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An introduction to neural networks: AI/ML for beginners

Posted on by Imarticus Learning

The field of AI and machine learning is overgrowing, with new advancements in algorithms happening nearly every day. One area with a lot of growth recently is neural networks, which are artificially intelligent systems built on an architecture inspired by the human brain. In this post, we will explore what precisely neural networks are and how they work so you can get started today!

What is a neural network?

Neural networks are machine learning algorithms that you can use to recognize objects in pictures or understand human speech. 

For example, imagine you wish to teach a convolutional neural network how to recognize pictures of cats. You might show the computer thousands of examples of what cats look like and let it learn from that data. Then, when somebody shows the computer a picture that isn’t a cat, it could determine whether or not this is an image of something else using its knowledge of cats.

A step-by-step tutorial on how to train the convolutional neural network and make predictions:

 

  • Choose your dataset:

 

The first step is choosing a dataset to train your neural network. It could be a data set of images, text, or anything else you want to predict.

 

  • Preprocess the data:

 

Before starting training your neural network, you need to preprocess the data. It includes cleaning and formatting the data to be ready to be used by the deep neural network.

 

  • Choose your model:

 

The next step is to choose a model for your neural network. There are many different models, so you need to choose one that will work best for your dataset.

 

  • Train the model:

 

Now it’s time to train the network. It is where you will feed in your data and let the neural network learn from it.

The future of AI/ML:

AI/ML is becoming more widely used today. AI/ML has many benefits for the world around us. Machine learning help diagnose diseases, drive cars and even write music!

  • Websites like Amazon use AI/ML to recommend products you may like based on what you have bought in the past.
  • Facebook uses AI/ML to determine which posts or status to show first in your newsfeed.
  • Google uses AI/ML to generate search results.

The possibilities are endless, and the future of AI/ML is inspiring!

Discover Artificial intelligence and machine learning course with Imarticus Learning

This Artificial intelligence and machine learning course is by industry specialists to assist students in learning real-world applications from the ground up and building sophisticated models to offer helpful business insights and forecasts. This AIML course is for recent graduates and early-career professionals (0-5 years) who want to further their careers in Data Science and Analytics, the most in-demand job skill.

Course Benefit For Learner: 

  • Students get a solid understanding of the fundamentals of data analytics and machine learning and the most in-demand data science tools and methodologies. 
  • Learn data science skills by participating in 25 in-class real-world projects and case studies from business partners. 
  • Impress employers & showcase skills with artificial intelligence courses recognized by India’s prestigious academic collaborations.

Using AI models for credit risk assessments can help financial institutions make smarter decisions to boost the customer life cycle

Posted on by Imarticus Learning

Credit risk may vary differently depending on the type and amount of credit, but now there are new methods to assess it. Artificial intelligence models are the next step up from traditional scoring systems and offer a more nuanced look at your customers.

The use of AI models for credit risk assessment has been on the rise in recent years. It’s easy to see, unlike traditional scoring systems, these models provide a more nuanced view of customers and their financial history.

With this information, you can ensure that reliable data will back any loans or other financial products offered.

This article helps you to understand AI-based credit scoring models and how they help make your business more profitable.

AI-based credit scoring model: 

It is about improving the transparency of credit through increased access to information, higher credit standards, and improved risk assessment.

The big contribution of the AI-based credit score model is not only figuring out people’s real identity or whether they are eligible for loans. Instead, it changes the old idea that people who make more money could easily get a loan.

The model evaluates borrowers not by personal income levels but by risk factors, including employment history, credit report, assets, and liabilities.

This way, when making decisions about an individual’s eligibility for loans without lessening the effect on others in society.

Consumers can be evaluated according to their implications rather than just their income level- thus decreasing the incidence of the poverty cycle.

Benefits of integrating AI credit risk assessment model 

AI is fast, smart, and efficient at making decisions without any biases or emotions getting involved. This means you can make more informed financial decisions based on scale data from the entire population.

