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Reasons Why Financial Modelling is Important in 2023

Posted on by Imarticus Learning

Making a quantitative depiction of a company’s financial status is called financial modelling. Making forecasts and estimations of a company’s future performance, financial situation and cash flows entail building a financial model, a tool that leverages financial data.

Financial Analysis

Financial models may incorporate a variety of financial data, such as revenue estimates, costs, taxes, depreciation and cash flows. They are often created using spreadsheet software like Microsoft Excel or Google Sheets.

Businesses and investors may use financial modelling to understand the economic effects of their activities and make wise strategic decisions.

Applications of financial modelling

Financial modelling is helpful in:

  • Making predictions about future performance and financial results
  • Assessing prospective purchases or investment opportunities
  • Examining the effects of different financial choices or situations
  • Creating a budget and making plans for the future
  • Preparing balance sheets, cash flow statements and income statements for economic use.

10 benefits of financial modelling

In 2023, financial modelling will still be a crucial tool for investors and enterprises. The following are a few of the main advantages of financial modelling in 2023:

  1. Precise forecasting: Using financial modelling, companies may make precise financial estimates based on past data, current market conditions and assumptions.
  2. Risk reduction: By examining multiple possibilities and making sensible assumptions, financial modelling may assist firms in identifying and reducing financial risks.
  3. Better decision-making: It gives firms a mathematical foundation for making decisions, allowing them to weigh their choices and select the best course of action.
  4. Improved communication: When expressing financial information to stakeholders like investors, lenders and board members, financial modelling offers a shared language.
  5. Resource optimisation: By locating the most lucrative investments and skillfully handling their finances, financial modelling assists organisations in making optimum use of their resources.
  6. Efficiency gain: Financial process automation can assist in cutting down on the time and resources needed for financial analysis and reporting.
  7. Improved strategic planning: It gives firms a comprehensive grasp of the financial ramifications of various situations, which helps them plan and carry out their strategic ambitions.
  8. Better capital allocation: By determining the most lucrative investments and managing their finances effectively, financial modelling may assist organisations in allocating their resources more effectively.
  9. Better risk-adjusted returns: By determining the ideal ratio of risk to return, financial modelling may assist organisations in achieving higher risk-adjusted returns.
  10. Better valuation: Businesses may make better investment choices by valuing their assets, initiatives and investments more precisely using financial modelling.

Career paths related to financial modelling

Financial modelling skills are highly valued in the finance and investment industry, and there are several career paths available for professionals with expertise in financial modelling. Here are some examples:

  1. Investment Banking: It is a key skill required for investment banking professionals who work on complex financial models to advise clients on mergers and acquisitions, capital raising and other strategic transactions.
  2. Private Equity: Private equity firms also require financial modelling skills to evaluate investment opportunities, assess risks and forecast financial performance.
  3. Equity Research: Equity research analysts use financial modelling to evaluate and forecast the financial performance of publicly traded companies, which investors use to make investment decisions.
  4. Corporate Finance: It is a key skill required for corporate finance professionals who create financial models to support budgeting, forecasting and capital allocation decisions.
  5. Consulting: It is used extensively in management consulting to analyse and evaluate business operations, identify growth opportunities and assess financial risks.
  6. Asset Management: Asset Managers use financial modelling to forecast financial performance, evaluate investment opportunities and manage risk in portfolios.
  7. Risk Management: Risk management professionals use financial modelling to analyse financial risks and create models that predict the likelihood of future events and their potential impact.

Why will financial modelling still be important in 2023?

Using spreadsheet programmes like MS Excel, financial modelling may provide a statement that quantifies the company’s past, present and potential future. It is an economical technique that assesses possible concepts and calculates risks. It aids businesses in gathering their financial data, analysing the results and reaching major business choices. Valium 2 mg

Also, it can assist in developing long-term solutions for the company’s various financial issues. Most significantly, it creates accurate financial statements that convey to outsiders the company’s financial health. It is an essential competency when the company is considering mergers and acquisitions, attempting to raise funds, or trying to draw in new investors.

Wrapping Up

A financial model is a vital corporate tool used often in the finance sector. The Financial Analysis Prodegree in Collaboration with KPMG programme by Imarticus provides students with a comprehensive understanding of financial analysis and its application in various business scenarios. The programme covers a wide range of topics, including financial statement analysis, ratio analysis, cash flow analysis and valuation techniques to help students develop the necessary skills to become financial analysts.

It is delivered through classroom lectures, hands-on exercises, case studies and online learning modules. Upon completion, students will receive a certification from Imarticus and KPMG, which can help enhance their career prospects.

Reasons Fintech Should Keep Its Faith in Blockchain Technology

Posted on by Imarticus Learning

Financial technology, also known as fintech, is a recently formulated technology that helps consumers to receive streamlined financial services. Fintech addresses almost all aspects of the finance sector. 

Fintech has revolutionised the world of finance. From updating bank transactions to formulating decisions regarding investments and stocks, every financial work can be accomplished by using either a mobile phone or a computer.

