Contact Us
×

Month: March 2020

How AI and Big Data Can Be Used to Fight Against Coronavirus?

Posted on by Imarticus Learning

COVID-19, a deadly virus that originated from Wuhan, China, has been declared as a pandemic by the WHO lately. The whole world is in quarantine to stop the spread of the on-going pandemic to further extend. The world has united to fight against the common cause. The results are most anticipated from the AI and Big Data to sustain through this so uncalled time.

Artificial Intelligence training is already helping many countries to fight against coronavirus and executives from Amazon, Google, Microsoft, and Apple met officials at Downing Street recently to discuss their role in the Coronavirus crisis. It is no secret that “Data” is the new gold; it is no less than a miracle that even on such a large scale shutdown of the economy the countries are doing well in providing necessities to the citizens. It is done by proper modeling and tracking of data.

What is modelling and tracking data?

Machine learning (ML) an advanced version of AI, has come to play a significant role in fighting CONVID-19. Five years ago, many were asking whether these models could be used to optimize corporate performance but now is the time when these models are helping daily to fight against coronavirus. Tracking the data using parameters and altering the matrix could come in handy in maintaining the resources and handling the outbreak in a more optimized way possible.

How to use the available resources to fight against the coronavirus?  

Countries like South Korea have an advanced digital platform for big data mining and they are already running government-run big data platform that stores citizen information and monitors foreign nationals and integrates all hospitals, government organizations, final institutions, and all other services too.

AI and Big Data have surely revolutionized the approach to fight the outbreak. Tracking and forecasting the path of infection and detect the most infected area to send instant help to limit the spread.

The difference is the quality of the data

Pumping huge amounts of data into AI and machine -learning systems is no guarantee of success and it makes it difficult to ensure that people focus on relevant information and not get mislead by hysteria. A recent update by Facebook stated the concern about the public reaction on the outbreak. They told us that they are monitoring people’s response to this outbreak and detecting the most affected areas across the world. This has come to be of great help in monitoring the outbreak on a global platform.

Using fresh data in these circumstances is of high priority as early detection of the virus can save other people from getting infected. Many countries have also introduced a quick reaction team and total isolation chambers to limit the contamination. Many drive-through labs are operational where you can get your results while sitting in the car and get treatment instantly if infected.  AI and Big Data-based start-ups are busy in making thermometers which can detect CONVID-19 at early stages.

Finding the cure using AI and Big Data Analysis

Exscienta, a British start-up became the first company to test AI-designed drug molecules on humankind. There are some limitations in finding the cure as it takes a long time to study the pattern and create algorithms

Conclusion

AI and Big Data have surely revolutionized the campaign again coronavirus in all aspects possible be it keeping the people comfortable and safe in quarantine or let it be the fight against coronavirus on the front ground and it’s no wonder why AI and Big Data analytics is booming globally and many companies are shifting their focus towards this upcoming mega technology.

What is the Difference Between Financial Mathematics, Financial Analysis and Financial Economics?

Posted on by Imarticus Learning

The terms financial mathematics and financial economics are often interchanged and thought of as the same discipline. Financial analysis is a different discipline all-together that uses elements from financial mathematics and financial economics in doing an in-depth analysis of the targeted firm regarding its financial sustainability.

Let’s compare and see how they differ.

What is Financial Mathematics?

Financial mathematics can be understood as the field that is concerned with mathematical applications in finance. Solving financial problems using mathematical methods is the main focus of financial mathematics. Financial mathematics is alternatively termed as quantitative finance and computational finance.

The major applications of quantitative mathematics are in the field of securities valuation, risk management, and portfolio structuring, etc. by financial institutions like investment banks, hedge funds, and insurance companies. Financial mathematics also plays a crucial role in commodities-based industries.

The valuation of various financial instruments requires mathematical modelling of financial markets.

What is Financial Analysis?

Financial analysis can be defined as the process of evaluating a business or a project using different techniques such as ratio analysis that helps to ascertain the suitability and viability of the business. Major applications of financial analysis are in evaluating economic trends, building financial policy, etc.