AI-based solutions transform credit scoring in several ways. Involving such a model can help the financial institutions as follows:

  • With the support of the AI credit score model, financial institutions can learn about their customer’s financial behavior based on historical data and potential income forecasting. Such analyses help the institutions to sell their credit plans to the right category of clients.
  • AI model offers greater speed without compromising quality or precision. The lending decision is much easier than traditional methods where banks used to apply decision trees, regression, and complicated arithmetic analyses to generate the client’s credit score.
  • AI smart applications are available to check the customer’s creditworthiness and maximum credit limit.
  • It has allowed the banks to increase the customers’ lifetime value by engaging with them continuously and intelligently to strengthen each relationship across diverse products and services.
  • Increased profits due to efficient targeting of low-risk loans. Both economic and efficiency terms benefit from reduced bankruptcy rates.

Learn more with Imarticus Learning:

Imarticus Learning presents credit risk management courses that help to understand India’s credit landscape and the entire loan assessment process.

Our Credit Risk Management Course USPs:

  • Quality learning experience through learning pedagogy consists of 145 hours of live lectures.
  • The comprehensive credit risk management courses strongly emphasize the digital innovation that is disrupting the lending space.
  • Helping to build a career in banks, NBFCs, and start-ups through resume enhancing workshops, interview preparation sessions, and mentorship.
  • Holistic, well-rounded, and practical curriculum designed and delivered in collaboration with Moody’s Analytics.

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

MBA degrees over the years and how to choose the right one

Posted on by Imarticus Learning

Traditionally, an MBA degree is offered as an on-campus option, where a student has to pause their career and cede their salary for it. But, in recent years, technology has shaped course delivery methods considerably, and most of the leading business schools are now offering part-time and online MBA courses. 

Due to the COVID-19 pandemic, a vast shift towards remote studies made aspirants opt for the online mode of education. Few of the world’s leading business schools have recently shifted to a complete online course curriculum. But, how do choose the right one? Here are the tips for settling on the right MBA course.

Tips to Choose a Right MBA Program 

Tip 1: Know Your Purpose  

While opting for an MBA program, it is vital to know the goals beforehand. Some aspirants wish to pursue the MBA to get a promotion, while others want to excel and get started in the business genre. For people who don’t want to leave their current job but want the degree to reach heights, an online MBA course will be perfect as physical meetings with new people, networking with industry experts, and reaching different companies might not be the primary priority. 

But, for a student who wants to get started in the business sector, physically meeting people is the primary criteria; hence, a traditional on-campus course will be ideal for them. 

Tip 2: Budget

While considering a business management course, budget is one of the primary deciding factors. While it is quite common to notice that on-campus courses are relatively costlier than digital MBA courses, it is essential to choose the suitable one.  

Few business programs also provide scholarships for deserving students. Hence, students who have a tight budget or are meritorious can check out the institution providing financial aids.   

Tip 3: Employability and Competition of Admission 

Institutions providing the best online MBA courses have a high employability rate. This factor is beneficial for students looking for a course that will provide them significant opportunities but within a limited budget. These online courses will groom them with all the amenities with options of industrial connection and a virtual networking ecosystem. The employability rate of a particular university is provided on the website itself, or it can be found on the internet if researched well. 

Additionally, an online business management course will give candidates options to study from their residence, with a minimum computer and fast internet connection requirement. Although most of the on-campus leading MBA courses have a high competition to get into due to their limited seat availability, these online programs have relatively more seats for students as institutions do not need to accommodate them on-campus. Hence, the competition to get into an online MBA degree course is somewhat easier for aspirants. 

It is easy to get lost in the different university prospectus when choosing an MBA program. Apart from these primary pointers, you can check – 

 

  • Class Profiles – 

 

Look at the profile of previously passed out candidates, where they are now, what positions are they serving, and others. This will provide you with the necessary idea of how the alumni have performed and your chances of success. 

 

  • Institution’s Reputation- 

 

Lastly, know about the national and international reputation of the university. A highly reputed institution will let its students connect with a large alumni base, thus widening the scope of employability and further studies. 

Hence, before choosing an MBA course, check out every above-mentioned factor to settle for an ideal one. Candidates should remember that both the digital and physical MBA’s have their pros and cons; hence it is crucial to choose accordingly. Imarticus offers some of the best management programs from some of the world’s renowned universities that one can choose from.