A lot of fintech’s success is riding upon blockchain. It has given birth to cryptocurrency, one of the most popular financial technology. However, with growing concerns over crypto’s instability, faith in blockchain, too, is receding. This article will discuss in detail why fintech must not lose its faith in blockchain technology.

Blockchain technology and cryptocurrency

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Blockchain is a kind of database that is distributed among various nodes of a computer system. A blockchain is quite different from a typical database. Here the data is usually stored in the form of blocks of varying sizes, while the database store data in the form of tables. 

Once a block with a certain size has been filled up with enough information, it is closed. Subsequently, that block automatically gets linked with the previously filled blocks and eventually forms a chain. This cycle keeps on continuing hence the data chain gets extended. 

Blockchain technology is mostly used in cryptocurrency to maintain a secure and hassle-free transaction over various computer networks. Cryptocurrency is an excellent example of fintech. It is a digital form of currency that is designed to be used across the internet without any assistance from third parties. 

Cryptocurrency is not issued by any central bank hence no government can control it. The first cryptocurrency, Bitcoin, was launched in 2008 and is still in immense demand.  

Reasons fintech should keep its faith in blockchain technology

Recently, blockchain’s reputation has been adversely affected due to the great downfall of various cryptocurrencies. Besides cryptocurrencies, many NFTs are also facing a downfall. However, fintech must not lose faith in blockchain as this technology has a lot to offer in the field of finance and beyond. 

Secured payments

Blockchains use technologies that do not allow any third party to intervene when there is an ongoing payment. Therefore, sectors that deal with fintech, like the banking sector or the investment sector opt for blockchain. 

Bring down cost

Blockchain technology will also assist fintech companies in reducing their infrastructure cost. Hence, banks can stop availing services from intermediaries or other third parties. Blockchain can also help various banks reduce the cost of creating new contracts. 

In a statistical prediction, it was stated that blockchain could reduce costs by $20 billion USD.

Rapid transaction of money

Blockchain technology can transfer money faster than traditional systems. It takes a few seconds to complete the entire transaction. Therefore, banks can execute rapid transactions.

Blockchain technology avoids any third party while executing a transaction. Therefore, the transaction time has reduced significantly, thus, increasing in number.  

Top-notch security

Fintech sectors deal with a large amount of capital. Hence, it requires a top-notch security level to safeguard all the capital. Blockchain technology can easily secure all transactions with its technology. 

No one can easily decode transaction details or information because implementing blockchain protects them from third parties. Secured transactions take place using two keys, one is the public key, and the other is the private key. Private key shares information of the transaction between two specific parties. 

Responsibility

Blockchain technology is essential for fintech because it carries all the responsibilities against fraud and misuse of assets. It reduces transaction errors that occur during the transfer of money. It also brings down cases of fabricated information. 

Conclusion

Blockchain still has a very promising future even after all setbacks it has witnessed. It will benefit financial technology in the future and create more job opportunities. Therefore, if anyone is interested in commencing a career in fintech, then they can opt for an online fintech certtification course. 

Imarticus Learning has brought the SP Jain Fintech certification course for those who are willing to make a career in fintech. This course will incorporate the required knowledge and skills, including API, cloud computing, machine learning, IoT, AI, taught by renowned faculty. The course is spanned over 3 months and opens gateways through partnerships with industry giants. Apart from access to an online library housing more than 900,000 books, students get the esteemed SP Jain alumni status. Enrol in this course to bag alluring job opportunities today! 

Steps to Become a Cybersecurity Analyst

Posted on by Imarticus Learning

Data of every nature, whether sensitive or not, is stored and transmitted online daily by everyone, from businesses to government entities and even individuals. Consequently, securing that data is of utmost importance, with hackers innovating more sophisticated tools to attack and corrupt data. The question lies in who can prevent and tackle these incidents. The answer is a cybersecurity analyst.

With the growing frequency of cyber threats and malware attacks, there is a constant rise in demand for cybersecurity professionals. Thus, it is the right time to build a career in cybersecurity. Read on to learn how.

Who is a cybersecurity analyst?

Cybersecurity analysts are specialists that protect the network and systems of a company from possible cyberattacks. They monitor databases, computer infrastructure and systems to identify potential anomalies in various applications, programmes, networks, etc.

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Also, cybersecurity analysts take protective measures to prevent malicious activities and external damage. They use the most effective and latest methods to abate any security vulnerability that can impair the company’s normal functioning and cause significant damage.

What are the roles of a cybersecurity analyst?

A cybersecurity analyst has multiple responsibilities. Some of those are:

  • Managing software

A cybersecurity analyst needs to install, update and maintain various software on the systems and networks of the company. The aim here is to secure the system from potential threats.

  • Monitoring network

Proper monitoring of the network is another principal role of a cybersecurity analyst. Network monitoring is required to nullify any potential threats seeping in.

  • Security planning

A cybersecurity analyst also needs to develop a security plan for the organisation. The analyst should consider every cybersecurity detail. They must also keep the organisation’s key members abreast of the security details.

