should you take up AI & ML courses? Let us find out!

The AI & ML courses are essential for anyone who wants to be at par with today’s world technology. There are many companies hiring people who have these certifications in their resumes. The scope for students taking artificial intelligence or machine learning courses would be high because there has been an increase in demand for these skills set by top companies worldwide! It is not surprising that students with a degree in artificial intelligence or machine learning have a bright future ahead of them!

AI & ML Courses – What to Expect?

If you are planning to take up AI & Machine Learning Certifications, then there are various things that you should know before making your decision. Short-term certification programs are available for people who want to gain expertise at the workplace but do not have time for full-length online classes. Some considerations to be made when choosing a machine learning course:

Performance: The quality of the course’s results is a fundamental factor, and one must prioritize algorithms that maximize performance. Depending on the issue, different metrics can help to analyze the results of the course. Selecting an excellent metric to evaluate course performance is a crucial task.

Explainability: In many scenarios, briefing the results of a course is paramount. Many algorithms work somewhat like black boxes, and the results can be hard to explain. The lack of explainability can be a breaker of situations. Understanding the level, of course, can help to interpret the result and let one choose the best!

Complexity: A challenging course or model may probably find more interesting patterns in data, but at the same time, it can be challenging to maintain. More complexity may help you better perform in specific fields. A complex setup may increase the impact on the entire learnings of the course.

Training time and cost: How long does it takes, and how much worth do you need to bear for the course? Some courses need to incorporate new knowledge in near real-time, and they can’t afford long training cycles. Balancing time, costs, and performance is crucial when designing a scalable solution.

Grow and Learn with Imarticus Learning:

Imarticus Learning offers 9-months extensive program that helps you prepare for the Data Scientist, Data Analyst, Machine Learning Engineer, & AI Engineer roles.

This E&ICT Academy, IIT Guwahati artificial intelligence course will bolster your foundational skills in Artificial Intelligence and goes a long way towards helping you unlock lucrative career opportunities in the coveted fields of Artificial Intelligence and Machine Learning.

What’s Unique About Our Artificial Intelligence & Machine Learning Program?

Cutting-Edge AIML Curriculum & Certification By E&ICT Academy, IIT Guwahati
The objective of this Certification Course is to prepare students for AI/ML job roles perfectly.
Students learn real-world ML & Deep Learning skills through multiple business projects.
Learn By Doing What AIML Engineers Do in Real-World Scenarios

For further details, contact us through the Live Chat Support system or visit our training centers in Mumbai, Thane, Pune, Chennai, Bengaluru, Hyderabad, Delhi, and Gurgaon.

Marketing of the Future: How Data Analytics is Changing

Posted on September 27, 2021January 24, 2022 by Imarticus Learning

Last Updated on 4 years ago by Imarticus Learning

The corporate industry is ever-evolving, and even more so when it comes to data analytics. From standard out-of-the-box reports to a tool operated by digital mediums, it has come a long way. In the past, it was mostly a way to decipher and keep records of bygone trends, but now it has turned into a tool that helps you predict future trends.

It even helps you understand your target customer better through their online habits. And as daily life gets more and more virtual-oriented, the need for machine learning and AI-oriented data analytics will keep growing. So will grow the number of positions for good data analysts.

Here, you have a chance to learn data analytics with Imarticus Learning’s new PG program to enhance your skills to the max.

Coming back to the evolution of data analytics throughout the years, there are a few major changes that need to be addressed. Such as the shift to digital analytics from standard reports, as well as live AI reports and alerts.

Digital Analytics from standard reports

The major difference that has gained traction in the last few years would be how virtualized the data analytics industry has become. In the past, most of the analysis process was done through tool-generated out-of-the-box reports which today are almost out of date. Now the main focus has shifted towards reading the targeted customer’s habits and needs and forming strategies to cater directly to it.

It becomes a lot less hassling on both parts as instead of guessing you’re actually finding out what your client needs and providing solutions for it. Now with the change in how data is extracted and processed the job of an analyst has also evolved. This is one of the reasons why data analytics courses in India have been getting more and more popular in recent years.