The results obtained after a rigorous financial analysis help different stakeholders in decision making. Internally conducted financial analysis helps managers to make an informed decision or study successful historical trends. Financial analysis is further classified into two parts -fundamental analysis and technical analysis, let’s delve into the details of both the techniques.

Fundamental Analysis

Fundamental analysis uses the approach of evaluating securities and conducting financial analysis by measuring the intrinsic value of the security. Fundamental analysis takes a more comprehensive view of the organisation and considers different factors such as economic conditions, industry outlook, management of the company, etc. It also evaluates the company on account of assets, liabilities, expenses, income generated, etc.

Technical Analysis

Technical analysis is different from the fundamental analysis technique for evaluating the viability of the company. It has only two inputs in the whole evaluation method. The two elements of this technique are stock prices and volume. It assumes that stock prices reflect other important fundamentals of the company. This method uses stock charts and other tools to predict the future trends of the stock.

What is Financial Economics?

Financial economics is a discipline in economics that is concerned with the analysis of the use and distribution of resources in the market. It studies how different factors such as opportunity cost, risk, time, etc. play a crucial role in creating incentives or disincentives for any specific decision.

It involves employing complex financial models to test the variables influencing a particular decision. Rational consumer behaviour is an important and common assumption with these models. Microeconomics, econometrics and basic accounting concepts form the pillar of this discipline of economics. Financial economics requires a basic understanding of the concept of probability and statistics.

Comparing the three

Now that we have individually learned about all three disciplines the difference is very evident. Let’s compare how they differ from each other, starting with the comparison between financial mathematics and financial analysis.

Financial mathematics is a field of applied mathematics that is concerned with the financial markets whereas financial analysis is the assessment of different elements that paints a picture of the functioning of the business which will help different stakeholders in informed decision making.

Financial economics is heavily concentrated on two aspects of finance, asset pricing and corporate finance. The role of a financial economist requires collecting and analysing statistical data using different sampling and econometric techniques.

What is the Next Big Thing in Fintech?

Posted on by Imarticus Learning

Breaking down the evolution

Have you ever wondered what would be the case if money wasn’t into the picture? To simplify even further imagine a world where the banking and finance industry is alien to people. Well, it was the case when money wasn’t used as a medium of exchange. The barter economy was running the whole game, exchanging goods for goods and finding people who needed them. It was a lot more complicated than it sounds.

It’s hard to imagine a time like the barter economy when we are sending money through smartphones using the Fintech companies.

When money was introduced as a medium of exchange, it simplified a lot of things that were no less than chaos earlier. From providing a unit of measurement to assigning a standard value for goods and services there was a lot that money solved. But there was still a whole lot that could be done with money as a medium of exchange.

Realizing the potential of this ultimate exchange medium, the bank started leveraging on the multiplier effect of money; there was no end to this revolution of wealth creation and wealth management. As we progressed further and became more technically advanced, technology was leveraged to create Fintech companies, an amalgamation of finance and technology that helped solved the loopholes of traditional banking methods.

Popular trends in Fintech

All the major economies which were earlier cash-oriented are going cashless; this observation is enough to say that the Fintech industry is thriving in the contemporary landscape. The introduction of the internet totally changed the game for almost every industry and the banking & finance industry were no exception to this chain of progressive evolution.

A few decades ago it was just a thought and now it has been the norm. The deep internet penetration and introduction of smartphones have paved the way for success for companies in the Fintech industry. The next big thing in the Fintech industry seems more in favor of digitisation and automation of financial services. The Fintech industry has been limitless so far, let’s look into some prominent developments within this sector that will come out as a game-changer.

Digital Lending Portals

There are companies in the Fintech domain whose main goal is to bring more transparency and convenience in the whole lending process. To minimize the cost of the loan and make it available for even the poorest individual has been the mission for these companies. All of this on a digital portal that can be accessed through your smartphones and laptops, it has been never this easy to get credit from the banks.