  • Reports

A cybersecurity analyst has to compile reports on flaws and vulnerabilities of the system and enlist the possible measures to resolve these issues.

  • Ensure compliance

A cybersecurity analyst ensures that the organisation is adhering to various regulatory standards. For example, if an organisation has chosen ISO 27001 as its information security standard, then the analyst ensures that the organisation follows it explicitly.

  • Research

Cybersecurity professionals should constantly research ways to improve security and keep themselves updated with every new malware. They need to look for all the latest technologies that can protect the system better.

What are the skills required for a cybersecurity analyst?

There is a range of skills that a cybersecurity analyst need to muster. Some of the more important ones are:

  • Programming

Skills in programming languages like C++ and JavaScript help detect encoded threats.

  • Hacking

Cybersecurity analysts also work as certified ethical hackers. It enables them to have a better understanding of incoming threats.

  • Knowledge of protocols

You should also know various protocols like privacy and access to building a system that is well protected.

  • Networking

Proper know-how of the networking elements enables one to understand threats better.

Steps to becoming a Cybersecurity Analyst

A cybersecurity analyst plays a critical role in any organisation. It requires proper knowledge and skills to become a cybersecurity specialist. Here are a few basic steps to becoming a cybersecurity analyst.

  • Learning the fundamentals

The first step is to learn the fundamentals of cybersecurity. A clear vision of the current threat to information systems, network architecture, firewalls and much more is required. Mostly, employers prefer a candidate with a degree or diploma in information technology or any related course. These courses provide all the fundamental skills and knowledge required. 

  • Earning a certificate

A certification means proficiency in a particular skill. And, that is what cybersecurity is about. Organisations often prefer candidates having certification from well-reputed institutes. Both entry-level and mid-career jobs require certification.

  • Experience gaining

Gaining experience is another crucial part of becoming a cybersecurity analyst. Some of the lower-level jobs don’t require a lot of experience. So, these jobs are more approachable for beginners. Having experience boosts the chances of landing the desired job. These experiences may include college internships, participation in online cybersecurity training labs, and part-time IT jobs.

  • Keeping updated

Cybersecurity is a rapidly evolving sphere in IT. Hackers discover new ways to attack systems daily. Hence, constant research and learning about the latest trends and advancements in technology is necessary to keep you in the loop.

  • Apply for job

After going through the previous four steps, the first thing is to look for a job that matches the skillset you have acquired. It often helps to land a job in cybersecurity if you have a good network. Having a little real-world experience can act as an added advantage.

Conclusion

If you are interested in cybersecurity, then the job of a cybersecurity analyst is a career you can consider. As the demand for cybersecurity is rising rapidly, you can land lucrative jobs with relevant skills.

Check out the postgraduate programme in cybersecurity brought to you by Imarticus. The course offers lab sessions on real-world problems and assured job placement. Become a certified ethical hacker under the guidance of industry experts.

How to Kickstart a Career in Digital Marketing

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Advertisements on the internet and creative ads on social media are unmissable features. Just as ads on the billboard we see on the highway are traditional marketing, any advertisement propagated via an electronic gadget, like the pop-up ads on websites, makes up digital marketing.

Digital communication has become an indispensable part of our lives and is an essential digital marketing tool. Through online communication with potential buyers, any business can yield a perfect marketing strategy by understanding the needs and requirements of customers. A good digital marketing course will help kickstart a successful digital marketing career, teaching the essentials of marketing.

How to approach digital marketing as a beginner?

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To have a versatile career in digital marketing, one has to start with the fundamentals. Here are the best ways to build your career in digital marketing from scratch:

  • Obtaining a degree: First comes awareness and thorough knowledge of the desired subject. Getting a bachelor’s degree in marketing or advertising will acquaint you with all the necessary information required at the basic level of online marketing techniques.
  • Garnering specific skills: Once you get the hang of the subject, you can start learning strategies and upskilling yourself, consequently setting yourself apart in the field. Some necessary digital marketing skills include learning to analyse data, write content and develop creative marketing strategies.
  • Getting experience: Any candidate is deemed more worthy if he/she has some prior experience in the field he/she is trying to pursue. Starting an entry-level digital marketing job will get you the needed experience and educate you on practical applications.
  • Strengthening your resume: Go for more courses, internships or even a boot camp on digital marketing. It will not only help grow your exposure to the subject, but the certificates will add to your CV and attract prospective recruiters. 

Inbound and outbound marketing

We’re often bombarded with lucrative emails with offers and discounts designed especially for our needs. This sort of marketing is known as outbound marketing, where an organisation tries to propagate its existence in every way possible, but it seldom warrants a favourable outcome. 

Digital marketing is traditionally considered a part of inbound marketing, where you appeal to already interested people. Instead of generating generic ads for all, you can create an individualistic and specific way to advertise your business. Thus, when starting with digital marketing, it is important to be creative and find economical ways to improve your business. 

Skills required for a digital marketing career

Search Engine Marketing 

A prominent ranking amongst search results should be implemented in your endeavour to make your business more relevant. Search engine optimisation (SEO) attempts to enhance results when you look for a topic on any search engine by attracting traffic to a particular website and increasing its rank when searched. 