Live AI reports and alerts

Another system that has become immensely popular is live AI reports that alert the clients to any ups and downs in trends. This helps them to develop quick strategies to respond to the changing trends. Not just that, the sources from which data is generated have changed drastically. Not just real-time market trends, data today can be sourced from online retails, social media, intelligence tools as well as pop-ups and advertising platforms.

The bandwidth of data has increased and along with it, the variety, responsibility, and positions of a data analyst job. As this process evolves more the scopes will increase even more in numbers. This is a good time to enhance your skills if you are thinking about career prospects. There are many institutes that offer good data analytics courses in India that might suit your preference. However, there are few things you should keep in mind when it comes to data analytics:

Many companies are venturing into exploring open-source software, but the majority is still using business analytics tools and visual analytics.
Data analysis has become a part of our daily lives. As a result, new and faster ways to generate data are coming out that need higher levels of skills.
The industry is constantly evolving, so it is necessary to always be top of your game as a data analyst.

Conclusion

Data analytics is one of the careers that has a firm base. More and more opportunities are going to open up in the future. This might be a good time to learn data analytics with a proper PG program that will help your career bloom.

Five painless Instagram Marketing techniques to kickstart engagements

Posted on September 27, 2021March 21, 2024 by Imarticus Learning

Last Updated on 2 years ago by Imarticus Learning

As a professional, it’s imperative to stay up-to-date on the latest marketing trends. One trend that has remained steady over the past few years is Instagram – it is the best social media site out there!

For this, students must learn SEO courses online and Social Media Marketing courses, which will enhance their skills and help them grow as the best Digital Marketer.

The site has around 1.16 billion monthly visitors. Customers spend roughly 8 minutes on the app, and 500 million people use Story every day. This blog post will explore five painless techniques marketers can implement to kickstart engagements with their target audience.

Five painless techniques to kickstart engagements

1. Post right hashtags in captions

Adding hashtags when posting about a topic on social media is vital because it gives posts more visibility and a whole new audience. Choosing the right hashtags for Instagram posts might make a big difference between being at the top of the feed or disappearing altogether.

Get creative with your hashtags

If one is using the same hashtag repeatedly, people are not likely to click on it. Instead of adding the same vapid hashtags time after time, come up with something new by asking a question or posting an opinion – that’s what is trending in Social Media Marketing today.

Use emoji hashtags

Instead of using the hashtag, try adding an emoji. It is a great way to give post character and stick out from the posts with only words. In addition, it’s another technique on how to get likes and comments!

Schedule your posts

Yes, this won’t be very creative, but it’s an effective way to get your posts out there! Social media platforms are notorious for having a lot of clutter on their feed, so whenever you schedule your posts, you can skip the rush and have them go live at a time where they are more likely to be seen. Just make sure that if you’ve added hashtags in the caption, they don’t get cut off when you schedule your post.

Cross-promote on Social Media

By cross-promoting your other Social Media Pages, you can get more views and build a larger audience for yourself, eventually leading to more engagements. For example, if you start an Instagram profile for your business but do not have any followers, go to your Facebook Page and share a link to your Instagram profile directly on your Timeline.

By following these five painless techniques to kickstart engagements, you can see your page get more attention, and it will help you get even closer to reaching your Social Media Marketing goals!

Enroll in a Digital marketing course online with Imarticus Learning

Imarticus Learning works on a simulated journey explicitly created for students and will allow them to use their theoretical knowledge in a real-world setting.

The student will get a 120-hour curriculum with practical projects and in-depth learning, making their program the most important Digital Marketing course online. SEO course online and social media marketing course also enhances student’s skills for digital marketing.

Some course USP:

The course lets the students learn skills that prepare them for an exciting Digital marketing career.
Impress employers & showcase skills with a certification endorsed by India’s most prestigious academic collaborations.
World-Class Academic Professors to learn from through live online sessions and discussions. It will help students understand the practical implementation with real industry projects and assignments.