5 tips to get started in Ethical Hacking

Posted on by Imarticus Learning

5 tips to get started in Ethical Hacking

When you have been online for a few years and you start to see the potential dangers you can encounter on the internet, many questions arise. One of them is whether it is possible to do something about it, in order to protect your data and your privacy. This can be done with some knowledge in cybersecurity. Then the question is, How do you get started in hacking or cybersecurity?

It seems like a simple question, but learning something like this requires effort and time, you need to prepare yourself and find the necessary tools to be able to train as a cybersecurity expert. In this article, we are going to mention 5 tips for you to start your career in Ethical Hacking

The most important thing is to find courses that are comprehensive enough and evaluated by experts so that you can acquire the necessary knowledge. Without a good course, you will never get off the ground as an Ethical Hacker.

That’s why, at Imarticus, we offer a 6-month ethical hacking course that gives you everything you need. In this complete hacking course, you will learn from a very basic level all the knowledge and techniques of hacking. 

With the ethical hacking training, you will learn a wide variety of techniques, tools, and the fundamentals on which they are based. The course is organized in several levels ranging from basic to more advanced levels. Here you will be able to learn directly from experts and apply your knowledge in 10 different industry-level real scenarios. To participate, no previous technical knowledge is required for this course.

Learning the basics of computers, how a computer works, how to put it to work, and recognizing the components necessary for it to work optimally, is paramount in cybersecurity. If any of these items sound unfamiliar to you, it’s best to start researching to feed your knowledge.

Start learning a bit of programming. You can start with Python because of its versatility, but the more you learn the better. Programming is the best tool you can have to be an ethical hacker.

Learn about networks and wireless networks, as well as technology related to networking. You must understand how the network works, browsers, protocols, and sending information. Most cyber-attacks come from networks so it is of utmost importance to understand how they work.

Start learning the basics of cyber security. As a hacker, you will have to deal with password-protected systems. Understanding how different encryption and decryption systems work will allow you to achieve your goals. Some of the operating systems you need to master to become an ethical hacker include Linux.

These are just a few tips we can give you to feed your curiosity about cybersecurity. At Imarticus we aim to help you delve deeper into each of the key elements to become an expert in cyber security. The ethical hacking training is aimed at all those who want to strengthen their knowledge of computer security based on hacking techniques, to see weaknesses in networks and software. For those who want to learn how to use different tools to develop their work.

In other words, it is especially dedicated to those who work or study in the areas of programming, security, and computer science. You will not regret taking this ethical hacking course, in the end, you will become an expert in cyber security thanks to the projects and your certification.

This course will allow you to enter the professional world without any problems. If you have any questions about the program, please do not hesitate to contact us so that we can answer your questions and you can start your career in Ethical Hacking.

Which languages should you learn for data analytics?

Posted on by Imarticus Learning

Data science is a fascinating topic to work in since it combines high statistical and mathematical abilities with practical programming experience. There are a variety of programming languages in which a prospective data scientist might specialize.

In this article, we will tell you how by learning machine learning and taking a python course you can obtain a Data analytics Certification. 

big data analytics courseWhile there is no one-size-fits-all solution, there are various factors to consider. Many factors will determine your performance as a data scientist, including:

  • Specificity: When it comes to sophisticated data science, re-inventing the wheel each time can only get you so far. Master the numerous packages and modules available in the language of your choice. The extent to which this is feasible is determined by the domain-specific packages that are initially accessible to you! 
  • Generality: A smart data scientist will be able to program in a variety of languages and will be able to crunch statistics. Much of data science’s day-to-day job is locating and processing raw data, sometimes known as ‘data cleaning.’ No amount of clever machine learning software can assist with this. 
  • Productivity: In the fast-paced world of commercial data science, getting the work done quickly has a lot of appeal. This, however, is what allows technical debt to accumulate, and only rational procedures may help to reduce it.
  • Performance: In some circumstances, especially when working with enormous amounts of mission-critical data, it’s crucial to maximize the performance of your code. Compile-time languages are often substantially quicker than interpreted languages and statically typed languages are far more reliable than dynamically typed languages. The clear trade-off is between efficiency and productivity.