Robo-Advisors

The future is all about automation; it has been rightly proved with this big thing in the Fintech circle. No, it’s not a sci-fi movie; robots are here to guide humans on their investment decision. These cutting edge technologies use algorithms to find the best investment solutions for individuals and businesses. It covers all aspects of your investments; from rebalancing to asset allocation you can trust your Robo-advisor for maximizing your investment returns.

RegTech

Leveraging technology to solve regulatory challenges for financial institutions is what RegTech is all about. It’s a combination of the words ‘regulatory’ and ‘technology’. When you are dealing with financial products and services the risks are endless, RegTech is here to save you by mitigating the risks and providing solutions to challenges using technology.

Conclusion

The next big thing in the Fintech industry is not about payment solutions using smartphones but it takes into consideration the broader set of challenges like financial inclusion, regulatory barriers for businesses, credit for the remotest consumer, etc. There is a major scope for companies that are willing to solve these set of challenges.

7 Tips to Create a Killer On-boarding Training Programs!

Posted on by Imarticus Learning

How do onboarding programs help?

The most important resource for any company is its labor force, the human resource we are talking about. What’s challenging in today’s scenario is finding the best talent from a pool of workforce, someone who will be a good fit for the firm given its culture and work environment.  Higher employee turnover ratio not only reflects badly on the firm but also drains profitability and revenue.

Firms in the 21st century have learned from their past mistakes and most of them have an outstanding onboarding program for the newcomers to make them feel more connected to the mission of the organization and their individual role in achieving the objectives. Let’s dive a little deeper into how the onboarding programs help the newcomers.

First and foremost, a robust onboarding program created by the company helps to increase the employee retention rate by providing them insightful information into the job they’ll be performing and the means to excel. Also, it helps to create a sense of belongingness for the employee and they are more likely to stick around for a longer period.

How to create effective onboarding programs?

Now that we know how onboarding programs help the firm in multiple ways, let’s talk about how we can create an effective onboarding program. Research studies have concluded that employee performance can be improved by up to 12% with a robust onboarding plan in place. Here are a few tips on how to create an effective onboarding program.

Creating a cross-departmental team for the onboarding process

Employees from different departments like HR, IT, Management, etc. can be included in the onboarding team. This will provide the employee with a holistic view of the company and also help the employee with a point of contact in every department.  Also, keeping a co-worker from the team can help the new recruit with understanding the day to day of the organization more effectively.

Communicating the firm’s expectation from the new hires

Communication is the key while hiring new talent; it helps with providing the new candidates with a benchmark as to what they’ll need to achieve the given objectives. It will also help the manager and the employee to evaluate the progress and take necessary measures to improve productivity.

Breaking the ice with team members

Planning an activity that’ll help the new candidate to gel-up and socialize with the team is paramount in the onboarding process. This not only helps with creating a positive work environment but also with developing a sense of belongingness for the new employee.

A proactive approach to the onboarding process

Maintaining communication with the new employee between the period of their hiring and their first day at the company is very crucial. People get anxious about their first day at the company, keeping in touch before their first day will create an inclusive environment for the newcomer.

Reminding the Hiring Manager

The hiring manager has a huge role to play when it comes to the success of a newcomer. Reminding the hiring manager to prepare for the first day of the candidate is a crucial step in the onboarding process. A reminder email to the hiring manager should focus on multiple aspects such as roles and responsibilities, buddy system, scheduled check-ins, etc. to be set up before the first day of the newcomer.

Giving access through QR codes

Using QR codes to provide the newcomers with important information such as training materials, company information, individual objectives, etc. can be an effective way to start the day instead of mailing a bunch of PDFs and handing out hard copies for sharing information.

Taking feedback from the newcomers

Newcomers not only bring new ideas but also have a different perspective on things. Taking feedback from the recruits as to how their onboarding experience was and what can be done to improve that experience is significant in designing better training programs for the future.