Contact us through the live chat support system or schedule a visit to training centers in Mumbai, Thane, Pune, Chennai, Bengaluru, Hyderabad, Delhi, and Gurgaon.

A Complete Guide to Data Science, Artificial Intelligence and Machine Learning

Posted on September 27, 2021March 26, 2024 by Imarticus Learning

Last Updated on 2 years ago by Imarticus Learning

Data science often referred to as the ‘oil of the 21^st century can be simply defined as the subject dealing with the collection, storage, analysis, deployment, and prediction of data. It collects the clean information from the raw data of the user and uses it for actionable insights. It is also used in predicting certain events in the future. Scientists define it as another form of statistics and YES! IT IS.:-

Data science vs AI vs Machine Learning

Data science obviously has the upper hand when compared with artificial intelligence and machine learning. Indeed machine learning and AI is a subset of data science.

After all data science, machine learning, and AI are associated with each other to build the technology.

By 2013, the total data created was 2.7 zettabytes which 9x times more than it was collected in the previous 92,000 years of humankind combined. And is 90% of entire world data has been created in just 2 years. YEP! That’s amazing.

And it is still growing at a rapid pace. By 2020, the total data created was 44 zettabytes and it is projected to a rise of 175 zettabytes at the dawn of 2025.

Processes in Data science:-

Understanding Business problem
Data Acquisition
Data preparation
Exploratory data analysis
Data modeling
Visualization and communication
Deploy and maintenance

Potential of data science:-

The power of data science is beyond our vision. We use it in our day-to-day life. It made our lives easier. Data science is being currently being used by many companies like Google, Instagram, Apple, etc. Whatever we browse, we watch is everything monitored from second to second.

Some of these determine its potential:-

Genomic data provides a deeper understanding of genetic issues.
Logistics companies like DHL and FedEx have discovered the best time and route to the ship.
Used to predict the employee artition and understand the variables that influence employee turnover.
Airline companies can now easily predict flight delays and notify passengers.

Applications of Data science:-

Data science plays a major role in many fields of the world like health, finance, Entertainment, cyber security, social networking, weather forecasting, etc.

Apps like Instagram Facebook YouTube collect the data from which we are interested and designs a user-friendly profile with recommendations popping up.
Data science is also used in detecting earthquakes’ location and magnitude by Seismograph.
Often used in cyber security and crime-related issues because data science has every single information of a person like his address, phone number, salary, what type of device he uses, etc.
Entertainment sites like Netflix and Prime video analyze the information from the videos which we have watched recently and creates our recommendations.

You might wonder which company has the most data. And the prize goes to google.

Because Google’s entire business is based on data science. Google uses apps like Google Maps to show us the best route in the traffic is an example often build by data science.

Companies like Apple supports the user’s privacy and does not allow the companies to go through our personal information using data science.

A tool like VPN helps in disguising or diverting our IP address from ISP and third parties.

Another segment to know under data science is hacking.

Hacking is done by hackers who are unauthorized users who break into one’s system and steal or destroy their personal information.

Hacking can be prevented by installing anti-software and keeping it up-to-date.

Another way of preventing hacking is setting up two-factor authentication.

Artificial intelligence like Siri, Alexa, etc are designed for user assistance and can be referred to as user-friendly software.

Future outlook:-

In the future foresight for sure, Dada signs will rise rapidly and will make our lives much easier with the better implementation of technology in the upcoming generations. For sure we can see the golden ages of artificial intelligence in the upcoming era.

We will be able to get the use of robots for better development. But how much ever it grows it must be always embedded in the limits because if it overtakes the human race it will be the end of Mankind. But it is difficult to equal the level of human intelligence.

Case Study:- Instagram algorithm

The main objective of the Instagram algorithm is to keep its users online for as much time as possible. Its algorithm works like popping the ads that users might be interested in. Now you might wonder how can Instagram know about its user’s interests.

Instagram algorithm stores the set of information of each user separately like how much time a user spends on a post or a real or what type of post he likes frequently or what type of ads the user visits.