Like SEO, search engine marketing (SEM) is also a significant part of digital marketing ━ websites, and businesses increase their rankings through payment to a particular search engine. This way, a small business might catch someone’s eye and attract an influx of customers. SEM helps businesses present the perfect online ad to convert buyers into loyal consumers. 

Content writing and communicating with buyers

Writing content online makes you accustomed to what a customer might or might not need, and you learn to communicate with buyers in a friendly yet dignified manner. It allows businesses to seem approachable yet strict when presenting themselves online. This is why having content writing as a skill is extremely important when venturing into digital marketing.

Communication is key in digital marketing. Once you understand what your customer wants, you can easily create a product suited for them and design distinct ads for any future undertakings.

Creative skills

Creativity indeed goes a long way in digital marketing; from catchy slogans to innovative campaigns, businesses depend greatly on what kind of content and advertisement they put out there. The better your ads, the more your customers are. This is what digital marketing will expect of a candidate, so honing your creativity will help you in this area. 

Conclusion

With a world that runs on technology, digital marketing has seen a massive boom. With almost everything being conducted online, we have come a long way from bumper sticker ads to finding what we are looking for online through a perfect advertisement. This makes handling digital marketing an important part of any business that wants to thrive online. 

Understanding how to deal with advertisements is fundamental to being good at marketing and having permanent customers. Imarticus’ digital marketing course can be the gateway to affluent employment. 

Data Science Using Tableau and Power BI

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Data science creates coherent knowledge from incoherent structures of data using statistical processes and tools. In a world where technology reigns supreme even for the most minor issues, data science is a rapidly developing sector and is quite beneficial for anyone interested in exploring this area. 

To understand and work with data science, one has to be familiar with the workings and significance of the tools used to arrange unstructured data. These include Tableau and Power BI, two of the most widely used analytical tools in organising data. 

Read on to learn how a knowledge of these tools can help make a successful career in data science.

Data visualisation and its endless merits

 

Before using tools like Tableau and Power BI, it is imperative to know what data visualisation is and how it works. Imagine reading about the population statistics of a country over decades- the changes in the numbers become a lot easier to understand when presented with graphs or pie charts.

These graphs and charts in data visualisation are often created with the help of the programming language Python. Data visualisation with Python allows it to use libraries like Bokeh and Matplotlib, which contain unique intrinsic units, to create the required graphs from a mass of unstructured collected data. Tableau and Power BI help enhance such effortlessly receptive graphs and more. They also employ linear regression when sorting data- it helps predict the outcome of the dependent variables on the independent ones during data analysis and assists in data visualisation.

Data science using Tableau – Its uses and importance

Any organisation needs its data to be analysed in order to understand its progress and targets and take corresponding action. Founded in 2003, Tableau enters this scenario as an essential analytical tool that helps process the sea of data into great visuals allowing Data Scientists to understand and extract relevant information from the same.

How Tableau operates

  • Tableau uses TDE, or the Tableau Data Extract, to store and collect mass data, sometimes also Hyper, a technology that enhances the collection and analysis of the data through fast and efficient processing. 
  • Sometimes, errors occur while data is being stored or analysed. Tableau uses ETL Integration (Extract Transform and Load) to recognise, extract and fix the error. With a visually appealing and easy-to-grasp interface, one can do all of this with the help of Tableau Prep Builder. 
  • Anyone examining and willing to explore all kinds of data can connect to a vast array of databases anywhere using the Tableau Desktop. 
  • Local businesses or non-profit enterprises also make use of Tableau Public which does all of the same things as Tableau Desktop, but the sharing of the reports of the data exploration remains limited to the public domain. 
  • Finally, Tableau Server and Tableau Online provide security, efficiency and a large amount of collective accessibility and inspection of the data in your organisation. 
  • Tableau even allows one system to connect with another for further examination and scrutiny. 

Data science and its dependency on Power BI

Like Tableau, Power BI is a Business Intelligence (BI) technology that provides clarity to unorganised data, like creating a meaningful image by putting puzzle pieces together. Power BI was in its initial design in 2010 and was launched in 2011 by Microsoft as a desktop application that allowed anyone to inspect, understand and visualise data on their own.

Basic features of Power BI 

  • Power BI’s major appeal is the connection it offers to several data sources, from Excel to Google Analytics and even social media platforms like Facebook, Twitter and Reddit. 
  • It also allows analysts to connect to IoT (Internet of Things) devices, collect that data and transform it into creative visualisation and interaction. 
  • The Power BI Pro enables anyone to gather any data they want, survey it and create dashboards suited to their required needs. 
  • One can also control who has access to the new, processed data. Power BI simultaneously protects and shares data and insights across the organisation. 
  • It is regulated per General Data Protection Regulation (GDPR) and is easily operable on mobiles. 
  • With its Premium well-suited for large organisations and businesses, Power BI is a real step up for Data Scientists.