For more details, you can also visit – https://www.linkedin.com/showcase/4821209/admin

Posted in COE

Why a Business Analyst is Important?

Posted on by Imarticus Learning

Business analysts (BA) can boost the productivity of project crews and this is, at times, ignored. While the development crew prepares the technical solutions, BAs offer insights, answer questions, remove hindrances and make sure that the technical solution is working well to cater to the expectations of stakeholders.

Business analysts not only bring immense value to every division that is a part of a project but also to the clientele. But having said that, people do ask about these values, and so these are some of the factors that highlight the importance of business analysts.

Curbing Overall Project Investment

Business analysts are essential in a company as they aid in curbing capital expenses (CAPEX) and operational expenses (OPEX). Even though it might look like the company is spending more cash as they would have to recruit and pay business analysts, yet eventually, BAs can help reduce the overall expenditure on the project they are contributing to. In a way, they help slash the project expenditure by eliminating the need for further re-work.

Consider this, when developers begin coding for a business runner, things might not go as expected and they might have to perform a re-check. In other words, anything that starts simple can scale up in its complexity level after customers’ requirements come in and you will see yourselves redoing the components you initially began your project with.

Business analysts, thus, can help address this rework as they are aware of the demands of business users and know how to convey them to the developers. This ward off project delays which can cost a fortune to organizations.

Secondly, it requires a considerable amount of effort for companies to discover the objectives of the project. This translates to regular meetings, which not only bother their bank accounts but also is a time-intensive affair.

This is when business analysts come in to play; they form a sound decision-making model; remind others about any suggestion they have made earlier and bridge those communication loopholes between various divisions contributing to the project. BAs would avoid myriads of meetings from taking place, which would ease off the burden on business expenses.

Improving the Odds of Potential Margins

Enhancing the value of projects can aid improve the possibilities of getting outsized returns as they are becoming more robust. Having the development crew create an array of, let’s say, 50 tasks that need to be achieved and sorting them by the domain of the system can result in further problems in the project as they might not have been sorted by value.

You might be giving priority to those things which actually are not that important by doing this. Not driving attention to prioritization leads to the devaluation of your project. Prioritization is among the crucial skills companies would desire in business analysts. With BAs in your organization to address the project requisites, the value of your project would scale up and, in turn, ensure potential margins.

Filling Communication Gaps

Having a well-experienced business analyst can be really beneficial for developers as they are solely focused on coding. Else, it would hamper the productivity if they interact with business users and understand their lengthy requirements in time-consuming meetings.

Developers would like to create a solution before going through the entire list of requirements that are essential for a project and, at times, this activity is not much appreciated by business users. As such, it creates inconvenience and confusion among business users and can negatively affect the overall project.

But BAs understand the stuff the developers have to go through to gill the loophole between technical and business requirements. Even though developers are highly capable to collaborate with business runners, it can lead to delays in projects and revamp because of the miscommunication.

Business analysts, hence, will bring value to organizations as they know technical as well as business demands. Moreover, they can communicate with developers as well as business users to make sure that there is no more any project delay.

 

What Is A Data Scientist’s Career Path?

Posted on by Imarticus Learning

The Data Career trajectory is probably the hottest career option you can do right now. As Glassdoor’s latest report shows, the $ 108,000 base salary is not only attractive to job seekers, but the Data Science career also boasts 4.2 out of 5.

Data Science Pipeline

A data science project is a whole process. It is important to understand this fact to get out of the labyrinth of data science.

Data science is not magic!

Embarking on a series of steps systematically first, the project goals are reached. Have you identified attractive business issues or market opportunities? You need to clarify what your company is trying to help you gain a competitive edge.

Next, you need to know where to collect data, plan resources, and coordinate people to do their job. The third part is data preparation. You must clear the data and investigate it carefully. The association begins to appear and the sample and the variable are corrected. The next step is to create, validate, evaluate and improve the form.

