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Day: August 10, 2022

How IIT Roorkee is creating a mark in the data science and machine learning industry

Posted on by Imarticus Learning

How IIT Roorkee is creating a mark in the data science and machine learning industry

India’s data science and machine learning industry is proliferating, fueled by the digital revolution. With the country witnessing a digital transformation, the demand for data analysts and software developers in India has proliferated over the past few years.

Several global corporations like Microsoft, Google, Amazon, Facebook, and Twitter also have their R&D centres in India to tap into their talented workforce. The country has seen several prominent players emerge in the last two years, focusing on building world-class ML projects.

Why IIT Roorkee? 

IIT Roorkee offers a two-year program aimed at helping students and researchers with AI and ML expertise to transition into corporate jobs. The program also seeks to create a pool of future talent for the Indian data science and AI ecosystem by connecting top research institutes, academia, and corporate partners.

It has been designed for researchers, academicians, and industry partners to realize their AI and ML aspirations through research and project collaboration. It also aims to build a talent pipeline for the industry.

Program participants create their projects based on their specific expertise and can also work on projects proposed by corporate partners. The program is supported by academic experts, technopreneurs, corporates, and investors. Participants can apply for the program through an online application process.

Why Build A Data Science And Machine Learning Centre?

Roorkee’s approach to AI and ML Development is one of the first Indian institutes to create a Centre for AI and ML Development. This decision to focus on artificial intelligence and machine learning was based on the need to prepare the country’s talent for an “AI-powered” future.

The vision is to become a world-class AI & ML research, training, and entrepreneurship centre. AI is a set of computer technologies that lets machines learn how to perform tasks, process data, and make decisions. It is also known as strong artificial intelligence (AI). ML studies how to automatically build systems that use AI to learn and improve over time.

Roorkee has also started an AI & ML development centre focusing on developing a strong talent pool in the country and building a robust ecosystem of AI & ML startups.

Major Projects at IIT Roorkee 

Some of the achievements from IIT Roorkee that have left an impression on its contemporaries include successful projects like the development of large-scale deep learning models for Breast Cancer Detection at a Multi-Institutional Collaboration Project and improvement over other research institutions such as Google Brain’s AlphaGo Zero result. Apart from traditional academic research, this Institute has also been involved in industry collaborations and has developed innovative products such as the IIT-developed speech recognition algorithm for Google Assistant.

Since its inception, IIT Roorkee has always strived to create an environment where students can develop their skills in cutting-edge research areas. This is done by providing a supportive infrastructure and encouraging interactions with industry partners. Several programs at this Institute are designed to help students get hands-on experience with real-world problems and solutions related to data science and machine learning. For instance, the mentor program pairs experienced professionals from various industries with bright college students for project guidance.

With an approach to a research-based, collaborative and integrative research model, the programme focuses on core research areas, including natural language processing (NLP), computer vision, and reinforcement learning. The programme helps participants transition from research to product development in a short time through a three-month boot camp and six-month programme.

They conduct workshops, lectures, and hands-on classes to help participants understand their research domain’s fundamental concepts and technologies. It also provides mentorship and guidance to help participants build a strong research team and business model. The Institute also provides support through research publications, grants, and internships.

Conclusion

With the demand for data scientists and machine learning experts rising rapidly in India, the country is facing a significant problem in finding such talent. This is where IIT Roorkee, with its innovative approach to AI and ML development, comes into the picture.

Over the last few years, the Indian Institute of Technology (IIT) Roorkee has emerged as one of the leading institutions in India for data science and machine learning research. The Institute facilitates research and development by building a talent pool in AI and ML. It is finding data scientists and machine learning experts, thus relieving this country of a major problem.

4 pillars for successful financial modeling

Posted on by Imarticus Learning

4 pillars for successful financial modeling

The field of finance is a space for various strategic implementations to make it a successful open house. While finance corporations require a brush-up of technical skills and fundamentals. An operative and successful financial model paves an elongated way for businesses to carry out their financial operations efficiently and therefore finance analysts need to back their research and process on an effective financial model. 