Conclusion

Data science as a branch has been extremely versatile in its growth in recent years, and a simple visual representation of anything complicated always yields a better understanding of the concept. This is why someone starting out with data science needs to be well-acquainted with Tableau and Power BI, their contribution to the field and their interchangeable usage because of their similarities. 

Imarticus’ PG in Data Science and Analytics is a data science course with job interview guarantee that will provide a thorough understanding of data science, the processes and tools needed to work with, the required training needed for upskilling, and consequent employment. 

Trading Derivatives: 3 Things to Keep in Mind

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Derivative trading involves buying and selling financial instruments that derive value from an underlying asset. These assets include foreign exchange, stocks, bonds and commodities, and are traded in derivatives. 

The trade life cycle of a derivative typically involves the initial agreement between buyers and sellers, followed by the fulfilment of the contract at a specified date in the future. 

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Derivative trading involves using a variety of financial instruments, including options, futures and swaps, to bet on the price changes of the underlying assets. The objective is to produce profits by correctly forecasting the direction of price changes and successfully managing risks. 

Let us look at the three most important points one should remember while trading in derivatives.

1. Risks involved

Market risk

It is the potential for losses brought on by fluctuations in the underlying asset’s value.

Since the value of a derivative is determined from the value of an underlying asset, this risk is significant in derivative trading.

Credit risk

It refers to the possibility that a counterparty will fail to meet its obligations, leaving the other party holding the bag.

Since derivative trading is frequently done on margin or with leverage, which raises the risk of losses in the event of a default, this risk is significant.

Liquidity risk

It refers to the chance of being unable to sell or get out of a position because neither buyers nor sellers are available.

Restricted liquidity in some products makes it challenging to close a position at a fair price when trading in the derivatives markets.

Operational risk

It is the potential for losses brought on by errors in operating procedures or system performance.

This risk is essential in derivative trading because it involves intricate systems and procedures requiring careful management and supervision to prevent losses.

Legal and regulatory risk

It refers to the possibility of financial losses brought on by modifications to the rules or legislation governing the trading of derivatives.

Regulations can change quickly and significantly influence the value of investments, making this risk particularly crucial.

Counterparty risk

It implies that the opposite party to trade won’t keep its end of the bargain.

This risk is significant because it includes intricate and frequently lengthy contracts that depend on mutual trust and confidence.

2. Importance of having a trading plan

Having a trading plan is crucial for success in trading derivatives. Here are a few reasons why:

  • Minimise emotions: A trading strategy reduces the influence of emotions, which can trigger irrational and impulsive behaviour, in trading decisions.
  • Provides clarity: A trading plan simplifies the steps to be taken in various market conditions, what to expect from the market and how to manage risk. It aids traders in making defensible decisions without being influenced by feelings or ideas.
  • Increases consistency: A trading plan enables traders to maintain consistency in their trading strategy, which is crucial for long-term success. It allows them to imitate their triumphs and prevents them from repeating the same mistakes.
  • Helps manage risk: Well-defined stop-loss levels and restrictions on the amount of cash that can be risked in each trade and articulating how to handle unforeseen events enable traders to control risk.
  • Improves discipline: By offering a precise framework for trading activity, a trading strategy aids traders in maintaining discipline. 

It guarantees that individuals trade in an organised manner, preventing rash judgements and lowering the likelihood of losses.

3. Strategies for successful derivative trading

Derivative trading is a complex and high-risk activity that requires a thorough understanding of the underlying assets and markets. 

Here are some strategies for successful derivative trading:

Educate yourself: It’s crucial to have a solid understanding of financial markets, asset classification and risk management before starting with derivative trading. 

A thorough understanding of the fundamentals of derivatives, such as options, futures, swaps and their associated trading strategies, is necessary.

Develop a trading plan: An effective trading strategy for derivatives must be carefully constructed. Your objectives, risk tolerance, entry and exit methods, and the amount of capital you are willing to risk on each trade should be outlined in the goal.

Manage your risk: Trading derivatives are inherently dangerous; thus, controlling your risk is essential. It entails placing stop-loss orders, attentively watching your trades and getting ready to leave a position if the market turns against you.

Use technical analysis: It entails examining charts and other market data to spot trends and patterns. It can also unearth trading opportunities and lead to better trading decisions.

Conclusion

Derivative trading is a complex activity that requires knowledge, skill and discipline. For those interested in pursuing a career in investment banking, expertise in derivative trading is a valuable asset.

Imarticus Learning’s Certified Investment Banking Operations Professional course provides comprehensive training on the complexities of foreign exchange and derivatives markets, trade life cycles and risk management. This course will help individuals develop the skills necessary to succeed in the competitive world of investment banking.

Gamification in Onboarding: Boosting Engagement and Retention

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Introduction

Effective onboarding is critical for the success of any company. The onboarding process is often the first real interaction that new hires have with their employer. It sets the tone for their entire employment experience. As such, it’s important to make sure that your onboarding programme is engaging, informative and memorable.

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One way to achieve this is through the use of gamification. Gamification is a method of using game design and mechanics in non-game settings like learning and training. Gamifying onboarding can improve the engagement, motivation and retention of new hires. It can be done by using elements such as competition, rewards and scoreboards.