Finally, you need to communicate your team experience in the data science process. The data must be compelling and compelling. In the final reporting stage, visualization is essential to telling the complete story.

What did you learn?

At Imarticus Learning, the role of the data science team is not exclusive technology. Programming and statistics are essential to the basic steps in the Data Science Training, but contextual skills are essential to the planning and reporting stages. 

A role in data science

In fact, the role of data scientists is a common part of many different fields. Data scientists are highly capable professionals who have a big picture and are a data programmer, statistician, and a good storyteller.

However, the data science team counts people with different roles, all of whom contribute in different ways. If your career path in the data world is your ultimate goal, there are many ways to reach it.

For example, as an analyst, your data science career will be involved in day-to-day tasks that focus on data collection, database structure, modeling and execution, trend analysis, recommendations, and storytelling. Business intelligence (BI) analysts, on the other hand, should be able to see the trend and get an overview and state of the business unit in the market.

BI analysts usually have experience in business, management, economics or similar fields. However, you should also “interact with data”. BI analysts process a great deal of information and spend most of their time analyzing and illustrating data collected from multiple sources.

Are you fascinated by marketing issues? Marketing analysts are a special kind of data analyst. However, their main competency is associated with analyzing customer activities data with the help of special programs and not involved in programming or machine learning.

Data Science at Work

Data science training equips you with the skills for suggesting smart solutions for performing machine learning for beer and food molecules. Preparing beer with the right molecules to match the most popular meal ingredients on the market will be fun and make money. Imagine the perfect mix of top-selling beers like burgers and tikka masala!

Is Python Good For Data Analysis?

Posted on by Imarticus Learning

Is Python Good For Data Analysis?

In order to understand if Python is a good fit for data analysis, it is important to know the exact role of a data analyst. With a clear understanding of the job role, one can make better choices of the Python library tools that will fit each requirement of data analysis.

Role of a data analyst

In day-to-day life, we tend to make decisions based on our previous experiences. The role of a data analyst is quite similar; the only difference is they do it from the perspective of a business house. Data analysts are basically responsible for taking down the data and analyzing the results with the use of statistical techniques and preparing reports. They are also responsible for obtaining data from primary or secondary data sources and thereby maintaining a database. Their prime intention is to extract useful information from different data and take decisions based on the analysis.

An idea about Python

Python is a programming language of high-level, used for web development at the server end, app development, software development, system scripting, and mathematics. It is basically a programming language used for general purposes. Python allows you to focus on the core functionality of an application by taking care of the common programming tasks. It was designed by Guido van Rossum and released in the year 1991.

Python’s popularity is growing in several industries like retail banking, aerospace, insurance, finance, healthcare, etc., particularly in Machine Learning projects. It is a dynamic language supporting both object-oriented programming and structured programming. In this present IT scenario, to learn Python is probably the easiest thing and its huge library makes the tasks a lot simpler.

What makes Python ideal for data analysis?

The following points prove why Python is ideal for data analysis:

  • Data can be present in different forms. Assuming data is present in huge excel sheets with a huge number of columns and rows it is a challenge to search for a particular type of data from that sheet. It can be really time consuming and cumbersome. But with Python’s libraries like Pandas and Numpy that uses parallel processing, the task can be accomplished with ease.
  • Acquiring data is another challenge. It is not always the situation that the data is readily available. At times one needs to dig into the data from the internet, which can be a challenge. In this case, the two libraries of Python Scrapy and Beautifulsoup prove to be helpful.
  • The next stage is a pictographic representation of the data. The best way of representing the data in the visualization mode is through bar graphs, pie-charts, and histograms. Python has solutions for this too. For this, libraries like Seaborn and Matplotlib give the ultimate results.
  • Now comes the most important part, i.e. machine learning. This involves substantial mathematics like probability, calculus and matrix operations including thousands of columns and rows. All these turn out to be very simple with the help of the Python library Scikit-learn.
  • When the data is in the form of images, Python has a solution for that too. These images can be operated with the help of an open-source library of Python named Opencv.