So it analyzes from all these statistics and organizes the homepage and search engine of one account to hold them online for most of the time. It might seem surprising and tactical but at the end of the day, it’s all business.

The ones who are interested in data science is a very good field of the subject to opt for.

One can opt for data engineering at the graduation level. They would have a very good scope of becoming a data scientist or a data engineer. And The mean average salary is around $90,000 to 120,000 $.

And that’s it in today’s blog. Hope you had an informative day.

Hasta la vista.

Article Credit –

“This blog was written and submitted by Ruthvik Rao, Hyderabad as a part of Imarticus National Blogging Contest. All views and opinions expressed within this article are the personal opinions of the author.

Disclaimer:

The facts and opinions appearing in the article do not reflect the views of Imarticus Learning and Imarticus Learning does not assume any responsibility or liability for the same.

How new-age banking solution geared with AI can help make wise investment decisions

Posted on September 27, 2021March 29, 2024 by Imarticus Learning

Last Updated on 2 years ago by Imarticus Learning

Due to the chaos propelled by COVID-19, banks are now required to undertake continuous reinvention of their economic models and solutions with the help of flexible and adaptive technologies, which will not only make them immune to adverse situations but will also increase their profits.

Banks are creating new technology frameworks to distinguish themselves, which include elements such as hybrid multi-cloud, microservices, API platforms, blockchain, intelligent automation, and AI. When coupled with an agile approach to design thinking and organizational integration, these technologies will have a bigger influence.

With better risk reduction and more efficient compliance operations, these new architectures are becoming more secure and compliant. A post-graduate diploma in banking and finance or a certificate course in banking and finance will teach you all these technologies and help you secure a good job in the banking industry.

Banking institutions have always been infamous for amassing massive amounts of user data but failing to spend it wisely. Years have passed since the tech transformation stormed through the banking industry, and these institutions now deal with massive amounts of information that are just ready to be mined.

However, aside from certain fundamental digital capabilities, these solutions do not produce enough, especially from a cognitive standpoint. But a financial revolution is about to take place!

Artificial intelligence is at the forefront of this digital transformation quest as a springboard, providing a facelift for these traditional banking systems through scalability, automation, the convenience of use, risk assessment, and decision-making aid, which can direct to a banking experience customized to every individual’s needs.

Organizations have become increasingly customer-centric as a result of data-driven tactics that leverage the customer experience as a competitive difference. By leveraging a data ecosystem enriched with structured, unstructured, alternate, and internal data, advanced cognitive technologies, and AI will aid in the evolution of the customer experience, providing more unified and personal digital journeys.

Chatbots, cognitive routing, and smart search are examples of AI-powered systems that will enhance customer satisfaction and involvement.

How are new innovations helping people make wiser financial decisions?

Financial institutions are working on providing exceptional customer service through hyper-personalization, which is based on intuitive, self-learning software that enables offers to be updated and added in response to consumer requests. This type of technology adds additional value and creates an altogether new customer experience.

Customer engagement may be improved by employing AI to better understand the customer and take advantage of real-time decision-making and predictive analysis. Product suggestion algorithms, for example, have proven to be effective in providing a personalized experience while increasing revenue.

Product recommendation engines are a type of artificial intelligence (AI) technology that provides recommendations for each user based on a variety of characteristics such as historical behavior, in-session activity, product economics, and the behaviors and preferences of similar users.

Many banks use advanced investment algorithms to improve portfolio management. Banks are targeting deployment in fraud detection and back-office services in their future business plans because digital transformation is an end-to-end process.

An AI prediction system can provide an instant evaluation of a user’s credit risk, allowing customer service agents to provide a suitable offer. This approach improves the efficiency of offers by speeding up the total credit risk evaluation process.

Conclusion:

The banking industry is one of such industries that we will always depend on. We need money and banking as long as we live. The whole banking industry has undergone a huge revolution with the introduction of new technologies.

A post-graduate diploma in banking and finance or a certificate course in banking and finance can help you go a long way in the banking industry. You just need to pick the right course and you’ll be good to go.