Many UAE companies are interested in gamifying the onboarding process. We have covered some tips and best practices to help you get started.

Benefits of gamification for onboarding

Research has shown that gamification can improve

  • learning outcomes,
  • increase engagement and
  • enhance motivation among employees.

Also, gamification can help to create a sense of community and teamwork among new hires.

Steps to effectively incorporate gamifying onboarding strategy

Setting clear objectives

You first need to identify clear objectives for the programme.

  • What do you want your new hires to learn?
  • What skills do you want them to develop?
  • What outcomes do you want to see because of the programme?

By answering these questions, you can design a gamified onboarding programme that aligns with your company’s goals and values.

Identifying the right gamification techniques

There are a variety of gamification techniques that you can use in your onboarding programme. Techniques like points, badges, leaderboards and quests can be effective. However, not all are suitable for every organisation. When selecting gamification techniques, consider factors such as your employees’:

  • demographics
  • learning styles
  • training needs

You should also ensure that the techniques you choose are aligned with your company’s culture and values.

Designing activities that align with your training goals

The next step is to design game-based activities that effectively teach new hires the skills and knowledge they need to succeed.

For example, you could create a scavenger hunt that takes new hires through various departments of your company to learn about distinct roles and responsibilities. Or, you could create a simulation that allows new hires to practice specific job-related tasks.

Incorporating feedback and rewards

Feedback and rewards are essential components of a successful gamification strategy. By providing feedback on new hires’ progress and rewarding them for their achievements, you can keep them engaged and motivated throughout the onboarding process.

You can provide feedback after each completed activity or offer badges and certificates for completing certain milestones.

Tracking progress and analysing data

To ensure that your gamification employee onboarding programme is effective, track progress and analyse data regularly. This allows data-driven decisions to improve the programme over time.

For example, you could use data to identify which activities are the most engaging for new hires. You may also evaluate the overall effectiveness of your feedback and reward systems.

Ensuring accessibility and inclusivity

When designing a gamified onboarding process, it’s important to ensure that it’s accessible and inclusive for all employees. This means creating activities and materials that are accessible to:

  • People with disabilities
  • People who speak languages other than English.

Different employees may have different learning styles or preferences. So, you should also consider providing multiple pathways for completing the onboarding process.

In conclusion

Incorporating gamification into your onboarding programme can increase engagement, motivation and retention among new hires. By following these best practices, UAE companies can create a memorable and effective onboarding experience for their employees.

Imarticus Learning offers gamified onboarding programmes designed to align with company objectives and values. We provide feedback and rewards to keep employees engaged and motivated throughout the process. This helps create a memorable onboarding experience. Connect with us today.

Logistic Regression vs. Linear Regression: Which is Better for Machine Learning?

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Logistic regression and linear regression are two integral algorithms of machine learning. These two are supervised learning methods that are used for making forecasts and predictions. 

Although both regression techniques are used for the purpose of machine learning, they still have a lot of differences in the way they are used. A good data analytics course with placement can help one understand the distinctions in a simple way. When talking about logistic regression vs linear regression, we need to understand that logistic regression is mainly used to solve classification problems, whereas linear regression is used to solve regression problems.

Read on to learn more about logistic regression vs linear regression – the regression techniques of machine learning.

Logistic Regression

Logistic regression is one of the most used machine learning algorithms. It is a supervised technique that is generally used for classification problems but can also be used for regression problems. It uses simple independent variables to make predictions on categorical and dependent ones.

Logistic regression works well with Python programming, which requires minimal coding and does the job of solving classification problems. the output of this technique ranges between 0 to 1. It is based on the institution of maximum likelihood estimate, which means observing data should be the most obvious.

Linear Regression

Linear regression is an old and simple form of a machine learning algorithm. It is a supervised technique that is specifically used for solving regression problems. This technique uses independent variables to make predictions on the continuous dependent variables. It is also compatible with Python programming, much like its counterpart. 

As the name suggests, a linear regression graph is always in the form of a straight line, and the main objective is to find the best-fit line for accurately denoting the variables. If only one independent variable is used, then it is called a simple linear regression, and if more than one independent variable is employed, then it is known as multiple linear regression.

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Logistic Regression vs Linear Regression – The Differences

Both regression techniques are widely used for machine learning, yet each one addresses particular problems. However, both can be used for data visualisation, and there are certain tasks that each performs separately. The key differences in logistic regression vs linear regression can be explained as follows:

Type of variable and output

Logistic regression is predominantly used to specifically predict and deal with the categorically dependent variables. A particular set of independent factors is associated with this regression technique. This technique is mostly based on probabilities.

On the other hand, linear regression uses only continuous dependent variables based on a particular set of independent variables. These independent variables take numeric values and reference from staying in any category or group.

Degree of complexity

Logistic regression has a more complex structure of equations, that makes it challenging to interpret and understand this model. 

Whereas linear regression is a simple model and involves a comparatively simple structure of equations. It is easily understandable and interpreted. 