Conclusion

So, we can see that Python forms a valuable part of the toolbox of a data analyst. With its help, data analysts can freely handle some of the toughest parts of their job making it interesting and attaining rewarding results. Thanks to the wide variety of Python libraries available. So it would be a wise choice to learn python which would help you in understanding the Data Analytics Courses.

What Is Fintech in Banking?

Posted on by Imarticus Learning

Understanding Fintech in General

Fintech can be explained as the use of cutting edge technology to provide solutions in the financial domain to increase customer satisfaction and bring more convenience to the final users.

This evolution within the financial industry aims to introduce new tech-savvy financial products and services most of which helps to automate repetitive processes that earlier required human intervention.

The digital world is fast-paced and the use of the internet has transformed every industry. To cater to the needs of an aggressive customer base, the evolution within the financial domain was indispensable. The collective approach towards problem-solving in the financial services domain using technology has produced a spin-off that has been coined as Fin-tech.

Fintech in the Banking domain

Fintech is totally on the verge of disrupting the consumer banking industry. The disruption is inevitable given the simplicity of services in the traditional form of banking which can now be rendered very conveniently through the use of technology.

If you look closely the technological advancements have also changed the financial procedures and the way these institutions function.

Let’s look contextually into some of the major aspects of banking that the Fintech has redefined.

Credit Services 

Fintech has expanded the horizons when it comes to providing loans. Some of the prominent Fintech companies have taken upon themselves to bring more transparency and ease in the borrowing process by eradicating unnecessary documentation and other loopholes. They aim to simplify the process of obtaining financial aids for businesses and individuals.

Payment Services

Not much long ago, payment transfers and related services was a challenge for the customer. From long queues to limited banking hours and whatnot, it was a painful experience. Today a service or a product is not just limited to providing a solution but providing a memorable experience for the customer.

Keeping the customer experience in mind the Fintech companies have revolutionized the payment services segment totally. Payment using smartphone applications and the internet is the norm today, thanks to the harbingers of the Fintech revolution.

Wealth Management Services

Fintech training has redefined the way people are saving and investing their hard-earned money.

There are companies like Robinhood whose mission is to reduce the income inequality that prevails in society by providing commission-free trading and investing platform.

Today people want what’s best for them and not a general best seller, realizing this gap Fintech companies are catering to customized investment solutions for people looking to invest their money.

How is it changing the banking domain?

Let’s see how the banking sector has been transformed drastically with the advent of Fintech companies in the context of digital & mobile banking services.

Digital Banking

One of the best things about the digital space is that it is not constrained by geographies. This feature alone has helped many banks to expand their customer base in multiple territories without having to invest in building a brick and mortar outlet. Other than this the services are available for the 21st-century consumers who don’t sleep, meaning 24*7 has been the norm.

Mobile Banking

With the proliferation of e-commerce and food-tech companies, the consumers needed a more convenient method to do the transaction. Realizing this need the Fintech companies have designed mobile banking solutions that just require your smartphone and bank details to start a transaction. Problem solved!

Conclusion

Fintech has totally changed the banking industry for good. Mobile banking and digital banking services have mended the follies of traditional banking services. Focusing more on the customer experience aspect the Fintech companies are boosting the customer satisfaction level to new heights making them more loyal and committed to the service providers.

What Is a Financial Modelling Course?

Posted on by Imarticus Learning

In these modern times, there are a lot of tools that a business typically makes use of on a regular basis, but among all of them, one that is crucial to determining its success is financial modelling. As an aspiring financial analyst, it is crucial for you to know the true potential of financial modelling and why it is so important. Thus, in this article, we will explain to you the meaning of financial modelling as well as outline the concepts that encompass a financial modelling course. Let’s get started.

What is A Financial Model?

In the simplest of terms, a financial model can be described as being a tool that is primarily built on Excel and used by companies to predict its future cash flow as well as potential income sources. The most common use of this tool is to predict the future of the company’s financials and how it can prepare itself for future challenges as well as opportunities.