Importance of Financial Models

Financial models are important for decision-making and performing exquisite financial analysis concerning raising capital, mergers and acquisitions, business valuations, ratio analysis, etc. A typical financial model is a tool that can be built on spreadsheet software like MS Excel. A financial is an extrinsic technique to forecast a business’ financial performance. Such can be based on a company’s past performance and assumptions made by the analyst. 

To build advanced types of models one can obtain a financial modeling certification to create complex algorithms and build a top-notch model such as discounted cash flow analysis (DCF model), mergers and acquisitions (M&A), leveraged-buyout (LBO), sensitivity analysis, etc.

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To obtain higher knowledge of the field and update your skills, you can also opt for the varied financial analyst training courses that are being offered by various institutes or obtain a financial analyst certification course to expedite the financial modeling process in your job and going forward in your career. 

Pillars of Financial Modeling

For successful financial modeling, the top 4 pillars comprise consistency, reliability, efficiency, and clarity. Without these essentials, a financial model shall be without soul and ultimately tough to navigate. These pillars ensure that your financial model is free of errors and minimizes the duplication of tasks. Also, it helps you to create a model with a higher readability score and an effective model on which to base your decision-making. Let us understand these fundamentals that act as a tower of strength to a successful financial model.

Consistency:

Consistency is an essential element in a model layout. It ensures that the reader can explicitly understand the logic behind the stats used and thus can be successfully carried through from one author to another. The consistency must be maintained in terms of the data affixed through the appropriate columns, the number of sheets, as well as the page, breaks serial numbers, formulas, and most importantly proper formatting. This ensures that parity is maintained in the model and the chances of errors can get minute along the way.

Efficiency:

An efficient model helps the analysts to cut down on extra costs for revamping the system consecutively. It allows both the creator of the model as well as the financial analyst to move towards effective cost and time management. This ensures that they can obtain trustworthy results by paying attention to the required formulas along with the inputs in the form of numbers that need to be clubbed onto the sheet. It also portrays how these numbers must be aligned and organized by keeping the end goal as the main feature and obtaining efficient results.

Clarity:

Without clarity in the financial model requisites and features, the financial model can tend to become highly complex. This in turn shall create hindrances in obtaining viable plans and analysis of the financials involved. The model, therefore, needs to have a simple outlook and structure. The model also needs to be transparent in terms of its presentations and must be easier to follow the process for not only the one creating the model but also the analysts and users who need to dissect the numbers and use them to forecast the financials of the enterprise.

Reliability:

It is substantial for a business to have a reliable financial model to minimize the upheavals in its decision-making process. A reliable financial model ensures the promulgation of technical as well as strategic assistance. Apart from being flexible and easy to understand, the reliability trait goes a long way for you to stick around the business and enhance your financial operations.

To create an effective financial model and also to learn how to create one, the best way is to practice. To do so requires years of experience and to reach the stage of becoming an expert at creating a structured financial model comes with several failed ones. But once you have mastered the techniques and skills, it would become a tad bit easier. 

An ideal approach to learning and analyzing a financial model is doing a thorough reading of equity research reports. This shall be an excellent way to compare your results and ultimately improve your methods. One of the most preferred ways is also to practice analyzing a company’s historical financials that have been built on a flat-line model and then calculate the net present value per share.

This helps to compare the current share price or the targeted prices of equity research reports and hence can brief you on how to improve your data build-up. You can also opt for courses that are available online. The bottom line is to craft your skills in such a manner to build a model that consists of all the 4 pillars that have been mentioned above to obtain successful results. 

The best moves for the fintech professionals for post-pandemic success

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The best moves for the fintech professionals for post-pandemic success

As the economy has now finally shifted from response mode to recovery one, various opportunities have emerged in the field of finance. Specifically, in terms of fintech. Fintech is the space where the technology conglomerates with finance and has been on the pathway to leverage its unique assets and seize the opportunities that have been created in the field.