Application and scope

Logistic regression is mainly applied to solving classification problems. It can, however, solve some regression problems as well. It is slightly wider in scope.

On the other hand, linear regression is only applied to solving regression problems. It cannot solve classification problems. Hence, it is comparatively narrow in scope.

Type of graphical representation

In logistic regression, the graph forms an S-shaped curve. Thus, the type of graph is S-shaped. It can be easily used for classifying samples.

In contrast, the graph of linear regression is a straight-line curve. It focuses on finding the proper fit line for precisely predicting the output. 

Method used

Logistic regression uses the maximum likelihood estimation method. In this method, the coefficients of the regression method are chosen to maximise the probability of the variables until the iteration processes are complete.

On the contrary, linear regression uses the ordinary least square estimation method. In this method, the coefficients of the recreation method are chosen to lower the sum of the squared variables to find the best-fit line.

Mathematical equation

The mathematical equation used in logistic regression is:

y(x) = e(a0 + a1x1 + a2x2 + … + aixi) / (1 + e(a0 + a1x1 + a2x2 + … + aixi))

On the other hand, the mathematical equation used in linear regression is:

y = a0 + a1x1 + a2x2 + … + aixi

Where, 

y stands for the response variable

xi stands for the predictor variable

ai stands for the average effect on y as xi increases by one (when all other predictors are fixed)

Type of output

Logistic regression must always have a categorical or numeric output that ranges between 0 to 1 or is present in the form of a clear yes or no answer, and so on.

On the other hand, the output of linear regression must always be in the form of a continuous value. For instance price, weight, age, and so on.

Correlation between independent variables

In the case of logistic regression, there exists no correlation between the independent variables.

On the contrary, in the case of linear regression, there may or may not exist a correlation between the independent variables. 

The Better Approach to Machine Learning

This is another debatable topic for many specialists. When it comes to supervised Machine Learning then linear regression is considered to be the best-suited approach. Linear regression is best suited for predicting continuous outcomes. 

For instance, linear regression can be used for predicting the weight of an individual if his height is known. 

The equation appears as follows:

Weight = 70 + 2 x (60) = 190 lbs

Where height= 60 inches

Here, the effect of one independent variable on the outcome is analysed. 

However, when unsupervised or semi-supervised Machine Learning is considered then logistic regression is the most suitable approach. It works best with assumptive data and refrains from using a rigid approach. Logistic regression is best suited for producing a discrete value.

For instance, logistic regression can be used for predicting whether a student will be able to crack a competitive examination or not. Whether a political candidate will win the election in an area or not. This approach presents binary outcomes where it has only two straightforward alternatives.

Conclusion

Regression is an essential Machine Learning tool that makes predictions and creates relationships among variables. Machine learning experts are in demand in every organisation nowadays. If you are looking to gain professional knowledge about regression approaches and learn what is logistic regression vs linear regression then a data analytics course with placement can help you to get a good piece of work. Sign up for the Postgraduate Program In Data Science And Analytics by Imarticus and kickstart your career in data science.

Trade Life Cycle in Investment Banking

Posted on by Imarticus Learning

The selling and buying of financial products and instruments are known as trade or deal. But, the process of completing a trade is not so simple. From the moment a trade is initiated through an order to when it is settled, the steps and stages involved in the process together form a trade life cycle. It is important for financial institutions like hedge funds, investment banks, pension funds, etc., to maintain their trade life cycle. 

Considerable planning and subsequent follow-up are required to complete a trade. This article will attempt to break down the entire trade life cycle and give an account of each step. Read on to learn in detail.

Why is a trade life cycle important in investment banking? 

Investment Banking Job

Trades involving financial products are worth a lot. Hence, even a small mistake in the steps can cause either party to lose a lot of money. Inefficient management can nullify a trade. Thus, financial companies that trade frequently need to keep track of the trade life cycle. 

If you are aiming for a career in investment banking, then apart from foreign exchange and derivatives markets, an in-depth understanding of the trade life cycle can give you the necessary professional edge to succeed. 

Stages in a trade life cycle 

Depending on different types of trades, there can be some different steps in the trade life cycle. It can also vary from institute to institute. However, when talking about the stages of a trade life cycle in the context of investment banking, the main stages involved are: 

Pre-trade prep 

In investment banking, the trade begins with preparing the deal. The institution ensures that the trade abides by local laws and that all the legal documents related to the case are in order. Evaluation of counterparty credit risk, management of risk and collateral collection are also part of the process. 

Beginning and execution of the trade 

After the preparation for the trade is done, both parties initiate the trade process. Following negotiations, the parties reach an understanding and the counterparty places an order with the institution which executes the trade. A trade confirmation is then sent to the client as proof of the deal. 

Trade capture 

The process of trade capture involves booking the trade into the various office systems of the institutions. It includes recording the trade in both the front, middle and back office systems. The main details usually recorded from the trade are price, quantity, underlying assets, the date and time of the trade, etc. 