In order to build a financial model or forecast, a number of factors need to be taken into account. This includes the history of the company’s performance and its assumptions for the future of its business among many others. Along with this, an income statement, cash flow statement and balance sheet need to be drafted which make up for the key fundamentals in developing a financial model.

A financial model can be used by a business for several applications including the creation of predictions for mergers and acquisitions, leveraged buyouts as well as discounted cash flow statements.

Uses Of A Financial Model

Now that you know about the basic concept of a financial model, let us quickly understand the main uses of a financial model in a business environment.

1. Raise Capital: One of the most common uses of a financial model is to raise capital from investors as well as take working capital if necessary.
2. Acquisition: Second to raising capital, companies often make use of financial models to acquire new markets, skills as well as shares.
3. Forecasting and Budgeting: Last but not the least, a well defined financial model helps companies forecast the way for the future, as well as decide on the budget for various of its activities.

What is a Financial Modelling Course?

Now that you are aware of what a financial model is, along with its main uses, let us take a look at the various components a financial modelling course encompasses.

1. Educate you on the basics of financial modelling and the skills you need to acquire in order to successfully create a financial model.
2. Analyse real-life case studies of companies that actively make use of financial models and how it has helped them grow.
3. Various skills in software that are used to create financial models, including Excel among others.
4. Educate you on the various types of financial models that can be created, those that are being actively used by businesses and their most common applications.

One of the most important pointers you need to take note of as a future financial analyst is the fact that in order to create sustainable models that provide solutions, one aspect that trumps others is regular practice and implementation.

Job Roles And Career Opportunities

Last but not the least, it is important for you to know the main job roles or designations that are responsible for creating financial models. This includes but is not limited to:

1. Investment Bankers
2. Financial Analysts
3. Accountants
4. Corporate Executives

Conclusion

Financial modelling as a skill is becoming more and more important to acquire on a regular basis and thus in order to better equip yourself for the future, it is necessary that you undertake a financial modelling course today.

 

 

Introduction

 Finance is gaining immense importance in recent times. Various tools have now come into the market that Finance professionals in their job can use. If you check out the financial analytics course syllabus, you will realize that some tools are essential for financial analysis. There are several online finance courses that you can take up to understand the application of financial modelling and its techniques.

Courses on Financial Modelling

There are specialized courses for financial analysts that offer an in-depth understanding of financial decision-making elements. You can pick an excellent financial analyst course from the online finance courses that are available out there. People who want to make a career in investment banking also opt-in for a chartered financial analyst course to understand the subject’s intricacies and make a successful career.

Strategy analysisThe online finance courses on Financial modeling teach you the basics of finance and the various tools and techniques involved in financial modelling.

The financial analytics course syllabus also includes the application of Excel in managing and working with finance. There are several courses for financial analysts online. You can pick one based on the requirement and the level of expertise needed.

Use of Financial modeling

 Financial modeling is used for efficient decision-making and to analyze the company’s economic performance. A financial analyst course will discuss various models and methodologies required to conduct an economic analysis of companies. Some uses of these models are:

  • Raising capital for the company
  • Acquiring or merging with new or existing companies
  • Asset management
  • Growth of business
  • Decisions on divesting or selling the existing assets and purchase of new assets
  • Budgeting
  • Financial forecasting
  • Allocation of Capital
  • Valuation of Businesses and their verticals
  • Analysis of financial statements and key financial ratios
  • Management accounting

A chartered financial analyst course will also give you similar insights, but the level of learning would be a notch higher. The chartered financial analyst course syllabus is more comprehensive compared to the financial analytics course syllabus. Therefore, you can pick one depending on the job you want to pursue.