The uncertainty of the covid pandemic had certainly created stress in the financial system, however, in the midst of all this, fintech has managed to strive to revive the whole industry and how. It initiated cost-saving measures and tightened the underwriting system of the financials and thus creating a sense of relief in the industry as opposed to the struggling traditional finance methods. This has indulged the younger generation to see finance and fintech particularly as a glorifying career and also for the fintech professionals who can expect a surge in the field as well as the job prospects

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So, let us now understand the best moves a fintech professional or someone looking to leverage his career in the field can opt for, to look at the post-pandemic success of fintech in the industry. 

Certification programs: For finance professionals, a fintech certification program can certainly pave the way for upgrading their skills and their technical know-how. A fintech certification can give an edge to their career after the post-pandemic fiasco and can lead a set way to better job prospects. It also shall help to update the financial models and financial terminologies one can be acquainted with. There are several certification courses available both online and offline today. This in the long term can help you to improvise your methods and career at the same time.

Technical upskill: Apart from upskilling one’s fundamental knowledge, it is also important in today’s time and specifically in the coming decade for professionals to update their technical skills such as data analysis, financial reporting, etc. Technical skills like data and machine learning are the future of the finance industry and must be elongated along with your advancement in the field. It shall become tricky to survive in a fintech industry without being relevant with the times, therefore technical upgradation can prove to be one of the best bets for the future.

Online training: If you are new to the industry, you can choose to enrol yourself in fintech online training. These are not much heavy on your pocket, unlike the traditional training methods. A fintech online course can teach you to deal with the upheaval of the industry and the essentials to survive in the field. Post-pandemic, many aspirants as well as fintech professionals have chosen to enrol in these courses, creating a surge in the demand, and therefore many popular institutes have created bets of fintech programs online.

Some of the best fintech courses in India are:

  • Masters in Fintech- MS in Finance (STEM) by SPJIMR
  • MBA in Fintech by IILM University
  • Decoding Fintech by SP Jain Institute of Management & Research
  • Advanced programming in FinTech and Financial Blockchain by IIM Calcutta
  • FinTech for Managers by S P Jain School of Global Management
  • Post Graduate Diploma in Fintech by BITS Pilani
  • Certificate Programme in Fintech and Financial Blockchain by IIM Indore
  • Fintech Certificate by Imarticus Learning

Blockchain Expertise: With the rising demand for blockchain experts, it is important to not be ignorant of the fact that the future nomenclature is in the finance industry.  The increase in nuances about bitcoin and its related notions can be directly tied to the explosion of cryptocurrency in the finance industry. Over this past decade, the unimaginable increase in the value of digital currencies like bitcoin has almost disrupted the core industry. Now, with the underlying technology, blockchain can use a single, shared consensus as well as a ledger-based system of transactions so that it can enable the lending, trading, and reconciling of money across the world.

Soft Skills: Fintech professionals also need to build up a great portfolio of soft skills to achieve long-term success in this industry. Fintech is set to be the biggest employer of new CFOs and CEOs, who will now need to have a deep understanding of information technology at all levels, as well as have the essential soft skills required to manage teams, and various personalities and be great decision-makers. People’s skills, analytical skills, creativity, etc are most underrated in the field but equally important in today’s world to move ahead in your career.

When you hear the word fintech, you are sure to conjure up visions of young professionals trading stocks, splitting the check with digital payment apps, and closing on a mortgage without setting foot in a bank, unlike the traditional times. Fintech is now a portmanteau of financial services and advancing technology.

It has certainly changed the stereotype that those applications that are web-based, and customer-centric are disruptive to the risk-averse industries. Also, the industry reach has become much broader and has extended to the back offices of financial corporations. Therefore this post-covid system of finance can give an edge to the fintech professionals in the field, given the right skills and the ability to grab the opportunity at hand.

Think You’re Cut Out to Do a PG in Machine Learning? Read On

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Think You’re Cut Out to Do a PG in Machine Learning? Read On

Are you intrigued by the concept of self-driving vehicles? Do you find your life made easier with voice assistants? Do you find your Netflix recommendations helpful in technology? If you’re eager to learn about the principles that drive such technologies, a degree in Machine Learning or Data Science is the right choice for you.