Trade validation and confirmation 

The back office system evaluates the trade before the trade settlement is confirmed. The limits and risks of the valuation are also checked in this stage. More details are added to the records while the professionals give a final check to identify any lingering problems or loopholes in the records.

If two institutes are working for different clients during an investment process, then all the details are verified and confirmed by both parties. 

Trade settlement

After every detail is confirmed and assessed by all the parties involved, they can finally move on to the settlement process. It involves the exchange of security and money; usually, the back office staff is in charge of overseeing a smooth and fair transaction. 

Based on the method, there are two types of settlement:

    • Delivery-versus-payment (DVP) – Cash and securities are exchanged simultaneously in this case.
  • Free-of-payment (FOP) – Here, securities and cash are delivered separately. Usually, in this case, the security is delivered before the payment is made. It is a risky affair since the counterparty may retract. 

A trade settlement, thus, should be done within 1-3 days of the trade date. This minimises associated risks. 

Termination 

Sometimes trades are terminated or expire before maturity. The termination of a trade can be a long process. 

Risk management 

Risk management at the end of a trade life cycle is extremely crucial for both the client and the institute. Measuring the profits and losses of the transaction, assessing the counterparty’s credit risk, preparing reports and managing trade reconciliation are all part of risk management after trade settlement. 

Conclusion 

It is important for any finance professional to know the different stages of a trade life cycle and why managing it is so important. If you want to build a career in investment banking, then learning the ins and outs of the trade life cycle will prove valuable. Thankfully, the Imarticus Certified Investment Banking Operations Professionals programme or CIBOP, is there to help you build your investment banking career. 

If you are a finance graduate with some work experience under your belt, then this course is perfect for you. You can avail yourself online live classes, experiential learning and career support. The course also comes with job assistance. So, boost your investment banking career with the Imarticus CIBOP course. 

UAE – Gamifying onboarding: A fun and interactive way to welcome new hires

Posted on by Imarticus Learning

Introduction

Looking for a fun and interactive way to welcome new hires? Look no further. We got you.

Onboarding gamification is a new trend in the UAE corporate world. It aims to make the employee onboarding process more engaging and interactive.

Gamified onboarding is a fresh approach to introducing new employees to a company’s culture and values. It provides them with the necessary knowledge and skills to succeed in their roles.

Why gamified solutions for onboarding are gaining popularity

The traditional onboarding process can be boring and overwhelming for new employees. Research has shown that gamification in onboarding can lead to higher levels of engagement, retention and motivation among new hires. In fact, a study found that 90% of employees are more productive when engaged in some form of gamification at their workplace.

How gamification can transform the onboarding process

Gamification can transform the onboarding process in several ways:

1. Increased engagement

Gamification can make the onboarding process more engaging and enjoyable for new employees. Game elements such as challenges, rewards and leaderboards can be added to the process. It’s more likely for new hires to remain engaged and motivated throughout.

2. Improved knowledge retention

Gamification can help new hires retain information better. Quizzes, simulations and mini-games are examples of interactive elements. New employees can learn and practise new skills in a fun and engaging way.

3. Faster onboarding

Breaking down the onboarding process into smaller tasks can make it more manageable for new employees. Providing immediate feedback can help them progress through the process more quickly.

4. Increased socialisation

Gamification can help new employees feel more connected to their colleagues and the company. Team challenges and social learning can help new employees interact with colleagues and build relationships. This can increase their sense of belonging in the company.

5. Reduced costs of onboarding

Gamification provides an engaging and efficient method. Companies can reduce the time and resources required for onboarding, which can result in cost savings.

Elements of successful onboarding programs

1. Learning portals

These are web-based platforms that provide access to various resources such as training modules, videos, assessments and other learning materials.

2. Responsive solutions

Gamified onboarding programs should be designed to work on various devices such as smartphones, tablets, and desktops.

3. Theme-based learning approaches

Gamification is all about creating an engaging experience for employees, and a theme-based approach is a great way to achieve this. Themes can be based on company culture, industry-specific concepts or even popular culture references.

4. Immersive designs

Immersive designs are used to create an engaging and memorable experience for new employees. This can include the use of interactive videos, simulations and virtual reality experiences.

5. High-recall visual imagery

Using high-quality images and graphics can help employees retain information better.

6. Story-based onboarding

It is a way that involves telling a story to new employees that shows the company’s culture, values and mission. This helps to create an emotional connection with the new employees and helps them to understand the company’s goals and objectives better.

How to implement onboarding gamification in your company

To implement onboarding gamification successfully, companies should:

  • Ensure that the game mechanics align with their business objectives and employee needs.
  • Provide clear instructions and feedback to employees.
  • Create a sense of competition among employees.
  • Recognise and reward employees’ achievements.

Parting note

With the benefits of engagement, retention and cost savings, it is no wonder that more companies are turning to gamified solutions for onboarding. It is becoming a popular strategy as it can help employees learn company procedures and culture while also having fun.

Imarticus Learning Onboarding is one such platform that offers gamified solutions for onboarding. It provides a fun and interactive way to welcome new hires. Visit Imarticus today!

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