Career Prospects

Financial modeling has a lot of applications. The financial analytics course syllabus covers all the significant financial modelling aspects that will prepare you for the job market. There are several industries where you can look for an appropriate job. Some of these popular industries or sectors are:

  1. Banks
  2. Institutions
  3. Corporates
  4. Public Accounting

You can get into several domains in a company after doing a course on financial modelling. Some of these popular domains or job functions are:

  1. Equity Research
  2. Sales and Trading
  3. Investment Banking
  4. Commercial Banking
  5. Due Diligence
  6. Transaction advisory
  7. Valuations
  8. FP&A
  9. Treasury
  10. Investor Relations
  11. Corporate Development
  12. Research
  13. Portfolio management
  14. Private Equity

A financial modelling course will help you in building various financial models that any company can use. These courses will lay down a solid foundation for your career in finance. You can also come up with a go-to model using the learnings of the financial modelling course.

What is Excel Data Financial Analysis?

Posted on by Imarticus Learning

Stakeholders bank on Excel to perform technical calculations or yield basic accounting ratios. Companies, on the other hand, can leverage Excel to perform risk analysis, capital budgeting analysis or discount cash flows. Moreover, options traders generally utilize Excel to perform Black-Scholes pricing. There are myriads of standard financial analysis frameworks that could be done via Excel. Also, there are some courses for financial analysis certification that can aid use Excel for various finance-related purposes.

Excel for Finance Experts

For those pursuing a career in financial analysis, extensive working knowledge of Excel and proficiency in general Excel features are quite worthwhile. Data, of any kind, can be entered and processed, as far as it is calculable.

While it can be unfeasible to have a firm grip on every function of Excel, it is important to know the approach to do functions that are essential for a particular analyst job. You can go for a technique similar to the 80-20 rule to your Excel purposes, that is, 80% of your outcome might be a result of 20% of your Excel shortcuts and functions.
Some of the common financial analysis features are pivot tables, data manipulation, lookup, formatting, and valuation equations.

Excel for Stakeholders

Stakeholders run financial analysis in either of the two major methods. The first method relies on data received from the financial statements (fundamental analysis) of an organization that can deliver Excel with the core elements for advanced equations. The second method emphasizes on probabilities, charting, and if-then analysis, which is the technical analysis.

For fundamental analysts, excel has proved to be more fruitful. Technical analyses usually depend significantly on interactive charts that are simpler to utilize and keep up-to-date swiftly via actual technical charting software in lieu of Excel.

Excel for Consultants and Accountants

At times, financial analysis is associated with repeating existing data rather than forecasting future data. For consultants and accountants, Excel can manage functions for amortization, depreciation, budgeting, and taxes.
Excel, by nature, offers itself to cost accounting owing to its excellent flexibility quotient. In contrast to financial accounting, which contains strict rules and a relatively standard approach, cost accounting must be adjusted to align with the individual requirements of the company.

In addition to this, Excel can monitor, upgrade and deliver information in a way that permits enhanced business decision making.

Using Excel Efficiently

Preparing a number of financial documents for various clients for various reasons is a time-prohibitive process. The actual issue is not creating multiple financial reports, but the approach of financial documentation and the technique utilized, which is burdensome and outdated.

A majority of organizations still rely on Excel spreadsheets. It is a surprising fact that organizations with multi-million or billion-dollar returns depend on Excel spreadsheets for their financial analysis & reporting.

Although Excel can be used to create financial budgets or reports or forecasts, their accuracy remains a question. It is easy to commit mistakes while creating reports using Excel and equally hard to check them.

The situation achieves another level of toughness when a larger number of people enter data into the same spreadsheet as this exposes you to problems like if someone by mistake entered incorrect data or on which version everyone did their updates on.

This problem is again intensified when you have to extract data that is required for report generation and later maintain all these Excel spreadsheets. Eventually, you end up spending a considerable amount of time in verifying the numbers to make sure it follows the correct data as well as to ensure the links and formulas are accurate.

In such a situation, going for cloud-based budgeting, reporting and planning software would be a great fit to make Excel perform financial analysis & reporting efficiently. It is an approach which business professionals are aware of and implies that when they login to the software tool, they can either enter their inputs on the web or via Excel add-in.