These can be viewed as a pair of axes to some extent (Generality-Specificity, Performance-Productivity). Each of the languages listed below can be found on one of these spectra. 

Let’s look at some of the more popular data science languages with these key ideas in mind. What follows is based on research as well as personal experience from myself, friends, and coworkers – but it is by no means exhaustive! Here they are, roughly in order of popularity:

    • R: R is a sophisticated language that excels in a wide range of statistical and data visualization applications, and it’s open-source, which means it has a vibrant community of contributors. Its current popularity is a reflection of how effective it is at what it accomplishes. 
    • Python: Python is a fantastic language for data research, and not only for beginners. The ETL process is at the heart of most of the data science processes (extraction-transformation-loading). Python’s generality is appropriate for this task. Python is a tremendously interesting language to work with for machine learning, thanks to libraries like Google’s Tensorflow.
    • SQL: SQL is best used as a data processing language rather than as a sophisticated analytical tool. Yet ETL is critical to so much of the data science process, and SQL’s endurance and efficiency demonstrate that it is a valuable language for the current data scientist to grasp. 
    • Java: There are several advantages to studying Java as a primary data science language. Many businesses will value the ability to easily incorporate data science production code into their existing codebase, and Java’s performance and type safety will be significant benefits. However, you won’t have access to the stats-specific packages that other languages provide. That said, it’s worth thinking about, especially if you’re already familiar with R and/or Python.

 

  • Scala: When it comes to working with Big Data using cluster computing, Scala + Spark are wonderful options. Scala’s characteristics will appeal to anybody who has worked with Java or other statically typed languages. However, if your application doesn’t deal with large amounts of data, you’ll likely discover that adopting alternative languages like R or Python will increase your productivity significantly.

 

Conclusion

At Imarticus we commit to giving the best quality education, so if you are interested in getting a data analytics certification, taking a python course, and learning machine learning come and visit us! 

Related Article:

https://imarticus.org/what-are-top-15-data-analyst-interview-questions-and-answers/

Understanding global securities settlements and reporting in investment banking operations

Posted on by Imarticus Learning

Conceptually speaking, global investment banking is an activity focused on obtaining and intermediating resources for the sale of companies, mergers, and acquisitions, issuing shares for the entry of new investors (traditionally carried out on stock exchanges), placing debt bonds in the market, or for the development of new companies or projects. In countries such as the United States, the United Kingdom, and those belonging to the European Union, investment banking has also been associated with the trading of securities in the capital markets.

Securities settlement systems (SSSs) are a fundamental component of the infrastructure of international financial markets. Over the last years, the volumes of trading and settlement have grown significantly as securities markets have developed to be an increasingly imperative channel for intermediating streams of resources between creditors and debtors, and because investors are being able to manage their portfolios of securities more dynamically, in part because of declining transaction costs. Cross-border trading and settlement volumes have grown particularly fast, reflecting the increasing integration of international markets.

best investment banking courses with placement in IndiaAny disruption in securities settlement has the potential to spill over to any of the payment systems used by the SSS or to any payment system used by the SSS to transfer collateral.

In the securities markets themselves, market liquidity depends critically on confidence in the safety and reliability of settlement arrangements; traders may be unwilling to trade if they have significant doubts about whether the transaction will actually be settled. 

Thus, in order for investment banking to be able to carry out its intermediation and resource management activity, it develops basic services and activities to identify the financial situation of the companies it supports. These activities are financial analysis and diagnosis, company valuation, and financial advisory services, the main one being the financial structuring of projects. These services are the same as those provided by financial consultancy firms.

In addition, some investment banking firms also specialize in the comprehensive development of the company’s strategic process vis-à-vis third parties. For example, if a company wants to sell a shareholding, the investment banking firm prepares a booklet for the sale of these shares, indicating the details of the activities to be carried out by the interested agents in order to purchase the shares. This is the importance of reporting in investment banking.

Why an Investment Banking Career? 