Machine Learning, a sub-topic under the umbrella of Artificial Intelligence, was introduced in the mid-20th century. It was followed by the invention of the Turing Machine in the year 1950 to examine the intelligence of computers. Harboring a career in these domains has become a popular choice for tech enthusiasts with the increasing amount of Big Data and the general Industry 4.0 requirements of Artificial Intelligence and Machine Learning technologies.

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Although having a bachelor’s degree in a related specialisation would aid in learning further, you can still prepare yourself for the upcoming advancements in Data Analytics and Machine Learning.

If you are looking for a holistic course, which can help you chart out a robust Machine Learning Career, then you must check out our Postgraduate Program in Data Analytics and Machine Learning developed by industry experts.

Our Data Analytics Course, equipped with Machine Learning, offers an optimal mix of various algorithms and techniques. Completing a postgraduation in this field prepares you for viable job opportunities in government and private organisations for roles like Machine Learning Engineer, Data Analyst, Data Scientist, Data Engineer, AI Engineer, Computer Vision Engineer, and more. It has been statistically predicted that the field of Data Science will be the fastest-progressing sector in the coming decade.

What Skills Do You Need to Study Machine Learning?

The learning path towards a Machine Learning career should provide great knowledge about these essential skills that make you job-ready –

  • Programming Skills – Preferably in Python or R, understanding and creating models using different algorithms account for the basic practice of Machine Learning professionals. The coding skills make working on real-world challenges possible.
  • Data Analysis and Visualization – Data is a very valuable resource and nearly all companies today rely on data and business analytics for better organisational design, increased revenue, and streamlined productivity.
  • Statistical Modeling – Most industries generate numerical or textual data in great proportions. Discovering the logic hidden inside that information is only possible through formulations of applied mathematics and statistics. The math behind Machine Learning models is what fosters better decision-making rules.

Why is Data Analytics and Machine Learning A Booming Field?

One of the major advantages of studying Machine Learning is that it enables you to understand the world more closely and identify the impacts of Artificial Intelligence in this digital era. You would be astonished to know how deeply we are surrounded by such technologies. From the personalised ads you see on YouTube to Spotify’s song recommendations, from the auto-pilot mode of Tesla to robotic surgeries, it is all Machine Learning.

Machine Learning complements the analysis of large amounts of data. A degree in the field will help you to identify hidden patterns and insights from data that would not make sense to humans in their raw format. However, feeding that data to machine learning models might flip the existing perspectives, providing another great advantage for tech and business advancements.

A PG in Machine Learning is the most advanced stage of deep research, where one can learn to create complex Machine Learning models and solve complex problems in finance, healthcare, education, or manufacturing.

Conclusion 

Machine Learning and Data Analytics have become the driving forces for all sorts of decision-making processes across businesses. All these skills require you to work on projects that have data at their core, which can be extracted and recorded in various formats – tabular, numerical, pictorial, graphical, etc. Data is being generated in every business, ranging from small startups to large multinational corporations. And machine learning is the tool that helps make sense of this pool of big data.

If you are searching for a comprehensive curriculum to begin or advance your Machine Learning journey, our PG course in the field of Data Analytics and Machine Learning is the ideal choice. Whether you are a beginner, who has just started to learn Artificial Intelligence and Machine Learning, or an expert in the field, completing a Postgraduate certification course will improve your job opportunities and/or growth significantly.

For more detailed information about the course or the career prospects in the field, feel free to contact us through chat support or visit our nearest training centers in Mumbai, Thane, Pune, Chennai, Bengaluru, Delhi, Gurgaon, and Ahmedabad.

 

The Perfect Guide To Understanding The Data Science Career Path

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The Perfect Guide To Understanding The Data Science Career Path

India is rising and shining brightly when it comes to adopting new and emerging technology. Almost every significant industry segment recruits data science specialists to help them gain meaningful insights from massive data. The analytics sector has seen considerable demand for highly qualified employees who understand business and technology. Organizations nowadays are always on the hunt for specialists who can cover this ever-growing talent gap.

What is data science?

To develop insights from data, data scientists mix probability, statistics, and machine learning with domain expertise. These insights range from forecasting outcomes and trends to displaying linkages and patterns.