Global Investment Banking professionals are among the highest-paid in the world, but with this great reward comes enormous responsibility. To be able to work in this field, you need to have many competencies and skills in concepts like global securities settlements and good reporting practices. In fact, the investment banking sector will deal with responsibilities such as the financing of companies through debt or equity, as you will decide things like whether to buy or sell companies or parts of it, risk hedging, or joint ventures.

Why Imarticus for investment banking courses in India?

In Imarticus we offer CIBOP Certified Investment Banking Operations Professional courses in India for everyone that needs to start from the basics of an Investment Banking Career. Visit our site today to start a career in global investment banking operations, and learn more about the importance of well understanding global securities settlements and reporting techniques in this field.  

Conclusion

To succeed in an Investment Banking Career, professionals must acquire and demonstrate a multidisciplinary profile with extensive financial knowledge. Concepts and skills such as global securities settlements and reporting must be well mastered. At Imarticus, we offer you the possibility to take online investment banking courses in India. Enroll today and start your Investment Banking Career.

5 tips for supply chain management and analytics in the age of AI

Posted on by Imarticus Learning

Undergoing supply chain management training is a prominent goal of several in the management industry. To become a supply chain analyst, one must complete a certification course. There are various certifications for supply chain professionals, available online. While pursuing this career one must understand how the SCM works in this new age of AI. 

Nowadays, AI is an integral part of the competitive market. Businesses are constantly increasing their profit margin using AI. The supply chain market is volatile with the change in several factors and using AI businesses can keep up with the changes and make necessary changes in their system as needed. 

There are several ways in which AI helps in supply chain management (SCM). One of the most prominent methods it adopts is to analyze the available data, both internal and external. Here are some tips for supply chain management in this AI era.  

 

  • Plan for the IoT Data

 

The various data applications in the supply chain make up one-third of the total IoT data. So it needs proper planning to collect, integrate and utilize it. Since the volume of data is ever-increasing, it needs proper tools to manage it effectively and AI comes in as the best option. It can handle data collection of any volume and streamline it properly. 

While doing so, make sure to bring in variety with the data so that it can help with unprecedented methods and ways that detect any anomalies or disruptions in the SCM system. 

 

  • Make use of external data

 

In supply chain management, the volume of internal data itself can be vast. When using AI, one must also think outside the box and bring in outside data such as the local weather, customer reviews from external sources, vendor details, details about the competitor, etc to have a comprehensive database. 

 

  • Increase reactivity faster with AI

 

AI helps achieve a competitive edge in terms of responsiveness to any issues. It can detect problems and create alerts to take necessary preventive steps or find alternatives. 

 

  • Prioritize root cause analysis

 

AI is an effective tool in detecting issues and finding the root cause of the said problem. It can save time by early detection and gives an unbiased analysis of the root cause. 

 

  • Automation in the management system

 

AI can automate the various steps involved in SCM. It can automate administrative jobs, shipment updates, warehouse management, route planning, quality control, and shipping processes. The collective efforts can improve overall customer satisfaction or supplier selection. 

What do you need to study to become a supply chain analyst?

Supply Chain Management is a popular career option and many are eager to become supply chain analysts. But, what do you need to study to become a supply chain analyst? It requires you to get some kind of supply chain management training. 

Supply Chain Management Certification Course

Though a bachelor’s degree seems to be the basic qualification mark, having a master’s degree is an added advantage. To become an analyst one must take certification courses in the form of Professional Certification In Supply Chain Management & Analytics that provide expert guidance and job placement assistance. 

Conclusion

The popular AI-assisted processes in supply chain management are GPS tracking of the shipment for both the company and customer, regular weather updates to help the shipping industry plan their shipments, keeping inventory to help with warehouse management, etc. Depending on AI has helped businesses to reduce their cost, customize their products, and reach more customers with better customer satisfaction. 

The Fintech Bubble: Principles of investing in Fintech

Posted on by Imarticus Learning

Since its emergence, fintech has been one of the growing industries worldwide. People immediately preferred fintech services over financial services offered by traditional banks. Many fintech start-ups came in recent years and, some of them even became successful. The market cap of fintech is continuously increasing due to more and more customers preferring digital transactions.

Traditional banks are arranging fintech training courses for their employees to undergo digital transformation. If you are looking to invest in a fintech start-up or start your fintech firm, you should have a basic understanding of the fintech bubble. Read on to know about some principles for investing in the fintech industry.

Did you notice the fintech bubble exploded?

Gone are the days when only a handful of fintech companies were there in the market. At present, many fintech firms are competing with each other. In 2015, there were more than 350 fintech start-ups that caught headlines. However, the number of fintech start-ups decreased as the fintech bubble exploded.

Many fintech firms had already established themselves at the top and it got hard for newcomers. However, this does not mean that fintech training courses are of no use.

Even if the fintech bubble exploded, the global market cap of the fintech industry is continuously increasing. The predicted CAGR (Compound Annual Growth Rate) for the fintech industry is also high. The only thing that is challenging in the fintech industry is the increased competition. At present, you will have to compete with many fintech giants to build your market share.

The top fintech firms have already gained the trust of customers and, it is hard to displace their market. However, with the right business strategies and reliable services, you can still become a fintech giant even after starting late.

Principles for investing in the fintech industry

Some of the principles for those looking to invest in the fintech industry are as follows:

  • If you are buying shares of any fintech company, look for those that are continuously innovating. There is no compulsion that you should buy shares of a fintech giant. A fintech company that is constantly innovating itself is moving in the right direction.

  • If you are investing in a fintech start-up, look for the technology stack used by the fintech platform. Invest in a fintech platform that uses blockchain for making digital transactions secure and fast. A financial technology course can help you understand the technologies used for creating a fintech platform.

  • Invest in a fintech platform that offers many financial services to customers. Besides facilitating customers with digital transactions, a fintech platform can also provide P2P lending, gold/stock trading, and many other services. Third-party integrations also make a fintech platform more popular than others.

  • Due diligence is required before investing in the fintech industry. If you are starting your fintech firm, perform due diligence to know the right time to start. You should consider market disruptions, trends, and financial reports before investing in the fintech industry or starting your fintech firm. A financial technology course can help you understand the driving features of a fintech platform.

How to learn financial technology?

You should obtain a fintech certificate online to become an expert in financial technology. We at Imarticus Learning ensure industry-oriented FinTech courses that can help in knowing the industry practices. Besides investors or entrepreneurs, our fintech courses are beneficial for young enthusiasts looking to build their careers.

Our fintech courses will make you work on several hands-on projects and case studies. Job aspirants will also receive placement support to kickstart their careers in the fintech industry. Obtain your fintech certificate online with Imarticus right away!

Regression and classification metrics with python in AI/ML

Posted on by Imarticus Learning

Python is one of the most popular languages used in data science. It has a massive library that makes it easy for anyone to conduct machine learning and deep learning experiments. In this blog, we will be discussing regression and classification metrics with python Programming in AI/ML.  

We will show how to use some of these metrics to measure the performance of your models, which can help you make decisions about what algorithm or architecture might work best for your application or dataset!

What is a regression metric?

A regression metric measures how accurately a machine learning model predicts future values. To calculate a regression metric, you first need to collect predicted and actual values data. Then, you can use various measures to evaluate how well the model performs. 

How to use classification metrics with python Programming in AI/ML?

A classification metric or accuracy score measures how accurately a machine learning model predicts the correct class label for each data point in your training dataset. Once you have a classification metric, you can evaluate your machine learning model’s performance. 

You can use many different classification metrics to measure performance for a classifier machine learning model. Common ones include accuracy score, precision, recall, actual positive rate, and recall at different false-positive rates. You can also calculate the Matthews correlation coefficient (MCC) to measure how well your model performs.

Accuracy Score:

Accuracy score measures how often the predicted value equals the actual value. It’s also known as error rate, accuracy, or simply classification accuracy. You can calculate the accuracy score by dividing the total number of correct predictions from all predictions made.

Precision:

Precision is the number of correct predictions divided by the number of predictions made. 

Recall:

Recall, or valid positive rate is the number of correct predictions divided by the number of positives. You can calculate how well your model performs for different classes by plotting a ROC curve and calculating the AUC.