Data science impacts practically everything we encounter, from tailored playlists to shopping recommendations to illness diagnoses. Data scientists are also pioneers in developing self-driving vehicles, chatbots, and our data-driven environment.

A Career Path of Data Science Professional

Many data scientists have difficulty locating data science profiles and determining whether their skills match the job description. Because this is a relatively new industry, most organizations are imaginative regarding titles and career routes. This is also since these titles have no apparent precedent. Data science careers are expanding rapidly, with numerous vacancies across all industries. 

How do you begin a career in data science?

    1. Get educated – A good data scientist must have a solid math and statistics foundation. Getting extra education is essential if you’re not strong in these areas.
    2. Start playing with data – Use publicly available data sets to practice your data wrangling, analysis, and visualization skills.
    3. Get involved in the community – The data science community is vibrant and supportive. There are many online forums where you can ask questions and get advice from experienced data scientists. Community involvement will help you learn and grow in your career.
    4. Find a mentor – A mentor can be a great asset in your data science journey. Find someone experienced in the field who can help guide you as you learn and grow in your career.
    5. Keep learning – As a data scientist, you must continuously learn new techniques and tools. Many excellent resources are available, such as online courses, books, and blog posts.

Explore a data science career with Imarticus Learning

Students may start in data science and machine learning with this certificate program. Students will master the fundamentals of data science and machine learning through this curriculum, developed in partnership with iHUB DivyaSampark @IIT Roorkee, and acquire the knowledge and abilities they need to apply these concepts to problems in the real world.

Course Benefits For Learners:

  • This five-month program, developed by renowned IIT faculty members, will instruct students in using Python to comprehend data mining and machine learning methodologies.
  • The data science course will be live via online sessions with India’s best educators.
  • With the help of this data science online training, students will be able to develop a solid foundation in data science.

Starch Derivatives Market Trends: Everything You Need To Know

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Starch Derivatives Market Trends: Everything You Need To Know

Starch derivatives are a group of ingredients used in producing and processing food products. You can find starch derivatives in cereals, bread, cookies, pastries, and other baked foods such as puddings, pies, cakes, and donuts. In addition to adding flavor to foods, starch derivatives also improve texture stability when cooked or stored at room temperature for extended periods.

What are starch derivatives?

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Starch is a complex carbohydrate found in plant products. Starch derivatives help to form the structure of foods, including bread and pasta. They also help thicken sauces and gravies and improve their texture.

A starch derivative is a molecule that gets derived from starch. The most common types of starch derivatives are:

  • Starch hydrolyzates (also known as dextrins)
  • Hydrolysates (also known as amylases)

There are several types of starch derivatives. The most common ones are:

  • You can use starch derivatives in food and beverage industries, such as soft drinks, ice cream, and baby formula.
  • The Paper pulp industry uses starch as a raw material for paper production.
  • The textile industry utilizes starch to manufacture fibers such as cotton or woolen fabrics.

What are the benefits of using starch derivatives?

Starch derivatives are one of the food industry’s most versatile and commonly used ingredients. They are used as a thickening and stabilizing agents and can be customized to meet the specific needs of each application. The main benefit of using starch derivatives is their ease of use. It can help you save money by avoiding costly applications or having to replace equipment when it becomes damaged by starch.

Starch derivatives also have several other benefits: they are biodegradable, so they do not require special disposal procedures; they do not leave residues on your packaging, and come in different viscosities.

How can you choose the right starch derivative for your application?

If you are looking to choose a suitable starch derivative for your application, you must understand its physical and chemical properties.

Starch derivatives are a versatile family of ingredients used in various applications. As the demand for sustainable and eco-friendly food products increases, there is an increasing need to develop substitutes for traditional starch. It has led to the development of a wide range of starch derivatives with unique properties, making them ideal replacements for conventional starch in different food applications.

Learn investment banking courses with Imarticus Learning

With the Certified Investment Banking Operations Professional curriculum, students may jumpstart their careers in investment banking. These banking and finance courses will give students the skills necessary to succeed in banking operations, treasury, and clearing services at all production levels.  The investment banking course syllabus covers Strategic planning and financial management, financing, and valuation of investment opportunities. 

Course Benefits for Learners:

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