False Positive:

False-positive is also known as Type I Error or alpha error in statistical hypothesis testing. It’s when your model predicts that an instance belongs to one class, but it belongs to another.

False Negative:

False-negative is also known as Type II Error or beta error in statistical hypothesis testing. It’s when your model predicts that an instance belongs to one class but belongs to another, and the actual value isn’t present in training data. 

Matthews Correlation Coefficient (MCC):

The Matthews correlation coefficient measures how well your model predicts the labels of unseen instances from training data. 

Area Under Curve (AUC):

The AUC score measures how well your model predicts future values by plotting a ROC curve and calculating the area under it.

Discover AIML course with Imarticus Learning

This artificial intelligence course is by industry specialists to help students understand real-world applications from the ground up and construct strong models to deliver relevant business insights and forecasts. 

Course Benefit For Learner: 

  • Students get a solid understanding of the fundamentals of data analytics and machine learning and the most in-demand data science tools and methodologies.
  • Learn data science skills by participating in 25 in-class real-world projects and case studies from business partners.
  • Impress employers & showcase skills with artificial intelligence courses recognized by India’s prestigious academic collaborations.

Contact us via the chat support system, or drive to one of our training centers in Mumbai, Thane, Pune, Chennai, Bengaluru, Delhi, Gurgaon. 

Understanding Python for Financial Analysis and Algorithmic Trading

Posted on by Imarticus Learning

The field of finance is as interesting, dynamic, and innovative as it gets. There are always new trends shaping the technologies under development, as well as those that have been around for a while. For people who are eager to learn financial analysis, there are countless opportunities in presential, online, and hybrid modes.

While financial theories are not something new, enrolling in a financial analyst course online, or in presential or hybrid modes, will allow you to understand in depth the mechanisms that affect the performance of projects, companies, budgets, and different financial transactions.

Just like any other field, the area of finance has had to evolve in order to keep track of the latest disruptive factors around the world, which is what has led to the progressive adoption of python as a tool for data processing and extraction. Not only has it permitted the improvement of existing procedures, but it has allowed the development of new techniques that provide more accurate and reliable results.

What is Python?

If you are new to all of this, you might be wondering what on Earth is python and why does it sound like a fascinating, magic solution for financial analysis. Well, the first thing to note is that financial analysis is not the only playground for python and that it has been an incredibly useful and powerful asset in numerous disciplines.

Python is a programming language whose flexibility and simplicity have turned it into the go-to option for software development, particularly in Fintech. It is easily readable, and its conciseness helps developers save time and effort when coding.

What is financial analysis?

Now that the first item on the list is clear, let’s pass to the second one and define financial analysis. This process consists of evaluating the appropriateness and the performance of financial transactions, businesses, budgets, among others, with the aim of determining its stability, solvency, profitability, or liquidity, in order to decide whether it is worth investing in it or not.

One way of learning all you need to know about financial analysis is signing up for a financial analyst course online from wherever you are! Although this would not compare to a bachelor’s in finance or economics, it will certainly give you practical knowledge and know-how in the area.

After having acquired significant expertise in financial analysis, one could also aim to be designated a chartered financial analyst (CFA) after taking a CFA course in India, or wherever you live. This evaluation will test your understanding of financial mechanisms and fundaments, asset valuation, wealth planning, and managing portfolios.

What is algorithmic trading?

The third and last term to go through corresponds to algorithmic trading. This process comprises the place of trade through an algorithm, which allows to increase revenue and save time. Why? Because algorithms are able to take human emotions out of the equation and make sounder decisions when placing the trades, apart from doing it at higher frequencies, increasing revenue over a defined period of time.

How can python be used for financial analysis and algorithmic trading?

As you can imagine now, python is an excellent tool for programming the algorithms used in algorithmic trading, and for analyzing the stock market, as it allows the financial analyst to handle large sets of data and to extract relevant information faster and more efficiently.

Fintech is just one of the many fields where python is leading change and allowing for improvements to take place across the globe. Whether we got you with the CFA course in India idea, or you were already determined to learn financial analysis, this is a promising path to follow.